Research on a multi-omics prognostic model of liver cancer based on stacked supervised deep learning

The high heterogeneity of Hepatocellular Carcinoma (HCC) limits prognostic prediction accuracy, and existing deep learning models often struggle to capture the complex interactions within multi-omics data. To address this limitation, this study proposes a novel Stacked Supervised Auto-Encoder (SSAE) framework. It integrates miRNA, mRNA, and DNA methylation data by embedding a Cox proportional hazards model into the hidden layers of sequentially stacked modules. Using the TCGA-LIHC dataset, we systematically evaluated Early Integration (EI-SSAE) and Late Integration (LI-SSAE) strategies while comparing them against traditional machine learning methods. The results demonstrate that the Late Integration strategy significantly outperforms both Early Integration and unsupervised variants. It achieved a high Concordance Index (CI) of 0.969 compared to 0.663 for EI-SSAE. Additionally, the LI-SSAE model exhibited superior calibration accuracy with a Brier score of 0.077 and successfully stratified patients into distinct risk groups with a P-value of 1.11e-08. Bioinformatic analysis further identified critical biomarkers such as FBXW10 and TRIP13. This study confirms that the LI-SSAE model effectively enhances prognostic prediction accuracy and offers a robust tool for clinical assessment and personalized treatment in HCC.