Establishment and validation of a prognostic prediction model for glioma based on key genes and clinical factors.

Background: Glioma is a common brain tumour that is associated with poor prognosis. Immunotherapy has shown significant potential in the treatment of gliomas. Herein, we proposed a new prognostic risk model based on immune- and mitochondrial energy metabolism-related differentially expressed genes (IR&MEMRDEGs) to enhance the accuracy of prognostic assessment in patients with glioma.

Methods: Data from samples from 671 glioma patients and 5 normal controls with available follow-up data and prognostic outcomes were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. All data were downloaded on 13 November 2023. IR&MEMRDEGs were screened from the GeneCards website and published literature. Prognostic prediction models were constructed and analysed using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, Kaplan-Meier (KM) curve, and receiver operating characteristic (ROC) curve analyses. Single-sample gene set enrichment analysis (ssGSEA) was further performed to ascertain the percentage of immune cell infiltration in the glioma specimens.

Results: Bioinformatics analysis of the GEO and TCGA databases identified eleven MEMRDEGs with dysregulated expression in gliomas: and . Further analysis identified , and as separate predictive factors for glioma, among which and exhibited superior accuracy [area under the ROC curve (AUC) >0.9], while , and demonstrated slightly lower accuracy (0.7< AUC <0.9), and displayed poor accuracy (0.5< AUC <0.7). Among these genes, the levels of , and were significantly higher in the high-risk group (HRG) compared with the low-risk group (LRG) (P<0.001), indicating a negative association with patient prognosis. In contrast, and were significantly downregulated in the HRG compared to the LRG (P<0.05), indicating a potential correlation with patient outcomes. Subsequently, prognostic models were constructed based on IR&MEMRDEG and MEMRDEGs to anticipate the outcomes of glioma patients, while the predictive efficacy of the model was validated via KM and ROC curve analysis. The results revealed that , and had superior accuracy in predicting glioma prognosis. The ssGSEA results showed that only was negatively linked to the amount of immune cell infiltration in the LRG, while displaying a positive connection in the HRG (r value>0), indicating that the expression levels of may have a distinct influence on the tumour immune microenvironment.

Conclusions: The present study confirmed the significant predictive value of for glioma prognosis, which may guide immunotherapy for glioma treatment.