Glutamine is characterized as the nutrient required in tumor cells. The study based on glutamine metabolism aimed to develop a new predictive factor for pan-cancer prognostic and therapeutic analyses and to explore the mechanisms underlying the development of cancer.
The RNA-sequence data retrieved from TCGA, ICGC, GEO, and CGGA databases were applied to train and further validate our signature. Single-cell RNA transcriptome data from GEO were used to investigate the correlation between glutamine metabolism and cell cycle progression. A series of bioinformatics and machine learning approaches were applied to accomplish the statistical analyses in this study.
As an individual risk factor, our signature could predict the overall survival (OS) and immunotherapy responses of patients in the pan-cancer analysis. The nomogram model combined several clinicopathological features, provided the GMscore, a readable measurement to clinically predict the probability of OS and improve the predictive capacity of GMscore. While analyzing the correlations between glutamine metabolism and malignant features of the tumor, we observed that the accumulation of TP53 inactivation might underlie glutamine metabolism with cell cycle progression in cancer. Supposedly, CAD and its upstream genes in glutamine metabolism would be potential targets in the therapy of patients with IDH-mutated glioma. Immune infiltration and sensitivity to anti-cancer drugs have been confirmed in the high-risk group.
In summary, glutamine metabolism is significant to the clinical outcomes of patients with pan-cancer and is tightly associated with several hallmarks of a malignant tumor.