Identification of glycometabolism-related genes for predicting the prognosis of patients with glioblastoma and its correlation with immune infiltration

Abnormal glucose metabolism has emerged as a prominent metabolic characteristic of various cancers, significantly influencing critical processes such as cell proliferation, metastasis, and invasion. Moreover, it plays an integral role in shaping the tumor immune microenvironment. Glioblastoma (GBM), recognized as the most prevalent malignant histopathology of the central nervous system (CNS), also exhibits notable abnormalities in glucose metabolism. Despite its clinical significance, comprehensive studies delineating the characteristics of glucose metabolism in GBM remain scarce. In this study, we aimed to systematically investigate the differential expression of glycometabolism-related genes (DEGs) in GBM. We utilized a combination of differential expression gene analysis and Weighted Correlation Network Analysis (WGCNA) to identify relevant DEGs.To enhance the robustness of our findings, we incorporated two independent datasets (GSE68848 and GSE108474) and performed batch effect correction using the “sva” R package. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate the potential molecular functions of these DEGs. To refine our focus, we employed least absolute shrinkage and selection operator (LASSO) regression analysis to pinpoint hub DEGs associated with glycometabolism. Expression level verification and prognosis analyses were conducted using Gene Expression Profiling Interactive Analysis 2 (GEPIA2). In addition, we highlighted the need for experimental validation of the 35 glycometabolism-related DEGs in clinical GBM samples to confirm their expression patterns and functional relevance. Our findings revealed that 11 genes—ADRA2A, CARTPT, LMNA, NPTX1, PRKCE, PTPRN, RTN2, DDIT4, EXT2, KIF5C, and PPIB—were differentially expressed between GBM tissues and healthy controls (HC). Notably, two hub genes, PPIB and PTPRN, demonstrated significant associations with the prognosis of GBM patients. Further analysis into immune infiltration, gene set enrichment analysis (GSEA), and mutation patterns was conducted on these glycometabolism-related hub DEGs.We also emphasize that experimental validation of these 35 candidate genes in patient samples is essential to confirm their clinical relevance and biological roles. This study highlights the critical role of glycometabolism-related genes in GBM, offering potential targets for therapeutic intervention. The identification of PPIB and PTPRN as hub genes associated with prognosis underscores their significance in GBM pathology. Future research should prioritize experimental validation of the 35 glycometabolism-related DEGs in clinical specimens and further investigate the mechanistic underpinnings of these genes in the context of GBM, paving the way for innovative therapeutic strategies.