Evaluation of genes and molecular pathway related pathogenesis of endometriosis: A bioinformatics approach

Mahin Khalilzadeh Seivani, Noushin Modabber, Marzieh Shadpirouz, Bahareh Shateri Amiri, Parvane Bahoorzahi, Sahar Bahraminia
In: Informatics in Medicine Unlocked · 2023 · vol. 41 , pp. 101324 · doi:10.1016/j.imu.2023.101324 · W4385704308
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Abstract

Although recent studies have focused on the role of genes in the pathogenesis of endometriosis, the investigation of genes and molecular pathways involved in the pathogenesis of endometriosis with a bioinformatics approach has not been addressed yet. Hence, we aimed to investigate this issue in our study. The gene expression datasets GSE7305 and GSE124010 were obtained from the GEO database, consisting of endometriosis and normal endometrial tissue samples. Differential expression analysis was performed using the R software and LIMMA package, applying cutoff criteria of p-value 1 to identify differentially expressed miRNAs (DEmiRs) and genes (DEGs). The MultiMiR package was used to predict target genes for DEmiRs, considering the overlapping predictions from multiple databases. Venn diagrams were used to establish miRNA-target relationships and identify interactions with DEGs. Functional enrichment analyses were conducted using the Enrichr analysis tool to predict the molecular function, biological processes, cellular components, and pathways associated with the target genes. Protein-protein interaction networks were constructed using the STRING database and visualized in Cytoscape, and hub genes were identified based on their centrality measures. Key hub genes were selected by considering those shared among all three centrality measures. The expression levels of hub genes were validated in endometriosis samples using ROC curves to assess diagnostic accuracy. The AUC was calculated to compare the diagnostic value of the hub genes, using GraphPad Prism version 9.1.0. The analysis identified six hub genes (CCNA2, ESR1, AURKA, MKI67, and VEGFA) in the protein-protein interaction network. Among the miRNAs, hsa-miR-29a-3p showed the highest number of interactions with these hub genes, making it the key miRNA in the study. These findings suggest the potential involvement of hsa-miR-29a-3p in regulating the expression of the identified hub genes in endometriosis. Identifying pathways related to the expression of hub genes can be useful in designing appropriate treatment strategies. On the other hand, examining the pathways activated by hub genes can be effective in preventing of endometriosis progression.

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endometriosis

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