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      Dissecting the heterogeneity of the microenvironment in primary and recurrent nasopharyngeal carcinomas using single-cell RNA sequencing

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          ABSTRACT

          Nasopharyngeal carcinoma (NPC) has a 10–15% recurrence rate, while no long term or durable treatment options are currently available. Single-cell profiling in recurrent NPC (rNPC) may aid in designing effective anticancer therapies, including immunotherapies. For the first time, we profiled the transcriptomes of ∼60,000 cells from four primary NPC and two rNPC cases to provide deeper insights into the dynamic changes in rNPC within radiation fields. Heterogeneity of both immune cells (T, natural killer, B, and myeloid cells) and tumor cells was characterized. Recurrent samples showed increased infiltration of regulatory T cells in a highly immunosuppressive state and CD8 + T cells in a highly cytotoxic and dysfunctional state. Enrichment of M2-polarized macrophages and LAMP3 + dendritic cells conferred enhanced immune suppression to rNPC. Furthermore, malignant cells showed enhanced immune-related features, such as antigen presentation. Elevated regulatory T cell levels were associated with a worse prognosis, with certain receptor-ligand communication pairs identified in rNPC. Even with relatively limited samples, our study provides important clues to complement the exploitation of rNPC immune environment and will help advance targeted immunotherapy of rNPC.

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          Most cited references69

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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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            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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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Journal
                Oncoimmunology
                Oncoimmunology
                Oncoimmunology
                Taylor & Francis
                2162-4011
                2162-402X
                25 January 2022
                2022
                25 January 2022
                : 11
                : 1
                : 2026583
                Affiliations
                [a ]Department of Radiation Oncology, Fudan University Shanghai Cancer Center; , Shanghai, China
                [b ]Department of Oncology, Shanghai Medical College, Fudan University; , Shanghai, China
                [c ]Department of Radiation Oncology, Eye, Ear, Nose & Throat Hospital of Fudan University; , Shanghai, China
                [d ]Department of Pathology, Fudan University Shanghai Cancer Center; , Shanghai, China
                Author notes
                CONTACT Xue-Guan Lu luxueguan@ 123456163.com Fudan University Shanghai Cancer Center; , 270 Dong An Road, Shanghai 200032, China
                Ting-Ting Xu dr_tingtingxu@ 123456163.com Fudan University Shanghai Cancer Center, 270 Dong An Road; , Shanghai 200032, China
                Chao-Su Hu hucsu62@ 123456yahoo.com Fudan University Shanghai Cancer Center; , 270 Dong An Road, Shanghai 200032, China.
                [✧]

                These authors contributed equally

                Article
                2026583
                10.1080/2162402X.2022.2026583
                8794254
                35096485
                92abc6bc-c425-47fc-92da-1c05a5150164
                © 2022 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: 0, References: 69, Pages: 1
                Categories
                Research Article
                Research Article

                Immunology
                nasopharyngeal carcinoma,recurrence,tumor microenvironment,immune cells,single-cell rna sequencing

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