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      SIRPα and PD1 expression on tumor-associated macrophage predict prognosis of intrahepatic cholangiocarcinoma

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          Abstract

          Background

          The phagocytosis checkpoints of CD47/SIRPα, PD1/PDL1, CD24/SIGLEC10, and MHC/LILRB1 have shown inhibited phagocytosis of macrophages in distinct tumors. However, phagocytosis checkpoints and their therapeutic significance remain largely unknown in intrahepatic cholangiocarcinoma (ICC) patients.

          Methods

          We analyzed sequencing data from the Cancer Genome Atlas (TCGA) and identified differently expressed genes between tumors and para‐tumors. Then, we investigated the expression of CD68, SIRPα, PD1, and SIGLEC10 by IHC in 81 ICC patients, and the clinical significance of these markers with different risk factors was also measured.

          Results

          Tumor infiltration immune cells analysis from the TCGA data revealed that macrophages significantly increased. Further analysis showed that M0 macrophages were significantly higher and M2 macrophages were significantly lower in ICC compared with paracancerous tissues, while there was no significant difference in M1 macrophages. We then examined some of M1 and M2 markers, and we found that M1 markers (iNOS, TNF, IL12A, and B) increased, while M2 markers (ARG1 and CD206) decreased in ICCs compared with paracancerous tissues. Furthermore, the expression of CD68, SIRPα, PD1, and SIGLEC10 increased significantly, but LILRB1 expression did not. We also examined the expression of CD68, SIRPα, PD1, and SIGLEC10 in 81 ICC patients by IHC, which revealed a similar expression pattern to that which emerged from the TCGA data. Upon analyzing the correlation between these markers and the progression of ICC patients, we found that the high expression of CD68, SIRPα, and PD1 are correlated with poor progression among ICC patients, while SIGLEC10 shows no correlation. More SIRPα + or PD1 + TAMs were observed in the tumor tissues of ICC patients with HBV infections compared to non‐HBV‐infected patients. Multivariate analysis indicated that SIRPα and PD1 expression are independent indicators of ICC patient prognosis.

          Conclusion

          Hyperactivated CD47/SIRPα and PD1/PD‐L1 signals in CD68 + TAMs in tumor tissues are negative prognostic markers for ICCs after resection. Furthermore, anti-CD47 in combination with anti-PD1 or CD47/PD1 bispecific antibody (BsAb) may represent promising treatments for ICC. Further studies are also required in the future to confirmed our findings.

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          Most cited references63

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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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              Macrophage activation and polarization: nomenclature and experimental guidelines.

              Description of macrophage activation is currently contentious and confusing. Like the biblical Tower of Babel, macrophage activation encompasses a panoply of descriptors used in different ways. The lack of consensus on how to define macrophage activation in experiments in vitro and in vivo impedes progress in multiple ways, including the fact that many researchers still consider there to be only two types of activated macrophages, often termed M1 and M2. Here, we describe a set of standards encompassing three principles-the source of macrophages, definition of the activators, and a consensus collection of markers to describe macrophage activation-with the goal of unifying experimental standards for diverse experimental scenarios. Collectively, we propose a common framework for macrophage-activation nomenclature. Copyright © 2014 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                zlyyxulinping1475@zzu.edu.cn
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                22 March 2022
                22 March 2022
                2022
                : 20
                : 140
                Affiliations
                [1 ]GRID grid.414011.1, ISNI 0000 0004 1808 090X, Department of Gastroenterology, , Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, ; Zhengzhou, 450003 Henan China
                [2 ]GRID grid.414008.9, ISNI 0000 0004 1799 4638, Department of Research and Foreign Affairs, , The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, ; Zhengzhou, 450008 China
                [3 ]GRID grid.412633.1, ISNI 0000 0004 1799 0733, Department of Hematology, , The First Affiliated Hospital of Zhengzhou University, ; Zhengzhou, 450052 Henan China
                Article
                3342
                10.1186/s12967-022-03342-6
                8939174
                35317832
                7e74b77a-ea67-40c6-9629-0ea6e878de46
                © The Author(s) 2022

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 4 January 2022
                : 6 March 2022
                Funding
                Funded by: Key Research Projects of Henan Higher Education Institutions
                Award ID: 21A320049
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Medicine
                tams,phagocytosis checkpoints,sirpα,pd1,siglec10
                Medicine
                tams, phagocytosis checkpoints, sirpα, pd1, siglec10

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