Identification and validation of cell senescence genes in recurrent spontaneous abortion via multiple bioinformatics algorithms.

ABSTRACT: Recurrent spontaneous abortion (RSA) represents a significant challenge in reproductive obstetrics, affecting approximately 5% of couples globally. Despite various treatments, the effectiveness of these interventions remains highly contentious. Emerging evidence suggests that cellular senescence plays a critical role in the pathogenesis of multiple diseases, potentially implicating its involvement in RSA as well. This study integrated two RNA sequencing datasets and employed Weighted Gene Co-Expression Network Analysis (WGCNA) alongside five machine learning algorithms (XGBoost, Boruta, LASSO, SVM-RFE, and Random Forest) to identify cellular senescence genes linked to RSA. Gene expression was validated at both the transcriptome and protein levels using qPCR, Western blot, and immunofluores

SUBMITTER: Wei Y 

PROVIDER: S-EPMC12514248 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature