Development of fibrotic gene signature and construction of a prognostic model in melanoma

Abstract

Abstract Purpose Skin cutaneous melanoma (SKCM) is a malignant tumor responsible for over 75% of skin cancer deaths, the relationship between fibrosis and cancer has been increasingly appreciated. The aim of this study is to investigate the fibrotic gene signature (FGS) in melanoma and construct a prognostic model based on FGS. Methods SKCM-related datasets were obtained from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. By weighted gene co-expression network analysis (WGCNA) of the TCGA-SKCM cohort and GSE65904 cohort, core modules and central genes highly associated with fibrotic features were identified and intersecting genes were defined as fibrotic gene signature (FGS). The least absolute shrinkage and selection operator (LASSO) regression analysis and the Akaike information criterion (AIC) method were conducted to construct a prognostic model based on the FGS gene set. The fibrotic gene signature enrichment score (FGES) and fibrotic gene signature risk score (FGRS) were used to analyze immune infiltration. For FGRS, the correlation between clinical characteristics and the expression of immune checkpoint genes between different risk groups was also analyzed in depth. Results A total of 301 genes were defined as FGS, and a robust eight-gene prediction model was constructed based on FGS, these 8 genes are SV2A, HEYL, OLFML2A, PROX1, ACOX2, PRRX1, PHACTR1 and LHX6. On the basis of the model, a nomogram consisting of FGRS could accurately predict prognosis. In addition, patients in the high-risk group showed immunosuppression, while patients in the low-risk group may benefit more from immunotherapy. However, there was no significant difference between the immune infiltration of different FGES groups. Conclusion In this study, taken together, we developed a fibrotic gene signature in melanoma, and construct an eight-gene prognostic model based on the FGS to provide a reference for prognosis estimation and treatment selection for melanoma patients.