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      Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets

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          Abstract

          Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.

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          miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients.

          The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer.
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            Identification of metastasis-related microRNAs in hepatocellular carcinoma.

            MicroRNAs (miRNAs) have been used as cancer-related biomarkers. Hepatocellular carcinoma (HCC) is an aggressive cancer with a dismal outcome largely due to metastasis and postsurgical recurrence. We investigated whether the expression of certain miRNAs are associated with HCC metastasis. We examined the miRNA expression profiles of 482 cancerous and noncancerous specimens from radical resection of 241 patients with HCC. Using a supervised algorithm and a clinically well-defined cohort of 131 cases, we built a unique 20-miRNA metastasis signature that could significantly predict (P < 0.001) primary HCC tissues with venous metastases from metastasis-free solitary tumors with 10-fold cross-validation. However, significant miRNAs could not be identified from the corresponding noncancerous hepatic tissues. A survival risk prediction analysis revealed that a majority of the metastasis-related miRNAs were associated with survival. Furthermore, the 20-miRNA tumor signature was validated in 110 additional cases as a significant independent predictor of survival (P = 0.009) and was significantly associated with both survival and relapse in 89 cases of early stage HCC (P = 0.022 and 0.002, respectively). These 20 miRNAs may provide a simple profiling method to assist in identifying patients with HCC who are likely to develop metastases/recurrence. In addition, functional analysis of these miRNAs may enhance our biological understanding of HCC metastasis.
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              Specificity, duplex degradation and subcellular localization of antagomirs

              MicroRNAs (miRNAs) are an abundant class of 20–23-nt long regulators of gene expression. The study of miRNA function in mice and potential therapeutic approaches largely depend on modified oligonucleotides. We recently demonstrated silencing miRNA function in mice using chemically modified and cholesterol-conjugated RNAs termed ‘antagomirs’. Here, we further characterize the properties and function of antagomirs in mice. We demonstrate that antagomirs harbor optimized phosphorothioate modifications, require >19-nt length for highest efficiency and can discriminate between single nucleotide mismatches of the targeted miRNA. Degradation of different chemically protected miRNA/antagomir duplexes in mouse livers and localization of antagomirs in a cytosolic compartment that is distinct from processing (P)-bodies indicates a degradation mechanism independent of the RNA interference (RNAi) pathway. Finally, we show that antagomirs, although incapable of silencing miRNAs in the central nervous system (CNS) when injected systemically, efficiently target miRNAs when injected locally into the mouse cortex. Our data further validate the effectiveness of antagomirs in vivo and should facilitate future studies to silence miRNAs for functional analysis and in clinically relevant settings.
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                Author and article information

                Contributors
                gyorffy.balazs@ttk.mta.hu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 June 2018
                15 June 2018
                2018
                : 8
                : 9227
                Affiliations
                [1 ]ISNI 0000 0004 0635 9129, GRID grid.429187.1, MTA TTK Lendület Cancer Biomarker Research Group, , Institute of Enzymology, ; Magyar Tudósok körútja 2, 1117 Budapest, Hungary
                [2 ]ISNI 0000 0001 0942 9821, GRID grid.11804.3c, Semmelweis University 2nd Dept. of Pediatrics, ; Tűzoltó utca 7-9, 1094 Budapest, Hungary
                Author information
                http://orcid.org/0000-0002-5772-3766
                Article
                27521
                10.1038/s41598-018-27521-y
                6003936
                29907753
                82361c09-ff72-4eb0-ac59-3d6c8dc796c6
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 November 2017
                : 8 May 2018
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