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      The role of m6A methylation genes in predicting poor prognosis in sepsis: identifying key biomarkers and therapeutic targets

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          Abstract

          Sepsis is one of the leading causes of death among seriously ill patients worldwide, affecting more than 30 million people annually and accounting for 1–2% of hospitalizations. By analyzing gene expression omnibus (GEO) data set, our team explored the relationship between m6A methylation gene and poor prognosis of sepsis. The purpose of this present study is to examine new detection markers for patients with poor prognosis, provide theoretical basis for timely intervention and improve the survival rate of patients. First, GSE54514 transcriptome data were extracted from the GEO database 31 patients with sepsis related death and 72 sepsis survivors. Key genes were screened from differentially expressed genes (DEGs), least absolute shrinkage and selection operator (LSAAO) and random forest (RF). And then, METTL3, WTAP and RBM15 were further verified by quantitative reverse transcription PCR (qRT-PCR). The constructed nomogram model showed high accuracy in predicting death. These three genes are mainly involved in chemokine signaling pathway, differentiation of monocytes and T cells, and phagocytosis of immune cells. The analysis showed that a high m6A score subtype is linked to lower immunosuppression and higher survival rates in clinical samples, suggesting better immune responses and outcomes for these patients. Finally, the protective effect of METTL3 in sepsis was demonstrated in mouse sepsis model applied with METTL3 inhibitor, by conducting cell flow cytometry analysis, enzyme-linked immunosorbent assay (ELISA) and hematoxylin–eosin (HE) staining. In conclusion, these findings provide potential biomarkers and targets for early precision diagnosis and treatment.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40001-024-02194-8.

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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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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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              Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

              The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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                Author and article information

                Contributors
                luzhonghua@ahmu.edu.cn
                sunyun9653@126.com
                Journal
                Eur J Med Res
                Eur J Med Res
                European Journal of Medical Research
                BioMed Central (London )
                0949-2321
                2047-783X
                19 December 2024
                19 December 2024
                2024
                : 29
                : 608
                Affiliations
                [1 ]The Second Affiliated Hospital of Anhui Medical University, ( https://ror.org/047aw1y82) 678 Furong Road, Hefei, 230601 Anhui Province China
                [2 ]School of Biomedical Engineering, Anhui Medical University, ( https://ror.org/03xb04968) 81 Meishan Road, Hefei, 230032 Anhui Province China
                Article
                2194
                10.1186/s40001-024-02194-8
                11657712
                39702336
                c82a5f8a-e122-4547-aef9-77760897a2e1
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

                History
                : 28 September 2024
                : 2 December 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Medicine
                sepsis,geo,qrt-pcr,mettl3
                Medicine
                sepsis, geo, qrt-pcr, mettl3

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