Metabolic deregulation in prostate cancer.

Area of Science:

• Oncology
• Metabolomics
• Genomics

Background:

• Prostate cancer (PCa) exhibits unique metabolic alterations during progression from neoplasia to aggressive disease and metastasis.
• Understanding PCa metabolism is crucial for developing novel diagnostic strategies, especially for early detection of aggressive forms.

Purpose of the Study:

• To identify and characterize distinct metabolic subgroups within prostate cancer.
• To correlate these metabolic subgroups with patient prognosis and genetic alterations.
• To evaluate the potential of metabolic profiles for early diagnosis of aggressive PCa.

Main Methods:

• Clustering of The Cancer Genome Atlas (TCGA) transcriptomics data from 498 PCa patients to define metabolic subgroups.
• Validation of subgroups using independent datasets and a multinomial regression classifier.
• Analysis of metabolic pathway deregulation using Pathifier and correlation with genetic alterations and clinical outcomes (disease-free survival, biochemical relapse).

Main Results:

• Six distinct metabolic subgroups (C1-C6) of PCa were identified, showing significant differences in disease-free survival (p < 0.0001).
• Subgroups C5 and C3 demonstrated poor prognosis with high relapse rates, linked to deregulation in lipid and carbohydrate metabolism pathways.
• Specific genetic alterations (e.g., BRCA1, TP53 in C5; RB1, STK11 in C3) were associated with distinct metabolic subgroups.
• Application of the classifier to blood protein profiles showed potential in reassigning active surveillance patients to poor-prognosis subgroups.

Conclusions:

• Metabolic expression profiles of PCa tumors can identify subgroups with distinct clinical outcomes, reinforcing the link between metabolism and prognosis.
• Elevated lipid and carbohydrate metabolism pathways may serve as more effective early diagnostic indicators for aggressive PCa than PSA levels.
• The identified metabolic subgroups and their associated genetic underpinnings provide new insights into PCa heterogeneity and progression.