陆军军医大学学报2026，Vol.48Issue(6)：809-821,13.DOI:10.16016/j.2097-0927.202512061
MoACG:一种基于自注意力机制和门控融合的多组学与临床数据整合模型用于多癌种预后预测
MoACG:A self-attention and gated fusion-based multi-omics and clinical data integration model for pan-cancer prognosis prediction
摘要
Abstract
Objective To perform in-depth integration and analysis of cancer multi-omics data and clinical data to enhance the predictive capability for cancer prognosis.Methods 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.Results 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).Conclusion 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.关键词
自注意力机制/癌症多组学/门控融合/癌症预后/可解释性Key words
self-attention mechanism/cancer multi-omics/gated fusion/cancer prognosis/interpretability分类
医药卫生引用本文复制引用
秦茂洋,沈俊杰,王龙昊,郭泓麟,伍亚舟..MoACG:一种基于自注意力机制和门控融合的多组学与临床数据整合模型用于多癌种预后预测[J].陆军军医大学学报,2026,48(6):809-821,13.基金项目
国家自然科学基金项目(82173621,82574207) Supported by the National Natural Science Foundation of China(82173621,82574207). （82173621,82574207）
