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Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet’s disease by leveraging gene microarray data

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Abstract

Behcet’s disease (BD) is a chronic inflammatory vasculitis and clinically heterogeneous disorder caused by immunocyte aberrations. Comprehensive research on gene expression patterns in BD illuminating its aetiology is lacking. E-MTAB-2713 downloaded from ArrayExpress was analysed to screen differentially expressed genes (DEGs) using limma. Random forest (RF) and neural network (NN) classification models composed of gene signatures were established using the E-MTAB-2713 training set and subsequently verified using GSE17114. Single sample gene set enrichment analysis was used to assess immunocyte infiltration. After identifying DEGs in E-MTAB-2713, pathogen-triggered, lymphocyte-mediated and angiogenesis- and glycosylation-related inflammatory pathways were discovered to be predominant in BD episodes. Gene signatures from the RF and NN diagnostic models, together with genes enriched in angiogenesis and glycosylation pathways, well discriminated the clinical subtypes of BD manifesting as mucocutaneous, ocular and large vein thrombosis involvement in GSE17114. Moreover, a distinctive immunocyte profile revealed T, NK and dendritic cell activation in BD compared to the findings in healthy controls. Our findings suggested that EPHX1, PKP2, EIF4B and HORMAD1 expression in CD14+ monocytes and CSTF3 and TCEANC2 expression in CD16+ neutrophils could serve as combined gene signatures for BD phenotype differentiation. Pathway genes comprising ATP2B4, MYOF and NRP1 for angiogenesis and GXYLT1, ENG, CD69, GAA, SIGLEC7, SIGLEC9 and SIGLEC16 for glycosylation also might be applicable diagnostic markers for subtype identification.

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Data availability

The dataset presented in this study can be found in ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) and Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) (GSE61399 and GSE17114).

Abbreviations

AAV :

ANCA-associated vasculitis

APC :

Antigen presenting cells

AUC :

Area under the curve

BD :

Behcet’s disease

BP :

Biological process

CCR :

Chemokine receptor

CMV :

Cytomegalovirus

C3 :

Complement 3

DEG :

Differentially expressed gene

FDR :

False discovery rate

GEO :

Gene Expression Omnibus

GO :

Gene Ontology

GWAS :

Genome-wide association study

HC :

Healthy control

HSV :

Herpes simplex virus

HLA :

Human leukocyte antigen

IBD :

Inflammatory bowel disease

iDC :

Immature dendritic cells

IFN :

Interferon

IL :

Interleukin

KEGG :

Kyoto Encyclopedia of Genes and Genomes

MB :

BD patients with mucocutaneous manifestations

MG :

Module genes

NK :

Natural killer cell

NN :

Neural network

OB :

BD patients with ocular involvement

PCR :

Polymerase chain reaction

pDC :

Plasmacytoid dendritic cell

PG :

Paramount genes

PPI :

Protein–protein Interaction

pSS :

Primary Sjögren’s syndrome

RA :

Rheumatoid arthritis

RF :

Random forest

ROC :

Receiver operating characteristic

SLE :

Systemic lupus erythematosus

ssGSEA :

Single sample gene set enrichment analysis

Tfh :

Follicular helper T cell

TNF :

Tumor necrosis-like factors

Th :

Helper T cell

Treg :

Regulatory T cell

VB :

BD patients with large vein thrombosis

WGCNA :

Weighted correlation network analysis

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Acknowledgements

The authors would like to express special gratitude to the researchers of the datasets of E-MTAB-2713, GSE17114 and GSE61399.

Funding

This research was supported by grants from the National Key Research and Development Program of China (2018YFE0207300), Beijing Municipal Natural Science Foundation Project (7234383) and the National Natural Science Foundation of China Grants (81871302).

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Author notes

  1. Haoting Zhan, Linlin Cheng and Haolong Li contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China

    Haoting Zhan, Linlin Cheng, Haolong Li, Yongmei Liu, Yuan Huang, Songxin Yan & Yongzhe Li

  2. Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China

    Xiaomeng Li

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Contributions

Professor YZL conceived and designed the research. PhD. HTZ extracted the data, performed the software analysis and visualized the graphs and tables. PhD. LLC and HLL supervised the statistical analyses. PhD. HTZ, YML, HLL, YH, XML and SXY categorised the graphs and tables. PhD. HTZ wrote the paper. All authors are accountable for all aspects of the study and attest to the accuracy and integrity of the results. The authors have read and approved the final manuscript as submitted.

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Zhan, H., Cheng, L., Li, H. et al. Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet’s disease by leveraging gene microarray data. Immunol Res 71, 860–872 (2023). https://doi.org/10.1007/s12026-023-09398-w

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