Identifying gene regulation modules associated with tumor metastasis using a network decomposition approach and combinatorial fusion analysis.

We systematically evaluated whether modular decomposition of molecular networks into gene regulatory modules (GRMs) enables the identification of metastasis‑associated genes. We developed an efficient bioinformatics framework that integrates subgraph extraction with combinatorial fusion analysis (CFA) to identify and prioritize metastasis‑associated GRMs in cancer networks. We validated top‑ranked GRMs using cancer hallmark annotations, enrichment analysis, drug-target associations, and survival data, and assessed GRM cooperativity through comparisons with prior metastasis studies. The proposed approach consistently outperformed existing methods in identifying metastasis‑associated GRMs. Robustness analyses across ten feature combinations and comparisons between three‑node and four‑node GRMs confirmed stable performance under diverse settings. Application to three independent KIRC metastasis cohorts further demonstrated improved identification of metastasis‑related GRMs. Overall, this integrated GRM‑based framework reliably captures coordinated regulatory patterns linked to metastasis and shows potential for identifying clinically relevant target genes and therapeutic drug candidates.