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Identification of dysregulated miRNAs and their regulatory signature in glioma patients using the partial least squares method.


ABSTRACT: Using microarray data, the present study identified differentially expressed microRNAs (miRNAs) and evaluated their regulatory characteristics in high-grade glioma patients, with the aim to further the understanding into the underlying etiology of the condition. Previously, studies have generally implemented regression or variance analysis, which ignores various background biological factors. However, in the present study, analysis was performed with microarray data collected from the Gene Expression Omnibus database using a partial least squares-based method, which is more sensitive in handling microarray data. Among the six identified differentially expressed miRNAs, hsa-miR-21 and hsa-miR-612 have been previously reported to be associated with glioma. In addition, the remaining miRNAs, hsa-miR-4680, hsa-miR-1908, hsa-miR-4656 and hsa-miR-4467, may also contribute to glioma progression since they are all associated with the tumorigenesis of other types of cancer. Moreover, the expression levels of hsa-miR-1908, hsa-miR-4656 and hsa-miR-4680 have been identified to significantly correlate with the survival rate. Enrichment analysis of the dysregulated target genes revealed that the selected miRNAs primarily affect biological processes in the nervous system and the protein phosphorylation process. Therefore, the results may offer a new understanding into the pathogenesis of high-grade glioma.

SUBMITTER: Shou J 

PROVIDER: S-EPMC4247319 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Identification of dysregulated miRNAs and their regulatory signature in glioma patients using the partial least squares method.

Shou Jiajun J   Gu Shixin S   Gu Wentao W  

Experimental and therapeutic medicine 20141031 1


Using microarray data, the present study identified differentially expressed microRNAs (miRNAs) and evaluated their regulatory characteristics in high-grade glioma patients, with the aim to further the understanding into the underlying etiology of the condition. Previously, studies have generally implemented regression or variance analysis, which ignores various background biological factors. However, in the present study, analysis was performed with microarray data collected from the Gene Expre  ...[more]

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