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

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.