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

Author:

Du Yongming1ORCID,Hu Yichao2,Zhang Fubin1,Guan Yutao1,Sheng Yuehua1ORCID

Affiliation:

1. Department of Gynecology The First Affiliated Hospital of Ningbo University Ningbo Zhejiang China

2. Department of Urology The First Affiliated Hospital of Ningbo University Ningbo Zhejiang China

Abstract

AbstractBackgroundEpidemiological 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.MethodsThe 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.ResultsWe 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.ConclusionOur 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.

Publisher

Wiley

Subject

Obstetrics and Gynecology

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