Analysis of key candidate genes and pathways of endometriosis pathophysiology by a genomics-bioinformatics approach

Zhang Z, Zhimin Zhang, Lin Ruan, Ruan L, Mingxuan Lu, Lu M, Xiaoguang Yao, Yao X
Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology · 2019 · vol. 35(7) , pp. 576–581 · doi:10.1080/09513590.2019.1576609 · PMID:30798642 · W2916899670
Other OA: closed CC0 ⤵ 10 in-corpus citations
View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-07 ⓘ

This study integrated gene expression datasets to identify 94 differentially expressed genes and 18 central node genes, primarily involved in the angiotensin system, smooth muscle contraction, cell junction organization, and lipoxin pathways, offering insights into endometriosis pathophysiology.

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

Abstract

Endometriosis is a common disease in women, but the signaling pathways and driven genes involved remain unclear. This study integrated four datasets to elucidate potential key candidate genes and pathways in endometriosis. Four expression profile datasets including 29 endometriosis lesions and 37 normal tissues were integrated and analyzed. Differentially expressed genes (DEGs) were sorted, and the gene ontology, pathway enrichment, and protein-protein interaction network of candidate genes were then analyzed. A total of 94 shared DEGs were identified from the four datasets. The DEGs were clustered based on functions and signaling pathways through the analysis of significant enrichment. Among the DEG protein-protein interaction network complex, 87 nodes/DEGs were identified. Furthermore, 18 central node genes were identified, and most of the corresponding genes were involved in the angiotensin system, smooth muscle contraction, cell junction organization, and lipoxin pathways. Through integrated bioinformatic analysis, we identified candidate genes and pathways in endometriosis, which could improve our understanding of endometriosis.

My notes (saved in your browser only)

Condition tags

endometriosis

MeSH descriptors

Databases, Genetic Databases, Genetic Endometriosis Gene Expression Profiling Gene Expression Profiling Genomics Genomics Computational Biology Computational Biology Endometriosis Endometriosis Female Female Humans Humans Signal Transduction Signal Transduction Signal Transduction Transcriptome Transcriptome

Citation neighborhood (2-hop)

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. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (40)

Cited by (10)

Source provenance

europepmc
last seen: 2026-10-13T06:10:10.580696+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pubmed
last seen: 2026-10-08T21:23:58.330360+00:00
unpaywall
last seen: 2026-10-13T06:25:51.201866+00:00
License: CC0 · commercial use OK