Identification of novel causally related genes in adenomyosis: An integrated summary data-based Mendelian randomization study and bioinformatics analysis

Qiaomei Yang, Fuchun Zhong, Xianhua Liu, Jingxuan Hong, Li Chen, L L Chen, Hao Lin, Jianhui Fu, Xinye Zheng, Junying Jiang
Medicine · 2025 · vol. 104(24) , pp. e42768 · doi:10.1097/md.0000000000042768 · PMID:40527793 · W4411398505
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Abstract

Adenomyosis (AM) is recognized as a complex gynecological and endocrine disorder that contributes to infertility and elevates the risk of pregnancy complications; however, its underlying genetic basis remains unidentified. This study aimed to identify potentially causative genes that may relate to AM. We conducted a summary data-based Mendelian randomization analysis using single nucleotide polymorphisms as an instrumental variable, along with expression quantitative trait loci data from whole blood and uterus as exposures and AM as the outcome. Summary data-based Mendelian randomization incorporating multiple single nucleotide polymorphisms was employed as a sensitivity analysis to reduce the false-positive rate. The false discovery rate was used to adjust for multiple tests. Furthermore, bioinformatics analysis was performed to elucidate the biological functions in which the novel target risk genes may be involved and to evaluate the diagnostic performance of risk genes based on data from the Gene Expression Omnibus database. We have identified 24 novel protein-coding genes potentially causally linked to AM, none of which have been previously reported in the context of this disease. The most relevant candidate genes are ARHGEF35, AMT, RCVRN, GMPPB, and INTS1. Bioinformatics analysis indicates that these genes play critical roles in essential biological functions, including base-excision repair, negative regulation of various cell cycle processes, and metabolism-related pathways in AM. Differential gene expression analysis was conducted using the Gene Expression Omnibus database, comparing data from AM patients and healthy controls. Specifically, DNA2 and INTS1 displayed high expression levels, whereas EFCAB2, HLA-DQA2, and RPS26 exhibited low expression levels. The receiver operating characteristic curve analysis for the Predictive Diagnostic Index revealed an area under the curve of 0.8 for the combined analysis of the 5 risk genes. Our study identifies novel potentially causal genes associated with AM, suggesting that these genes hold promise as therapeutic targets and biomarkers for early diagnosis. These findings significantly enhance our understanding of the underlying mechanisms and provide new insights into potential curative targets for AM.

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Condition tags

adenomyosisinfertility

MeSH descriptors

Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Computational Biology Computational Biology

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