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, Hu Y, Zhang F, Fubin Zhang, Yutao Guan, Guan Y, Yuehua Sheng
Journal of Obstetrics and Gynaecology Research · 2023 · vol. 49(8) , pp. 2135–2150 · doi:10.1111/jog.15720 · PMID:37340502 · W4381469888
article OA: closed CC0 ⤵ 1 in-corpus citation
View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-08 ⓘ

This study identified 262 shared genes, primarily involved in cytokine-cytokine receptor interaction, between endometriosis and endometriosis-associated ovarian cancer, pinpointing EDNRA and OCLN as key diagnostic genes.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

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.

My notes (saved in your browser only)

Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (48)

Cited by (1)

Source provenance

europepmc
last seen: 2026-10-06T06:14:42.462031+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pubmed
last seen: 2026-10-06T06:13:58.716141+00:00
unpaywall
last seen: 2026-10-06T06:46:56.404536+00:00
License: CC0 · commercial use OK