Single-cell RNA-sequencing analyses along with abundant machine learning methods develop and validate a novel metabolism-related prognostic model in SKCM

Abstract

Skin cutaneous melanoma (SKCM) is a fatal form of skin cancer. Metabolic-related genes (MRGs) are a set of genes which can mediate and control all of metabolic related pathways. In this study, the expression levels of MRGs were used to classify all patients into three molecular subtypes. Then the differentially expressed genes (DEGs) among the three MRGs molecular clusters were applied into the LASSO and COX regression analysis to identify five signature genes. Moreover, we constructed a prognostic model for forecasting the prognosis of SKCM and assess the response of the SKCM patients to immunotherapy according to the signature genes. Single-cell RNA-sequencing analyses are used to explore the expression of the five signature genes in immune or non-immune cells. Notably, different expression levels of the signature genes between normal melanocytes and SKCM cells were determined using in vitro experiments including western blot (WB), quantitative PCR (qPCR) and immunohistochemical (IHC) methods. In addition, silencing the expression of SLC5A3 in SKCM cells using small interfering RNAs (siRNAs) would inhibit the proliferation and migration of cells which could be observed by the EdU Fluorescence Labeling, CCK8 and cell transfection methods.