Abstract
Lipid metabolism reprogramming is critical in various biological processes and is considered a hallmark in cancers. The expression data of mRNA and corresponding follow-up information were obtained from TCGA used as a training set and the CGGA used as a validating set. Based on the expression of genes involved in lipid metabolism, 550 glioma samples of the training set were clustered by unsupervised classification method. Then, we construct a lipid metabolism-related risk signature based on the Lasso regression algorithm. The biological mechanism related to risk score was investigated by gene sets enrichment analysis (GSEA). 67 lipid metabolism- and immune-related genes were identified. Two robust groups were yielded by consensus clustering of the 550 samples. Subgroup2 correlated with a significantly better clinical outcome compared with Subgroup1. A 16-genes risk signature was constructed, and the overall survival of patients is dramatically better in the low-risk than the high-risk group. Consistently, the 16-gene signature showed pretty prognostically predicting ability by the receiver operating characteristic curve with areas under curve more than 0.8 in both TCGA and CGGA. Furthermore, the risk score was identified as an independent prognostic factor for glioma. Moreover, samples with a high-risk score were correlated with a higher level of immune infiltration and associated with a higher expression of immune checkpoints, which indicated an inhibitory tumor immune microenvironment. Our study demonstrated a new sight of lipid metabolism-related and immune-associated genes and constructed a 16-gene risk signature to predict prognosis and immunotherapy for glioma patients.