Integrative multi-omics analysis and machine learning refine global histone modification features in prostate cancer

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Prostate cancer (PCa) is a leading cause of male cancer mortality.
• Tumor heterogeneity and treatment resistance are key challenges in PCa management.
• Histone modifications' role in PCa progression and therapy resistance is not well understood.

Purpose of the Study:

• To investigate histone modification-driven heterogeneity in prostate cancer.
• To develop a machine learning model for classifying PCa subtypes.
• To identify potential therapeutic targets based on identified subtypes.

Main Methods:

• Integrative multi-omics analysis and machine learning.
• Development of the Comprehensive Machine Learning Histone Modification Score (CMLHMS).
• Single-cell RNA sequencing and drug sensitivity analysis.

Main Results:

• CMLHMS classifies PCa into high and low subtypes with distinct biological and clinical features.
• High-CMLHMS tumors show increased proliferation, metabolic activity, and link to castration-resistant PCa (CRPC).
• Low-CMLHMS tumors exhibit stress-adaptive and immune-regulatory phenotypes; distinct drug sensitivities were observed for each subtype.

Conclusions:

• The CMLHMS model effectively stratifies PCa, offering insights into heterogeneity.
• This study identifies novel therapeutic strategies for advanced PCa.
• Findings contribute to precision oncology for prostate cancer patients.