Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures

BMC Genomics

Research

Srinivasulu Yerukala Sathipati¹  Hui-Ling Huang²  Shinn-Ying Ho² 

[1] Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan;

关键词: Glioma Cell;    Support Vector Regression;    Glioma Cell Line;    miRNA Expression Profile;    Support Vector Regression Model;   

DOI  :  10.1186/s12864-016-3321-y

来源: Springer

【 摘 要 】

BackgroundThough glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy in adults, clinical treatment still faces challenges due to poor prognoses and tumor relapses. Recently, microRNAs (miRNAs) have been extensively used with the aim of developing accurate molecular therapies, because of their emerging role in the regulation of cancer-related genes. This work aims to identify the miRNA signatures related to survival of GBM patients for developing molecular therapies.ResultsThis work proposes a support vector regression (SVR)-based estimator, called SVR-GBM, to estimate the survival time in patients with GBM using their miRNA expression profiles. SVR-GBM identified 24 out of 470 miRNAs that were significantly associated with survival of GBM patients. SVR-GBM had a mean absolute error of 0.63 years and a correlation coefficient of 0.76 between the real and predicted survival time. The 10 top-ranked miRNAs according to prediction contribution are as follows: hsa-miR-222, hsa-miR-345, hsa-miR-587, hsa-miR-526a, hsa-miR-335, hsa-miR-122, hsa-miR-24, hsa-miR-433, hsa-miR-574 and hsa-miR-320. Biological analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on the identified miRNAs revealed their influence in GBM cancer.ConclusionThe proposed SVR-GBM using an optimal feature selection algorithm and an optimized SVR to identify the 24 miRNA signatures associated with survival of GBM patients. These miRNA signatures are helpful to uncover the individual role of miRNAs in GBM prognosis and develop miRNA-based therapies.

【 授权许可】

CC BY   
© The Author(s). 2016

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