Abstract
BACKGROUND: Glioblastoma multiforme (GBM) is the most prevalent fatal central nervous system tumor. Notably, the survival rates after surgical intervention and active radiotherapy are not optimistic. Therefore, identifying new GBM-related biomarkers is a top priority in current research. METHODS: Transcriptome and clinical information of patients with GBM were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. According to the SMIM20 expression levels, the samples were divided into high- and low-expression groups and used for differential expression gene (DEG) analysis. Functional enrichment analyses, including Gene Ontology (GO), gene set enrichment analysis, and immune cell infiltration, were performed on SMIM20-related DEGs. Subsequently, univariate and multivariate Cox regression analyses were performed to screen the risk factors associated with the poor prognosis of SMIM20, and the clinical significance of SMIM20 in GBM was explored by constructing a prognostic nomogram. RESULTS: In total, 156 DEGs were screened, of which 131 were upregulated and 25 were downregulated. Kaplan-Meier analysis revealed that the total survival time of the SMIM20 high expression group was significantly lower than that of the SMIM20 low-expression group. Finally, the nomogram map had good predictive value for evaluating GBM prognosis of patients. CONCLUSIONS: High expression of SMIM20 is associated with poor outcomes in GBM. The DEGs and pathways identified in this study reveal potential molecular mechanisms underlying the occurrence and progression of GBM. Our study identifies potential new biomarkers and therapeutic targets for the treatment of GBM.