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bioRxiv · 10.1101/2025.11.17.688950

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

Astuti, A.·Schweikert, C.·Hsu, D. F.·KALOK, N.
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Abstract

The hypothesis that modular decomposition of molecular networks into gene regulatory modules (GRMs) enables identification of metastasis-associated gene candidates was systematically evaluated. We developed an efficient bioinformatics approach to identify metastasis-associated GRMs in cancer networks. Using a subgraph method to extract GRMs, we applied combinatorial fusion analysis (CFA) to prioritize them based on relevance. To validate the top-ranked GRMs, we employed the hallmark of cancer annotations, enrichment analysis, drug-target gene evaluation, and survival analysis. The cooperativity effect of GRMs was examined through comparative analysis with previously published studies. Results demonstrate that the proposed approach could effectively identifies metastasis-associated GRMs relative to existing methods. Robustness was assessed through ten feature combinations and comparisons of three-node versus four-node GRMs. Results consistently confirmed the methods reliability across different scenarios. This integrated approach combines the subgraph method and CFA to uncover metastasis-associated GRMs effectively. Validation through enrichment analysis, drug-target gene insights, and survival data demonstrates its potential for identifying metastasis-associated target genes and discovering therapeutic drug candidates. Compared to three KIRC cohorts metastasis studies, our approach more effectively identifies GRMs associated with metastasis.

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Astuti, A., Schweikert, C., Hsu, D. F., KALOK, N.. 2025-11-18. Identifying gene regulation modules associated with tumor metastasis using a network decomposition approach and combinatorial fusion analysis. https://doi.org/10.1101/2025.11.17.688950

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