5-Hydroxymethylcytosine profiles in circulating cell-free DNA serve as potential biomarkers for diagnosis and classification of adenomyosis

5-Hydroxymethylcytosine profiles in circulating cell-free DNA serve as potential biomarkers for diagnosis and classification of adenomyosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 5-Hydroxymethylcytosine profiles in circulating cell-free DNA serve as potential biomarkers for diagnosis and classification of adenomyosis Lei zhang, Xiaotong Han, Hangyu Chen, Chunliang Shang, Long Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3253918/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: At present, clinical diagnosis and classification of adenomyosis are mainly dependent on MRI or pathological examination, and little research is carried out on molecular biomarkers for the diagnosis and classification of adenomyosis. Our aim is to identify molecular biomarkers for the diagnosis and classification of adenomyosis. Methods: 5hmC-Seal technique was used to obtain genome-wide 5hmC profiles from plasma cfDNA and tissue genomic DNA samples. Patients were divided into the training (n = 26) and the validation group (n = 25), and a 5hmC-based Logistic regression model from the training group was developed to verify the diagnostic capability of the model. Meanwhile, we investigated whether 5hmC molecular markers could be used to classify the two main subtypes of adenomyosis. Results: In this study, ten 5hmC markers were identified by using machine learning techniques. The diagnostic ability reached 0.88 sensitivity and 0.87 specificity (AUC = 0.91). Next, we found that 5hmC markers can be used as markers for adenomyosis classification. Conclusions: Our results showed that 5hmC markers of cfDNA had the potential to be used for diagnosis and classification in adenomyosis patients, and 5hmC-Seal may be a clinically applicable and minimally invasive method for the diagnosis and classification of adenomyosis in the future. Adenomyosis 5hmC Machine learning Diagnosis Classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Adenomyosis is a common disease in women of childbearing age, with a prevalence of 5%-70% 1 , 2 . It is a progressive disease that seriously affects women's health and quality of life. Timely diagnosis and treatment can control disease progression to a certain extent, improve quality of life, improve prognosis, and reduce the likelihood of hysterectomy. At present, surgical pathology remains the gold standard for the diagnosis of adenomyosis. However, it is an invasive procedure that requires a relatively large amount of tissue biopsies, which is difficult for advanced-staged patients with scarce biopsies and sometimes leads to repeated biopsies that are time-consuming and painful for patients. Therefore, the diagnosis of adenomyosis is increasingly dependent on non-operative methods such as clinical symptoms, imaging, and serological examination. However, the symptoms of adenomyosis are non-specific and about 30% of patients are asymptomatic 3 . Studies have shown that the sensitivity and specificity of TVS and MRI in diagnosing adenomyosis are unstable 4-8 , and the sensitivity is as low as 12% when examined by radiologists and non-gynecologists 9 , 10 . Although carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) have a potential role in the diagnosis of adenomyosis, it lacks specificity in distinguishing adenomyosis from other diseases 11 , 12 . Adenomyosis is a heterogeneous disease. Kishi et al. classified adenomyosis into 4 subtypes according to the position of adenomyosis lesions and other structural components on MRI 13 . Different subtypes may be associated with different pathogenesis, resulting in different clinical symptoms and treatment effects 13-16 . However, the molecular biomarkers of different subtypes of adenomyosis are less well studied. Due to the limitations of the current diagnostic and classification methods of adenomyosis, it is urgent to establish a non-invasive, accurate, and early serological diagnosis and classification method. Liquid biopsy is becoming increasingly popular among clinicians and patients due to its non-invasiveness, sampling convenience, dynamic tracking, and other characteristics 17-20 . Meanwhile, a large amount of evidence has proven that cell-free DNA (cfDNA) isolated into plasma is closely related to the occurrence and development of diseases and has shown better diagnostic and predictive ability than clinical indicators 21-25 . 5hmC is an important epigenetic marker, which is closely related to not only organ such as brain development 26 but also the occurrence and development of human diseases such as neurodegenerative diseases 27 , 28 and cancers 29 , 30 . In the human genome, 5-methylcytosines (5mCs) are dynamic and reversible 31 , and can be oxidized into 5-hydroxymethylcytosines (5hmCs) through the Ten–Eleven Translocation (TET) enzymes in an active DNA-demethylation process 31-34 . In addition, according to recent studies, 5hmC enrichment in gene body regions can promote gene transcription 32 and displays a tissue-specific distribution 35-37 . Therefore, we hypothesized that 5hmC profiles might have potential value in adenomyosis diagnosis and classification. In this study, we used 5hmC-Seal technique 38 to obtain genome-wide 5hmC profiles in plasma cfDNA and tissue genomic DNA from 51 patients with adenomyosis, 46 healthy persons, 26 matching patients’ tissue samples from adenomyosis, and 21 normal uterine tissues. Our results demonstrated that 5hmC markers could be used to diagnose and classify adenomyosis patients. Materials and methods Study participants From 2020 to 2021, 51 adenomyosis (AM) patients from department of obstetrics and gynecology, Peking University Third Hospital were included in this study. Next, 47 matching tissue samples were obtained from patients who underwent surgical resection and freshly frozen at -80℃ until use. All patients had signed the patient consent form. Informed consent was obtained from each patient prior to tissue collection and the study was approved by Peking University Third Hospital Medical Science Research Ethics Committee (M2021682). The detailed clinical and pathological information is shown in Table 1. All the tissues from Peking University Third Hospital were histologically diagnosed. In addition, a total of 46 plasma samples from healthy people were collected between 2016 and 2019 at Peking University. All healthy donors were not having any acute or chronic illnesses or receiving any medications at the time of blood donation. Study design This study aims to diagnose and distinguish different types of adenomyosis patients using 5hmC sequencing technology. A total of 144 samples were included in the cohort analysis, including 51 adenomyosis patients, 46 healthy persons, 26 matching patients’ tissue samples from adenomyosis, and 21 normal uterine tissues. All 5hmC libraries were sequenced using Illumina Next500. Meantime, in data processing, we divided 51 adenomyosis patients and 46 healthy persons into a training cohort and a validation cohort in a 1 to 1 ratio. Firstly, we identified candidate differential 5hmC modification markers in adenomyosis patients and healthy persons from the training cohort. Secondly, 26 matching tissue samples were used to identify 5hmC markers origin from adenomyosis tissue. These 5hmC markers are then used to construct the diagnostic models. Thirdly, we diagnose adenomyosis patients in the validation cohort using the model developed. Lastly, we found 5hmC markers origin from adenomyosis tissue and used these 5hmC markers to classify the patients with adenomyosis (Figure 1A). cfDNA extractions and quantity assessments 5 ml of the patient's peripheral blood was collected in a BD Vacutainer ® EDTA tube for cfDNA extraction (Becton, Dickinson and Company, Cat# 367525). The blood was transported to the laboratory within 24 hours and centrifuged for 1350 g and 12 min for plasma preparation. The upper plasma was removed and placed into a 2 mL centrifuge tube (AXYGEN, McT-200-c) for 1350 g and 12 min to remove the leukocytes in the plasma. The upper plasma was removed and placed into a new 2 mL centrifuge tube (AXYGEN, McT-200-c). Centrifugation was performed again for 13500 g for 5 min to completely remove the red blood cell fragments in the suspended plasma and immediately stored at -80 ℃. Plasma cfDNA was extracted using Quick-cfDNA Serum & Plasma Kit (ZYMO, Cat# D4076) and quantified by Qubit3.0 (Thermo, Cat# Q33216). Then store at -80 °C. Before the library was established, nucleic acid electrophoresis was conducted to observe the fragment size (~180 bp) and then 5hmC library was established. gDNA extractions and quantity assessments Tissue samples from patients, including adenomyosis lesions and normal myometrium tissue samples, were stored at -80 °C after surgical resection. After thawing, 25 mg tissue was cut and collected. gDNA was isolated from tissues using the quick-DNA TM miniprep Plus Kit (ZYMO, Cat# D4069) and quantified by Qubit3.0 (Thermo Cat# Q33216). Then store at −80 °C. Before the library was established, nucleic acid electrophoresis was conducted to observe the fragment size (1000 bp~) and then 5hmC library was established. 5hmC-Seal library construction and sequencing Library construction was performed using a chemically selective labeling method 38 , in which bacteriophage T4 β-glucosyltransferase was used to transfer engineered glucose fragments containing azide groups to hydroxyl groups of 5hmC in the human genome. Then biotin was used to chemically modify the azide group to effectively enrich DNA fragments containing 5hmC, which could efficiently bind and capture hydroxymethylation sites on DNA. First, according to the requirements of second-generation sequencing, Qubit3.0 (Thermo, Cat# Q33216) accurately quantified cfDNA (1-10ng) and GDNA (1-50ng) were used to interrupt GDNA by enzymatic reaction, and KAPA Hyper Prep Kit (KAPA, Cat# KK8514) was used for terminal repair. It is then connected with adapters (Purkary, Cat# PKR2015, PKR2016, PKR2017, and PKR2018) that are compatible with Illumina. The linked DNA was reacted in a 25 μL solution containing 50 mM HEPES buffer (pH 8.0), 25 mM MgCl2, 100 μM UDP6-N3-GLC and 1 μM T4 β-GT (NEB, Cat# M0357L) at 37℃ for 2 h. The DNA was then purified using DNA Clean& ConcentratorTM-5 (ZYMO, Cat# D4014). The purified DNA was fully mixed with 1µ L DBCO-PEG4-biotin (Click Chemistry Tools, 4.5 mM stock in DMSO) and reacted for 2h at 37℃. Similarly, DNA was purified using DNA Clean & ConcentratorTM-5 (ZYMO, Cat# D4014). At the same time, 2.5 μL Thermo Life Technologies (Thermo, Cat# 65305) was inoculated in 1 × buffer solution (5 mM Tris pH 7.5, 0.5 mM EDTA, 1 M NaCl, 0.2% Tween 20) was directly added into the mixture and reacted at room temperature for 30 min, and then gently rotated. Finally, rinse with buffer 1-4 8 times for 5 minutes. The beads were then re-suspended in RNase-free water (Tarara, Cat# 9012) for 14-16 PCR amplification cycles. The amplified products are purified using Pure Beads (KAPA, Cat# KK8001). The concentration of the library was measured with Qubit3.0 (Thermo, Cat# Q33216). Peer 38 bp high-throughput sequencing was performed on the NextSeq 500 platform. 