Aims
In the past few decades, the prognosis of glioma patients has not significantly improved. Therefore, to provide more precise medical services for glioma patients, it is urgent to identify more clinically meaningful subtypes, establish more robust clinical prediction models, and find more effective therapeutic targets. Materials and
Methods
Four distinct metabolic-associated subtypes were identified by the NMF algorithm based on metabolic genes (MEGs). A robust scoring system was constructed based on the differentially expressed genes (DEGs) screened from the four metabolic-associated subtypes with the LASSO regression algorithm and multivariate Cox regression analysis. Further analysis of scoring systems was done by different R packages. In addition, the ATP1A3 gene was screened and bioinformatics analysis of it was conducted on several public websites. GSEA software was utilized to search hallmark signaling pathways closely related to ATP1A3. Cytological experiments were used to investigate the role of ATP1A3 in the malignant progression of glioblastoma (GBM) cells. Key findings: Four metabolic-associated subtypes with significantly different clinicopathological characteristics were identified, and a robust scoring system with outstanding clinical application value was established. In addition, a tumor suppressor gene ATP1A3 was found, which is expected to be a potential therapeutic target for glioma. Significance: This study is of great significance in the diagnosis, prognosis, and prediction of the response to immune checkpoint blockers (ICBs) for glioma patients. More importantly, this study found a potential therapeutic target for glioma.
Significance
This study is of great significance in the diagnosis, prognosis, and prediction of the response to immune checkpoint blockers (ICBs) for glioma patients. More importantly, this study found a potential therapeutic target for glioma.