Introduction: The liver is one of the most important organs of the human body and any disorder in this vital organ causes acute problems for the patient. Because the liver plays an important role in eliminating and excreting natural and artificial toxins from the blood . MicroRNAs, on the other hand, have significant properties in controlling cancer cells and can be considered as key targets for understanding the pathways that lead to cancer. miR-101 is one of the Tumor suppressor microRNAs that is associated with decreased expression in hepatocellular carcinoma, this prevents the cells from dividing and metastasizing abnormally to the surrounding tissues and prevents abnormal cell death, so miR-101 targets the Mcl1 gene, which is a suppressor gene. Tumor in the TGF_B molecular pathway of the liver prevents the spread of tumorigenesis in the liver and adjacent tissues. Therefore, the aim of this study was to evaluate the bioinformatics of miR-101 in inhibiting tumorigenesis of liver carcinoma cells by acting on the tumor suppressor gene Mcl1 in the TGF_B molecular pathway and preventing its metastasis to other organs using databases and algorithms. To further investigate the role of this microRNA as a metastasis suppressor and inhibitor of tumorigenesis, as well as its effect on the Mcl1 gene as a diagnostic / prognostic biomaker in the treatment of liver cancer.
Methods: In this study, using bioinformatics algorithms, first to investigate the role of microRNA-101 in liver cancer, to look for changes in the expression of microRNA-101 in this disease at OncomiR site and then to investigate the role of microRNA-101 in pathways Interfering signaling in liver cancer pathogenicity, miR-101 is examined in the bioinformatics site mirPath v.3. Then, by entering the full name of microRNA-101 in the window related to the KEGG database and selecting the TarBase V.7 algorithm, we will examine the molecular paths and thermal map of the miR-101. Finally, to ensure the accuracy of the results and prevent false positive results, 11 known online gene target prediction databases are used in the miRWalk 0.2 database. R software is also used in further interaction studies (first to control the existing raw data using the Limma package, available in the Graph prism and R software by PCA) and then to normalize the microarrays using It is done by LOESS method and between samples by several methods. Criteria for gene identification are evaluated by differential expression and significance of Fold Change> 2 and Pvalue <0.05.
Results: The relationship miR-101 with HCC from bioinformatic data in HCC was mined. Using bioinformatics algorithms and statistical analysis with R and Graph prism software Information profiling uncovered that when By reducing the expression of miR-101 gene, it has tumor suppressive properties.
Conclusion: The value of miR-101-3p and miR-101-5p has been evaluated as biomarkers for early diagnosis of HCC; Because it reduced the expression of PTEN and Mcl1 genes in the TGF-B signaling pathway, and in addition, their molecular mechanisms provide the results of a deeper understanding of the role of miR-101-3p/5p in HCC.
Keywords: Liver Cancer - Biomarker - Algorithm-HCC