Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC).

ABSTRACT: Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor cells from clinical samples are poorly understood, and their impact on clinical outcomes are unknown. Our objective was to identify the metabolic features in the tumor compartment that are most clinically impactful. Methods: A computational deconvolution approach using the DeMixT algorithm was applied to bulk RNASeq data from The Cancer Genome Atlas to determine the proportion of each gene's expression that was attributable to the tumor compartment. A machine learning algorithm desig

SUBMITTER: Pervin J 

PROVIDER: S-EPMC10643182 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature