TY - JOUR
T1 - Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer
AU - Zhang, Lin
AU - Li, Shan
AU - Hao, Chunxiang
AU - Hong, Guini
AU - Zou, Jinfeng
AU - Zhang, Yuannv
AU - Li, Pengfei
AU - Guo, Zheng
N1 - Funding Information:
Funding: This work was supported in part by the National Natural Science Foundation of China ( 30970668 , 31100901 , 81071646 , 91029717 ), and the Excellent Youth Foundation of Heilongjiang Province ( JC200808 ).
PY - 2013/9/10
Y1 - 2013/9/10
N2 - 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.
AB - 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.
KW - Cancer
KW - Diagnosis
KW - Gene expression profiling
KW - Protein interaction networks
KW - Reproducibility of biomarkers
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U2 - 10.1016/j.gene.2013.05.011
DO - 10.1016/j.gene.2013.05.011
M3 - Article
C2 - 23707927
AN - SCOPUS:84880710519
SN - 0378-1119
VL - 526
SP - 232
EP - 238
JO - Gene
JF - Gene
IS - 2
ER -