MoACG: A self-attention and gated fusion-based multi-omics and clinical data integration model for pan-cancer prognosis prediction

To perform in-depth integration and analysis of cancer multi-omics data and clinical data to enhance the predictive capability for cancer prognosis.

Multi-omics data (mRNA, lncRNA, and miRNA profiles) and clinical data for 3 cancer types, namely ovarian cancer, liver cancer, and colorectal cancer, were retrieved from The Cancer Genome Atlas (TCGA) database. A novel cancer prognosis prediction model, MoACG (Multi-omics Attention Clinical Gating Model), was constructed based on the self-attention mechanism to explore potential associations among different omics layers, and gated fusion was employed to adaptively integrate multi-omics information with clinical information(age, sex, and treatment modality). The effectiveness of the model was validated through comparison with multiple machine learning methods, and the interpretability algorithm DeepLIFT was utilized to quantify gene contributions to the model and identify core prognostic genes.

In the ovarian cancer, liver cancer, and colorectal cancer datasets, five-fold cross-validation yielded the area under curve (AUC) values of receiver operating characteristic (ROC) curve of (0.793±0.042), (0.791±0.065), and (0.789±0.086), respectively, and the AUC values of precision-recall curve (AUPR) were (0.915±0.020), (0.855±0.058), and (0.917±0.039), respectively. The comprehensive performance surpassed that of 9 other machine learning models. Ablation experiments demonstrated that the 3-omics data integration model exhibited optimal predictive performance across all cancer types. The DeepLIFT algorithm identified MED8, DLGAP4, and NABP2 as genes associated with liver cancer, showing high concordance with existing research findings and effectively stratifying patient survival risk based on expression levels (P<0.005).

Compared with previous studies, the MoACG model, constructed by integrating multi-omics and clinical data, effectively enhances the predictive performance for cancer prognosis, thereby providing a novel approach for cancer diagnosis, treatment, and prognostic research.