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      Macrophage M2 Co-expression Factors Correlate With the Immune Microenvironment and Predict Outcome of Renal Clear Cell Carcinoma

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          Abstract

          Purpose: In the tumor microenvironment, the functional differences among various tumor-associated macrophages (TAM) are not completely clear. Tumor-associated macrophages are thought to promote the progression of cancer. This article focuses on exploring M2 macrophage-related factors and behaviors of renal clear cell carcinoma.

          Method: We obtained renal clear cell carcinoma data from TCGA-KIRC-FPKM, GSE8050, GSE12606, GSE14762, and GSE3689. We used the “Cibersort” algorithm to calculate type M2 macrophage proportions among 22 types of immune cells. M2 macrophage-related co-expression module genes were selected using weighted gene co-expression network analysis (WGCNA). A renal clear cell carcinoma prognosis risk score was built based on M2 macrophage-related factors. The ROC curve and Kaplan–Meier analysis were performed to evacuate the risk score in various subgroups. The Pearson test was used to calculate correlations among M2 macrophage-related genes, clinical phenotype, immune phenotype, and tumor mutation burden (TMB). We measured differences in co-expression of genes at the protein level in clear renal cell carcinoma tissues.

          Results: There were six M2 macrophage co-expressed genes (F13A1, FUCA1, SDCBP, VSIG4, HLA-E, TAP2) related to infiltration of M2 macrophages; these were enriched in neutrophil activation and involved in immune responses, antigen processing, and presentation of exogenous peptide antigen via MHC class I. M2-related factor frequencies were robust biomarkers for predicting the renal clear cell carcinoma patient clinical phenotype and immune microenvironment. The Cox regression model, built based on M2 macrophage-related factors, showed a close prognostic correlation (AUC = 0.78). The M2 macrophage-related prognosis model also performed well in various subgroups. Using western blotting, we found that VSIG4 protein expression levels were higher in clear renal cell carcinoma tissues than in normal tissues.

          Conclusion: These co-expressed genes were most related to the M2 macrophage phenotype. They correlated with the immune microenvironment and predicted outcomes of renal clear cell carcinoma. These co-expressed genes and the biological processes associated with them might provide the basis for new strategies to intervene via chemotaxis of M2 macrophages.

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          Most cited references37

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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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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              Proteomics. Tissue-based map of the human proteome.

              Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                22 February 2021
                2021
                : 12
                : 615655
                Affiliations
                [1] 1Department of Urology, The First Hospital of China Medical University, China Medical University , Shenyang, China
                [2] 2Department of Dermatology, The First Hospital of China Medical University, China Medical University , Shenyang, China
                Author notes

                Edited by: Chunjie Jiang, University of Pennsylvania, United States

                Reviewed by: Jingting Yu, Salk Institute for Biological Studies, United States; Yi Zhang, Dana-Farber Cancer Institute, United States; Kuixi Zhu, University of Arizona, United States

                *Correspondence: Jianbin Bi bijianbin@ 123456hotmail.com

                This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics

                †These authors have contributed equally to this work

                Article
                10.3389/fgene.2021.615655
                7938896
                33692827
                c439a324-1663-4247-be12-5ad4d34f0a2c
                Copyright © 2021 Wang, Yan, Lin, Li and Bi.

                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
                : 09 October 2020
                : 07 January 2021
                Page count
                Figures: 9, Tables: 2, Equations: 0, References: 37, Pages: 14, Words: 5929
                Categories
                Genetics
                Original Research

                Genetics
                m2 macrophage,weighted gene co-expression network analysis,tumor-associated macrophage,immune phenotype,risk model

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