Background: 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. Machine learning algorithms were applied to identify characteristic genes. CIBERSORT deconvolution algorithm was used to explore the difference in tumor immune microenvironment. Furthermore, diagnostic nomogram was constructed and evaluated for supporting clinical practicality.
Results: We identified 262 shared genes between EAOC and endometriosis via WGCNA analysis. They were mainly enriched in cytokine-cytokine receptor interaction. After protein-protein interaction 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. Survival analysis indicated that dysregulated expressions of EDNRA and OCLN were closely correlated with prognosis of ovarian cancer patients. gene set enrichment analyses revealed that the two characteristic genes were mainly enriched in the cancer- and immune-related pathways.
Conclusion: 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. More research is required to determine the exact mechanisms by which these two hub genes affecting the development and progression of EAOC from endometriosis.
Keywords: WGCNA; endometriosis; endometriosis-associated ovarian cancer; machine learning algorithms.
© 2023 Japan Society of Obstetrics and Gynecology.