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

Zhimin Zhang, Lin Ruan, Mingxuan Lu, Xiaoguang Yao
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
article OA: closed CC0 ⤵ 10 in-corpus citations
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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.

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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.

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Condition tags

endometriosis

MeSH descriptors

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

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Cited by (10)

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