5hmC-Seal profiling Paired-end 38 bp high-throughput sequencing was performed on the NextSeq 500 platform. FASTQC (version 0.11.5) was used to assess the sequence quality. Raw reads were aligned to the human genome (version hg19) with bowtie2 (version 2.2.9) (Langmead and Salzberg, 2012) and further filtered with Samtools (version 1.3.1) (Li et al., 2009) to retain unique non-duplicate matches to the genome. Pair-end reads were extended and converted into bedgraph format normalized to the total number of aligned reads using bedtools (version 2.19.1) (Quinlan, 2014), and then converted to bigwig format using bedGraphToBigWig from the UCSC Genome Browser for visualization in the Integrated Genomics Viewer. Potential hMRs were identified using MACS (version 1.4.2), and the parameters used were macs 14-p 1e-3-f BAM-g hs (Consortium et al., 2007). Peak calls were merged using bedtools merge, and only those peak regions that appeared in more than ten samples and less than 1000bp were retained. Blacklisted genomic regions that tend to show artifact signals, according to ENCODE, were also filtered. Mapping and identifying 5hmC-enriched regions FASTQC (version 0.11.5) was used to assess the sequence quality. Raw reads were aligned to the human genome (version hg19) with bowtie2 (version 2.2.9) 39 and further filtered with Samtools (version 1.3.1) 40 , (parameters used: Samtools view -f 2 -F 1548 -q 30 and Samtools rmdup) to retain unique non-duplicate matches to the genome. Pair-end reads were extended and converted into bedgraph format normalized to the total number of aligned reads using bedtools (version 2.19.1) 41 , and then converted to bigwig format, using bedGraphToBigWig from the UCSC Genome Browser for visualization in the Integrated Genomics Viewer. Potential 5hmC-enriched regions (hMRs) were identified using MACS (version 1.4.2) and the parameters used were macs 14 -p 1e-3 -f BAM -g hs 40 . Peak calls were merged using bedtools merge and only those peak regions that appeared in more than 10 samples and that were less than 1000bp were retained. Blacklisted genomic regions that tend to show artifact signals, according to ENCODE, were also filtered. The hMRs for each patient were generated by intersecting the individual peak call file with the merged peak file. The hMRs within chromosome X and chromosome Y were excluded and used as input for the downstream analyses. Feature Selection, model training, and validation AM patients were randomly divided into training and validation cohorts with a 2:1 ratio, using train_test_split in Scikit-Learn (version 0.22.1) package in Python (version 3.6.10) 34 , the logistic regression CV (LR) model was chosen to establish diagnosis models 35 In the training cohort, we identified Differentially 5hMc-enriched Regions (DhMRs) using DESeq2 package (version 1.30.0) in R (version 3.5.0), with the filtering threshold (p-value<0.05 & |log2FoldChange|≥0.5). To avoid overfitting, 5 rounds of 10-fold cross-validation were performed. The details were as follows: the training cohort was randomly divided into five folds, four of which were selected as the training subset, and the remaining one was the test subset. Then, the process performed was repeated 100 times using the recursive feature elimination algorithm (RFECV) in Scikit-Learn (parameters used: estimator=LogisticRegressionCV (class_weight='balanced', cv=2, max_iter=1000), scoring='accuracy') to further filtered. Meanwhile, 10-fold cross-validation was repeated 100 times in each round 39 , and the final markers observed in at least 3 rounds were used to build the final diagnosis model in the training cohort. Next, we trained the logistic regression CV model (LR) with the features selected from DhMRs (parameter used: maxiter=100, method="lbfgs"). Finally, the trained LR model was used to diagnose AM patients in the validation cohort. Receiver operating characteristics (ROC) analysis was used to evaluate model performance. Exploring functional relevance of the 5hmC markers We used the ChIPseeker R Package (version 1.20.0) 42 to annotate the DhMRs, and genes closest to the marker regions were used for the following functional analyses. The gene ontology (GO) enrichment analysis (Biological Process) was done by the ClueGO (version 2.5.5) and CluePedia (version 1.5.5) plug-in from Cytoscape software (version 3.7.2). We used the following parameters: Medium Network Specificity, Bonferroni step down pV Correction, and Two-sided hypergeometric test. Statistical analysis Statistical analysis in table 1 was conducted in GraphPad Prism 8. We used two-tailed t-tests (paired or unpaired depending on the experiments) for normally distributed data. We used the percentile method to calculate 95% CIs and p-values <0.05 was considered statistically significant. Results Clinical characteristics of adenomyosis (AM) patients Table 1 shows the baseline characteristics of the patients. The volunteers in the adenomyosis patient group and the control group were 51 and 46 respectively, with an average age of 45.29 and 48.30 years, respectively. There were 27 (52.94%), 26 (50.98%), and 5 (9.80%) patients with adenomyosis combined with severe dysmenorrhea (visual analog scale score (VAS)≥7), menorrhagia and infertility, respectively. Deep infiltrating endometriosis (DIE) was found in 12 patients (23.53%), chocolate ovarian cysts in 13 patients (25.49%), and uterine myoma in 31 patients (16.45%). 37 patients in the adenomyosis group were tested for CA125, among which 29 patients showed elevated CA125, with a diagnostic accuracy of 78.38%. A total of 36 patients were tested for CA199, among which 8 patients showed elevated CA199, and the diagnostic accuracy was 22.22%. A total of 51 patients underwent TVS examination, and 41 patients were diagnosed with adenomyosis by TVS, with a diagnostic accuracy of 80.39%. A total of 35 patients underwent MRI examinations, including 20 intrinsic patients and 15 extrinsic patients. A total of 32 patients were diagnosed with adenomyosis by MRI, with a diagnostic accuracy of 91.43%. 15 adenomyosis patients (29.41%) had been pretreated with gonadotropin-releasing hormone agonist (GnRHa) 6 months before surgery. 5hmC profiles differ between adenomyosis patients and healthy people in the training cohort Plasma is an easy-to-obtain biological sample, and plasma cfDNA differential analysis will be an ideal method for disease diagnosis. To explore the potential diagnostic value of plasma cfDNA 5hmC and to find more effective biomarkers, adenomyosis patients and healthy subjects were divided into a training cohort (n=49) and a validation cohort (n=48). The adenomyosis patients in the training cohort all had perfectly matched tissues. Meanwhile, we compared the two subtypes of adenomyosis, and the diagnosis and classification of adenomyosis could be made by liquid biopsy (Figure 1A). Cluster analysis based on 5hmC shows two different clusters (Figure 1B). One group represented adenomyosis patients and the other healthy control, suggesting that differential expression of 5hmC could distinguish adenomyosis patients from healthy control. Subsequently, we observed overall downregulation of the distribution of 5hmC expression sites between adenomyosis patients and healthy subjects, mainly in introns, intergenic regions, and promoter regions (Figure 1C). Meanwhile, we conducted differential analysis (p-value<0.05 & |log2FoldChange|≥0.5) and observed 1500 DhMRs, including upregulate (n=966) and downregulate (n=544) regions in adenomyosis compared to healthy control (Figure 1D, Supplementary Table 1). For example, CCDC149 (P=0.015) was significantly enriched in adenomyosis patients (Figure 1E) and PSMB1 (P=0.00001) healthy subjects (Figure 1F) compared with healthy subjects. We also analyzed the 5hmC genome characteristics of the up-regulated and down-regulated loci and found multiple 5hmC peaks. Most of the enriched 5hmC loci were distributed in intronic, intergenic, and promoter regions, which was consistent with the previous results (Figure 1G-1H). We used unsupervised hierarchical clustering of the first 100 plasma cfDNA 5hmC differential loci to generally distinguish healthy control from adenomyosis patients in the training group (Figure 1I). The 5hmC marker demonstrated the ability to distinguish adenomyosis from healthy control. GO signal pathway and functional enrichment analysis Pathway analysis of differential hydroxymethylation genes (DhMGs) in adenomyosis patients (Supplementary Table 1, Table 1) showed that some typical pathways were rich in functions (Figure 2A-B). We observed that the signal pathways of DhMGs enrichment were closely related to diseases. For example, the top of down-regulated genes was related to phosphatidylinositol 3- kinase activity (Figure 2A), while up-regulated genes were mainly enriched the positive regulation of cell migration (Figure 2B). It is known that estrogen acts in adenomyosis through MAPK and PI3K/AKT/mTOR pathways, which increases the possibility that targeting PI3K pathway may provide a new method for the treatment of adenomyosis and other gynecological diseases 43 . In addition, other researchers have reported that the development of adenomyosis was closely related to cell migration 44-46 . DhMGs may be related to adenomyosis and other gynecological diseases. At the same time, the protein interaction network (Figure 2C-D) showed that these genes including JAK2 that regulate macrophages, CDC42 that regulate cell morphological migration, MAPK1 that participate in immunomodulation, EGFR activation, and cell proliferation, HIF1A that participate in energy metabolism and activating gene transcription, and ITGB1 that participate in cell repair and metastasis 47-52 , are significantly related to the molecular mechanism and clinical symptoms of adenomyosis. The landscape of 5hmC profiles in tissue genomic DNA from adenomyosis patients To confirm the association between plasma cfDNA 5hmC markers and disease focus, we analyzed tissue samples from adenomyosis patients and normal myometrium tissue samples from non-adenomyosis patients using 5hmC-Seal technique. PCA clustering was compared according to the expression level of 5hmC, and it was found that most patient samples were separated from healthy pairs by photo clustering (Figure 3A). At the same time, the overall site distribution of 5hmC was compared between the two groups, and the expression of 5hmC was down-regulated at the tissue level in adenomyosis patients compared with healthy control. In addition, we observed overall downregulation of the distribution of 5hmC expression sites between adenomyosis patients and healthy subjects, mainly in intronic, intergenic, and promoter regions (Figure 3B), which is consistent with the results of plasma cfDNA (Figure 1C). Compared with healthy control, we found 2058 DhMRs in adenomyosis patients, including upregulate (n=1055) and downregulate (n=1003) (p-value<0.05 & |log2FoldChange|≥0.5) (Figure 3C, Supplementary Table 2). The top 200 markers were selected for unsupervised cluster analysis, with statistically significant differences between the two groups (p-value<0.05 & |log2FoldChange|≥0.5) (Figure 3D). Next, to verify the correlation between tissue genomic DNA and 5hmC cfDNA plasma, we performed correlation analysis on the samples of the same tissue and plasma. We found that 5hmC markers in plasma cfDNA correlated with 5hmC markers in pathological tissue genomic DNA (Figure 3E). For example, SIRT1 was significantly correlated (R=0.551) (Figure 3F, Figure S1A). These results validate the significance of plasma cfDNA in disease diagnosis. In order to search for characteristic markers of 5hmC reflected in plasma from focal tissues, we screened 141 DhMGs co-expressed in tissues and plasma, of which 51 genes were down-regulated and 90 genes were up-regulated (Figure 3G-H, Supplementary Table 3). We analyzed the signaling pathway enrichment of 141 DhMGs and found that signaling pathway enrichment was related to vascular smooth muscle contraction, neuronal differentiation regulation, adhesion assembly and cell-substrate junction (Figure 3I) 53-59 . Meanwhile, studies showed that genes including the neurodevelopment-related gene ( AUTS2 ), estrogen-related gene ( BCAS3 ), and immune-related gene ( FOXO3 ), are associated with the development of adenomyosis (Figure 3J). Diagnostic biomarker performance of cfDNA Similarly, we generated genome-wide 5hmC profiles for patients in the validation set, including 23 healthy and 25 adenomyosis patients. By using the recursive feature elimination algorithm based on the logistic regression CV estimator, we further reduced the number of 5hmC markers from 141 to 10, which achieved the best cross-validation score (Figure 4A). Further, we found based on PCA principal component analysis and unsupervised clustering that the ten 5hmC markers (Table 2), selected by the LR model, could distinguish adenomyosis patients from healthy in both the training and validation cohorts (Figure 4B-E). Meantime, these ten 5hmC markers could effectively diagnose adenomyosis patients in the training (AUC = 1) and the validation cohorts (AUC = 0.91) (Figure 4H), achieving 1 sensitivity and 1 specificity in the training cohort (Figure 4F) and 0.88 sensitivity and 0.87 specificity in the validation cohort (Figure 4G). Finally, we also calculated the individual AUC for each of the ten 5hmC markers in the training and validation cohorts (Figure S2A). It has been reported that the accuracy of detection and diagnosis of serum CA125 and CA199 is lower than the diagnostic level of 5hmC 12 . Liquid biopsy with 5hmC markers can be used to diagnose adenomyosis sensitively and significantly improve the accuracy of preoperative diagnosis. 