Bioinformatics analysis and prediction of hsa-miR-32 target genes

PENG Can-can; MA Wen-li; XIA Wei; HUANG Zheng-liang; ZHENG Wen-ling

doi:10.16462/j.cnki.zhjbkz.2016.06.017

Volume 20 Issue 6

Jun. 2016

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2016 > 20(6): 609-613

PENG Can-can, MA Wen-li, XIA Wei, HUANG Zheng-liang, ZHENG Wen-ling. Bioinformatics analysis and prediction of hsa-miR-32 target genes[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2016, 20(6): 609-613. doi: 10.16462/j.cnki.zhjbkz.2016.06.017

Citation:

PENG Can-can, MA Wen-li, XIA Wei, HUANG Zheng-liang, ZHENG Wen-ling. Bioinformatics analysis and prediction of hsa-miR-32 target genes[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2016, 20(6): 609-613. doi: 10.16462/j.cnki.zhjbkz.2016.06.017

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Bioinformatics analysis and prediction of hsa-miR-32 target genes

doi: 10.16462/j.cnki.zhjbkz.2016.06.017

1. Institute of Genetic Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China;
2. Department of Clinical Laboratory, NO. 421 Hospital of PLA, Guangzhou 510318, China

Received Date: 2015-12-24
Rev Recd Date: 2016-03-27

Abstract

Abstract

Objective Bioinformatics software and database were applied to predict and analyze target genes and functions of hsa-miR-32, in order to provide a basis for the study of the mechanism of hsa-miR-32 and its target genes in cancer. Methods The sequence of miR-32 was got from miRBase database. The microarray data of disease were downloaded from the Gene Expression Omnibus(GEO) and the expression level of hsa-miR-32 in disease was analysed by miRGator and Qlucore Omics Explorer. PicTar, DIANA-microT-CDS, PITA and miRanda algorithm were used to predict target genes of hsa-miR-32. Combined with validated target genes, the gene set was analyzed by gene ontology(GO) and pathway enrichment. Results miR-32 was highly conserved among different species. Different expression levels of hsa-miR-32 were observed in different cancer tissues compared with adjacent normal tissues(all P<0.05). Gene ontology analysis indicated that 168 target genes were mainly enriched in positive regulation of gene expression, negative regulation of cell proliferation, negative regulation of signal transduction, cell death and other biology processes(all P<0.05). KEGG pathway analysis showed that these genes were mostly involved in small cell lung cancer, prostate cancer, glioma, melanoma, pathways in cancer, p53 signaling pathway and cell cycle(all P<0.05). Conclusions The target genes of hsa-miR-32 may have extensive functions and be closely related with cancer.
- Carcinoma,
- Genes,
- Computational biology,
- Forecasting

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