Integrated Multi-Omics Data Reveals the Molecular Subtypes of Prostate Cancer

Abstract

Prostate cancer (PCa), the second most common male malignancy, is the fifth leading cause of cancer-related death and places notable burdens on medical resources. Most of the previous subtypes only focused on one or fewer types of data or ignored the genomic heterogeneity among PCa patients with diverse genetic backgrounds. Therefore, it is essential to precisely identify the specific molecular features and judge potential clinical outcomes based on multi-omics data. In the current study, we first identified the PCa multi-omics classification (PMOC) system based on the multi-omics, including mRNA, miRNA, lncRNA, DNA methylation, and gene mutation, using a total of ten state-of-the-art clustering algorithms. The PMOC1 subtype, also called the inflammatory subtype, contains the highest expression levels of immune checkpoint proteins, moderate activated immune-associated pathways. The PMOC2 tumor-activated subtype demonstrated the worst prognosis, which might be impacted by the activated cell cycle and DNA repair pathways, and also characterized by the most genetic alterations of mutant TP53, mutant APC and copy number alteration of 8q24.21 region. The PMOC3 subtype is likely to be a balance subtype, with the activated oncogenic signaling pathways, including hypoxia, angiogenesis, epithelial mesenchymal transition, and PI3K/AKT pathways. As well as with the activated proinflammatory pathways, including IL6/JAK/STAT3, IL2/STAT5, Notch and TNF- signaling. Additionally, PMOC3 subtype also linked with the activation of the androgen response and the high response rate of ARSI treatment. Taken together, we defined the PMOC system for PCa patients via multi-omics data and consensus results of ten algorithms, this multi-omics consensus PCa molecular classification can further assist in the precise clinical treatment and development of targeted therapy.