Machine Learning Unveils Sphingolipid Metabolism's Role in Tumour Microenvironment and Immunotherapy in Lung Cancer

Abstract

TME is a core player in the development of a cancerous lesion, the immune evasive potential of the lesion, and its response to therapy. Sphingolipid metabolism, which governs a number of cellular processes, has been recognised as a player involved in the control of immune heterogeneity within the TME. Sphingolipid metabolism-related genes prevalent in the TME of LUAD and LUSC were identified using transcriptomic analysis and clinical samples from the TCGA and GTEx databases. Lasso regression and survival SVM in the Etra Application were employed as machine learning algorithms to determine patient outcomes and to reveal key immune factors associated with gene expression and chemotherapeutic response. Gene expression in lung cancer cells was explored through scRNA-seq data. Thereafter, mediation impact analysis was further performed to explain the defined relation between the immune cell subsets and sphingolipid metabolites and their risk impact on lung cancers. Genes involved in sphingolipid metabolism were dysregulated in lung cancer, correlating with immune cell infiltration and TME remodelling. Lasso regression identified ASAH1 and SMPD1 as strong prognostic markers. scRNA-seq revealed higher gene expression in T cells, macrophages and fibroblasts. Sphingomyelin partially mediated the link between T lymphocyte abundance and lung cancer risk. High-risk phenotypes exhibited enhanced immune evasion via altered regulatory T cell and macrophage polarisation. This research highlights the contribution of sphingolipid metabolism in shaping the TME and its implications for immunotherapy.