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      Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

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          Abstract

          The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

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          Hepatocellular Carcinoma

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            A global view of hepatocellular carcinoma: trends, risk, prevention and management

            Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related death worldwide. Risk factors for HCC include chronic hepatitis B and hepatitis C, alcohol addiction, metabolic liver disease (particularly nonalcoholic fatty liver disease) and exposure to dietary toxins such as aflatoxins and aristolochic acid. All these risk factors are potentially preventable, highlighting the considerable potential of risk prevention for decreasing the global burden of HCC. HCC surveillance and early detection increase the chance of potentially curative treatment; however, HCC surveillance is substantially underutilized, even in countries with sufficient medical resources. Early-stage HCC can be treated curatively by local ablation, surgical resection or liver transplantation. Treatment selection depends on tumour characteristics, the severity of underlying liver dysfunction, age, other medical comorbidities, and available medical resources and local expertise. Catheter-based locoregional treatment is used in patients with intermediate-stage cancer. Kinase and immune checkpoint inhibitors have been shown to be effective treatment options in patients with advanced-stage HCC. Together, rational deployment of prevention, attainment of global goals for viral hepatitis eradication, and improvements in HCC surveillance and therapy hold promise for achieving a substantial reduction in the worldwide HCC burden within the next few decades.
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              The lasso method for variable selection in the Cox model.

              I propose a new method for variable selection and shrinkage in Cox's proportional hazards model. My proposal minimizes the log partial likelihood subject to the sum of the absolute values of the parameters being bounded by a constant. Because of the nature of this constraint, it shrinks coefficients and produces some coefficients that are exactly zero. As a result it reduces the estimation variance while providing an interpretable final model. The method is a variation of the 'lasso' proposal of Tibshirani, designed for the linear regression context. Simulations indicate that the lasso can be more accurate than stepwise selection in this setting.
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                Author and article information

                Contributors
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                24 May 2021
                2021
                : 8
                : 636991
                Affiliations
                [ 1 ]The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
                [ 2 ]Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
                Author notes

                Edited by: Nikolay Mikhaylovich Borisov, Moscow Institute of Physics and Technology, Russia

                Reviewed by: Yu-hong Li, Sun Yat-sen University Cancer Center, China

                Surendra Kumar Shukla, University of Nebraska Medical Center, United States

                [†]

                These authors have contributed equally to this work

                This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

                Article
                636991
                10.3389/fmolb.2021.636991
                8181399
                34109210
                b2c93bf1-3ba2-4e68-8843-f7f3e264a245
                Copyright © 2021 Huang, Jiang, Huang, Zhao, Li and Liu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 04 December 2020
                : 04 May 2021
                Categories
                Molecular Biosciences
                Original Research

                hepatocellular carcinoma,immune-related gene,overall survival,risk-scoring model,co-expression network construction

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