Identification of Key Immune Infiltration Related Genes Involved in Aortic Dissection Using Bioinformatic Analyses and Experimental Verification

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Abstract

Purpose

Immune microenvironment plays an important role in aortic dissection (AD). Therefore, novel immune biomarkers may facilitate AD prevention, diagnosis, and treatment. This study aimed at mining key immune-related genes and relevant mechanisms involved in AD pathogenesis.

Patients and Methods

Key immune cells in AD were identified by ssGESA algorithm. Next, genes associated with key immune cells were screened by weighted gene coexpression network analysis (WGCNA). Then hub immune genes were picked from protein-protein interaction network of overlapped genes from differential expression and WGCNA analyses by cytohubba plug-in. Their diagnostic potential was evaluated in two independent cohorts from GEO database. In addition, the expressions of hub immune genes were determined by quantitative RT-PCR, immunohistochemistry, and Western blotting in dissected and normal aortic tissues.

Results

Activated B cells, CD56dim natural killer cells, eosinophils, gamma delta T cells, immature B cells, natural killer cells and type 17 T helper cells were identified as key immune cells in AD. Thereafter, a gene module significantly correlated with key immune cells were found by WGCNA method. Subsequently, KDR, IGF1, NOS3, PECAM1, GAPDH, FLT1, DLL4, CDH5, VWF, and TEK were identified as hub immune cell related genes by PPI network analysis, which may be potential diagnostic markers for AD, as evidenced by ROC curves. Moreover, the decreased expression of VWF in AD was validated at both mRNA and protein levels, and its expression was significantly positive correlated with the marker of smooth muscle cells, ACTA2, in AD. Further immunofluorescent results showed that VWF was colocalized with ACTA2 in aortic tissues.

Conclusion

We identified key immune cells and hub immune cell-related genes involved in AD. Moreover, we found that VWF was co-expressed with the smooth muscle cell marker ACTA2, indicating the important role of VWF in smooth muscle cell loss in AD pathogenesis.

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Most cited references 55

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NIH Image to ImageJ: 25 years of image analysis

Kevin W Eliceiri, Caroline A Schneider, Wayne S Rasband (2019)

For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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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): 05 April 2024

Publication date Collection: 2024

Volume: 17

Pages: 2119-2135

Affiliations

[1 ]Department of Vascular Surgery, the Second Hospital, Shanxi Medical University , Taiyuan, 030001, People’s Republic of China

[2 ]Department of Cardiology, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital , Taiyuan, 030032, People’s Republic of China

[3 ]Department of Cardiac Surgery, the Second Hospital of Hebei Medical University , Shijiazhuang, People’s Republic of China

Author notes

Correspondence: Lihua Chen, The Second Hospital of Hebei Medical University, 215 Hepingxi Street, Shijiazhuang, 050000, People’s Republic of China, Tel +86 188 4679 1714, Fax +86 0311-87045297, Email lin0928@fjmu.edu.cn

[*]

These authors contributed equally to this work

Author information

Lin Zheng http://orcid.org/0000-0001-7771-5483

Yusi Yang http://orcid.org/0000-0002-9539-8968

Jie Liu http://orcid.org/0009-0005-1757-2691

Tianliang Zhao http://orcid.org/0000-0003-0630-9976

Xin Zhang http://orcid.org/0009-0009-9671-4019

Lihua Chen http://orcid.org/0009-0005-4290-7262

Article

Publisher ID: 434993

DOI: 10.2147/JIR.S434993

PMC ID: 11003470

PubMed ID: 38595338

SO-VID: 325388df-91d4-42b9-a51d-8b063cdeddf3

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 : 11 August 2023

Date accepted : 29 March 2024

Page count

Figures: 8, Tables: 1, References: 56, Pages: 17

Funding

Funded by: funding;

There is no funding to report.

Identification of Key Immune Infiltration Related Genes Involved in Aortic Dissection Using Bioinformatic Analyses and Experimental Verification

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Purpose

Patients and Methods

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Most cited references 55

NIH Image to ImageJ: 25 years of image analysis

WGCNA: an R package for weighted correlation network analysis

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

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