Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer.

ABSTRACT:

Background

Pancreatic cancer (PAC) is one of the most malignant cancer types and immunotherapy has emerged as a promising treatment option. PAC cells undergo metabolic reprogramming, which is thought to modulate the tumor microenvironment (TME) and affect immunotherapy outcomes. However, the metabolic landscape of PAC and its association with the TME remains largely unexplored.

Methods

We characterized the metabolic landscape of PAC based on 112 metabolic pathways and constructed a novel metabolism-related signature (MBS) using data from 1,188 patients with PAC. We evaluated the predictive performance of MBS for immunotherapy outcomes in 11 immunotherapy cohorts from both bulk-RNA and single-cell perspectives. We validated our results using immunohistochemistry, western blot

SUBMITTER: Guo Y 

PROVIDER: S-EPMC10533800 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature