ArticleFrontiers in genetics2021
Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Analysis of microarray and single-cell RNA-seq finds gene co-expression, cell-cell communication, and tumor environment associated with cytoskeleton protein in epithelial-mesenchymal transition in ovarian cancer.Discover oncology · 2026Article
- Node properties of biomarkers within the protein-protein interaction network derived from breast cancer-associated genes.PloS one · 2026Article
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- Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.Bioinformatics (Oxford, England) · 2025Article
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- Sensitivity analysis on protein-protein interaction networks through deep graph networks.BMC bioinformatics · 2025Article
- Pan-cancer analysis and experimental verification of cytochrome B561 as a prognostic and therapeutic biomarker in breast cancer.Discover oncology · 2025Article
- Breast cancer prediction based on gene expression data using interpretable machine learning techniques.Scientific reports · 2025Article
- PICALM as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltration in Breast Cancer.Applied biochemistry and biotechnology · 2024Article
- Association of ADP‑ribosylation factor family genes with prognosis and immune infiltration of breast cancer.Oncology letters · 2024Article
- Integrative Bioinformatics Analysis Reveals Potential Target Genes and PTEN Signaling in Breast Cancer and Effect of Zingiber officinale (Ginger) and Allium sativum (Garlic) extract on It.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Mutational signatures for breast cancer diagnosis using artificial intelligence.Journal of the Egyptian National Cancer Institute · 2023Article
- Article
- AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA).PloS one · 2023Article
- Identification of circulating immune landscape in ischemic stroke based on bioinformatics methods.Frontiers in genetics · 2022Article
- Lung Cancer Stage Prediction Using Multi-Omics Data.Computational and mathematical methods in medicine · 2022Article
- A Framework to Predict the Molecular Classification and Prognosis of Breast Cancer Patients and Characterize the Landscape of Immune Cell Infiltration.Computational and mathematical methods in medicine · 2022Article
- HAUS5 Is A Potential Prognostic Biomarker With Functional Significance in Breast Cancer.Frontiers in oncology · 2022Article
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8 authors.
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Abstract
Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further
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