Integrative multi-omics identifies a diagnostic T cell signature for cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (cSCC) involves complex immune interactions. This study aimed to identify a T cell-related gene signature to characterize the immune landscape and aid in molecular diagnosis. We integrated single-cell RNA sequencing (scRNA-seq) and five bulk microarray datasets, utilizing an independent RNA-seq cohort for external validation. Feature genes were identified from the intersection of scRNA-seq-defined T cell-related genes (TRGs) and bulk differentially expressed genes using machine learning. A diagnostic nomogram was constructed, and its performance was assessed via ROC curves. In addition, immune infiltration, immunofluorescence staining, drug interactions, and clinical expression (qRT-PCR) were evaluated. Screening yielded 28 T cell-related DEGs enriched in extracellular matrix functions. machine learning selected a core signature: APOE, CYBA, and S100A2. The diagnostic model demonstrated high diagnostic performance in the studied cohorts (AUC > 0.9) across training and external validation cohorts. Clinically, qRT-PCR supported significant upregulation of CYBA and S100A2. APOE exhibited distinct immunomodulatory connectivity, correlating positively with Th17 cells and negatively with Tregs, whereas CYBA and S100A2 were associated with Treg infiltration. Immunofluorescence results revealed significantly elevated levels of S100A2 and Foxp3 in cSCC tissues compared to the control group. Pharmacogenetic analysis highlights the association of these genes, particularly the APOE gene, with drug response. This T cell-associated signature highlights the potential link between molecular diagnosis and immune characterization. Specifically, CYBA and S100A2 are identified as promising diagnostic candidate signatures, while APOE may reflect immunomodulatory heterogeneity. These findings offer insights for developing diagnostic strategies and targeted immunotherapies in cSCC.