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      Single-cell landscape reveals active cell subtypes and their interaction in the tumor microenvironment of gastric cancer

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          Abstract

          Background: Gastric cancer remains the third most common cause of cancer-related death worldwide. The development of novel therapeutic strategies for gastric cancer requires a deep understanding of the tumor cells and microenvironment of gastric cancer.

          Methods: We performed the single-cell RNA sequencing (scRNA-seq) on nine untreated non-metastatic gastric cancer patients. The transcriptomic atlas and ligand-receptor-based intercellular communication networks of the single cells were characterized.

          Results: Here, we profiled the transcriptomes of 47,304 cells from nine patients with gastric cancer. Tregs cells were significantly enriched in the gastric tumor tissues with increased expression of immune suppression related genes, which suggest a more immunosuppressive microenvironment. We also observed the absence of separate exhausted CD8+ T cell cluster, and the low expression level of exhaustion markers PDCD1, CTLA4, HAVCR2, LAG-3, and TIGIT in those specific cells. These may serve as molecular-level evidence for the limited benefit of immunotherapy among gastric cancer patients. In addition, we found ACKR1 specifically expressed in tumor endothelial cells, associated with poor prognosis in the cohort data and potentially provided a novel target of gastric cancer treatment. Furthermore, the tight interaction between endothelial cells and fibroblast implied the important roles of fibroblast in tumor angiogenesis and the maintenance of tumor vasculature.

          Conclusions: In conclusion, this single-cell atlas provide understanding the cellular heterogeneity from molecular level in gastric cancer and will serve as a valuable resource for developing innovative early and companion diagnostics, as well as discovering novel targeted therapies for gastric cancer.

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          Most cited references56

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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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            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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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                Theranostics
                Theranostics
                thno
                Theranostics
                Ivyspring International Publisher (Sydney )
                1838-7640
                2022
                9 May 2022
                : 12
                : 8
                : 3818-3833
                Affiliations
                [1 ]Lanzhou University Second Hospital, Lanzhou 730030, China.
                [2 ]BIOPIC, Beijing Advanced Innovation Center for Genomics, School of Life Sciences, Peking University, Beijing 100871, China.
                [3 ]Clabee Genomics, Urban Garden Building, Bookstore Road, Luohu District, Shenzhen 518000, Guangdong, China.
                [4 ]Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
                [5 ]Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, United States.
                [6 ]National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
                [7 ]Center on Translational Neuroscience, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China.
                [8 ]Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, USA.
                Author notes
                ✉ Corresponding authors: George Church (email: gchurch@ 123456genetics.med.harvard.edu ), Jie He (email: hejie@ 123456cicams.ac.cn ) and Yingtai Chen (email: yingtaichen@ 123456126.com ).

                #These authors contributed equally to this work.

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                thnov12p3818
                10.7150/thno.71833
                9131288
                35664061
                52eb0d4f-ecc8-4b07-85c5-44e27d2ccb58
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 8 February 2022
                : 24 April 2022
                Categories
                Research Paper

                Molecular medicine
                gastric cancer,tumor microenvironment,single-cell rna sequencing.
                Molecular medicine
                gastric cancer, tumor microenvironment, single-cell rna sequencing.

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