Predicting Bladder Cancer Prognosis by Integrating Multiple Omics Data Through an Adversarial Autoencoder-Based Cox Proportional Hazards Network.

Bladder cancer prognosis is a critical factor in determining optimal treatment strategies. However, the heterogeneity of multi-omics data and the high dimensionality of gene features pose significant challenges for accurate survival prediction. Traditional single-omics or naive integration methods often struggle to capture complex inter-omics relationships and are vulnerable to noise from redundant genes. To address these issues, a novel deep learning framework--AAE-Cox-M, is proposed to integrate mRNA, miRNA, DNA methylation, and CNV data for survival analysis. This method features a two-stage integration strategy based on Cross-omics Pre-training, which first pretrains on one omics type and then incorporates additional omics data to enhance cross-layer feature learning. Additionally, a differential expression-based feature selection module is employed to reduce dimensionality, eliminate irrelevant signals, and highlight biologically meaningful genes. To improve representation robustness, an adversarial autoencoder is employed, combining survival loss with adversarial regularization to better model the underlying data distribution while resisting overfitting. Experiments conducted on the TCGA-BLCA dataset and four independent GEO datasets demonstrate that AAE-Cox-M consistently outperforms existing linear and deep models. Ablation studies further verify the contributions of each module.