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      Development and validation of LRP1B mutation-associated prognostic model for hepatocellular carcinoma

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          Abstract

          Purpose: To develop a lipoprotein receptor-related protein 1B ( LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.

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          Most cited references28

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Robust enumeration of cell subsets from tissue expression profiles

            We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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              Regularization Paths for Generalized Linear Models via Coordinate Descent

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                Author and article information

                Contributors
                Role: Data curationRole: Supervision
                Role: Resources
                Role: SupervisionRole: Funding acquisition
                Role: Data curationRole: ValidationRole: Investigation
                Role: MethodologyRole: Writing—original draft
                Role: MethodologyRole: Writing—original draft
                Role: InvestigationRole: Visualization
                Journal
                Biosci Rep
                Biosci Rep
                bsr
                Bioscience Reports
                Portland Press Ltd.
                0144-8463
                1573-4935
                30 September 2021
                31 August 2021
                : 41
                : 9
                : BSR20211053
                Affiliations
                [1 ]Tianjin Second People’s Hospital, Tianjin 300192, China; Tianjin Institute of Hepatology, Tianjin 300192, China
                [2 ]Department of Immunology, Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
                [3 ]School of Pharmacy, Tianjin Medical University, Tianjin, China
                Author notes
                Correspondence: Jian Xu ( 58364386@ 123456qq.com ) or Bo Zhang ( bozhang@ 123456tmu.edu.cn )
                [*]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-1540-1013
                Article
                BSR20211053
                10.1042/BSR20211053
                8415215
                34386813
                09975fe5-a5f0-44a8-9d6f-35abff09f561
                © 2021 The Author(s).

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

                History
                : 04 May 2021
                : 28 July 2021
                : 29 July 2021
                : 13 August 2021
                Page count
                Pages: 11
                Categories
                Cancer
                Bioinformatics
                Gene Expression & Regulation
                Research Articles

                Life sciences
                hepatocellular carcinoma,immune cell infiltration ratio,immune checkpoint,lrp1b,prognostic model

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