LaCONIC: A Label-Aware and Graph-Guided Contrastive Multi-Omics Collaborative Learning Model for Cancer Risk Prediction

Abstract

Investigating accurate cancer survival prediction models has important clinical value for optimizing therapeutic strategies and improving clinical outcomes. Although an increasing number of models have shifted from relying solely on clinical variables to integrating multi-omics data, there remains insufficient exploitation and integrative utilization of gene regulatory network (GRN) structures and cancer subtype information, which limits the predictive accuracy and biological interpretability of multi-omics models. Therefore, this study proposes label-aware and graph-guided multi-omics collaborative learning framework, termed LaCONIC, to achieve accurate and interpretable cancer survival prediction. To obtain biologically meaningful regulatory features of different molecular types, we first construct a gene regulatory network comprising multiple molecular entities and their relations, and design a heterogeneous graph self-supervised pre-training module to obtain unified graph-based gene representations. To fully leverage multiomics and multi-modal information, we develop an adaptive cross-omics representation learning module that performs intraomics modality alignment (i.e., graph and expression modals) and inter-omics representation alignment, thereby achieving synergistic integration of multi-source information. Moreover, to comprehensively capture global cross-omics molecular interactions, we present a graph-guided cross-omics interactive learning module that explicitly encodes gene regulatory network priors within a Transformer-based architecture. Finally, we introduce cancer subtype information to construct a label-aware constraint mechanism that improves predictive performance while enhancing inter-class separability and intra-class consistency of the learned representations. Experiments on multiple cancer datasets show that LaCONIC outperforms existing state-of-the-art methods in various metrics. SHAP-based interpretability analysis on breast cancer further identifies survival-associated regulatory modules and high-risk molecules (e.g., hsa-miR-148a-3p, ARMC1, TMEM242), highlighting its potential for biological mechanism elucidation and prognostic assessment. The source code is available at https://github.com/Liangyushi/LaCONIC.