Abstract
Skin cutaneous melanoma (SKCM) is a fatal form of skin cancer. Metabolism-related genes (MRGs) comprise a group of genes that possess the ability to modulate and regulate metabolic pathways. In this study, the expression levels of MRGs were used to classify SKCM 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. Furthermore, we developed a prognostic model to predict the prognosis of SKCM patients and evaluate the response of SKCM patients to immunotherapy based on the expression of signature genes. Pathological features were extracted using the ResNet50 deep learning framework and Cellprofiler software, and feature selection was performed using elastic regression and univariate Cox analysis to obtain 13 pathological features related to the MRGs prognostic model. Single-cell RNA sequencing (scRNA-seq) analysis identified the expression of MRGs in multiple cell types and found that SLC5A3 + malignant cells may mediate potential communication with tumor-associated fibroblasts through the PI3K-AKT pathway and cholesterol metabolism pathway. It is worth noting that, through in vitro experiments, including western blot (WB), quantitative PCR (qPCR), and immunohistochemistry (IHC) techniques, we found differences in the expression levels of signature genes between normal melanocytes and SKCM cells. In addition, suppressing the expression of the signature gene, SLC5A3, in a SKCM cell line through the utilization of small interfering RNAs (siRNAs) could inhibit the proliferation and migration of cells, as evidenced by the implementation of colony formation assay, CCK8, and cell transfection techniques.