title: Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer.
abstract: In microarray-based case-control studies of a disease, people often attempt to identify a few diagnostic or prognostic markers amongst the most significant differentially expressed (DE) genes. However, the reproducibility of DE genes identified in different studies for a disease is typically very low. To tackle the problem, we could evaluate the reproducibility of DE genes across studies and define robust markers for disease diagnosis using disease-associated protein-protein interaction (PPI) subnetwork. Using datasets for four cancer types, we found that the most significant DE genes in cancer exhibit consistent up- or down-regulation in different datasets. For each cancer type, the 5 (or 10) most significant DE genes separately extracted from different datasets tend to be significantly coexpressed and closely connected in the PPI subnetwork, thereby indicating that they are highly reproducible at the PPI level. Consequently, we were able to build robust subnetwork-based classifiers for cancer diagnosis.
authors.affiliation: Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. linzhang.bioinformatics@gmail.com
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.forename: Shan
authors.initials: S
authors.lastname: Li
authors.forename: Chunxiang
authors.initials: C
authors.lastname: Hao
authors.forename: Guini
authors.initials: G
authors.lastname: Hong
authors.forename: Jinfeng
authors.initials: J
authors.lastname: Zou
authors.forename: Yuannv
authors.initials: Y
authors.lastname: Zhang
authors.forename: Pengfei
authors.initials: P
authors.lastname: Li
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D016022:Case-Control Studies; D019295:Computational Biology; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000378:metabolism; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor
keywords: Cancer; DE; Diagnosis; FDR; Gene expression profiling; PO; POD; PON; PPI; Protein interaction networks; RFE; Reproducibility of biomarkers; SAM; SVM; differentially expressed; false discovery rate; percentage of overlap; percentage of overlap in the PPI network; percentage of overlapping deregulations; protein–protein interaction; recursive feature elimination; significance analysis of microarray; support vector machine