Robust Prognostic Subtyping of Muscle-Invasive Bladder Cancer Revealed by Deep Learning-Based Multi-Omics Data Integration.

Abstract

Muscle-invasive bladder cancer (MIBC) is the most common urinary system carcinoma associated with poor outcomes. It is necessary to develop a robust classification system for prognostic prediction of MIBC. Recently, increasing omics data at different levels of MIBC were produced, but few integration methods were used to classify MIBC that reflects the patient’s prognosis. In this study, we constructed an autoencoder based deep learning framework to integrate multi-omics data of MIBC and clustered samples into two different subgroups with significant overall survival difference (P = 8.11 × 10-5). As an independent prognostic factor relative to clinical information, these two subtypes have some significant genomic differences. Remarkably, the subtype of poor prognosis had significant higher frequency of chromosome 3p deletion. Immune decomposition analysis results showed that these two MIBC subtypes had different immune components including macrophages M1, resting NK cells, regulatory T cells, plasma cells, and naïve B cells. Hallmark gene set enrichment analysis was performed to investigate the functional character difference between these two MIBC subtypes, which revealed that activated IL-6/JAK/STAT3 signaling, interferon-alpha response, reactive oxygen species pathway, and unfolded protein response were significantly enriched in upregulated genes of high-risk subtype. We constructed MIBC subtyping models based on multi-omics data and single omics data, respectively, and internal and external validation datasets showed the robustness of the prediction model as well as its ability of prognosis (P < 0.05 in all datasets). Finally, through bioinformatics analysis and immunohistochemistry experiments, we found that KRT7 can be used as a biomarker reflecting MIBC risk.