ArticleScientific reports2021
Novel cancer subtyping method based on patient-specific gene regulatory network.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.Bioinformatics (Oxford, England) · 2026Article
- Decoding non-coding SNPs: systems genomics modelling dissects the heterogeneity of IBD.Molecular systems biology · 2026Article
- Using Machine Learning to Identify Factors Affecting Antibody Production and Adverse Reactions After COVID-19 Vaccination.Vaccines · 2026Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Network analysis reveals causal relationships among individual background risk factors leading to influenza susceptibility.Scientific reports · 2025Article
- Multiomics Signature Reveals Network Regulatory Mechanisms in a CRC Continuum.International journal of molecular sciences · 2025Article
- Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information.Scientific reports · 2024Article
- Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.Arteriosclerosis, thrombosis, and vascular biology · 2023Review
- Article
- Exploration of hub genes, lipid metabolism, and the immune microenvironment in stomach carcinoma and cholangiocarcinoma.Annals of translational medicine · 2022Article
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Authors and funding
6 authors.
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Abstract
The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.
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