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. 2023 May 24;24(1):413.
doi: 10.1186/s12891-023-06550-3.

Identification of potential biomarkers for ankylosing spondylitis based on bioinformatics analysis

Dongxu Li  1   2 , Ruichao Cao  1   2 , Wei Dong  1   2 , Minghuang Cheng  1   2 , Xiaohan Pan  1   2 , Zhenming Hu  1   2 , Jie Hao  3   4
Affiliations

Identification of potential biomarkers for ankylosing spondylitis based on bioinformatics analysis

Dongxu Li et al. BMC Musculoskelet Disord. .

Abstract

Objective: The aim of this study was to search for key genes in ankylosing spondylitis (AS) through comprehensive bioinformatics analysis, thus providing some theoretical support for future diagnosis and treatment of AS and further research.

Methods: Gene expression profiles were collected from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/ ) by searching for the term "ankylosing spondylitis". Ultimately, two microarray datasets (GSE73754 and GSE11886) were downloaded from the GEO database. A bioinformatic approach was used to screen differentially expressed genes and perform functional enrichment analysis to obtain biological functions and signalling pathways associated with the disease. Weighted correlation network analysis (WGCNA) was used to further obtain key genes. Immune infiltration analysis was performed using the CIBERSORT algorithm to conduct a correlation analysis of key genes with immune cells. The GWAS data of AS were analysed to identify the pathogenic regions of key genes in AS. Finally, potential therapeutic agents for AS were predicted using these key genes.

Results: A total of 7 potential biomarkers were identified: DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP and SORL1. ROC curves showed good prediction for each gene. T cell, CD4 naïve cell, and neutrophil levels were significantly higher in the disease group than in the paired normal group, and key gene expression was strongly correlated with immune cells. CMap results showed that the expression profiles of ibuprofen, forskolin, bongkrek-acid, and cimaterol showed the most significant negative correlation with the expression profiles of disease perturbations, suggesting that these drugs may play a role in AS treatment.

Conclusion: The potential biomarkers of AS screened in this study are closely related to the level of immune cell infiltration and play an important role in the immune microenvironment. This may provide help in the clinical diagnosis and treatment of AS and provide new ideas for further research.

Keywords: Ankylosing spondylitis; Bioinformatics; Biomarkers; GWAS; Immune infiltration.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Fig. 1
Fig. 1
Identification of differentially expressed genes in AS. a and b The interchip batch effect was reduced after SVA algorithm correction. c and d Volcano plot and heatmap of DEGs between normal and disease groups, with differentially expressed gene screening conditions of P < 0.05 and |logFC|> 0.585; (e) PPI network of DEGs
Fig. 2
Fig. 2
Functional enrichment analysis of DEGs
Fig. 3
Fig. 3
WGCNA. a Clustering heatmap of the control and AS groups; (b) scale-free exponent and mean connectivity; (c) dendrogram of gene clusters; (d) heatmap of the correlations. The purple module had the highest correlation. (e) A Venn diagram was generated to show the intersection of the purple module with differentially expressed genes
Fig. 4
Fig. 4
Predictive efficacy of key genes for the disease. The AUC values of the seven key genes suggest their good predictive performance for AS
Fig. 5
Fig. 5
Confirmation of key genes in the pathogenic region of ankylosing spondylitis using GWAS data. a Q-Q plot showing significant single nucleotide polymorphism (SNP) loci associated with AS; (b) key SNP loci distributed in the enriched region; (c-i) SNP pathogenic regions corresponding to each of the seven genes (DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP, SORL1)
Fig. 6
Fig. 6
Immune cell infiltration analysis. a Immune cell volume; (b) Pearson correlation between 22 immune cells. c Violin plot of the differences in infiltrating immune cells between the AS (red) and control (blue) groups
Fig. 7
Fig. 7
Correlation between key genes (DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP, SORL1) and immune cells (a–g)
Fig. 8
Fig. 8
GSEA. a-g Specific signalling pathways associated with the seven key genes (DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP, and SORL1)
Fig. 9
Fig. 9
Analysis of key gene regulatory network. a and b Results of enrichment analysis of cumulative recovery curves. c Sequences of enriched genes and their corresponding transcription factors
Fig. 10
Fig. 10
Differential analysis of pathogenic genes in ankylosing spondylitis. a Differential expression of AS pathogenic genes between the control (blue) and AS (pink) groups. b Correlation analysis between AS-related genes and key genes
Fig. 11
Fig. 11
Inverse prediction of seven key genes using the miRcode database yielded 85 miRNAs, with a total of 234 mRNA‒miRNA relationship pairs
Fig. 12
Fig. 12
Chemical structures of the four potential drugs: (a) ibuprofen; (b) forskolin; (c) bongkrek-acid; (d) cimaterol
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References

    1. Taurog JD, Chhabra A, Colbert RA. Ankylosing Spondylitis and Axial Spondyloarthritis. N Engl J Med. 2016;374(26):2563–2574. doi: 10.1056/NEJMra1406182. - DOI - PubMed
    1. Lindström U, et al. Impact of extra-articular spondyloarthritis manifestations and comorbidities on drug retention of a first TNF-inhibitor in ankylosing spondylitis: a population-based nationwide study. RMD Open. 2018;4(2):e000762. doi: 10.1136/rmdopen-2018-000762. - DOI - PMC - PubMed
    1. Dean LE, et al. Global prevalence of ankylosing spondylitis. Rheumatology (Oxford) 2014;53(4):650–657. doi: 10.1093/rheumatology/ket387. - DOI - PubMed
    1. Brown MA, et al. Recurrence risk modelling of the genetic susceptibility to ankylosing spondylitis. Ann Rheum Dis. 2000;59(11):883–886. doi: 10.1136/ard.59.11.883. - DOI - PMC - PubMed
    1. Brown MA, Kenna T, Wordsworth BP. Genetics of ankylosing spondylitis–insights into pathogenesis. Nat Rev Rheumatol. 2016;12(2):81–91. doi: 10.1038/nrrheum.2015.133. - DOI - PubMed
-