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      Unravelling infiltrating T‐cell heterogeneity in kidney renal clear cell carcinoma: Integrative single‐cell and spatial transcriptomic profiling

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          Abstract

          Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single‐cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high‐density weighted gene co‐expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell‐associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK‐8 assays, plate cloning assays, wound‐healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid‐ to late‐stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG‐based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T‐cell heterogeneity in KIRC using single‐cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

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          Most cited references106

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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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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

              Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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                Author and article information

                Contributors
                guanhuyang@gmail.com
                maigang68@hotmail.com
                liboer2002@126.com
                chihao7511@163.com
                Journal
                J Cell Mol Med
                J Cell Mol Med
                10.1111/(ISSN)1582-4934
                JCMM
                Journal of Cellular and Molecular Medicine
                John Wiley and Sons Inc. (Hoboken )
                1582-1838
                1582-4934
                21 June 2024
                June 2024
                : 28
                : 12 ( doiID: 10.1111/jcmm.v28.12 )
                : e18403
                Affiliations
                [ 1 ] Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital Southwest Medical University Luzhou China
                [ 2 ] School of Clinical Medicine, The Affiliated Hospital Southwest Medical University Luzhou China
                [ 3 ] Department of General Surgery (Hepatopancreatobiliary Surgery) Deyang People's Hospital Deyang China
                [ 4 ] Department of General Surgery Dazhou Central Hospital Dazhou China
                [ 5 ] Department of Pathology Sixth People's Hospital of Yibin Yibin China
                [ 6 ] Department of Specialty Medicine Ohio University Athens Ohio USA
                Author notes
                [*] [* ] Correspondence

                Guanhu Yang, Department of Specialty Medicine, Ohio University, Athens, OH, USA.

                Email: guanhuyang@ 123456gmail.com

                Gang Mai and Bo Li, Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, China.

                Email: maigang68@ 123456hotmail.com and liboer2002@ 123456126.com

                Hao Chi, School of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China.

                Email: chihao7511@ 123456163.com

                Author information
                https://orcid.org/0000-0002-5210-0770
                Article
                JCMM18403 JCMM-03-2024-258.R1
                10.1111/jcmm.18403
                11190954
                07b1ba6a-daf3-4418-b17d-bc31566e7a1a
                © 2024 The Author(s). Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 May 2024
                : 26 March 2024
                : 07 May 2024
                Page count
                Figures: 15, Tables: 0, Pages: 29, Words: 13900
                Funding
                Funded by: The Sichuan Provincial Science and Technology Department's Program
                Award ID: 22ZDYF1898
                Funded by: Sichuan Medical Association Project
                Award ID: S21048
                Funded by: Dazhou Science and Technology Bureau project
                Award ID: 21ZDYF0025
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                June 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.4 mode:remove_FC converted:21.06.2024

                Molecular medicine
                immune microenvironment,machine learning,multi‐omics,single‐cell analysis,spatial transcriptome,t cells,tumour heterogeneity

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