ArticleCommunications medicine2025
Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Inflammatory Proteomic Network Architecture of Visceral Adiposity and Cardiometabolic Traits.Research square · 2026Article
- Pooled single-cell screen in colorectal cancer defines transcriptional modules linked to oncogenes.Molecular systems biology · 2026Article
- In Silico Analysis of the Dual Role of Tumor Microenvironment on Colon Cancer Subtypes.Cancer informatics · 2026Article
- CD45⁺ hybrid circulating cells may reflect tumor-immune interactions and serve as transcriptomic indicators of metastatic potential in prostate cancer.Theranostics · 2026Article
- Multi-omics analysis reveals the role of tumor-infiltrating CD4BMC cancer · 2025Article
- Immune and inflammatory resolution pathways through multi-omics using the AI-based Network Integration.Research square · 2025Article
- Novel treatment-specific causal biomarkers for colorectal cancer by omics integration.NAR genomics and bioinformatics · 2025Article
- Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Gene expression biomarkers differentiate overall survival of colorectal cancer upon targeted therapies.Research square · 2024Article
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23 authors.
Funding
Abstract
backgroundGene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions.
methodsWe constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication.
resultsWe identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related.
conclusionsThis study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab.
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