Identification of microRNAs associated with glioma diagnosis and prognosis

// Xinyun Ye 1, * , Wenjin Wei 1, * , Zhengyu Zhang 1, * , Chunming He 1 , Ruijin Yang 1 , Jinshi Zhang 1 , Zhiwu Wu 1 , Qianliang Huang 1 , Qiuhua Jiang 1 1 Department of Neurosurgery, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, Jiangxi 341000, China * These authors have contributed equally to this work Correspondence to: Qiuhua Jiang, email: jiangqh1968@163.com Keywords: glioma, miRNA, diagnosis, prognosis, meta-analysis Received: August 23, 2016      Accepted: December 15, 2016      Published: January 02, 2017 ABSTRACT The sensitivity and specificity of microRNAs (miRNAs) for diagnosing glioma are controversial. We therefore performed a meta-analysis to systematically identify glioma-associated miRNAs. We initially screened five miRNA microarray datasets to evaluate the differential expression of miRNAs between glioma and normal tissues. We next compared the expression of the miRNAs in different organs and tissues to assess the sensitivity and specificity of the differentially expressed miRNAs in the diagnosis of glioma. Finally, pathway analysis was performed using GeneGO. We identified 27 candidate miRNAs associated with glioma initiation, progression, and patient prognosis. Sensitivity and specificity analysis indicated miR-15a, miR-16, miR-21, miR-23a, and miR-9 were up-regulated, while miR-124 was down-regulated in glioma. Ten signaling pathways showed the strongest association with glioma development and progression: the p53 pathway feedback loops 2, Interleukin signaling pathway, Toll receptor signaling pathway, Parkinson’s disease, Notch signaling pathway, Cadherin signaling pathway, Apoptosis signaling pathway, VEGF signaling pathway, Alzheimer disease-amyloid secretase pathway, and the FGF signaling pathway. Our results indicate that the integration of miRNA, gene, and protein expression data can yield valuable biomarkers for glioma diagnosis and treatment. Indeed, six of the miRNAs identified in this study may be useful diagnostic and prognostic biomarkers in glioma.