Identification of a Treg-related gene signature for predicting prognosis and immunosuppression in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is an aggressive malignancy where regulatory T cells (Tregs) drive an immunosuppressive tumor microenvironment, resulting in poor prognosis. Thus, there was an urgent need to identify Treg-related molecular biomarkers to optimize SKCM's prognostic assessment and therapeutic strategies. In this study, SKCM-related datasets were acquired from public databases. First, gene modules associated with Tregs screened using Weighted Correlation Network Analysis were intersected with differentially expressed genes to obtain Treg-DEGs. Subsequently, univariate Cox proportional hazards regression, LASSO regression, and multivariate Cox proportional hazards regression were employed to construct a prognostic biomarker signature. Furthermore, the biological functions of the prognostic biomarkers were explored by integrating functional enrichment analysis, molecular regulatory network construction, and drug prediction analysis. Finally, in cellular experiments, the mRNA and protein expression levels of the biomarkers were validated using qRT-PCR and Western blot. The risk model constructed based on the 10 prognostic biomarkers (PTPRF, ULK1, TGM3, CRABP2, SV2A, HLA-DQB2, KHDRBS3, VWA5A, CRIP1, and TFAP2C) could well predict the overall survival of SKCM patients. Functional enrichment analyses indicated that high-risk patients were enriched in keratinization pathways, whereas low-risk patients showed activation of autoimmune and infection-related pathways. NEAT1 might have regulated CRABP2 via miR-375. Additionally, 54 potential drugs, including resveratrol and metronidazole, were predicted for targeted therapy. qRT-PCR and Western blot confirmed PTPRF, ULK1, TGM3, and CRABP2 were upregulated at both the mRNA and protein levels. These findings indicate that the Treg-related signature serves as robust prognostic biomarkers and may guide personalized immunotherapy in SCKM.