5hmC markers can be used to classify adenomyosis Patients with adenomyosis were evaluated by MRI imaging. Intrinsic, extrinsic, and control tissue samples were compared with 5hmC markers to explore differences in pathogenesis. PCA results showed significant differences between adenomyosis patients and control in the two subtypes (Figure 5A). Total DhMRs were most common in intronic, intergenic, and promoter regions, and statistically significant differences were found between intron and extrinsic for any genomic feature type, with greater differences in intron regions (Figure 5B). In order to further find different 5hmC modified intrinsic and extrinsic DhMRs, we chose 200 DhMRs (p-value<0.05 & |log2FoldChange|≥0.5), the intrinsic and extrinsic unsupervised and hierarchical cluster analysis (Figure 5C), and then we directly compared the intrinsic and extrinsic DhMRs in plasma, Significant differences were found between the two genotypes (Figure S4A-F). Compared with the external genotypes, 360 DhMRs were significantly up-regulated internally and 324 DhMRs were significantly down-regulated (p-value<0.05 & |log2FoldChange|≥0.5 ) (Figure 5D, Supplementary Table 4). Most of the up-regulated genes and down-regulated genes were distributed in intronic, intergenic, and promoter regions (Figure S4C-D). Next, we compared the overall distribution of 5hmC in tissues, which was consistent with the difference in plasma (Figure S5A-H, Supplementary Table 5). 5hmC characteristic markers were extracted from the lesion sites reflected in plasma. Compared with intrinsic and extrinsic genes, 100 DhMGs were significantly up-regulated (Figure 5E) and 57 DhMGs were significantly down-regulated (Figure 5F). In addition, in order to further explore the biological significance and mechanism of differential genes among different genotypes, we conducted signal pathway enrichment analysis for 100 up-regulated and 57 down-regulated DhMGs (Supplementary Table 6). GO functional enrichment analysis showed that, compared with external differential genes, extrinsic significant differential genes were mainly enriched in interleukin-12 regulation and production, angiogenesis, and other signaling pathways (Figure 5G). Compared with intrinsic genes, extrinsic genes are mainly enriched in the regulation of signaling pathways such as immune response, amyloidosis, nervous system development, and DNA damage (Figure 5H). Studies showed that intrinsic and extrinsic lesions with different pathogenesis and clinical symptoms, mainly related to the change of hormone levels, clinical manifestation of hemorrhage increased, more and the external main performance for tissue fibrosis, clinical manifestations, such as menstrual pain due to adenomyosis of the uterus at the same time with the characteristics of inflammatory disease caused by a variety of inflammatory response 13-16 . We found that the biological significance of enrichment into this pathway is closely related. In order to further explore the differences between extrinsic and intrinsic mechanisms of action, and to look for characteristic markers that can distinguish the two types, we selected APP and NCOA2 that are highly correlated with the disease or participate in a variety of signaling pathways, which are related to tissue fibrosis and hormone regulation. CD47 , CD226 , CD36 , NFKB1 are associated with inflammation, while EPHA4 , AUTS2 are strongly associated with neurodevelopment. Its correlation with diseases has been confirmed to some extent 60 , 61 . Then 31 DhMGs that were significantly enriched in the signaling pathway were extracted for unsupervised cluster analysis. It was found that the 31 DhMGs extracted could also effectively separate the two types (Figure 5I). Our result demonstrated that intrinsic and extrinsic pathogenesis are distinct, and that 5hmC markers can distinguish between the two types. Discussion Principal findings At present, there are limitations in clinical diagnoses and classification of adenomyosis. There is a lack of molecular markers and an urgent need for a non-invasive liquid biopsy technique. Herein, we introduce a non-invasive method to classify patients with adenomyosis. In this study, we aimed to develop a model to diagnose and classify adenomyosis patients based on the 5hmC profiles derived from plasma cfDNA using 5hmC-Seal sequencing method. Results in the Context of What is Known In our cohort, we found an overall 5hmC down-regulation in adenomyosis patients compared to healthy control, with 1500 DhMRs detected by differential analysis method, including up-regulated (n = 966) and down-regulated (n = 544). Next, we performed GO functional enrichment analysis to study the biological significance of differentially hydroxymethylated genes (DhMGs). We found that genes with upregulated 5hmC were mainly enrich the cell migration positive regulation pathway, protein phosphorylation pathway, MAPK cascade regulation pathway, etc. Genes with decreased 5hmC were enriched in phosphatidylinositol 3- kinase activity signaling pathway, hormone response pathway, Hemostasis pathway, etc. Studies have shown that adenomyosis is an inflammatory disease, and its molecular mechanism is related to inflammation, immunity, nerve, and fibrosis 60-62 . As is known to all, cfDNA is not only derived from the disease itself, but also from the changes in the microenvironment caused by the disease 63 . The composition of human microenvironments is complex, including matrix elements, extracellular matrix, inflammation and immune cells, etc. 64 , which are closely related to the occurrence, development, and treatment of diseases 65 , 66 . Therefore, we can see that these 5hmC marker genes may be closely correlated with the occurrence and development of diseases. cfDNA is not only derived from focal tissue cells, but also from other somatic cells 67 . We selected 5hmC differential markers derived from the location of lesions reflected in plasma cfDNA to establish a diagnostic model. Ten 5hmC markers were selected based on machine learning algorithms to differentiate control from adenomyosis patients in the training and validation cohort. At the same time, the logistic regression CV model established by ten 5hmC markers was superior to existing clinical serological indicators CA125 and CA199 12 , with sensitivity of 0.88 and specificity of 0.87 (AUC = 0.91). Our results show that 5hmC markers extracted from cfDNA can be used as an effective biomarker for the non-invasive diagnosis of adenomyosis in patients. Based on 5hmC markers, we divided the adenomyosis patients into intrinsic and extrinsic, and the two groups could be clustered inward and separated outward. Signal pathway enrichment analysis based on the selected differential genes highly associated with the lesion revealed the different biological function changes in intrinsic and extrinsic. This provides clues for adenomyosis classification and potential targets for better treatment of adenomyosis in the future. Clinical Implications In conclusion, our results suggest that 5hmC markers derived from cfDNA can serve as effective epigenetic biomarkers for minimally noninvasive diagnosis and classification of adenomyosis. We also explore the molecular mechanism of the pathogenesis and development of the disease and the molecular mechanism between intrinsic and extrinsic adenomyosis. Strengths and Limitations Taken together, the ten 5hmC marker associated with adenomyosis identified by genome-wide 5hmC profiling in plasma cell free DNA can be used for the liquid biopsy of adenomyosis. This is superior to existing diagnostic methods. At the same time, we found that 5hmC markers can be used to classify the two subtypes of adenomyosis at the molecular level. We also found that two specific 5hmC markers, APP and NCOA2 , have the potential on distinguishing subtypes. There are still some limitations in this study. First, the sample size is relatively small and may not be fully representative of all adenomyosis patients. The performance of our model needs to be evaluated in a larger study cohort. Secondly, the sample included in this study was Chinese women, which may not represent patients of other races. Thirdly, Comparing the longitudinal distance of the imaging lumen, muscle thickness, and adenomyosis thickness, differences were found between intrinsic, extrinsic, mixed, and adenomyomatous lesions, but the sample included in this study was only intrinsic and extrinsic. In the future, our goal is to increase the sample size of patients and find more stable and reliable 5hmC marker genes for adenomyosis diagnosis and classification. Abbreviations 5hmC 5-hydroxymethylcytosine CA125 carbohydrate antigen 125 CA199 carbohydrate antigen 199 AUC Area Under the Curve MRI Magnetic Resonance Imaging TVS Transvaginal Ultrasonography APP Amyloid Beta Precursor Protein NCOA2 Nuclear Receptor Coactivator 2 cfDNA cell-free DNA DhMRs Differentially 5hMc-enriched Regions GO Gene ontology DhMGs Differential hydroxymethylation genes DIE Deep Infiltrating Endometriosis IHC Immunohistochemistry VAS visual analog scale score Declarations Acknowledgments We would like to acknowledge the essential contributions of all staff and students who participated in this work. Authors’ contributions LZ and X-TH conceived the study and designed the experiments. LZ performed the experiments with the help of H-YC. LZ analyzed data with help from LC. LZ and X-TH wrote the manuscript with input and comments from H-YC All authors read and approved the final manuscript, H-YG participated in study design and data interpretation, JL participated in in study design, data interpretation and writing of the paper. Funding This work was supported by the Natural Science Foundation of China (82274034), and National Key R&D Program of China (Grantnumber:2022YFC2704003). Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. All other datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The study was conducted according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Peking University Third Hospital. Written informed consent was obtained from all participants. Consent for publication Not applicable. 