Bioinformatics analysis for identifying hub genes in endometriosis and recurrent implantation failure: molecular pathways to enhanced IVF success

Parvin Hakimi, Mahshid Alborzi, Nahideh Afshar Zakariya, Khadijeh Pouya, Maryam Rezazadeh, Soudeh Ghafouri‐Fard
In: Middle East Fertility Society Journal · 2025 · vol. 30(1) · doi:10.1186/s43043-025-00247-4 · W4413946256
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This bioinformatics study identified 43 shared differentially expressed genes in endometriosis and recurrent implantation failure, highlighting pathways like IL-6 signaling and pinpointing ESR1, SOCS3, MYH11, CYP11A1, and CLU as potential hub genes.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-08 ⓘ

This bioinformatics study integrated three human endometrial gene-expression datasets from GEO—two for endometriosis (GSE7305, GSE11691) and one for recurrent implantation failure (RIF; GSE26787)—to identify shared differentially expressed genes using standardized microarray preprocessing and limma-based differential expression, then intersected DEG lists across conditions. Functional enrichment and protein–protein interaction analyses highlighted pathways and processes including interleukin-6 signaling, FOXO-mediated transcription, smooth muscle contraction, semaphorin interactions, and signal transduction/apoptosis regulation, yielding 43 shared DEGs. Hub genes proposed as key candidates included ESR1, SOCS3, MYH11, CYP11A1, and CLU, but the study’s conclusions are limited by reliance on public microarray datasets and small sample sizes and the use of different Affymetrix platforms (mitigated by analyzing the endometriosis datasets independently). This paper is centrally about endometriosis — it computationally identifies hub genes and shared molecular pathways linking endometriosis with recurrent implantation failure.

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Abstract

Abstract Background Recurrent embryo implantation failure (RIF) poses a considerable obstacle in the management of in vitro fertilization (IVF), as IVF failure has been linked to the presence of endometriosis, the growth of endometrial-like tissue outside the uterus. Therefore, this study aimed to reveal the molecular mechanisms connecting endometriosis and RIF, offering valuable knowledge on potential therapeutic targets and biomarkers. Methods A comprehensive investigation was conducted on gene expression data from the GEO database, focusing on three datasets related to endometriosis and RIF, which revealed distinct gene expression patterns and facilitated functional enrichment analysis to identify significant biological processes and molecular pathways associated with these differentially expressed genes. Protein–protein interaction networks were also established to identify critical genes. Results A total of 43 differentially expressed genes (DEGs) were identified, shared between endometriosis and RIF, with enrichment analysis highlighting pathways related to interleukin-6 signaling, FOXO-mediated transcription, smooth muscle contraction, and semaphorin interactions. Gene ontology studies revealed the significance of signal transduction and apoptosis regulation. ESR1, SOCS3, MYH11, CYP11A1, and CLU were identified as hub genes with potential as therapeutic targets and diagnostic indicators. Conclusion This study advances our understanding of the molecular framework underlying endometriosis and RIF. This presents potential possibilities for tailored treatment approaches and enhanced therapeutic results for individuals experiencing repeated or severe reproductive difficulties.

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Condition tags

endometriosis

Citation neighborhood

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. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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