Introduction: The microarray technique has been established as the reference method for studying the genes that are in the control of miRNAs. However, the high cost of this method has hampered its use in most research centers. On the other hand, the improvement of the bioinformatical algorithms and computer modeling systems has led to the development of the bioinformatical softwares that can predict mRNA targets for miRNAs. Therefore, the aim of this theoretical study was bioinformatically evaluation of the effect of miR-5011 on genes that can be involved in colorectal cancer, by using various specific softwares.
Methods/Materials: By Using different algorithms in TargetScan, DIANA and miRWalk databases, the potential gene targets of miR-5011 were identified. Then, a score table from the candidate genes was prepared based on the affinity of the seed region of miR-5011 and the number of MRE in the 3`-UTR region of genes. Finally, genes with highest scores were chosen as the candidates for practical analysis.
Results: The results of the bioinformatical analysis showed that SMAD6 and SMAD7 genes in TGFB signaling pathway and WNT3A and LRP6 genes in WNT signaling pathway are the most potential genes that might be affected by miR-5011 in colorectal cancer.
Discussion: It seems that WNT3A, LRP6, SMAD6 and SMAD7 suppressed by miR-5011 and this microRNA maybe act as a tumor suppressor and downregulated in colorectal cancer, Therefore this microRNA and its target genes can be considered as a suitable new candidate for experimental evaluation.
Appasani K. MicroRNAs: from basic science to disease biology. Cambridge University Press. 2008;23(4) :123-87.
Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):128-90.
Shi M, Liu D, Duan H, Shen B, Guo N. Metastasis-related miRNAs, active players in breast cancer invasion, and metastasis. Cancer and Metastasis Reviews. 2010;29(4):785-99.
Shivdasani RA. MicroRNAs: regulators of gene expression and cell differentiation. Blood. 2006;108(12):3646-53.
Gao F-B. Context-dependent functions of specific microRNAs in neuronal development. Neural development. 2010;5(1):25-6.
Ren, Dong A, Tsoi H. Detection of miRNA as non-invasive biomarkers of colorectal cancer. International journal of molecular sciences. 2015;16(2):2810-2823.
Grady W. Transforming growth factor β signaling in colorectal cancer. Current Colorectal Cancer Reports. 2007;3(2):65-70.
Morin P , Sparks A, Barker N, Clevers H. Activation of β-catenin-Tcf signaling in colon cancer by mutations in β-catenin or APC. Science.1997; 275(2):1787-1790.
Quesne J, Caldas C. Micro-RNAs and breast cancer. Molecular oncology. 2010;4(3):230-41.
Friedman RC, Farh KK-H, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome research. 2009;19(1):92-105.
Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, et al. DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic acids research. 2009;37(2): 273-6.
Dweep H, Sticht C, Pandey P, Gretz N. miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. Journal of biomedical informatics. 2011;44(5):839-47.
Wrana J. Regulation of Smad activity. Cell. 2010;100(2):189-192.
Zhou S, Kinzler K, Vogelstein B. Going Mad with Smads. New England Journal of Medicine. 2010;341(15):1144-1146.
Li Q. Inhibitory SMADs: Potential Regulators of Ovarian Function. Biology of reproduction. 2015;92(2):111-117.
Edlund S, et al. Interaction between Smad7 and beta-catenin: importance for transforming growth factor beta-induced apoptosis.Mol, Cell Biol. 2007;25 (4):1475–1488.
Cheruku H. Mohamedali A, Cantor D. Transforming growth factor-β, MAPK and Wnt signaling interactions in colorectal cancer. EuPA Open Proteomics. 2015;8(98):104-115.