Establishment of a prognostic-related microRNAs risk model for glioma by bioinformatics analysis

BACKGROUND: To explore the specific prognosis related microRNAs (miRNAs) of glioma. METHODS: The miRNA-Seq data and clinical information of glioma patients were downloaded from the TCGA (510 cases) and GEO (GSE112009, 25 cases) database. LASSO & COX regression was used to develop a miRNA-based model for predicting patient survival in the training set (n=255), to carry out glioma prognostic related miRNAs screening, and to construct a linear risk model based on the expression profiles of seven miRNAs. COX regression analysis was used to determine whether the miRNAs risk model was an independent prognostic factor. RESULTS: Seven survival-related miRNAs (miR-140-5p, miR-145-5p, miR-148a-3p, miR-183-5p, miR-222-3p, miR-223-3p, and miR-374a-5p) were identified in the training set. This showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group in the training set, prediction set, and validation set (P<0.05). Further analysis revealed that age and Karnofsky score both affected the risk of glioma. By crossing seven potential target genes of microRNAs, 620 effective target genes were obtained and GO analysis showed that these were related to the positive regulation of cell migration, neuron migration, and the response of transforming growth factor, and KEGG analysis showed they were related to the TGF-beta signaling pathway, MAPK signaling, and AGE-RAGE signaling pathway in diabetic complications. CONCLUSIONS: Seven miRNAs which regulate target genes to participate in related signaling pathways and lead to a poor prognosis were identified as biomarkers of glioma.