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      Integrated Analysis of Immune Infiltration and Hub Pyroptosis-Related Genes for Multiple Sclerosis

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          Abstract

          Purpose

          Studies on overall immune infiltration and pyroptosis in patients with multiple sclerosis (MS) are limited. This study explored immune cell infiltration and pyroptosis in MS using bioinformatics and experimental validation.

          Methods

          The GSE131282 and GSE135511 microarray datasets including brain autopsy tissues from controls and MS patients were downloaded for bioinformatic analysis. The gene expression-based deconvolution method, CIBERSORT, was used to determine immune infiltration. Differentially expressed genes (DEGs) and functional enrichments were analyzed. We then extracted pyroptosis-related genes (PRGs) from the DEGs by using machine learning strategies. Their diagnostic ability for MS was evaluated in both the training set (GSE131282 dataset) and validation set (GSE135511 dataset). In addition, messenger RNA (mRNA) expression of PRGs was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in cortical tissue from an experimental autoimmune encephalomyelitis (EAE) model of MS. Moreover, the functional enrichment pathways of each hub PRG were estimated. Finally, co-expressed competitive endogenous RNA (ceRNA) networks of PRGs in MS were constructed.

          Results

          Among the infiltrating cells, naive CD4 + T cells (P=0.006), resting NK cells (P=0.002), activated mast cells (P=0.022), and neutrophils (P=0.002) were significantly higher in patients with MS than in controls. The DEGs of MS were screened. Analysis of enrichment pathways showed that the pathways of transcriptional regulatory mechanisms and ion channels associating with pyroptosis. Four PRGs genes CASP4, PLCG1, CASP9 and NLRC4 were identified. They were validated in both the GSE135511 dataset and the EAE model by using qRT-PCR. CASP4 and NLRC4 were ultimately identified as stable hub PRGs for MS. Single-gene Gene Set Enrichment Analysis showed that they mainly participated in biosynthesis, metabolism, and organism resistance. ceRNA networks containing CASP4 and NLRC4 were constructed.

          Conclusion

          MS was associated with immune infiltration. CASP4 and NLRC4 were key biomarkers of pyroptosis in MS.

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          Most cited references67

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          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.

            Paul Shannon, Andrew Markiel, Owen Ozier … (2003)
            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

              Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)
              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
                Journal ID (nlm-ta): J Inflamm Res
                Journal ID (iso-abbrev): J Inflamm Res
                Journal ID (publisher-id): jir
                Title: Journal of Inflammation Research
                Publisher: Dove
                ISSN (Electronic): 1178-7031
                Publication date (Electronic): 13 September 2023
                Publication date Collection: 2023
                Volume: 16
                Pages: 4043-4059
                Affiliations
                [1 ]Department of Neurology, The First Affiliated Hospital of Chongqing Medical University , Chongqing, People’s Republic of China
                Author notes
                Correspondence: Xinyue Qin, Department of Neurology, The First Affiliated Hospital of Chongqing Medical University , 1st Youyi Road, Yuzhong District, Chongqing, People’s Republic of China, Tel +86 023-89012008, Email qinxinyuecqmu@163.com
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0009-0009-4191-1410
                Article
                Publisher ID: 422189
                DOI: 10.2147/JIR.S422189
                PMC ID: 10505586
                PubMed ID: 37727371
                SO-VID: 59d041df-5bf8-46bf-a268-da5ba2555365
                Copyright © © 2023 Zhang et al.
                License:

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                Date received : 08 June 2023
                Date accepted : 02 September 2023
                Page count
                Figures: 9, Tables: 1, References: 67, Pages: 17
                Categories
                Subject: Original Research

                ScienceOpen disciplines: Immunology
                Keywords: immune infiltration,pyroptosis,multiple sclerosis,bioinformatics,cerna
                Data availability:
                ScienceOpen disciplines: Immunology
                Keywords: immune infiltration, pyroptosis, multiple sclerosis, bioinformatics, cerna

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