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      Immune infiltration and immune gene signature predict the response to fluoropyrimidine-based chemotherapy in colorectal cancer patients

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          ABSTRACT

          Fluoropyrimidine-based chemotherapy is an essential component of systemic chemotherapy for colorectal cancer (CRC). The immune response is implicated in chemotherapy-induced cytotoxicity. Here, we reported an immune risk (Imm-R) model for prognostic prediction in patients receiving fluoropyrimidine-based chemotherapy. Gene expression profiles and corresponding clinical information were collected from four data sets and divided into training set (n = 183) and validation set (validation set1: n = 34; validation set2: n = 99). The composition of 22 tumor-infiltrating immune cells (TIICs) populations was characterized with the CIBERSORT deconvolution algorithm. A prognostic Imm-R model for predicting overall survival was established by performing least absolute shrinkage and selection operator (LASSO) penalized COX regression analysis. T follicular helper cells and M0 macrophages were associated with better survival, while eosinophils were associated with worse survival. TIICs signature was constructed based on the above three immune cell types. Furthermore, a Imm-R model was created by integrating TIICs signature with immune-related genes (IRGs), which effectively in distinguishing CRC patients with poorer prognosis. The Imm-R model was associated with activation of the TGF-beta signaling and suppression of DNA damage. Results of this research provide new insights into the role of immunity for in fluoropyrimidine-based chemotherapy as well as a useful tools to predict the outcome of CRC patients receiving fluoropyrimidine-based chemotherapy.

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          Most cited references74

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            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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              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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                Author and article information

                Journal
                Oncoimmunology
                Oncoimmunology
                Oncoimmunology
                Taylor & Francis
                2162-4011
                2162-402X
                19 October 2020
                2020
                19 October 2020
                : 9
                : 1
                : 1832347
                Affiliations
                [a ]Division of Colorectal & Anal Surgery,Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital; , Nanning, Guangxi Zhuang Autonomous Region, the People’s Republic of China
                [b ]Guangxi Clinical Research Center for Colorectal Cancer; , Nanning, Guangxi Zhuang Autonomous Region, the People’s Republic of China
                [c ]Department of Research, Guangxi Medical University Cancer Hospital; , Nanning, Guangxi Zhuang Autonomous Region, the People’s Republic of China
                Author notes
                CONTACT Weizhong Tang tangweizhong@ 123456gxmu.edu.cn Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning 530021, Guangxi Zhuang Autonomous Region, the People’s Republic of China
                Jungang Liu liujungang@ 123456gxmu.edu.cn Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, China
                [*]

                Contributed equally

                Author information
                https://orcid.org/0000-0003-0877-8557
                Article
                1832347
                10.1080/2162402X.2020.1832347
                7575007
                4fa926f1-f835-49af-9cdc-ea54a921e0d7
                © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 6, Tables: 1, References: 74, Pages: 1
                Categories
                Research Article
                Original Research

                Immunology
                colorectal cancer,fluoropyrimidine,immune-related-gene,tumor-infiltrating immune cells

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