A bioinformatics analysis of gene expression in endometrial cancer, endometriosis and obesity

Shan Shibu, Shrinal Vasa, Swayamprabha Samantaray, S. Samantaray, Nidhi Joshi, Dolatsinh Zala, Rajeev Chaudhari, Rajeshkumar G Chaudhari, Kartik Chauhan, Harsh Patel, Bhavin Parekh, Anupama Modi
Women & health · 2024 · vol. 65(1) , pp. 60–70 · doi:10.1080/03630242.2024.2437493 · PMID:39653677 · W4405174470
article OA: closed CC0 ⤵ 2 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-09 ⓘ

This bioinformatics analysis identified shared genetic links between endometrial cancer, endometriosis, and obesity, highlighting IGF-1 as a potential prognostic marker for endometrial cancer and recurrent hub gene alterations as therapeutic targets.

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Abstract

Endometrial cancer (EC), endometriosis (ENDO), and obesity (OBY) are interconnected conditions in women that may share underlying genetic pathways. This study aimed to identify shared genetic pathways and differential gene expressions across these conditions to uncover potential therapeutic targets. A bioinformatics pipeline was applied using gene expression datasets from the GEO database, incorporating differential expression analysis, functional and pathway enrichment, PPI network construction, survival analysis, and mutational profiling across 198 samples. The analysis revealed 26 shared differentially expressed genes (DEGs), with IGF-1, CREBBP, EP300, and PIAS1 identified as key hub genes. Elevated IGF-1 expression was significantly linked to poorer survival outcomes in EC patients (p < .05). Frequent mutations were observed in these hub genes, suggesting their critical role in disease mechanisms. This study highlights genetic links among EC, ENDO, and OBY, emphasizing high IGF-1 expression as a potential prognostic marker in EC and recurrent alterations in hub genes as promising therapeutic targets. These findings provide insights into the shared genetic underpinnings of these conditions and present new avenues for targeted therapies.

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Condition tags

endometriosis

MeSH descriptors

Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology

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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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