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      Evaluation of CD47 in the Suppressive Tumor Microenvironment and Immunotherapy in Prostate Cancer

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          Abstract

          Background

          CD47 has high levels of expression in malignant cancer cells, which binds to SIRP- α to release the “don't eat me” signal and prevents mononuclear macrophages from phagocytosing the cells. Resistance to drugs and metastases are potential barriers for prostate cancer endocrine therapy. Although immunotherapy for tumors has developed rapidly in the last few decades, its effectiveness in treating prostate cancer is unsatisfactory. Prostate cancer has a high-expression level of CD47. Therefore, a novel approach for potential immunotherapy may be provided by investigating the relationship among CD47 and the infiltration of immune cells in the prostate carcinoma.

          Methods

          The GEPIA database was utilized to compare the abundance of CD47 in malignant tissues with tissues that were normal. Furthermore, the function of CD47 in prostate carcinoma was assessed by CancerSEA. The association among CD47 and the tumor microenvironment was assessed utilizing the TISCH single cell data database. By using TIMER, the connection among CD47 and immunological invasion of prostate cancer was explored. Moreover, macrophages were cocultured with mouse prostate cancer cell RM-1 blocked by CD47 antibody to observe the changes in phagocytosis efficiency in vitro.

          Results

          Expression level of CD47 is upregulated in prostate carcinoma, and it is closely connected with prostate cancer's inadequate immune invasion. CD47 antibody blocking promotes macrophage phagocytosis of RM-1.

          Conclusion

          Our research demonstrates a closely relationship among CD47 and the immunological microenvironment of prostate cancer, and blocking CD47 can promote macrophages to phagocytosis of prostate cancer cells. Therefore, CD47 may provide novel strategies for potential immunotherapy of prostate cancer.

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          Most cited references52

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

            Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
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              TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

              Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.
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                Author and article information

                Contributors
                Journal
                J Immunol Res
                J Immunol Res
                jir
                Journal of Immunology Research
                Hindawi
                2314-8861
                2314-7156
                2023
                9 September 2023
                : 2023
                : 2473075
                Affiliations
                1Department of Urology, School of Medicine, South China University of Technology, Guangzhou 510006, China
                2Department of Urology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
                Author notes

                Academic Editor: Lalit Batra

                Author information
                https://orcid.org/0000-0003-4137-7931
                https://orcid.org/0009-0004-2656-3466
                https://orcid.org/0000-0002-5875-0491
                https://orcid.org/0000-0002-4854-5523
                https://orcid.org/0000-0002-9738-6733
                https://orcid.org/0000-0003-0132-6494
                https://orcid.org/0000-0002-2593-0285
                Article
                10.1155/2023/2473075
                10505079
                37719086
                e4682ff6-f163-4a09-8f03-3e14485cd55a
                Copyright © 2023 Qianqian Wang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 March 2023
                : 2 August 2023
                : 18 August 2023
                Funding
                Funded by: Prostate Cancer Precision Diagnosis
                Funded by: Guangdong Provincial People's Hospital
                Award ID: KS0120220267
                Award ID: KS0120220268
                Categories
                Research Article

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