Computational identification of potential microRNA network biomarkers for the progression stages of gastric cancer

Authors:
Le Lu;Yanda Li;Shao Li
Affiliations:
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.;MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.;MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2011

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Abstract

MicroRNAs (miRNAs) are potential biomarkers in the diagnosis of human disease. In this study, a novel concept, the miRNA network biomarker, was proposed for the selection of biomarkers. Each miRNA network biomarker contains miRNA targets, as well as Transcription Factors (TFs), that affect the miRNA expression. The obtained biomarkers were applied to classifying expression data sets in different progression stages from chronic gastritis to gastric cancer. Furthermore, these biomarkers could accurately (94%) discriminate gastric cancer samples from normal samples in another data set. Angiogenesis-related pathways and genes were found to be enriched in these network biomarkers.