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

Authors: Le Lu; Yanda Li; Shao Li

Addresses: 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

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.

Keywords: microRNA; biomarkers; transcription factors; TSS; transcription start site; miRNA targets; angiogenesis; gastric cancer; chronic gastritis; TFBS; transcription factor binding sites; bioinformatics.

DOI: 10.1504/IJDMB.2011.043031

International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.5, pp.519 - 531

Received: 01 Oct 2009
Accepted: 24 Dec 2009
Published online: 24 Jan 2015 *

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