Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblasts Signature for Predicting Prognosis and Immunotherapeutic Responses in Skin Cutaneous Melanoma

Objective: Cancer-associated fibroblasts (CAFs) are the most prominent cellular component in the stroma of skin cutaneous melanoma (SKCM). The aim of this study was to explore the CAFs-related genes and to develop a CAFs-related model to predict the prognosis and immunotherapeutic efficacy of SKCM. Method: We collected mRNA expression and clinical information for 449 SKCM patients from the TCGA (The Cancer Genome Atlas) database and 210 patients in the GSE65904 dataset of the GEO (Gene expression Omnibus) database. CAFs infiltrations were quantified by the estimate the proportion of immune and cancer cells (EPIC) method. Weighted gene coexpression network analysis (WGCNA) was used to identify genes CAFs-related genes. A CAFs risk signature was then developed using the univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. Gene set enrichment analysis (GSEA) and single sample gene set enrichment analysis (ssGSEA) were applied to elucidate the molecular mechanisms. The tumor immune dysfunction and exclusion (TIDE) algorithm is further used to assess immunotherapeutic responses. Results: A six-gene (NOTCH3, HEYL, BGN, COL5A1, SULF1, COL1A1) CAFs prediction model was constructed. SKCM patients were divided into high– and low–CAFs-risk groups according to the median CAFs risk score. patients in the CAFs high-risk group had a significantly worse prognosis. GSEA showed that the ECM receptor-interacting and basal cell oncogene set was highly and significantly enriched in the high–CAFs-risk group. The ssGSEA results showed that CAFs risk scores were significantly positively correlated with basal cell carcinoma, melanogenesis and Notch signaling pathways, and significantly negatively correlated with regulation of autophagy and Toll-like receptor signaling pathways. ssGSEA results also showed that patients in the high–CAFs-risk group had a poorer response to immunotherapy. Conclusion: The six CAFs signature genes explored in this study not only reliably predicted prognosis, but also assessed clinical immunotherapy response in SKCM patients, which may have important clinical implications for tailoring anti-CAFs therapy and immunotherapy regimen for each SKCM patient.