ArticleFrontiers in molecular biosciences2026
Integrated bioinformatics analysis and experimental validation of
Article in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely linked to cellular senescence. Identifying senescence-related biomarkers is crucial for discovering potential diagnostic markers and therapeutic strategies for MASLD. Methods: Senescence-related genes from GenAge were integrated with GEO datasets (GSE89632, GSE63067). Overlapping genes among differentially expressed genes, WGCNA modules, and senescence sets were screened using five machine learning algorithms (LASSO, Random Forest, Boruta, GBM, SVM-RFE). Immune infiltration and functional enrichment were assessed by single-sample gene set enrichment analysis (ssGSEA) and gene set enrichment analysis (GSEA), respectively. Finally, the expression of these four hub genes was confirmed by RT-qPCR in animal models, cell lines, and clinical samples, while SRF protein levels were further validated by Western blotting. Single-cell RNA sequencing (scRNA-seq) was utilized for cell-type-specific characterization and Results: A total of 46 genes associated with both MASLD and cellular senescence were identified. After screening by ML and validation with external datasets, Conclusion:
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