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      Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases

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          Abstract

          Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recurrence in HCC patients using databases (TCGA, AMC and Inserm) and machine learning methods and obtain the gene signature that could predict early relapse of HCC. Statistical methods, such as feature selection, survival analysis and Chi-Square test in R software, were used to analyze and select mutant genes related to disease free survival (DFS), race and vascular invasion. In addition, whole-exome sequencing was performed on 10 HCC patients recruited from our center, and the sequencing results were compared with the databases. Using the databases and machine learning methods, the prediction model of recurrence was constructed and optimized, and the selected mutant genes were verified in the test group. The accuracy of prediction was 74.19%. Moreover, these 10 patients from our center were used to verify these mutant genes and the prediction model, and a success rate of 80% was achieved. Collectively, we discovered recurrence-related genes and established recurrence prediction model of recurrence for HCC patients, which could provide significant guidance for clinical prediction of recurrence.

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          Most cited references18

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          Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

          (2017)
          Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole exome sequencing and DNA copy number analyses, and 196 HCC also by DNA methylation, RNA, miRNA, and proteomic expression. DNA sequencing and mutation analysis identified significantly mutated genes including LZTR1 , EEF1A1 , SF3B1 , and SMARCA4 . Significant alterations by mutation or down-regulation by hypermethylation in genes likely to result in HCC metabolic reprogramming ( ALB , APOB , and CPS1 ) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1. Multiplex molecular profiling of human hepatocellular carcinoma patients provides insight into subtype characteristics and points toward key pathways to target therapeutically.
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            Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.

            Liver cancer, which is most often associated with virus infection, is prevalent worldwide, and its underlying etiology and genomic structure are heterogeneous. Here we provide a whole-genome landscape of somatic alterations in 300 liver cancers from Japanese individuals. Our comprehensive analysis identified point mutations, structural variations (STVs), and virus integrations, in noncoding and coding regions. We discovered mutational signatures related to liver carcinogenesis and recurrently mutated coding and noncoding regions, such as long intergenic noncoding RNA genes (NEAT1 and MALAT1), promoters, CTCF-binding sites, and regulatory regions. STV analysis found a significant association with replication timing and identified known (CDKN2A, CCND1, APC, and TERT) and new (ASH1L, NCOR1, and MACROD2) cancer-related genes that were recurrently affected by STVs, leading to altered expression. These results emphasize the value of whole-genome sequencing analysis in discovering cancer driver mutations and understanding comprehensive molecular profiles of liver cancer, especially with regard to STVs and noncoding mutations.
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              Significance of presence of microvascular invasion in specimens obtained after surgical treatment of hepatocellular carcinoma.

              Partial hepatectomy and liver transplantation are potentially curative treatments in selected patients with hepatocellular carcinoma (HCC). Unfortunately, a high postoperative tumor recurrence rate significantly decreases long-term survival outcomes. Among multiple prognostic factors, the presence of microvascular invasion (MVI) has increasingly been recognized to reflect enhanced abilities of local invasion and distant metastasis of HCC. Unfortunately, MVI can only currently be identified through histopathological studies on resected surgical specimens. Accurate preoperative tests to predict the presence of MVI are urgently needed. This paper reviews the current studies on incidence, pathological diagnosis, and classification of MVI; possible mechanisms of MVI formation; and preoperative prediction of the presence of MVI. Furthermore, focusing on how the postoperative management can be improved on histopathologically confirmed patients with HCC with MVI, and the potential roles of using predictive tests to estimate the risk of presence of MVI, helps in preoperative therapeutic decision-making in patients with HCC.
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                Author and article information

                Contributors
                baoruiliu@nju.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 March 2020
                10 March 2020
                2020
                : 10
                : 4435
                Affiliations
                [1 ]ISNI 0000 0001 2314 964X, GRID grid.41156.37, Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, ; Nanjing, 210008 Jiangsu Province China
                [2 ]Shanghai Biotecan Pharmaceuticals Co., Ltd., Pudong New District, Shanghai, China
                Article
                61298
                10.1038/s41598-020-61298-3
                7064516
                32157118
                b7a3cfa7-7dd5-4809-8068-150bb3cdf33f
                © The Author(s) 2020

                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
                : 10 April 2019
                : 24 February 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                tumour biomarkers,cancer genomics,hepatocellular carcinoma
                Uncategorized
                tumour biomarkers, cancer genomics, hepatocellular carcinoma

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