ArticleBiochemical genetics2026
Anoikis-Related Genes Can Accurately Predict the Occurrence of Endometriosis: A Retrospective Cohort Study via Machine Learning Analysis.
Article in Biochemical genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Advancing the frontiers of phytotherapy: a comprehensive review of botanical interventions targeting mitochondrial quality control to ameliorate endometriosis.Frontiers in cell and developmental biology · 2026Review
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7 authors.
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Abstract
Endometriosis is one of the most common benign gynecological disorders, characterized by persistent pain and challenges with fertility. Anoikis, a specific form of apoptosis, occurs when cells detach from the extracellular matrix. Recent researches have revealed that anoikis resistance is the most important prerequisite for endometriosis development, but it's still not entirely apparent, yet, what part anoikis plays in endometriosis pathogenesis. We obtained the GSE141549 dataset from the GEO database, which served as our training cohort. Anoikis-related genes (ANRGs), sourced from the GeneCards database, were integrated with differentially expressed genes (DEGs) identified in GSE141549. Consequently, we obtained a collection of differentially expressed anoikis-related genes (DE-ANRGs). Subsequently, we employed machine learning algorithms to screen for key diagnostic DE-ANRGs and developed a nomogram model to predict and diagnose endometriosis. Ultimately, we validated our findings through in vitro experiments and an online endometriosis database. Within the training cohort, a total of 47 DE-ANRGs were identified. Furthermore, three machine learning methods pinpointed four diagnostic genes (CAV1, PDK4, CSPG4, SERPINE1). Based on these genes, we constructed a nomogram to facilitate the prediction and clinical diagnosis of endometriosis. To assess model's predictive accuracy, clinical adaptability, and discriminative ability. We performed calibration curves, Receiver Operating Characteristic curves and decision curve analysis. All assessments demonstrated our model's outstanding performance. Ultimately, consistent expression trends of four genes were observed in GSE7305 test cohort, clinical specimens, and the Turku database when compared to the training cohort. In addition, we also have revealed the immune landscape differences, which may offer new promising immunotherapeutic targets for endometriosis patients in the future. In addition to offering novel perspectives on the role of anoikis in endometriosis pathogenesis, our analysis also identified a panel of distinctive biomarkers with significant diagnostic potential.
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