Volume 12, Issue 1 (2020)                   JMBS 2020, 12(1): 89-101 | Back to browse issues page

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Mortazavi S S, Gharbi S, Shahali M. Bioinformatics Prediction of microRNAs Regulating Epithelial-to-Mesenchymal Transition in Cancer Cells. JMBS 2020; 12 (1) :89-101
URL: http://biot.modares.ac.ir/article-22-39000-en.html
1- Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
2- Department of Quality Control, Research and Production Complex Pasteur Institute of Iran, Tehran , m_shahali@pasteur.ac.ir
Abstract:   (1175 Views)
 ABSTRACT
Aims: Epithelial to mesenchymal transition (EMT) is an essential step in the developmental process, wound healing and cancer progression. In many cancers, EMT can increase aggressive properties including invasion, metastasis and Tumor resistance to apoptosis. Recently, miRNAs as a new class of non-coding RNAs that post-transcriptionally regulate gene expression have been demonstrated to have a crucial role in the regulation of EMT. However, the detailed mechanisms of miRNAs involvement in EMT in human cancer cells are still unclear. This study aimed to clarify this issue by using bioinformatics tools for predicting competent miRNAs target the main gens in EMT.
Materials and Methods:  To ascertain an effective miRNA for the EMT, we assessed five genes from EMT/MET as key genes. Then, to predict the most suitable miRNA: target interactions, different online databases including DIANA, TargetScan, and miRSystem were applied.
Results: Possible targeting effects of different miRNAs on candidate genes were analyzed. Merging data from databases has shown that 11 miRNAs with strong possibility communally can be involved in EMT/MET.
Conclusion: To conclude, it can be predicted that according to high interaction scores of these elected miRNAs with candidate genes in the above-mentioned databases, these miRNAs probably can have critical roles in EMT/MET. Hence, these miRNAs can be introduced as appropriate candidates for future investigations.
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Article Type: Original Research | Subject: Bioinformatics
Received: 2019/12/14 | Accepted: 2020/10/31 | Published: 2020/12/30

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