Upregulation of miR-1266-5p serves as a prognostic biomarker of hepatocellular carcinoma and facilitates tumor cell proliferation, migration and invasion

SV40 miR-S1 and Cellular miR-1266 Sequester Each Other from Their Targets, Enhancing Telomerase Activity and Viral Replication

Virus-encoded microRNAs (miRNAs) target viral and host mRNAs to repress protein production from viral and host genes, and regulate viral persistence, cell transformation, and evasion of the immune system. The present study demonstrated that simian virus 40 (SV40)-encoded miRNA miR-S1 targets a cellular miRNA miR-1266 to derepress their respective target proteins, namely, T antigens (Tags) and telomerase reverse transcriptase (TERT). An in silico search for cellular miRNAs to interact with viral miR-S1 yielded nine potential miRNAs, five of which, including miR-1266, were found to interact with miR-S1 in dual-luciferase tests employing reporter plasmids containing the miRNA sequences with miR-S1. Intracellular bindings of miR-1266 to miR-S1 were also verified by the pull-down assay. These miRNAs were recruited into the Ago2-associated RNA-induced silencing complex. Intracellular coexpression of miR-S1 with miR-1266 abrogated the downregulation of TERT and decrease in telomerase activity induced by miR-1266. These effects of miR-S1 were also observed in miR-1266-expressing A549 cells infected with SV40. Moreover, the infected cells contained more Tag, replicated more viral DNA, and released more viral particles than control A549 cells infected with SV40, indicating that miR-S1-induced Tag downregulation was antagonized by miR-1266. Collectively, the present results revealed an interplay of viral and cellular miRNAs to sequester each other from their respective targets. This is a novel mechanism for viruses to manipulate the expression of viral and cellular proteins, contributing to not only viral lytic and latent replication but also cell transformation observed in viral infectious diseases including oncogenesis.

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.