LaCONIC: A Label-Aware and Graph-Guided Multi-Omics Collaborative Learning Model for Cancer Survival Prediction
TL;DR
LaCONIC is proposed, a label-aware and graph-guided multi-omics collaborative learning framework that bridges fine-grained molecular regulation and coarse-grained patient prognosis and consistently outperforms 14 representative survival baselines.
Abstract
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.