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Tables Table 1: Adenomyosis patients characteristics Adenomyosis(n=51) Age (years) 45.29±5.84 MRI classification Type A (intrinsic) (%) 20/51 (39.22) Type B (extrinsic) (%) 15/51 (29.41) Type C (NA) 16/51 (31.37) Menstruation Severe dysmenorrhea (%) 27/51 (52.94) Menorrhagia (%) 26/51 (50.98) History of gestation Gravidity (%) 43/51 (84.31) Vaginal delivery (%) 22/51 (43.14) Cesarean section (%) 18/51 (35.29) Dilatation and curettage (%) 29/51 (56.86) Abortion or stillborn (%) 5/51 (9.80) Infertility (%) 5/51 (9.80) Complication DIE (%) 12/51 (23.53) Ovarian chocolate cyst (%) 13/51 (25.49) Uterine myoma (%) 31/51 (16.45) Examination CA125 (accuracy) (%) 29/37 (78.38) CA199 (accuracy) (%) 8/36 (22.22) Hemoglobin (g/l) 112.51±27.45 TVS (accuracy) (%) 41/51 (80.39) MRI (accuracy) (%) 32/35 (91.43) Preoperative treatment GnRHa (%) 15/51 (29.41) None (%) 36/51 (70.59) Table 2: Characteristics and model coefficients of 10 5hmC markers Markers coef std err z P>|z| [0.025 0.975] const -1.7485 0.686 -2.547 0.011 -3.094 -0.403 JAK2 0.0739 0.027 2.717 0.007 0.021 0.127 NAPB 0.1364 0.047 2.927 0.003 0.045 0.228 PAIP1 0.0316 0.021 1.487 0.137 -0.01 0.073 PBX1 0.0867 0.028 3.128 0.002 0.032 0.141 QKI 0.0692 0.024 2.854 0.004 0.022 0.117 SIRPA 0.0486 0.022 2.238 0.025 0.006 0.091 SPRY1 -0.0763 0.026 -2.902 0.004 -0.128 -0.025 ST3GAL4 0.061 0.023 2.632 0.008 0.016 0.106 STK17B 0.0722 0.025 2.845 0.004 0.022 0.122 Note: SE: standard error of coefficient; Z: The Z-statistic of Wald Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx SupplementarymaterialsTable1.xlsx SupplementarymaterialsTable2.xlsx SupplementarymaterialsTable3.xlsx SupplementarymaterialsTable4.xlsx SupplementarymaterialsTable5.xlsx SupplementarymaterialsTable6.xlsx FigureS1.pdf FigureS2.pdf FigureS3.pdf FigureS4.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3253918","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":228271505,"identity":"34b47b2d-ae29-4305-ad54-3d697de85d45","order_by":0,"name":"Lei zhang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"zhang","suffix":""},{"id":228271506,"identity":"f195e5cb-824a-446d-93c4-86041baaca04","order_by":1,"name":"Xiaotong Han","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Han","suffix":""},{"id":228271507,"identity":"6f3eaf15-6ef1-4639-9e22-97df2fe8efb8","order_by":2,"name":"Hangyu Chen","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hangyu","middleName":"","lastName":"Chen","suffix":""},{"id":228271508,"identity":"4cb3cb2e-73cb-4d7a-84a7-55d5cad1a7f8","order_by":3,"name":"Chunliang Shang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunliang","middleName":"","lastName":"Shang","suffix":""},{"id":228271509,"identity":"340fc6c6-6565-4eba-aaf1-fab33c3597c1","order_by":4,"name":"Long Chen","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Chen","suffix":""},{"id":228271510,"identity":"af0e40f2-e1fa-46f0-a273-0cc645263f3d","order_by":5,"name":"Jian Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACPmYGBoMEBgY5Bh4Ql40ILWxQLcYkaIHSiQ3Ea2HnPVDwoOZO+naeMwYMH8oOM/DPbiDkML4Eg4Rjz3J39vYYMM44d5hB4s4BQlp4DAwS2A7nbjjPY8DM23aYwUAigRgt/w6nG4C0/CVaS2Lb4QSDsz0GzIzEa+k7bLiz51jBwZ5z6TwSNwho4ec/Y2b449theXOe5I0PfpRZy/HPIKAFZJEBiAQRB4CYh6B6IGB+ANMyCkbBKBgFowArAABmATvdr7dVDgAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Lin","suffix":""},{"id":228271511,"identity":"89757284-aa16-4f19-91e5-6323e3497163","order_by":6,"name":"Hongyan Guo","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2023-08-11 02:44:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3253918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3253918/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42152818,"identity":"780852c1-3890-4033-9155-965775ae8534","added_by":"auto","created_at":"2023-08-25 17:40:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of 5hmC in plasma of adenomyosis and healthy subjects. \u003c/strong\u003eA. Schematic diagram of research design overview. B. PCA differentiates healthy people from adenomyosis patients (healthy in green, adenomyosis patients in red). C. Characteristics of the distribution of 5hmC in plasma cfDNA of adenomyosis patients and healthy subjects in a training cohort (n = 49) with genome-wide distribution of 5hmC in different genomic feature groups (healthy in green, adenomyosis in red). D. Volcano map (healthy vs. adenomyosis). HMR significantly changed (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) are marked in red (up) or green (down). The black dots represent the HMR with no difference. E-F. The pie chart shows the percentage of 5hmC peaks for up-regulated and down-regulated genes in each category of genomic traits. The Promoter region is defined as 2KB around TSS. G-H. Box diagram of \u003cem\u003eCCDC149\u003c/em\u003e and \u003cem\u003ePSMD1\u003c/em\u003e in healthy persons and adenomyosis group. I. Heatmap of 23 healthy and 26 adenomyosis patients based on top 200 DhMRs (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) (healthy in green, adenomyosis patients in red). Unsupervised hierarchical clustering was performed across genes and samples.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/66756488b74623cc5ae5131e.png"},{"id":42155005,"identity":"f5fc3136-64a1-449d-b77f-6023513582dd","added_by":"auto","created_at":"2023-08-25 17:56:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO enrichment analysis and function exploration of 5hmC markers using Metascape software (\u003c/strong\u003ep-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5\u003cstrong\u003e) . \u003c/strong\u003eA. Metascape bar graph for viewing down markers in GO enrichment clusters. B Metascape bar graph for viewing up markers in GO enrichment clusters. C. PPI network analysis of PI3K-mTOR-AKT signaling pathway. D. PPI network analysis of positive regulation of cell migration.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/868310d1146ba741547339c1.png"},{"id":42155688,"identity":"3d8f4bba-6f60-4a08-8e9f-1eccadb93e0f","added_by":"auto","created_at":"2023-08-25 18:04:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of tissue and plasma characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PCA differentiation between control and adenomyosis patients (control in green, adenomyosis patients in red). B. Characteristics of 5hmC distribution in tissue gDNA of adenomyosis patients and control (n = 47) genome-wide 5hmC distribution in different genomic features grouped (control in green, adenomyosis patients in red). C. Volcano plot. Significantly altered genes (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) are highlighted in red (up) or green (down) using the control group as the reference. Black dots represent the genes that are not differentially expressed. D. Heatmap of 22 control and 26 adenomyosis patients based on top 200 DhMRs (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) (control in green, adenomyosis patients in red). Unsupervised hierarchical clustering was performed across genes and samples. E. Tissue was associated with plasma markers (the abscissa is tissues; the ordinate is plasma) F. SIRT1 is correlated with tissue in plasma. G-H. Biomarkers in plasma overlap the biomarkers in tissue (plasma in red, tissue in green) I. 141 markers in GO enrichment bar plot (*p = 0.005-0.05, **p = 0.0005-0.005). J. 141 markers in GO enrichment and Gene‑Concept Network. The node size is proportional to the p-value calculated from the network.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/2f26833b2a170e065a4e8fb0.png"},{"id":42153884,"identity":"1e6c552b-6a62-4fde-a6ca-7586a6ed3804","added_by":"auto","created_at":"2023-08-25 17:48:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5hmC markers’ diagnosis adenomyosis in the training and validation cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Workflow for building the diagnostic model. B. PCA identification of healthy and adenomyosis patients in the training cohort (healthy in green, adenomyosis patientsin red). C, Heatmaps of ten 5hmC markers with adenomyosis in the training cohort (healthy in green, adenomyosis patients in red) Unsupervised hierarchical clustering was performed across genes and samples. D. PCA identification of healthy and adenomyosis patients in the validation cohort (healthy in green, adenomyosis patients in red). E. Heatmaps of ten 5hmC markers with adenomyosis in the validation cohort (healthy in green, adenomyosis patients in red). Unsupervised hierarchical clustering was performed across genes and samples. F-G. Confusion matrices built from the diagnostic model in the training (F) and validation (G) validation cohorts. H. Receiver operating characteristic (ROC) curve of the diagnosis model with DhMRs in training and validation cohorts for progression.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/47f4ad5d8fc9fe651163358d.png"},{"id":42152822,"identity":"1b7a069a-2da3-4006-876e-61d68514551c","added_by":"auto","created_at":"2023-08-25 17:40:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5hmC feature marker to distinguish different types and mechanisms of adenomyosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PCA differentiation between control, intrinsic and extrinsic tissue (control in green, extrinsic in blue, intrinsic in pink). B. Characteristics of 5hmC distribution in tissue gDNA of control, intrinsic and extrinsic tissue in adenomyosis patients (n= 35) (control in green, intrinsic in blue, extrinsic in pink). C. Heatmap of 21 control, 20 intrinsic and 15 extrinsic patients,based on top 200 DhMRs (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) or higher (control in green, intrinsic patientsin blue, extrinsic patients in pink). D. Volcano map (intrinsic patients vs. extrinsic patients). HMR significantly changed (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) are indicated in pink (up) or blue (down). The black dots represent The HMR with no difference. E-F. Biomarkers in plasma overlap the biomarkers in tissue (plasma in red, tissue in green). G-H. Down 57 markers (G) and up 100 markers (H) in GO enrichment bar plot (* P = 0.005-0.05, * * P = 0.0005-0.005). I. Based on the differences between 31 genes (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|≥0.5) in heatmaps of 20 intrinsic patients and 15 extrinsic patients (pink in intrinsic patients, blue in extrinsic patients).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/92e9291ce4f432cec7871dea.png"},{"id":42177857,"identity":"b9775368-0574-43bd-a0da-ad501b084c40","added_by":"auto","created_at":"2023-08-26 09:52:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2815103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/6a1e5b16-aae5-4883-9a9e-27524d88b979.pdf"},{"id":42152824,"identity":"7750c6f7-7533-4bdd-a169-2e15bed27d81","added_by":"auto","created_at":"2023-08-25 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17:41:00","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":6967889,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3253918/v1/17ca95cf273fbeea474beb95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"5-Hydroxymethylcytosine profiles in circulating cell-free DNA serve as potential biomarkers for diagnosis and classification of adenomyosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdenomyosis is a common disease in women of childbearing age, with a prevalence of 5%-70%\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e. It is a progressive disease that seriously affects women\u0026apos;s health and quality of life. Timely diagnosis and treatment can control disease progression to a certain extent, improve quality of life, improve prognosis, and reduce the likelihood of hysterectomy.