Functional prediction of candidate micrornas for CRC management using in silico approach

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dc.contributor.authorFadaka, Adewale Oluwaseun
dc.contributor.authorPretorius, Ashley
dc.contributor.authorKlein, Ashwil
dc.date.accessioned2023-02-13T07:32:31Z
dc.date.available2023-02-13T07:32:31Z
dc.date.issued2019
dc.description.abstractApproximately 30–50% of malignant growths can be prevented by avoiding risk factors and implementing evidence-based strategies. Colorectal cancer (CRC) accounted for the second most common cancer and the third most common cause of cancer death worldwide. This cancer subtype can be reduced by early detection and patients’ management. In this study, the functional roles of the identified microRNAs were determined using an in silico pipeline. Five microRNAs identified using an in silico approach alongside their seven target genes from our previous study were used as datasets in this study. Furthermore, the secondary structure and the thermodynamic energies of the microRNAs were revealed by Mfold algorithm. The triplex binding ability of the oligonucleotide with the target promoters were analyzed by Trident. Finally, evolutionary stage-specific somatic events and co-expression analysis of the target genes in CRC were analyzed by SEECancer and GeneMANIA plugin in Cytoscape. Four of the five microRNAs have the potential to form more than one secondary structure. The ranges of the observed/expected ratio of CpG dinucleotides of these genes range from 0.60 to 1.22.en_US
dc.identifier.citationFadaka, A. O. et al. (2019). Functional prediction of candidate micrornas for CRC management using in silico approach. International Journal of Molecular Sciences, 20(20), 5190. https://doi.org/10.3390/ijms20205190en_US
dc.identifier.issn1422-0067
dc.identifier.urihttps://doi.org/10.3390/ijms20205190
dc.identifier.urihttp://hdl.handle.net/10566/8411
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectBiotechnologyen_US
dc.subjectColorectal canceren_US
dc.subjectPublic healthen_US
dc.subjectPatientsen_US
dc.titleFunctional prediction of candidate micrornas for CRC management using in silico approachen_US
dc.typeArticleen_US

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