ArticleBriefings in bioinformatics2021
Sample-specific perturbation of gene interactions identifies breast cancer subtypes.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- IRDRM: Exploring Biological Traits and Clinical Significance in Breast Cancer Through Gene Pairs and Somatic Mutation Profiles.Interdisciplinary sciences, computational life sciences · 2026Article
- MicroSSNet: an R package for microbial network construction and analysis at the single-sample and aggregated levels.BMC bioinformatics · 2026Article
- A gene interaction network-based molecular taxonomy of dilated cardiomyopathy highlighting immune and inflammatory heterogeneity.Clinical and experimental medicine · 2026Article
- Lactate metabolism-related interaction perturbation network enables robust stratification of hepatocellular carcinoma.Discover oncology · 2026Article
- The GFPT2/O-GlcNAcylation/c-Myc axis promotes bladder cancer progression.Journal of translational medicine · 2026Article
- A squamous epithelial gene interaction perturbation network index for risk stratification in esophageal squamous cell carcinoma.Journal of Cancer · 2026Article
- Article
- Feature learning augmented with sampling and heuristics (FLASH) improves model performance and biomarker identification.NPJ systems biology and applications · 2025Article
- Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer.International journal of molecular sciences · 2025Article
- Subgroup Analysis of Differential Networks with Latent Variables.Statistics and computing · 2025Article
- Metabolic genes interaction perturbation network identified and validated CD24 as a novel prognostic gene in hepatocellular carcinoma.Discover oncology · 2025Article
- Sample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma.iScience · 2025Article
- Targeting Matrix Metalloproteinase-9 to Alleviate T Cell Exhaustion and Improve Sepsis Prognosis.Research (Washington, D.C.) · 2025Article
- Using single-sample networks to identify the contrasting patterns of gene interactions and reveal the radiation dose-dependent effects in multiple tissues of spaceflight mice.NPJ microgravity · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Selective enhanced cytotoxicity of amino acid deprivation for cancer therapy using thermozyme functionalized nanocatalyst.Journal of nanobiotechnology · 2024Article
- Patient-specific analysis of co-expression to measure biological network rewiring in individuals.Life science alliance · 2024Article
- Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment.iScience · 2023Article
- Comprehensive analyses of brain cell communications based on multiple scRNA-seq and snRNA-seq datasets for revealing novel mechanism in neurodegenerative diseases.CNS neuroscience & therapeutics · 2023Review
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.
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