\u003c/p\u003e\n\u003cp\u003eAt present, surgical pathology remains the gold standard for the diagnosis of adenomyosis. However, it is an invasive procedure that requires a relatively large amount of tissue biopsies, which is difficult for advanced-staged patients with scarce biopsies and sometimes leads to repeated biopsies that are time-consuming and painful for patients.\u003c/p\u003e\n\u003cp\u003eTherefore, the diagnosis of adenomyosis is increasingly dependent on non-operative methods such as clinical symptoms, imaging, and serological examination. However, the symptoms of adenomyosis are non-specific and about 30% of patients are asymptomatic\u003csup\u003e3\u003c/sup\u003e. Studies have shown that the sensitivity and specificity of TVS and MRI in diagnosing adenomyosis are unstable\u003csup\u003e4-8\u003c/sup\u003e, and the sensitivity is as low as 12% when examined by radiologists and non-gynecologists\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e. Although carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) have a potential role in the diagnosis of adenomyosis, it lacks specificity in distinguishing adenomyosis from other diseases\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdenomyosis is a heterogeneous disease. Kishi et al. classified adenomyosis into 4 subtypes according to the position of adenomyosis lesions and other structural components on MRI\u003csup\u003e13\u003c/sup\u003e. Different subtypes may be associated with different pathogenesis, resulting in different clinical symptoms and treatment effects\u003csup\u003e13-16\u003c/sup\u003e. However, the molecular biomarkers of different subtypes of adenomyosis are less well studied.\u003c/p\u003e\n\u003cp\u003eDue to the limitations of the current diagnostic and classification methods of adenomyosis, it is urgent to establish a non-invasive, accurate, and early serological diagnosis and classification method. Liquid biopsy is becoming increasingly popular among clinicians and patients due to its non-invasiveness, sampling convenience, dynamic tracking, and other characteristics\u003csup\u003e17-20\u003c/sup\u003e. Meanwhile, a large amount of evidence has proven that cell-free DNA (cfDNA) isolated into plasma is closely related to the occurrence and development of diseases and has shown better diagnostic\u003csup\u003e \u003c/sup\u003eand predictive ability than clinical indicators\u003csup\u003e21-25\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003e5hmC is an important epigenetic marker, which is closely related to not only organ such as brain development\u003csup\u003e26\u003c/sup\u003e but also the occurrence and development of human diseases such as neurodegenerative diseases\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e and cancers\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e. In the human genome, 5-methylcytosines (5mCs) are dynamic and reversible\u003csup\u003e31\u003c/sup\u003e, and can be oxidized into 5-hydroxymethylcytosines (5hmCs) through the Ten\u0026ndash;Eleven Translocation (TET) enzymes in an active DNA-demethylation process\u003csup\u003e31-34\u003c/sup\u003e. In addition, according to recent studies, 5hmC enrichment in gene body regions can promote gene transcription\u003csup\u003e32\u003c/sup\u003e and displays a tissue-specific distribution\u003csup\u003e35-37\u003c/sup\u003e. Therefore, we hypothesized that 5hmC profiles might have potential value in adenomyosis diagnosis and classification. \u003c/p\u003e\n\u003cp\u003eIn this study, we used 5hmC-Seal technique\u003csup\u003e38\u003c/sup\u003e to obtain genome-wide 5hmC profiles in plasma cfDNA and tissue genomic DNA from 51 patients with adenomyosis, 46 healthy persons, 26 matching patients\u0026rsquo; tissue samples from adenomyosis, and 21 normal uterine tissues. Our results demonstrated that 5hmC markers could be used to diagnose and classify adenomyosis patients. \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2020 to 2021, 51 adenomyosis (AM) patients from department of obstetrics and gynecology, Peking University Third Hospital were included in this study. Next, 47 matching tissue samples were obtained from patients who underwent surgical resection and freshly frozen at -80℃ until use. All patients had signed the patient consent form. Informed consent was obtained from each patient prior to tissue collection and the study was approved by Peking University Third Hospital Medical Science Research Ethics Committee (M2021682). The detailed clinical and pathological information is shown in Table 1. All the tissues from Peking University Third Hospital were histologically diagnosed. In addition, a total of 46 plasma samples from healthy people were collected between 2016 and 2019 at Peking University. All healthy donors were not having any acute or chronic illnesses or receiving any medications at the time of blood donation. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to diagnose and distinguish different types of adenomyosis patients using 5hmC sequencing technology. A total of 144 samples were included in the cohort analysis, including 51 adenomyosis patients, 46 healthy persons, 26 matching patients\u0026rsquo; tissue samples from adenomyosis, and 21 normal uterine tissues. All 5hmC libraries were sequenced using Illumina Next500. Meantime, in data processing, we divided 51 adenomyosis patients and 46 healthy persons into a training cohort and a validation cohort in a 1 to 1 ratio. Firstly, we identified candidate differential 5hmC modification markers in adenomyosis patients and healthy persons from the training cohort. Secondly, 26 matching tissue samples were used to identify 5hmC markers origin from adenomyosis tissue. These 5hmC markers are then used to construct the diagnostic models. Thirdly, we diagnose adenomyosis patients in the validation cohort using the model developed. Lastly, we found 5hmC markers origin from adenomyosis tissue and used these 5hmC markers to classify the patients with adenomyosis (Figure 1A). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ecfDNA extractions and quantity assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e5 ml of the patient\u0026apos;s peripheral blood was collected in a BD Vacutainer\u003csup\u003e\u0026reg;\u003c/sup\u003eEDTA tube for cfDNA extraction (Becton, Dickinson and Company, Cat# 367525). The blood was transported to the laboratory within 24 hours and centrifuged for 1350 g and 12 min for plasma preparation. The upper plasma was removed and placed into a 2 mL centrifuge tube (AXYGEN, McT-200-c) for 1350 g and 12 min to remove the leukocytes in the plasma. The upper plasma was removed and placed into a new 2 mL centrifuge tube (AXYGEN, McT-200-c). Centrifugation was performed again for 13500 g for 5 min to completely remove the red blood cell fragments in the suspended plasma and immediately stored at -80 ℃. Plasma cfDNA was extracted using Quick-cfDNA Serum \u0026amp; Plasma Kit (ZYMO, Cat# D4076) and quantified by Qubit3.0 (Thermo, Cat# Q33216). Then store at -80 \u0026deg;C. Before the library was established, nucleic acid electrophoresis was conducted to observe the fragment size (~180 bp) and then 5hmC library was established. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003egDNA extractions and quantity assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue samples from patients, including adenomyosis lesions and normal myometrium tissue samples, were stored at -80 \u0026deg;C after surgical resection. After thawing, 25 mg tissue was cut and collected. gDNA was isolated from tissues using the quick-DNA\u003csup\u003eTM\u003c/sup\u003e miniprep Plus Kit (ZYMO, Cat# D4069) and quantified by Qubit3.0 (Thermo Cat# Q33216). Then store at \u0026minus;80 \u0026deg;C. Before the library was established, nucleic acid electrophoresis was conducted to observe the fragment size (1000 bp~) and then 5hmC library was established. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e5hmC-Seal library construction and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLibrary construction was performed using a chemically selective labeling method\u003csup\u003e38\u003c/sup\u003e, in which bacteriophage T4 \u0026beta;-glucosyltransferase was used to transfer engineered glucose fragments containing azide groups to hydroxyl groups of 5hmC in the human genome. Then biotin was used to chemically modify the azide group to effectively enrich DNA fragments containing 5hmC, which could efficiently bind and capture hydroxymethylation sites on DNA. First, according to the requirements of second-generation sequencing, Qubit3.0 (Thermo, Cat# Q33216) accurately quantified cfDNA (1-10ng) and GDNA (1-50ng) were used to interrupt GDNA by enzymatic reaction, and KAPA Hyper Prep Kit (KAPA, Cat# KK8514) was used for terminal repair. It is then connected with adapters (Purkary, Cat# PKR2015, PKR2016, PKR2017, and PKR2018) that are compatible with Illumina. The linked DNA was reacted in a 25 \u0026mu;L solution containing 50 mM HEPES buffer (pH 8.0), 25 mM MgCl2, 100 \u0026mu;M UDP6-N3-GLC and 1 \u0026mu;M T4 \u0026beta;-GT (NEB, Cat# M0357L) at 37℃ for 2 h. The DNA was then purified using DNA Clean\u0026amp; ConcentratorTM-5 (ZYMO, Cat# D4014). The purified DNA was fully mixed with 1\u0026micro; L DBCO-PEG4-biotin (Click Chemistry Tools, 4.5 mM stock in DMSO) and reacted for 2h at 37℃. Similarly, DNA was purified using DNA Clean \u0026amp; ConcentratorTM-5 (ZYMO, Cat# D4014). At the same time, 2.5 \u0026mu;L Thermo Life Technologies (Thermo, Cat# 65305) was inoculated in 1 \u0026times; buffer solution (5 mM Tris pH 7.5, 0.5 mM EDTA, 1 M NaCl, 0.2% Tween 20) was directly added into the mixture and reacted at room temperature for 30 min, and then gently rotated. Finally, rinse with buffer 1-4 8 times for 5 minutes. The beads were then re-suspended in RNase-free water (Tarara, Cat# 9012) for 14-16 PCR amplification cycles. The amplified products are purified using Pure Beads (KAPA, Cat# KK8001). The concentration of the library was measured with Qubit3.0 (Thermo, Cat# Q33216). Peer 38 bp high-throughput sequencing was performed on the NextSeq 500 platform. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e5hmC-Seal profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePaired-end 38 bp high-throughput sequencing was performed on the NextSeq 500 platform. FASTQC (version 0.11.5) was used to assess the sequence quality. Raw reads were aligned to the human genome (version hg19) with bowtie2 (version 2.2.9) (Langmead and Salzberg, 2012) and further filtered with Samtools (version 1.3.1) (Li et al., 2009) to retain unique non-duplicate matches to the genome. Pair-end reads were extended and converted into bedgraph format normalized to the total number of aligned reads using bedtools (version 2.19.1) (Quinlan, 2014), and then converted to bigwig format using bedGraphToBigWig from the UCSC Genome Browser for visualization in the Integrated Genomics Viewer. Potential hMRs were identified using MACS (version 1.4.2), and the parameters used were macs 14-p 1e-3-f BAM-g hs (Consortium et al., 2007). Peak calls were merged using bedtools merge, and only those peak regions that appeared in more than ten samples and less than 1000bp were retained. Blacklisted genomic regions that tend to show artifact signals, according to ENCODE, were also filtered. