A prognosis-related based method for miRNA selection on liver hepatocellular carcinoma prediction. (April 2021) - Liang, Guangmin; Wu, Jin; Xu, Lei
Graphical abstract: Data downloaded from TCGA are analyzed by Limma Test and Cox regression or Kaplan-meier, then related miRNAs are selected. The results are verified on miTarBase platform. Survival curve of related miRNAs are shown. The curves are based on Kaplan-meier method and Log-rank test. From(a)-(e), the line of high risk group and the line low risk group are totally separated, which demonstrated that the five selected miRNAs are related to prognosis. Highlights: An efficient computational method for miRNA selection related to liver hepatocellular carcinoma prediction is proposed. The expression profile of miRNAs of human hepatocellular carcinoma in cancer tissue is compared with their adjacent tissue samples, then the genes with significant difference are selected. Five related miRNAs are selected and have been verified on miTarBase. Abstract: Hepatocellular carcinoma (HCC) is considered as the sixth most common cancer in the world, and it is also considered as one of the causes of death. Moreover, the poor prognosis of recurrence of HCC after surgery and metastasis is also a big problem for human health. If the disease can be diagnosed earlier, the survival rate of the patients will be improved significantly. In the early stage of hepatocellular carcinoma, the expression of miRNAs is likely to become abnormal. In our work, the expression profile of miRNAs of human HCC in cancer tissue is compared with their adjacent tissue samples collected from tumor cancer genomic Atlas (TCGA) platform, then the genes with significant difference are selected by Limma test. Selected genes are referred to predict miRNAs related to the prognosis of HCC patients. Finally, miRNAs regulated by target genes are selected by our method, and the experimental results demonstrated that our method is more efficient than biology wet experimental method with lower cost.