Overall Survival analysis in metabolism-based classes. (A) Nonnegative matrix factorization clustering algorithm by metabolism genes in training cohort. (B) Kaplan–Meier curve of patients in the training cohort between metabolism-based classes patients for overall survival (OS). (C) Nonnegative matrix factorization clustering algorithm by metabolism genes in validation cohort. (D) Kaplan–Meier curve of patients in the validation cohort between metabolism-based classes patients for OS.

Metabolic reprogramming in cancer significantly impacts immune responses within the tumor microenvironment, but its influence on cancer immunotherapy effectiveness remains uncertain. This study aims to elucidate the prognostic significance of metabolic genes in cancer immunotherapy through a comprehensive analytical approach. Utilizing data from the IMvigor210 trial (n = 348) and validated by retrospective datasets, we performed patient clustering using non-negative matrix factorization based on metabolism-related genes. A metabiotic score was developed using a “DeepSurv” neural network to assess correlations with overall survival (OS), progression-free survival, and immunotherapy response. Validation of the metabolic score and key genes was achieved via comparative gene expression analysis using qPCR. Our analysis identified four distinct metabolic classes with significant variations in OS. Notably, the metabolism-inactive and hypoxia-low class demonstrated the most pronounced benefit in terms of OS. The metabolic score predicted immunotherapeutic benefits with high accuracy (AUC: 0.93 at 12 months). SETD3 emerged as a crucial gene, showing strong correlations with improved OS outcomes. This study underscores the importance of metabolic profiling in predicting cancer immunotherapy success. Specifically, patients classified as metabolism-inactive and hypoxia-low appear to derive substantial benefits. SETD3 is established as a promising prognostic marker, linking metabolic activity with patient outcomes, advocating for the integration of metabolic profiling into immunotherapy strategies to enhance treatment precision and efficacy.

Article Access: https://doi.org/10.1002/mef2.89

More about MedComm-Future Medicine: https://onlinelibrary.wiley.com/journal/27696456

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