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      Neutrophil extracellular trap related risk score exhibits crucial prognostic value in skin cutaneous melanoma, associating with distinct immune characteristics

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          Abstract

          Background

          Neutrophil extracellular traps (NETs) are related to the prognosis of cancer patients. Nevertheless, the potential prognostic values of NETs in skin cutaneous melanoma (SKCM) remains largely unknown.

          Materials and methods

          The NET‐related gene signature was constructed by LASSO Cox regression analysis using the TCGA‐SKCM cohort. The overall survival (OS) and immune status in SKCM patients between the high‐ and low‐NET score (high‐score, low‐score) groups were explored. The scRNA‐seq dataset GSE115978 was used to understand the role of NET score in SKCM at single cell resolution.

          Results

          A five NET genes‐based signature (TLR2, CLEC6A, PDE4B, SLC22A4 and CYP4F3) was constructed as the NET‐related prognostic model for SKCM. The OS of SKCM patients with low‐score was better than that in patients with high‐score. Additionally, NET score was negatively associated with infiltration of some immune cells (e.g. type I Macrophages, CD8‐T cells, CD4‐T cells). Moreover, patients with high‐score had low stromal, immune and ESTIMATE scores. Furthermore, drug sensitivity analysis results showed that Lapatinib, Trametinib and Erlotinib may have better therapeutic advantages in patients with high‐score.

          Conclusion

          We established a NET‐related five gene signature in SKCM and found that the NET‐related signature may exhibit a good predictive ability for SKCM prognosis. The NET score may not only predict the survival outcome and drug sensitivity in SKCM, but also reflect the immune conditions of SKCM patients.

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          Most cited references58

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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                Author and article information

                Contributors
                summer890208@sina.com
                Journal
                Skin Res Technol
                Skin Res Technol
                10.1111/(ISSN)1600-0846
                SRT
                Skin Research and Technology
                John Wiley and Sons Inc. (Hoboken )
                0909-752X
                1600-0846
                21 August 2024
                August 2024
                : 30
                : 8 ( doiID: 10.1111/srt.v30.8 )
                : e70008
                Affiliations
                [ 1 ] Department of Hand and Foot Surgery Zibo Central Hospital Zibo China
                [ 2 ] Dermatology&S.T.D. Department Zibo Central Hospital Zibo China
                Author notes
                [*] [* ] Correspondence

                Congcong Wang, Dermatology & S.T.D. Department, Zibo Central Hospital, No. 54 Gongqingtuan West Road, Zhangdian District, Zibo 255036, China.

                Email: summer890208@ 123456sina.com

                Author information
                https://orcid.org/0009-0009-1850-5094
                Article
                SRT70008
                10.1111/srt.70008
                11337913
                39167030
                6ba26564-01f9-4568-8a48-0ee23b6cab1c
                © 2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 12 June 2024
                : 05 August 2024
                Page count
                Figures: 7, Tables: 0, Pages: 15, Words: 6110
                Categories
                Original Article
                Original Article
                Custom metadata
                2.0
                August 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.7 mode:remove_FC converted:21.08.2024

                bioinformatic analysis,deg,melanoma,neutrophil extracellular traps,scrna‐seq

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