title: Identification of microRNA as sepsis biomarker based on miRNAs regulatory network analysis.
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.
authors.affiliation: Systems Sepsis Biology Team, Soochow University Affiliated Children's Hospital, Suzhou 215003, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Huang
authors.affiliation: Systems Sepsis Biology Team, Soochow University Affiliated Children's Hospital, Suzhou 215003, China ; Center for Systems Biology, Soochow University, Suzhou 215006, China.
authors.forename: Zhandong
authors.initials: Z
authors.lastname: Sun
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, China ; Suzhou Zhengxing Translational Biomedical Informatics Ltd., Taicang 215400, China ; Taicang Center for Translational Bioinformatics, Taicang 215400, China.
authors.forename: Wenying
authors.initials: W
authors.lastname: Yan
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, China.
authors.forename: Yujie
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, China.
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Lin
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, China ; Taicang Center for Translational Bioinformatics, Taicang 215400, China.
authors.forename: Jiajai
authors.initials: J
authors.lastname: Chen
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, China.
authors.forename: Bairong
authors.initials: B
authors.lastname: Shen
authors.affiliation: Systems Sepsis Biology Team, Soochow University Affiliated Children's Hospital, Suzhou 215003, China.
authors.forename: Jian
authors.identifier: 0000-0002-9214-3302
authors.initials: J
authors.lastname: Wang
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism*; D060066:Protein Interaction Maps / Q000235:genetics; D012372:ROC Curve; D018805:Sepsis / Q000175:diagnosis* / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics
chemical_list: D015415:Biomarkers; D035683:MicroRNAs