Exploration of prognosis and immune infiltration characteristics in Glioblastoma Multiforme based on Lipid Metabolism related Genes

Objectives Lipid metabolism reprogramming, a key adaptive mechanism used by tumor cells to sustain their rapid proliferative rates, involves changes in the expression of lipid metabolism-related genes (LMRGs) that regulate the synthesis, breakdown, and transport of lipids. Given the crucial role of lipid metabolism in cancer, the aim of this study was to identify novel LMRGs-related prognostic biomarkers and therapeutic targets for glioblastoma multiforme (GBM). Methods The clinical and transcriptomic data of GBM patients were extracted from TCGA databases, and the LMRGs were obtained from the GSEA database. The prognosis-related genes were screened by univariate Cox regression analysis, and the patients were stratified into two molecular subtypes through consensus clustering analysis. The immune cell infiltration status associated with the molecular subtypes was evaluated using ESTIMATE, QUANTISEQ algorithm and ssGSEA. The relevant signaling pathways were screened by the GO and KEGG analyses, GSEA and GSVA. The hub genes with prognostic value were screened through LASSO-Cox regression analysis, and a nomogram was constructed to predict prognosis of GBM patients. The distribution of the hub genes in GBM cells was determined by single-cell sequencing analysis website. Results Based on the LMRGs, we divided GBM patients into two clusters, of which cluster 2 (C2) had worse prognosis compared to cluster 1 (C1) (P = 0.0269). The abundance of M2 macrophages, NK cells, and dendritic cells (DCs) was higher in C2. Furthermore, the GO and KEGG analysis showed that the 40 prognostic LMRGs between the two clusters were mainly enriched in pathways related to lipid metabolism responses, including steroid metabolism, phospholipid metabolism and glycerolipid metabolism. The results of GSEA showed significant enrichment of β-alanine metabolism and tyrosine metabolism in C1. The results of GSVA indicated significant enrichment of phospholipid metabolism and steroid metabolism in C1. Four hub genes were identified through LASSO-Cox regression, including INPP5F , PTEN , MTMR2 and IDI1 , of which INPP5F was the risk gene with hazard ratio > 1. A nomogram integrating these genes with clinical features accurately predicted the prognosis of GBM patients. Finally, analysis of single-cell sequencing data indicated that the oligodendrocytes made up a significant proportion of INPP5F -positive cells. Conclusion We identified four prognostic LMRGs, including INPP5F , PTEN , MTMR2 and IDI1 , of which INPP5F was the risk gene. Our findings provide novel insights into the mechanisms underlying GBM progression, along with new prognostic biomarkers and targets for immunotherapeutic strategies.