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      Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs

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          Abstract

          Acute-on-chronic liver failure (ACLF) is a critical and refractory disease and a hepatic disorder accompanied by immune dysfunction. Thus, it is essential to explore key immune-related genes of ACLF and investigate its mechanisms. We used two public datasets (GSE142255 and GSE168048) to perform various bioinformatics analyses, including WGCNA, CIBERSORT, and GSEA. We also constructed an ACLF immune-related protein–protein interaction (PPI) network to obtain hub differentially expressed genes (DEGs) and predict corresponding miRNAs. Finally, an ACLF rat model was established to verify the results. A total of 388 DEGs were identified in ACLF, including 162 upregulated and 226 downregulated genes. The enrichment analyses revealed that these DEGs were mainly involved in inflammatory-immune responses and biosynthetic metabolic pathways. Twenty-eight gene modules were obtained using WGCNA and the coral1 and darkseagreen4 modules were highly correlated with M1 macrophage polarization. As a result, 10 hub genes and 2 miRNAs were identified to be significantly altered in ACLF. The bioinformatics analyses of the two datasets presented valuable insights into the pathogenesis and screening of hub genes of ACLF. These results might contribute to a better understanding of the potential molecular mechanisms of ACLF. Finally, further studies are required to validate our current findings.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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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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                Author and article information

                Contributors
                gyb@ccmu.edu.cn
                19950035@ccmu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 August 2022
                18 August 2022
                2022
                : 12
                : 14052
                Affiliations
                [1 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, School of Traditional Chinese Medicine, , Capital Medical University, ; Beijing, China
                [2 ]Beijing Key Laboratory of TCM Collateral Disease Theory Research, Beijing, 100069 China
                Article
                18396
                10.1038/s41598-022-18396-1
                9388648
                35982134
                ca1d4ab4-c891-4fe4-bd2e-716e4cb73f36
                © The Author(s) 2022

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 January 2022
                : 10 August 2022
                Funding
                Funded by: National Key Research and Development Program of China
                Award ID: 2018YFC1704102
                Award ID: 2018YFC1704102
                Award ID: 2018YFC1704102
                Award ID: 2018YFC1704102
                Award ID: 2018YFC1704102
                Award ID: 2018YFC1704102
                Award ID: 2018YFC1704102
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 82074237
                Award ID: 82074237
                Award ID: 82074237
                Award ID: 82074237
                Award ID: 82074237
                Award ID: 82074237
                Award ID: 82074237
                Award Recipient :
                Funded by: Beijing TCM Science & Technology Development Foundation
                Award ID: JJ-2020-51
                Award ID: JJ-2020-51
                Award ID: JJ-2020-51
                Award ID: JJ-2020-51
                Award ID: JJ-2020-51
                Award ID: JJ-2020-51
                Award ID: JJ-2020-51
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                genome informatics,hepatitis,liver diseases,hepatology
                Uncategorized
                genome informatics, hepatitis, liver diseases, hepatology

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