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMapping and identifying 5hmC-enriched \u003c/strong\u003e\u003cstrong\u003eregions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFASTQC (version 0.11.5) was used to assess the sequence quality. Raw reads were aligned to the human genome (version hg19) with bowtie2 (version 2.2.9)\u003csup\u003e39\u003c/sup\u003e and further filtered with Samtools (version 1.3.1)\u003csup\u003e40\u003c/sup\u003e, (parameters used: Samtools view -f 2 -F 1548 -q 30 and Samtools rmdup) to retain unique non-duplicate matches to the genome. Pair-end reads were extended and converted into bedgraph format normalized to the total number of aligned reads using bedtools (version 2.19.1) \u003csup\u003e41\u003c/sup\u003e, and then converted to bigwig format, using bedGraphToBigWig from the UCSC Genome Browser for visualization in the Integrated Genomics Viewer. Potential 5hmC-enriched regions (hMRs) were identified using MACS (version 1.4.2) and the parameters used were macs 14 -p 1e-3 -f BAM -g hs\u003csup\u003e40\u003c/sup\u003e. Peak calls were merged using bedtools merge and only those peak regions that appeared in more than 10 samples and that were less than 1000bp were retained. Blacklisted genomic regions that tend to show artifact signals, according to ENCODE, were also filtered. The hMRs for each patient were generated by intersecting the individual peak call file with the merged peak file. The hMRs within chromosome X and chromosome Y were excluded and used as input for the downstream analyses. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature Selection, model training, and validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAM patients were randomly divided into training and validation cohorts with a 2:1 ratio, using train_test_split in Scikit-Learn (version 0.22.1) package in Python (version 3.6.10) \u003csup\u003e34\u003c/sup\u003e, the logistic regression CV (LR) model was chosen to establish diagnosis models\u003csup\u003e35\u003c/sup\u003e In the training cohort, we identified Differentially 5hMc-enriched Regions (DhMRs) using DESeq2 package (version 1.30.0) in R (version 3.5.0), with the filtering threshold (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|\u0026ge;0.5). To avoid overfitting, 5 rounds of 10-fold cross-validation were performed. The details were as follows: the training cohort was randomly divided into five folds, four of which were selected as the training subset, and the remaining one was the test subset. Then, the process performed was repeated 100 times using the recursive feature elimination algorithm (RFECV) in Scikit-Learn (parameters used: estimator=LogisticRegressionCV (class_weight=\u0026apos;balanced\u0026apos;, cv=2, max_iter=1000), scoring=\u0026apos;accuracy\u0026apos;) to further filtered. Meanwhile, 10-fold cross-validation was repeated 100 times in each round\u003csup\u003e39\u003c/sup\u003e, and the final markers observed in at least 3 rounds were used to build the final diagnosis model in the training cohort. Next, we trained the logistic regression CV model (LR) with the features selected from DhMRs (parameter used: maxiter=100, method=\u0026quot;lbfgs\u0026quot;). Finally, the trained LR model was used to diagnose AM patients in the validation cohort. Receiver operating characteristics (ROC) analysis was used to evaluate model performance. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExploring functional relevance of the 5hmC markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the ChIPseeker R Package (version 1.20.0) \u003csup\u003e42\u003c/sup\u003e to annotate the DhMRs, and genes closest to the marker regions were used for the following functional analyses. The gene ontology (GO) enrichment analysis (Biological Process) was done by the ClueGO (version 2.5.5) and CluePedia (version 1.5.5) plug-in from Cytoscape software (version 3.7.2). We used the following parameters: Medium Network Specificity, Bonferroni step down pV Correction, and Two-sided hypergeometric test. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis in table 1 was conducted in GraphPad Prism 8. We used two-tailed t-tests (paired or unpaired depending on the experiments) for normally distributed data. We used the percentile method to calculate 95% CIs and p-values \u0026lt;0.05 was considered statistically significant. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical characteristics of adenomyosis (AM) patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the baseline characteristics of the patients. The volunteers in the adenomyosis patient group and the control group were 51 and 46 respectively, with an average age of 45.29 and 48.30 years, respectively. There were 27 (52.94%), 26 (50.98%), and 5 (9.80%) patients with adenomyosis combined with severe dysmenorrhea (visual analog scale score (VAS)\u0026ge;7), menorrhagia and infertility, respectively. Deep infiltrating endometriosis (DIE) was found in 12 patients (23.53%), chocolate ovarian cysts in 13 patients (25.49%), and uterine myoma in 31 patients (16.45%). 37 patients in the adenomyosis group were tested for CA125, among which 29 patients showed elevated CA125, with a diagnostic accuracy of 78.38%. A total of 36 patients were tested for CA199, among which 8 patients showed elevated CA199, and the diagnostic accuracy was 22.22%. A total of 51 patients underwent TVS examination, and 41 patients were diagnosed with adenomyosis by TVS, with a diagnostic accuracy of 80.39%. A total of 35 patients underwent MRI examinations, including 20 intrinsic patients and 15 extrinsic patients. A total of 32 patients were diagnosed with adenomyosis by MRI, with a diagnostic accuracy of 91.43%. 15 adenomyosis patients (29.41%) had been pretreated with gonadotropin-releasing hormone agonist (GnRHa) 6 months before surgery. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e5hmC profiles differ between adenomyosis patients and healthy people in the training cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma is an easy-to-obtain biological sample, and plasma cfDNA differential analysis will be an ideal method for disease diagnosis. To explore the potential diagnostic value of plasma cfDNA 5hmC and to find more effective biomarkers, adenomyosis patients and healthy subjects were divided into a training cohort (n=49) and a validation cohort (n=48). The adenomyosis patients in the training cohort all had perfectly matched tissues. Meanwhile, we compared the two subtypes of adenomyosis, and the diagnosis and classification of adenomyosis could be made by liquid biopsy (Figure 1A). Cluster analysis based on 5hmC shows two different clusters (Figure 1B). One group represented adenomyosis patients and the other healthy control, suggesting that differential expression of 5hmC could distinguish adenomyosis patients from healthy control. Subsequently, we observed overall downregulation of the distribution of 5hmC expression sites between adenomyosis patients and healthy subjects, mainly in introns, intergenic regions, and promoter regions (Figure 1C). Meanwhile, we conducted differential analysis (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|\u0026ge;0.5) and observed 1500 DhMRs, including upregulate (n=966) and downregulate (n=544) regions in adenomyosis compared to healthy control (Figure 1D, Supplementary Table 1). For example, \u003cem\u003eCCDC149\u003c/em\u003e (P=0.015) was significantly enriched in adenomyosis patients (Figure 1E) and \u003cem\u003ePSMB1\u003c/em\u003e (P=0.00001) healthy subjects (Figure 1F) compared with healthy subjects. We also analyzed the 5hmC genome characteristics of the up-regulated and down-regulated loci and found multiple 5hmC peaks. Most of the enriched 5hmC loci were distributed in intronic, intergenic, and promoter regions, which was consistent with the previous results (Figure 1G-1H). We used unsupervised hierarchical clustering of the first 100 plasma cfDNA 5hmC differential loci to generally distinguish healthy control from adenomyosis patients in the training group (Figure 1I). The 5hmC marker demonstrated the ability to distinguish adenomyosis from healthy control. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eGO signal pathway and functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway analysis of differential hydroxymethylation genes (DhMGs) in adenomyosis patients (Supplementary Table 1, Table 1) showed that some typical pathways were rich in functions (Figure 2A-B). We observed that the signal pathways of DhMGs enrichment were closely related to diseases. For example, the top of down-regulated genes was related to phosphatidylinositol 3- kinase activity (Figure 2A), while up-regulated genes were mainly enriched the positive regulation of cell migration (Figure 2B). It is known that estrogen acts in adenomyosis through MAPK and PI3K/AKT/mTOR pathways, which increases the possibility that targeting PI3K pathway may provide a new method for the treatment of adenomyosis and other gynecological diseases\u003csup\u003e43\u003c/sup\u003e. In addition, other researchers have reported that the development of adenomyosis was closely related to cell migration\u003csup\u003e44-46\u003c/sup\u003e. DhMGs may be related to adenomyosis and other gynecological diseases. At the same time, the protein interaction network (Figure 2C-D) showed that these genes including \u003cem\u003eJAK2\u003c/em\u003e that regulate macrophages, \u003cem\u003eCDC42\u003c/em\u003e that regulate cell morphological migration, \u003cem\u003eMAPK1\u003c/em\u003e that participate in immunomodulation, EGFR activation, and cell proliferation, \u003cem\u003eHIF1A\u003c/em\u003e that participate in energy metabolism and activating gene transcription, and\u003cem\u003e ITGB1 \u003c/em\u003ethat participate in cell repair and metastasis\u003csup\u003e47-52\u003c/sup\u003e, are significantly related to the molecular mechanism and clinical symptoms of adenomyosis. \u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eThe landscape of 5hmC profiles in tissue genomic DNA from adenomyosis patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm the association between plasma cfDNA 5hmC markers and disease focus, we analyzed tissue samples from adenomyosis patients and normal myometrium tissue samples from non-adenomyosis patients using 5hmC-Seal technique. PCA clustering was compared according to the expression level of 5hmC, and it was found that most patient samples were separated from healthy pairs by photo clustering (Figure 3A). At the same time, the overall site distribution of 5hmC was compared between the two groups, and the expression of 5hmC was down-regulated at the tissue level in adenomyosis patients compared with healthy control. In addition, we observed overall downregulation of the distribution of 5hmC expression sites between adenomyosis patients and healthy subjects, mainly in intronic, intergenic, and promoter regions (Figure 3B), which is consistent with the results of plasma cfDNA (Figure 1C). Compared with healthy control, we found 2058 DhMRs in adenomyosis patients, including upregulate (n=1055) and downregulate (n=1003) (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|\u0026ge;0.5) (Figure 3C, Supplementary Table 2). The top 200 markers were selected for unsupervised cluster analysis, with statistically significant differences between the two groups (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|\u0026ge;0.5) (Figure 3D). Next, to verify the correlation between tissue genomic DNA and 5hmC cfDNA plasma, we performed correlation analysis on the samples of the same tissue and plasma. We found that 5hmC markers in plasma cfDNA correlated with 5hmC markers in pathological tissue genomic DNA (Figure 3E). For example, \u003cem\u003eSIRT1\u003c/em\u003e was significantly correlated (R=0.551) (Figure 3F, Figure S1A). These results validate the significance of plasma cfDNA in disease diagnosis. In order to search for characteristic markers of 5hmC reflected in plasma from focal tissues, we screened 141 DhMGs co-expressed in tissues and plasma, of which 51 genes were down-regulated and 90 genes were up-regulated (Figure 3G-H, Supplementary Table 3). We analyzed the signaling pathway enrichment of 141 DhMGs and found that signaling pathway enrichment was related to vascular smooth muscle contraction, neuronal differentiation regulation, adhesion assembly and cell-substrate junction (Figure 3I)\u003csup\u003e53-59\u003c/sup\u003e. Meanwhile, studies showed that genes including the neurodevelopment-related gene (\u003cem\u003eAUTS2\u003c/em\u003e), estrogen-related gene (\u003cem\u003eBCAS3\u003c/em\u003e), and immune-related gene (\u003cem\u003eFOXO3\u003c/em\u003e), are associated with the development of adenomyosis (Figure 3J). