胶质母细胞瘤脂质代谢相关基因的生物信息学分析OA

Objective To identify glioblastoma(GBM)-related lipid metabolism genes using bioinformatics methods.Methods The GSE68848 dataset was obtained from the Gene Expression Omnibus(GEO)database,and differential expression genes(DEGs)were screened and then intersected with lipid metabolism-related genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the functions of lipid metabolism-related DEGs.Hub genes were further identified via protein-protein interaction(PPI)network analysis and validated using the gene expression profiling interactive analysis database.Gene set enrichment analysis was used to analyze the signaling pathways significantly enriched by key hub genes.The tumor immune estimation resource database was applied to evaluate the correlation between the expression of key hub genes and immune cell infiltration.The correlations between key hub genes and immune checkpoints,stromal score,immune score,and ESTIMATE score were analyzed using the"reshape2"package and the ESTIMATE algorithm.Results Two of the 148 lipid metabolism-related DEGs were identified as key hub genes.Enrichment analysis showed that lipid metabolism-related DEGs significantly regulated various lipid metabolism processes.One important module and 10 key hub genes were identified by the MCODE and MCC algorithms,respectively.CYP27A1 and HSD3B7 were identified as key hub genes,both of which were associated with poor prognosis in GBM.Immune analysis revealed that CYP27A1 and HSD3B7 were significantly correlated with various immune cells,multiple immune checkpoints,stromal score,immune score,and ESTIMATE score.Conclusion CYP27A1 and HSD3B7 are identified as key lipid metabolism-related genes that may be involved in GBM,providing novel insights and perspectives for exploring the pathogenesis and potential therapeutic targets of GBM.