Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

Maryam Hosseini, Maryamsadat Hosseini, Behnaz Hammami, Mohammad Kazemi
Journal of assisted reproduction and genetics · 2023 · vol. 40(10) , pp. 2439–2451 · doi:10.1007/s10815-023-02903-y · PMID:37555920 · W4385684451
article OA: green CC0 ⤵ 4 in-corpus citations
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⚙ AI-generated summary by claude@2026-06+body, 2026-06-07 ⓘ

This study identified 119 differentially expressed genes and constructed a regulatory network to discover hub genes that enabled a diagnostic model with 0.98 AUC and identified rofecoxib and retinoic acid as potential therapeutic agents for endometriosis.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This study aimed to identify potential diagnostic biomarkers and therapeutic targets for endometriosis by merging three GEO gene expression datasets, identifying differentially expressed genes, and building a TF–mRNA–miRNA regulatory network. Using the DEGs, the authors applied multiple algorithms to detect hub genes and then trained a GaussianNB diagnostic model with reported AUC values of 0.98 in training and 0.92 in validation, while also performing molecular docking to evaluate drug-like compounds. They reported 119 DEGs between endometriosis and non-endometriosis samples, a regulatory network of 52 mRNAs, 249 miRNAs, and 37 transcription factors, five hub genes (including GATA6, HMOX1, HS3ST1, NFASC, and PTGIS), and potential therapeutic effects of rofecoxib and retinoic acid based on docking. The authors note that identified targets demand more experimental confirmation. This paper is centrally about endometriosis — it uses bioinformatics and machine learning to propose diagnostic biomarkers (hub genes) and therapeutic targets (docking-identified compounds).

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Condition tags

mesh:D004715endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis MicroRNAs MicroRNAs MicroRNAs MicroRNAs

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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