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic\u003c/strong\u003e\u003cstrong\u003ebiomarker performance of cfDNA \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, we generated genome-wide 5hmC profiles for patients in the validation set, including 23 healthy and 25 adenomyosis patients. By using the recursive feature elimination algorithm based on the logistic regression CV estimator, we further reduced the number of 5hmC markers from 141 to 10, which achieved the best cross-validation score (Figure 4A). Further, we found based on PCA principal component analysis and unsupervised clustering that the ten 5hmC markers (Table 2), selected by the LR model, could distinguish adenomyosis patients from healthy in both the training and validation cohorts (Figure 4B-E). Meantime, these ten 5hmC markers could effectively diagnose adenomyosis patients in the training (AUC = 1) and the validation cohorts (AUC = 0.91) (Figure 4H), achieving 1 sensitivity and 1 specificity in the training cohort (Figure 4F) and 0.88 sensitivity and 0.87 specificity in the validation cohort (Figure 4G). Finally, we also calculated the individual AUC for each of the ten 5hmC markers in the training and validation cohorts (Figure S2A). It has been reported that the accuracy of detection and diagnosis of serum CA125 and CA199 is lower than the diagnostic level of 5hmC\u003csup\u003e12\u003c/sup\u003e. Liquid biopsy with 5hmC markers can be used to diagnose adenomyosis sensitively and significantly improve the accuracy of preoperative diagnosis. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e5hmC markers can be used to classify adenomyosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with adenomyosis were evaluated by MRI imaging. Intrinsic, extrinsic, and control tissue samples were compared with 5hmC markers to explore differences in pathogenesis. PCA results showed significant differences between adenomyosis patients and control in the two subtypes (Figure 5A). Total DhMRs were most common in intronic, intergenic, and promoter regions, and statistically significant differences were found between intron and extrinsic for any genomic feature type, with greater differences in intron regions (Figure 5B). In order to further find different 5hmC modified intrinsic and extrinsic DhMRs, we chose 200 DhMRs (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|\u0026ge;0.5), the intrinsic and extrinsic unsupervised and hierarchical cluster analysis (Figure 5C), and then we directly compared the intrinsic and extrinsic DhMRs in plasma, Significant differences were found between the two genotypes (Figure S4A-F). Compared with the external genotypes, 360 DhMRs were significantly up-regulated internally and 324 DhMRs were significantly down-regulated (p-value\u0026lt;0.05 \u0026amp; |log2FoldChange|\u0026ge;0.5 ) (Figure 5D, Supplementary Table 4). Most of the up-regulated genes and down-regulated genes were distributed in intronic, intergenic, and promoter regions (Figure S4C-D). Next, we compared the overall distribution of 5hmC in tissues, which was consistent with the difference in plasma (Figure S5A-H, Supplementary Table 5). 5hmC characteristic markers were extracted from the lesion sites reflected in plasma. Compared with intrinsic and extrinsic genes, 100 DhMGs were significantly up-regulated (Figure 5E) and 57 DhMGs were significantly down-regulated (Figure 5F). In addition, in order to further explore the biological significance and mechanism of differential genes among different genotypes, we conducted signal pathway enrichment analysis for 100 up-regulated and 57 down-regulated DhMGs (Supplementary Table 6). GO functional enrichment analysis showed that, compared with external differential genes, extrinsic significant differential genes were mainly enriched in interleukin-12 regulation and production, angiogenesis, and other signaling pathways (Figure 5G). Compared with intrinsic genes, extrinsic genes are mainly enriched in the regulation of signaling pathways such as immune response, amyloidosis, nervous system development, and DNA damage (Figure 5H). Studies showed that intrinsic and extrinsic lesions with different pathogenesis and clinical symptoms, mainly related to the change of hormone levels, clinical manifestation of hemorrhage increased, more and the external main performance for tissue fibrosis, clinical manifestations, such as menstrual pain due to adenomyosis of the uterus at the same time with the characteristics of inflammatory disease caused by a variety of inflammatory response\u003csup\u003e13-16\u003c/sup\u003e. We found that the biological significance of enrichment into this pathway is closely related. In order to further explore the differences between extrinsic and intrinsic mechanisms of action, and to look for characteristic markers that can distinguish the two types, we selected \u003cem\u003eAPP\u003c/em\u003e and \u003cem\u003eNCOA2\u003c/em\u003e that are highly correlated with the disease or participate in a variety of signaling pathways, which are related to tissue fibrosis and hormone regulation. \u003cem\u003eCD47\u003c/em\u003e, \u003cem\u003eCD226\u003c/em\u003e, \u003cem\u003eCD36\u003c/em\u003e, \u003cem\u003eNFKB1\u003c/em\u003e are associated with inflammation, while \u003cem\u003eEPHA4\u003c/em\u003e, \u003cem\u003eAUTS2\u003c/em\u003e are strongly associated with neurodevelopment. Its correlation with diseases has been confirmed to some extent\u003csup\u003e60\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e61\u003c/sup\u003e . Then 31 DhMGs that were significantly enriched in the signaling pathway were extracted for unsupervised cluster analysis. It was found that the 31 DhMGs extracted could also effectively separate the two types (Figure 5I). Our result demonstrated that intrinsic and extrinsic pathogenesis are distinct, and that 5hmC markers can distinguish between the two types. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePrincipal findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt present, there are limitations in clinical diagnoses and classification of adenomyosis. There is a lack of molecular markers and an urgent need for a non-invasive liquid biopsy technique. Herein, we introduce a non-invasive method to classify patients with adenomyosis. In this study, we aimed to develop a model to diagnose and classify adenomyosis patients based on the 5hmC profiles derived from plasma cfDNA using 5hmC-Seal sequencing method. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults in the Context of What is Known\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our cohort, we found an overall 5hmC down-regulation in adenomyosis patients compared to healthy control, with 1500 DhMRs detected by differential analysis method, including up-regulated (n = 966) and down-regulated (n = 544). Next, we performed GO functional enrichment analysis to study the biological significance of differentially hydroxymethylated genes (DhMGs). We found that genes with upregulated 5hmC were mainly enrich the cell migration positive regulation pathway, protein phosphorylation pathway, MAPK cascade regulation pathway, etc. Genes with decreased 5hmC were enriched in phosphatidylinositol 3- kinase activity signaling pathway, hormone response pathway, Hemostasis pathway, etc. Studies have shown that adenomyosis is an inflammatory disease, and its molecular mechanism is related to inflammation, immunity, nerve, and fibrosis\u003csup\u003e60-62\u003c/sup\u003e. As is known to all, cfDNA is not only derived from the disease itself, but also from the changes in the microenvironment caused by the disease\u003csup\u003e63\u003c/sup\u003e. The composition of human microenvironments is complex, including matrix elements, extracellular matrix, inflammation and immune cells, etc.\u003csup\u003e64\u003c/sup\u003e, which are closely related to the occurrence, development, and treatment of diseases\u003csup\u003e65\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e66\u003c/sup\u003e. Therefore, we can see that these 5hmC marker genes may be closely correlated with the occurrence and development of diseases. \u003c/p\u003e\n\u003cp\u003ecfDNA is not only derived from focal tissue cells, but also from other somatic cells\u003csup\u003e67\u003c/sup\u003e. We selected 5hmC differential markers derived from the location of lesions reflected in plasma cfDNA to establish a diagnostic model. Ten 5hmC markers were selected based on machine learning algorithms to differentiate control from adenomyosis patients in the training and validation cohort. At the same time, the logistic regression CV model established by ten 5hmC markers was superior to existing clinical serological indicators CA125 and CA199\u003csup\u003e12\u003c/sup\u003e, with sensitivity of 0.88 and specificity of 0.87 (AUC = 0.91). Our results show that 5hmC markers extracted from cfDNA can be used as an effective biomarker for the non-invasive diagnosis of adenomyosis in patients. \u003c/p\u003e\n\u003cp\u003eBased on 5hmC markers, we divided the adenomyosis patients into intrinsic and extrinsic, and the two groups could be clustered inward and separated outward. Signal pathway enrichment analysis based on the selected differential genes highly associated with the lesion revealed the different biological function changes in intrinsic and extrinsic. This provides clues for adenomyosis classification and potential targets for better treatment of adenomyosis in the future. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, our results suggest that 5hmC markers derived from cfDNA can serve as effective epigenetic biomarkers for minimally noninvasive diagnosis and classification of adenomyosis. We also explore the molecular mechanism of the pathogenesis and development of the disease and the molecular mechanism between intrinsic and extrinsic adenomyosis. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaken together, the ten 5hmC marker associated with adenomyosis identified by genome-wide 5hmC profiling in plasma cell free DNA can be used for the liquid biopsy of adenomyosis. This is superior to existing diagnostic methods. At the same time, we found that 5hmC markers can be used to classify the two subtypes of adenomyosis at the molecular level. We also found that two specific 5hmC markers, \u003cem\u003eAPP\u003c/em\u003e and \u003cem\u003eNCOA2\u003c/em\u003e, have the potential on distinguishing subtypes.