Increasing evidence indicates that cellular senescence, metabolic dysfunction, stromal remodeling, and immune perturbation collectively contribute to disease progression. However, senescence-related biomarkers with diagnostic potential and microenvironmental relevance in sarcopenia remain insufficiently defined. We integrated bulk transcriptome data from sarcopenia and control samples, with senescence-associated gene sets from the GenAge and CellAge databases to identify senescence-related differentially expressed genes (DEGs). Transcriptomic landscape was characterized via principal component analysis (PCA), volcano plot visualization, heatmap clustering, functional enrichment analysis, and gene set enrichment analysis (GSEA). Candidate diagnostic genes were screened using three complementary machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF). Diagnostic performance was assessed by receiver operating characteristic (ROC) analysis, nomogram construction, and decision curve analysis (DCA). Immune and stromal infiltration patterns were estimated via single-sample gene set enrichment analysis (ssGSEA) and xCell deconvolution. Single-cell RNA sequencing (scRNA-seq), pseudotime trajectory analysis, hub gene-centered GSEA/GSVA, weighted gene co-expression network analysis (WGCNA), immune checkpoint correlation analysis, and ligand-receptor communication analysis were further performed to investigate the cellular localization, dynamic expression patterns, and potential regulatory roles of hub genes. Animal experiment was conducted to validate the reliability of the main analyses. A total of 42 senescence-related DEGs were identified in sarcopenia. Functional enrichment analyses indicated significant involvement of kinase activity regulation, receptor tyrosine kinase-related signaling, JAK-STAT signaling, AMPK signaling, ERK1/2 cascade regulation, and extracellular matrix-related pathways. GSEA revealed positive enrichment of NABA Core Matrisome and negative enrichment of the citric acid cycle and respiratory electron transport pathway in sarcopenia. Integrative machine learning analysis converged on PCK1, EGFR, and MAPKAPK3 as senescence-related hub genes. PCK1 and EGFR were significantly upregulated in sarcopenia, whereas MAPKAPK3 was significantly downregulated. The individual AUC values for diagnosis were 0.777 (PCK1), 0.764 (EGFR), and 0.751 (MAPKAPK3), while the combined three-gene model achieved an improved AUC of 0.801. Immune infiltration analysis showed that PCK1 and EGFR were positively associated with fibroblast-related stromal signatures, while MAPKAPK3 showed an opposite trend and was more closely linked to Th1-cell-related immune features. Single-cell and pseudotime analyses further demonstrated that these hub genes exhibited distinct cellular localization and dynamic expression patterns across the myogenic lineage. WGCNA and immune checkpoint analyses supported their participation in broader regulatory networks associated with sarcopenia. In addition, EGFR-centered virtual knockout analysis revealed significantly altered intercellular communication, especially involving fibroblast- and muscle-related compartments. Experimental validation in a rat sarcopenia model confirmed the bioinformatics findings: PCK1 and EGFR mRNA and protein levels were significantly upregulated, while MAPKAPK3 was significantly downregulated in the sarcopenia group compared with controls (all P < 0.05), supporting the reliability of the identified hub genes. PCK1, EGFR, and MAPKAPK3 are senescence-related candidate biomarkers in sarcopenia and may reflect distinct yet interconnected biological processes involving metabolic adaptation, stromal remodeling, immune microenvironment alteration, and myogenic dysregulation. Among them, EGFR may represent an important signaling node associated with skeletal muscle microenvironmental communication in sarcopenia.
