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May 8, 2026Frontiers in Reproductive Health0 citationsOpen Access

Multi-omics Mendelian randomization identifies mitochondrial genes associated with immune microenvironment signatures in endometriosis

SWSha WangXJXiaoyu JiMMMaako Moriyama

Key Points

  • This study aims to identify causal relationships between mitochondrial genes and endometriosis through multiomics analysis.
  • Analyzed 1,133 mitochondrial-related genes from MitoCarta3.0 using eQTL, pQTL, and mQTL data.
  • Performed two-sample Mendelian randomization with FinnGen endometriosis GWAS data and machine learning algorithms for feature selection.
  • Conducted immunohistochemical validation on endometrial samples from patients and controls.
  • Identified 128 mitochondrial genes significantly associated with endometriosis, enriched in steroid biosynthesis and mitochondrial gene expression pathways.
  • Selected five key feature genes (PHYH, GPD2, C12orf65, MRPS6, RPL21) with GPD2 and MRPS6 showing predictive value (AUC > 0.6).
  • High expression of GPD2 and MRPS6 in samples was validated immunohistochemically, confirming transcriptomic analysis.

Abstract

Background Endometriosis is a complex gynaecological disorder that affects 10%–15% of reproductive-age women and is characterized by chronic inflammation, pelvic pain, and infertility. Although mitochondrial dysfunction is implicated in endometriosis pathogenesis, the causal relationships between mitochondrial genes and endometriosis remain unclear. This study aims to elucidate these relationships through multiomics bioinformatics analysis and experimental validation. Methods We analysed 1,133 mitochondrial-related genes from the MitoCarta3.0 database, incorporating eQTL, pQTL, and mQTL data from the eQTLGen, DECODE, and Brisbane databases. Two-sample Mendelian randomization and summary data Mendelian randomization were performed using FinnGen endometriosis GWAS data to establish causal relationships. Protein–protein interaction networks were constructed, followed by GO/KEGG functional enrichment analysis. Machine learning algorithms, including LASSO, random forest, and Boruta, were applied to the GSE51981 and GSE7305 datasets for feature gene selection. Single-sample gene set enrichment analysis was used to assess correlations with immune cells. Additionally, immunohistochemical validation was performed on endometrial samples from endometriosis patients and controls. Results Mendelian randomization revealed 128 mitochondrial genes with significant causal relationships to endometriosis that were significantly enriched in the steroid biosynthesis, fatty acid elongation, and mitochondrial gene expression pathways. Five key feature genes (PHYH, GPD2, C12orf65, MRPS6, and RPL21) were selected, with GPD2 and MRPS6 demonstrating preliminary predictive value (AUC 0.6) in both the training and validation cohorts. These genes were significantly associated with macrophages and NK cells, suggesting that mitochondrial dysfunction may contribute to endometriosis through immune modulation. Immunohistochemical validation confirmed high expression of the marker genes GPD2 and MRPS6, which was consistent with the transcriptomic findings. Conclusions This study systematically elucidated the causal roles of mitochondrial genes in endometriosis, identifying GPD2 and MRPS6 as potential exploratory biomarkers and therapeutic targets. These findings provide a foundation for precision diagnostics and targeted therapeutic strategies for endometriosis management.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69fd7cd4bfa21ec5bbf05c50https://doi.org/10.3389/frph.2026.1747031
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