Abstract
Aberrant lipid metabolism is an early event in tumorigenesis and has been found in a variety of tumor types, especially prostate cancer (PCa). Therefore, We hypothesize that PCa can be stratified into metabolic subgroups based on glycolytic and cholesterogenic related genes, and the different subgroups are closely related to the immune microenvironment. Bioinformatics analysis of genomic, transcriptomic, and clinical data from a comprehensive cohort of PCa patients was performed. Datasets included the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) dataset, GSE70768, our previously published PCa cohort. The unsupervised cluster analysis was employed to stratify PCa samples based on the expression of metabolic-related genes. Four molecular subtypes were identified, named Glycolytic, Cholesterogenic, Mixed, and Quiescent. Each metabolic subtype has specific features. Among the 4 subtypes, the cholesterogenic subtype exhibited better median survival, whereas patients with high expression of glycolytic genes showed the shortest survival. The mitochondrial pyruvate carriers (MPC) 1 exhibited expression difference between PCa metabolic subgroups, but not for MPCs 2. Glycolytic subtypes had lower immune cell scores, while Cholesterogenic subgroups had higher immune cell scores. Our results demonstrated that metabolic classifications based on specific glycolytic and cholesterol-producing pathways provide new biological insights into previously established subtypes and may guide develop personalized therapies for unique tumor metabolism characteristics.