A lipid metabolism-related gene model reveals the prognosis and immune microenvironment of cutaneous melanoma

Abstract

Abstract Objectives Lipid metabolic dysregulation plays a critical role in the biological behavior of skin cutaneous melanoma (SKCM). Hence, we aimed to identify lipid metabolism-related genes (LMGs) and possible prognostic models for SKCM, perform prognostic risk assessment, and predict possible effective therapies. Methods SKCM patient data were downloaded from The Cancer Genome Atlas (TCGA) and used as the training set; GSE65904 was used as the validation dataset. A prognostic risk model was established by multivariate Cox regression analysis and the LASSO algorithm. The samples in training and validation sets were grouped into high- and low-risk groups, respectively, in accordance with the risk model, and risk score (RS) distribution and survival ROC curve were obtained. The ‘limma’ package in R3.6.1 Version 3.34.7 was used to filter significant differentially expressed genes (DEGs) in the training set between the high- and low-risk groups. For DEGs, functional enrichment and immune infiltration analyses were used to reveal potential disease mechanisms and responses to immunotherapy. The expression level of LMGs involved in the prognostic risk was verified by diverse methods. Results A predictive model comprising four LMGs, including ADH4, ALDH7A1, HADH, and HADHA, was established to predict SKCM patient survival. Functional enrichment has revealed enriched immune-associated pathways. Different immune microenvironments were identified by immune infiltration analysis. HPA immunohistochemical analysis, Real-time PCR analysis, and Western blotting revealed the upregulation of HADH and HADHA and the downregulation of ADH4 and ALDH7A1 in melanoma tissues or cell lines compared to normal skin tissues and melanocyte cells. Conclusions LMGs, including ADH4, ALDH7A1, HADH, and HADHA involved in the predictive model may play a critical role in the biological behaviors and therapeutic response of melanoma. The model we constructed may serve as a prospective biological marker to predict the prognosis and therapeutic response of melanoma patients.