Ruyue Ma
Yu Zheng
*The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Molecular Targets and Therapeutics
Volume 14 - 2024 |
doi: 10.3389/fonc.2024.1387860
Identification of key genes associated with endometriosis and endometrial cancer by bioinformatics analysis
Provisionally accepted- Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Endometriosis (EMS) is one of the risk factors for endometrial cancer(EC), but the disease progression mechanism remains unclear. We aim to use bioinformatics to identify critical genes and pathways that may be shared between EMS and EC, providing potential therapeutic biomarkers for the pathogenesis of endometriosis. We downloaded four endometriosis datasets and one endometrial cancer dataset from the Gene Expression Omnibus (GEO) database. We determined differentially expressed genes (DEGs) in EMS and EC groups compared to the control group using the limma package. Then, we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) analyses to identify enriched pathways related to DEGs. Subsequently, we validated gene expression and conducted survival analysis in the TCGA database. We constructed a gene regulatory network using the NetworkAnalyst online tool involving various transcription factors and searched for potential drugs in the Drug-Gene Interaction database. The results revealed 141 common DEGs between EMS and EC compared to the control group. GO analysis indicated that these genes mainly regulate growth and development and signaling pathways. KEGG analysis highlighted their association with the JAK-STAT signaling pathway and leukocyte transendothelial migration.Furthermore, PPI analysis revealed ten hub genes (APOE, FGF9, TIMP1, BGN, C1QB, MX1, SIGLEC1, BST2, ICAM1, MME) with high connectivity, where APOE, BGN, C1QB, and BST2 gene expressions correlated with cancer genomic atlas data and were associated with tumor immune infiltration. Notably, only APOE and BGN were significantly correlated with patient prognosis. In conclusion, through comprehensive bioinformatics analysis, this study identified key genes as potential biomarkers for EC. These findings contribute to our understanding of the molecular mechanisms underlying the pathogenesis and prognosis of EC and the identification of potential drug targets.
Keywords: bioinformatics, biomarker, Gene Expression, Endometriosis, endometrial cancer
Received: 18 Feb 2024; Accepted: 28 Oct 2024.
Copyright: © 2024 Ma, Zheng, Wang, Xu, Zhang, Xie, Zhang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yu Zheng, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Jianing Wang, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Hong Xu, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Ruirui Zhang, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Zhijia Xie, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Lei Zhang, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
Ruiheng Zhao, Suzhou Ninth People's Hospital, Suzhou, Jiangsu Province, China
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