\u003c/p\u003e\n\u003cp\u003eThere are still some limitations in this study. First, the sample size is relatively small and may not be fully representative of all adenomyosis patients. The performance of our model needs to be evaluated in a larger study cohort. Secondly, the sample included in this study was Chinese women, which may not represent patients of other races. Thirdly, Comparing the longitudinal distance of the imaging lumen, muscle thickness, and adenomyosis thickness, differences were found between intrinsic, extrinsic, mixed, and adenomyomatous lesions, but the sample included in this study was only intrinsic and extrinsic. In the future, our goal is to increase the sample size of patients and find more stable and reliable 5hmC marker genes for adenomyosis diagnosis and classification. \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003e5hmC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003e5-hydroxymethylcytosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003ecarbohydrate antigen 125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003ecarbohydrate antigen 199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eTVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eTransvaginal Ultrasonography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eAPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eAmyloid Beta Precursor Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eNCOA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eNuclear Receptor Coactivator 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003ecfDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003ecell-free DNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eDhMRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eDifferentially 5hMc-enriched Regions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eGene ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eDhMGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eDifferential hydroxymethylation genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eDIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eDeep Infiltrating Endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003eImmunohistochemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.10144927536232%\" valign=\"top\"\u003e\n \u003cp\u003eVAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.89855072463769%\" valign=\"top\"\u003e\n \u003cp\u003evisual analog scale score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the essential contributions of all staff and students who participated in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZ and X-TH conceived the study and designed the experiments. LZ performed the experiments with the help of H-YC. LZ analyzed data with help from LC. LZ and X-TH wrote the manuscript with input and comments from H-YC All authors read and approved the final manuscript, H-YG participated in study design and data interpretation, JL participated in in study design, data interpretation and writing of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China (82274034), and National Key R\u0026amp;D Program of China (Grantnumber:2022YFC2704003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files. All other datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Peking University Third Hospital. Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGraziano A, Lo Monte G, Piva I, et al. 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Annals of the New York Academy of Sciences\u003cem\u003e. \u003c/em\u003e2008;1137:1-6.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1:\u0026nbsp;Adenomyosis\u0026nbsp;patients\u0026nbsp;characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003eAdenomyosis(n=51)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e45.29\u0026plusmn;5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMRI classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eType A (intrinsic) (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e20/51 (39.22)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eType B (extrinsic) (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e15/51 (29.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eType C (NA)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e16/51 (31.37)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eMenstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eSevere dysmenorrhea (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e27/51 (52.94)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eMenorrhagia (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e26/51 (50.98)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHistory of gestation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eGravidity (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e43/51 (84.31)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eVaginal delivery (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e22/51 (43.14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eCesarean section (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e18/51 (35.29)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eDilatation and curettage (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e29/51 (56.86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eAbortion or stillborn (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e5/51 (9.80)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eInfertility (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e5/51 (9.80)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eComplication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eDIE (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e12/51 (23.53)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eOvarian chocolate cyst (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e13/51 (25.49)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eUterine myoma (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e31/51 (16.45)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eExamination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eCA125 (accuracy) (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e29/37 (78.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eCA199 (accuracy) (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e8/36 (22.22)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eHemoglobin (g/l)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e112.51\u0026plusmn;27.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eTVS (accuracy) (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e41/51 (80.39)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eMRI (accuracy) (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e32/35 (91.43)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePreoperative treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eGnRHa (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e15/51 (29.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.43055555555556%\" valign=\"top\"\u003e\n \u003cp\u003eNone (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.56944444444444%\" valign=\"top\"\u003e\n \u003cp\u003e36/51 (70.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Characteristics and model coefficients of 10 5hmC markers\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eMarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003ecoef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003estd err\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e[0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.975]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003econst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e-1.7485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e-2.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e-3.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e-0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eJAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.0739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e2.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eNAPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.1364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e2.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003ePAIP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.0316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e1.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003ePBX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.0867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e3.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eQKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.0692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e2.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eSIRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.0486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e2.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eSPRY1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e-2.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eST3GAL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e2.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eSTK17B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.0722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.343915343915343%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.520282186948853%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.582010582010582%\" valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: SE: standard error of coefficient; Z: The Z-statistic of Wald\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adenomyosis, 5hmC, Machine learning, Diagnosis, Classification","lastPublishedDoi":"10.21203/rs.3.rs-3253918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3253918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt present, clinical diagnosis and classification of adenomyosis are mainly dependent on MRI or pathological examination, and little research is carried out on molecular biomarkers for the diagnosis and classification of adenomyosis. Our aim is to identify molecular biomarkers for the diagnosis and classification of adenomyosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e5hmC-Seal technique was used to obtain genome-wide 5hmC profiles from plasma cfDNA and tissue genomic DNA samples. Patients were divided into the training (n = 26) and the validation group (n = 25), and a 5hmC-based Logistic regression model from the training group was developed to verify the diagnostic capability of the model. Meanwhile, we investigated whether 5hmC molecular markers could be used to classify the two main subtypes of adenomyosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, ten 5hmC markers were identified by using machine learning techniques. The diagnostic ability reached 0.88 sensitivity and 0.87 specificity (AUC = 0.91). Next, we found that 5hmC markers can be used as markers for adenomyosis classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results showed that 5hmC markers of cfDNA had the potential to be used for diagnosis and classification in adenomyosis patients, and 5hmC-Seal may be a clinically applicable and minimally invasive method for the diagnosis and classification of adenomyosis in the future.\u003c/p\u003e","manuscriptTitle":"5-Hydroxymethylcytosine profiles in circulating cell-free DNA serve as potential biomarkers for diagnosis and classification of adenomyosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-25 17:40:54","doi":"10.21203/rs.3.rs-3253918/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84ab8582-c941-4f0c-9428-74b95c061cc1","owner":[],"postedDate":"August 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-26T09:44:13+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-25 17:40:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3253918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3253918","identity":"rs-3253918","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}