ssNetShift: single-sample metabolic network rewiring reveals hidden prognostic subtypes beyond clinical staging in gastric cancer.

Abstract

Current metabolomics approaches predominantly rely on group-level differential abundance screening. While effective for identifying biomarkers, this paradigm often overlooks upstream regulatory hubs and fails to resolve the inter-individual heterogeneity inherent in complex diseases. To bridge this gap, we present ssNetShift, a single-sample network framework that identifies personalized topological driver metabolites by quantifying topological rewiring rather than static concentration deviations. By integrating linear interpolation-based network estimation with an extended neighbor-shift metric, ssNetShift systematically characterizes how specific metabolites alter their connectivity and centrality within individual patient networks. We applied ssNetShift to a multicohort gastric cancer dataset comprising 389 patients and 313 controls. Benchmarking analyses demonstrated that ssNetShift consistently outperformed conventional approaches: unlike group-level methods (e.g. NetShift), it recovered survival-associated driver metabolites masked by population averaging; unlike single-sample abundance methods (e.g. personalized perturbation profiles), it prioritized silent drivers, metabolites with stable abundance but drastic topological reorganization, thereby capturing system-level dysregulation. Crucially, ssNetShift revealed hidden prognostic subtypes within the same clinical stage, separating patients with identical tumor-node-metastasis (TNM) staging into distinct risk classes characterized by specific metabolic wiring patterns (e.g. nucleotide and tryptophan hubs) and significantly divergent survival outcomes. Collectively, ssNetShift provides a risk stratification dimension orthogonal to traditional staging, offering a robust tool for uncovering mechanistic drivers and refining prognostic resolution in heterogeneous malignancies.