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Updated: Jan 28, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
Published on: May 28, 2021
Analysis of key candidate genes and pathways of endometriosis pathophysiology by a genomics-bioinformatics approach
Zhimin Zhang1, Lin Ruan2, Mingxuan Lu3
1a The Department of Obstetrics , The Fourth Hospital of Shijiazhuang City , Shijiazhuang , China.
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.
Insights
This study identified key genes and pathways in endometriosis, a common women's disease. Findings reveal novel insights into the molecular mechanisms driving endometriosis progression.
Area of Science:
- Reproductive biology
- Genomics
- Bioinformatics
Background:
- Endometriosis is a prevalent gynecological condition affecting women's health.
- The underlying molecular mechanisms, including specific signaling pathways and genes, remain incompletely understood.
Purpose of the Study:
- To identify key candidate genes and signaling pathways implicated in endometriosis.
- To enhance the understanding of endometriosis pathogenesis through integrated bioinformatic analysis.
Main Methods:
- Integration and analysis of four gene expression profile datasets (29 endometriosis lesions, 37 normal tissues).
- Identification and analysis of differentially expressed genes (DEGs), gene ontology, pathway enrichment, and protein-protein interaction networks.
- Identification of central node genes within the DEG interaction network.
Main Results:
- A total of 94 shared DEGs were identified across the four datasets.
- The protein-protein interaction network analysis revealed 87 nodes/DEGs.
- Eighteen central node genes were identified, primarily associated with the angiotensin system, smooth muscle contraction, cell junction organization, and lipoxin pathways.
Conclusions:
- Integrated bioinformatic analysis successfully identified potential key candidate genes and pathways in endometriosis.
- These findings contribute to a deeper comprehension of endometriosis pathophysiology.
- The identified genes and pathways may serve as future therapeutic targets.
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