EgoNet: identification of human disease ego-network modules

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• 作者：Rendong Yang (10) (9)
  Yun Bai (11)
  Zhaohui Qin (9)
  Tianwei Yu (9)
  
  10. Minnesota Supercomputing Institute for Advanced Computational Research (MSI) ; University of Minnesota ; Minneapolis ; MN ; USA
  9. Department of Biostatistics and Bioinformatics ; Rollins School of Public Health ; Emory University ; 1518 Clifton Rd ; N.E ; Atlanta ; GA ; USA
  11. Department of Pharmaceutical Sciences ; School of Pharmacy ; Philadelphia College of Osteopathic Medicine ; Suwanee ; GA ; USA
  
• 关键词：Gene expression ; Network medicine ; Machine learning ; Cancer biology ; Biological networks ; Microarray
• 刊名：BMC Genomics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：808 KB
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• 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
• 出版者：BioMed Central
• ISSN：1471-2164

文摘

Background Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks. Results We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes. Conclusions Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases.