Abstract
Glutamine metabolism is pivotal in cancer biology, profoundly influencing tumor growth, proliferation, and resistance to therapies. Cancer cells often exhibit an elevated dependence on glutamine for essential functions such as energy production, biosynthesis of macromolecules, and maintenance of redox balance. Moreover, altered glutamine metabolism can contribute to the formation of an immune-suppressive tumor microenvironment characterized by reduced immune cell infiltration and activity. In this study on lung adenocarcinoma, we used consensus clustering and applied 101 machine learning methods to systematically identify key genes associated with glutamine metabolism. This comprehensive approach yielded a refined understanding of how these metabolic pathways contribute to cancer progression and patient outcomes. Importantly, a robust risk model was constructed using these identified genes to predict overall survival in lung adenocarcinoma patients. This model not only enhances our ability to stratify patient risk but also provides potential targets for therapeutic intervention aimed at disrupting glutamine metabolism and sensitizing tumors to existing treatments. Such insights underscore the critical role of glutamine metabolism in cancer and highlight avenues for personalized medicine in oncology research.