Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 594350, 12 pages
http://dx.doi.org/10.1155/2014/594350
Research Article

Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

1Systems Sepsis Biology Team, Soochow University Affiliated Children’s Hospital, Suzhou 215003, China
2Center for Systems Biology, Soochow University, Suzhou 215006, China
3Suzhou Zhengxing Translational Biomedical Informatics Ltd., Taicang 215400, China
4Taicang Center for Translational Bioinformatics, Taicang 215400, China

Received 17 January 2014; Accepted 3 March 2014; Published 6 April 2014

Academic Editor: Junfeng Xia

Copyright © 2014 Jie Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.