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      Biostatistics mining associated method identifies AKR1B10 enhancing hepatocellular carcinoma cell growth and degenerated by miR-383-5p

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          Abstract

          Previous studies have reported that the aberrantly expressed AKR1B10 is associated with many cancer development, however the functional roles of AKR1B10 and its regulatory mechanisms in hepatocellular carcinoma (HCC) have been limited studied. In this project, we identified AKR1B10 functional as an oncogene in HCC through tumor/normal human tissue comparison from both GEO microarray and TCGA RNAseq dataset. Further experimental validations from three HCC cell lines (SMMC-7721, HePG2 and HeP3B) also suggested the ontogenetic functions of AKR1B10 in HCC tumor growth. By knocking down AKR1B10 through shRNA in HCC HeP3B cells, we showed it significantly induced cell cycle arrest and inhibited cell growth. Interestingly, integrative analysis of TCGA RNAseq data and miRNA-seq data predicted that miR-383-5p, a novel post-transcriptional tumor suppressor, is negatively associated with AKR1B10 expression. To further investigate the role of miR-383-5p in regulating AKR1B10 in HCC, we performed Dual-luciferase reporter assay experiments. Results showed that miR-383-5p is an upstream modulator targeting AKR1B10 in the post-transcriptional stage. Thus, we report AKR1B10 modulated regulated by miR-383-5p, promotes HCC tumor progress, and could be potentially a therapeutic target for precision medicine in HCC.

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma.

            Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Here, we performed high-resolution copy-number analysis on 125 HCC tumors and whole-exome sequencing on 24 of these tumors. We identified 135 homozygous deletions and 994 somatic mutations of genes with predicted functional consequences. We found new recurrent alterations in four genes (ARID1A, RPS6KA3, NFE2L2 and IRF2) not previously described in HCC. Functional analyses showed tumor suppressor properties for IRF2, whose inactivation, exclusively found in hepatitis B virus (HBV)-related tumors, led to impaired TP53 function. In contrast, inactivation of chromatin remodelers was frequent and predominant in alcohol-related tumors. Moreover, association of mutations in specific genes (RPS6KA3-AXIN1 and NFE2L2-CTNNB1) suggested that Wnt/β-catenin signaling might cooperate in liver carcinogenesis with both oxidative stress metabolism and Ras/mitogen-activated protein kinase (MAPK) pathways. This study provides insight into the somatic mutational landscape in HCC and identifies interactions between mutations in oncogene and tumor suppressor gene mutations related to specific risk factors.
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              Gene Expression Patterns in Human Liver Cancers

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                Author and article information

                Contributors
                wangjunqingmd@hotmail.com
                cyj10651@rjh.com.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 July 2018
                23 July 2018
                2018
                : 8
                : 11094
                Affiliations
                [1 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of Surgery, Ruijin Hospital, , Shanghai Jiao Tong University School of Medicine, ; 197, Rui Jin Er Road, Shanghai, 20025 People’s Republic of China
                [2 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Shanghai Institute of Digestive Surgery, Ruijin Hospital, , Shanghai Jiao Tong University School of Medicine, ; 197, Rui Jin Er Road, Shanghai, 20025 People’s Republic of China
                [3 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Shanghai Key Laboratory of Gastric Neoplasms, Ruijin Hospital, , Shanghai Jiao Tong University School of Medicine, ; 197, Rui Jin Er Road, Shanghai, 20025 People’s Republic of China
                [4 ]ISNI 0000 0004 1937 0407, GRID grid.410721.1, Department of Data Science, , University of Mississippi Medical Center, ; Jackson, MS 39216 USA
                [5 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of Pathology, Ruijin Hospital, , Shanghai Jiao Tong University School of Medicine, ; 197, Rui Jin Er Road, Shanghai, 20025 People’s Republic of China
                Author information
                http://orcid.org/0000-0001-6874-9614
                Article
                29271
                10.1038/s41598-018-29271-3
                6056456
                30038373
                eae40e61-d0bf-40dd-9363-3d004cb39448
                © 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
                : 26 February 2018
                : 25 June 2018
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