LaCONIC: A Label-Aware and Graph-Guided Multi-Omics Collaborative Learning Model for Cancer Survival Prediction
Accurate cancer survival prediction is important for risk stratification and personalized treatment. However, patient prognosis is shaped by complex molecular regulation, cross-omics dependencies, and subtype-dependent tumor heterogeneity, making accurate and interpretable prediction challenging. Existing multi-omics survival models often treat omics profiles mainly as feature views and learn coarse patient-level representations, leaving fine-grained gene regulatory topology and subtype-structured prognostic heterogeneity underexploited. We propose LaCONIC, a label-aware and graph-guided multi-omics collaborative learning framework that bridges fine-grained molecular regulation and coarse-grained patient prognosis. LaCONIC first learns topology-aware gene structural representations through heterogeneous regulatory graph pretraining on a multi-entity disease gene regulatory network. It then performs adaptive cross-omics representation learning for intra-omics expression-graph fusion and inter-omics alignment, followed by ceRNA-guided attention to model miRNA-mRNA-lncRNA interactions under biological priors. During training, LaCONIC requires subtype annotations and leverages them through subtype classification and multi-level contrastive constraints to preserve subtype-dependent survival heterogeneity and improve risk-discriminative representation learning. Experiments across multiple TCGA cancer datasets demonstrate that LaCONIC consistently outperforms 14 representative survival baselines, while SHAP-based analyses identify prognosis-associated regulatory modules and biomarkers.