Dataset Information

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]

PMID: 25452796

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Project description:In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

Dataset Information

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

Publications

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

Similar Datasets

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