TransMarker: Unveiling dynamic network biomarkers in cancer progression through cross-state graph alignment and optimal transport.

Abstract

The identification of state specific biomarkers that reflect dynamic changes in gene regulatory networks is critical for understanding cancer progression and enhancing diagnostic precision. While multilayer network models have been proposed for analyzing disease evolution, most existing methods rely solely on topological features, neglecting structural rewiring and expression variability across disease states. In this study, we introduce TransMarker, a framework designed to detect genes with regulatory role transitions, those with meaningful shifts in regulatory roles during disease progression, as dynamic biomarkers via cross-state alignment of multi-state single-cell data. TransMarker encodes each disease state as a distinct layer in a multilayer graph, integrating prior interaction data with state-specific expression to construct attributed gene networks. Contextualized embeddings for each stage are generated for each state using Graph Attention Networks (GATs), and structural shifts are quantified via Gromov-Wasserstein optimal transport. Genes with significant changes are ranked using a Dynamic Network Index (DNI), which captures their regulatory variability. These prioritized biomarkers are then applied in a deep neural network for disease state classification. We validate our approach on synthetic simulated and real world dataset of gastric adenocarcinoma (GAC), to evaluate performance across diverse scenarios and assess generalizability. TransMarker outperforms existing multilayer network ranking techniques in classification accuracy, robustness, and biomarker relevance. Ablation studies confirm the contribution of each step to overall performance. Our findings suggest that combining regulatory rewiring, temporal expression dynamics, and cross-state alignment provides a powerful strategy for identifying biologically meaningful biomarkers and modeling disease progression at single cell resolution.