Identification and validation of shared genes and key pathways in endometriosis and endometriosis-associated ovarian cancer by weighted gene co-expression network analysis and machine learning algorithms

Yongming Du, Yichao Hu, Fubin Zhang, Tianhong Zhu, Yutao Guan
In: Research Square · 2023 · doi:10.21203/rs.3.rs-2542861/v1 · W4319300478
preprint OA: green CC0
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study identified shared genes and pathways between endometriosis and endometriosis-associated ovarian cancer using WGCNA and machine learning, pinpointing EDNRA and OCLN as predictive biomarkers for EAOC.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This preprint used ovarian carcinoma and endometriosis gene expression datasets from GEO (plus TCGA and GTEx for validation) to apply weighted gene co-expression network analysis (WGCNA) and identify genes and pathways shared between endometriosis-associated ovarian cancer (EAOC) and endometriosis. The authors report 262 shared genes enriched mainly in cytokine–cytokine receptor interaction pathways, and then used protein–protein interaction analysis and multiple machine learning methods to derive two characteristic hub genes, EDNRA and OCLN, along with a diagnostic nomogram with strong predictive performance. They report that OCLN is upregulated in ovarian cancer tissues while EDNRA is downregulated, and that dysregulated expression of both genes relates to ovarian cancer prognosis, with GSEA implicating cancer- and immune-related pathways; a caveat is that the work is presented as an unreviewed preprint. Relevance to endometriosis: the study directly analyzes shared gene networks between endometriosis and EAOC and proposes EDNRA/OCLN as biomarkers for EAOC arising in the context of endometriosis, so it is centrally about endometriosis-associated ovarian cancer biology.

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Abstract

Abstract Background: Endometriosis is a widespread disease in reproductive age. Epidemiological studies reported that patients with endometriosis had an increased risk of developing endometriosis-associated ovarian cancer (EAOC). The present study aimed to identify shared genes and key pathways that commonly interacted between EAOC and endometriosis. Methods: The expression matrix of ovarian cancer and endometriosis were collected from the Gene Expression Omnibus database. The weighted gene co-expression network analysis (WGCNA) was used to construct co-expression gene network. Functional enrichment analyses were conducted to clarify the potential regulatory mechanisms. Protein-protein interaction (PPI) network and machine learning algorithms were applied to identify characteristic genes. CIBERSORT deconvolution algorithm was used to explore the difference in tumor immune microenvironment. Receiver operating characteristic curves were utilized to assess the clinical diagnostic ability of hub genes. Furthermore, diagnostic nomogram was constructed and evaluated for supporting clinical practicality. Results: We identified 262 shared genes between EAOCand endometriosis via WGCNA analysis. They were mainly enriched in cytokine-cytokine receptor interaction, which may be considered a common mechanism between EAOC and endometriosis. After PPI network and machine learning algorithms, we recognized two characteristic genes (EDNRA, OCLN) and established a nomogram that presented an outstanding predictive performance. The hub genes demonstrated remarkable associations with immunological functions. OCLN were highly upregulatedin ovarian cancer compared to non-tumor tissues, while expression levels of EDNRA were significantly downregulated in ovarian cancer samples. Survival analysis indicated that dysregulated expressions of EDNRA and OCLNwere closely correlated with prognosis of ovarian cancer patients. GSEA analyses revealed that the two characteristic genes were mainly enriched in the cancer- and immune-related pathways. Gene drug interaction analysis found 15 drugs compound that interacted with the hub genes. Conclusion: We identified two hub genes (EDNRA, OCLN) and constructed a nomogram to predict the risk of EAOC based on WGCNA analyses and machine learning algorithms. They can be used as effective predictive biomarkers for detecting EAOC. Our findings pave the way for further investigation of potential candidate genes and will aid in improving the diagnosis and treatment of EAOC in endometriosis patients.

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endometriosis

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