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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.<\/jats:p>","DOI":"10.1093\/bib\/bbag264","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:46:07Z","timestamp":1778154367000},"source":"Crossref","is-referenced-by-count":0,"title":["ssNetShift: single-sample metabolic network rewiring reveals hidden prognostic subtypes beyond clinical staging in gastric cancer"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0648-6876","authenticated-orcid":false,"given":"Genjin","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University , 4221 Xiang'an South Road, Xiang'an District, Xiamen 361005 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