Gene Expression Profiling Analysis of Patients With Ankylosing Spondylitis

Shi, Zhicai; Quan, Li; Cai, Bin; Ran, Bo; Li, Ming

doi:10.1097/bsd.0000000000000266

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…While the etiology of AS is unclear, it is thought to be possibly immune-mediated. It has a strong genetic association with the human leukocyte antigen (HLA)-B*27, endopeptidase endoplasmic reticulum aminopeptidase (ERAP1), interleukin-23 receptor (IL23R), IL7R, and caspase recruitment domain-containing protein 9 (CARD9) . The most effective current medication, which basically consists of tumor necrosis factor (TNF-α) or IL-17 inhibitors, seemed to work to arrest bone erosion or syndesmophyte formation.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics

Liu

et al. 2020

J. Proteome Res.

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Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory rheumatic disease that affects 0.2% of the population. Current diagnostic criteria for disease activity rely on subjective Bath Ankylosing Spondylitis Disease Activity Index scores. Here, we aimed to discover a panel of serum protein biomarkers. First, tandem mass tag (TMT)-based quantitative proteomics was applied to identify differential proteins between 15 pooled active AS and 60 pooled healthy subjects. Second, cohort 1 of 328 humans, including 138 active AS and 190 healthy subjects from two independent centers, was used for biomarker discovery and validation. Finally, biomarker panels were applied to differentiate among active AS, stable AS, and healthy subjects from cohort 2, which enrolled 28 patients with stable AS, 26 with active AS, and 28 healthy subjects. From the proteomics study, a total of 762 proteins were identified and 46 proteins were up-regulated and 59 proteins were down-regulated in active AS patients compared to those in healthy persons. Among them, C-reactive protein (CRP), complement factor H-related protein 3 (CFHR3), α-1-acid glycoprotein 2 (ORM2), serum amyloid A1 (SAA1), fibrinogen γ (FG-γ), and fibrinogen β (FG-β) were the most significantly up-regulated inflammation-related proteins and S100A8, fatty acidbinding protein 5 (FABP5), and thrombospondin 1 (THBS1) were the most significantly down-regulated inflammation-related proteins. From the cohort 1 study, the best panel for the diagnosis of active AS vs healthy subjects is the combination of CRP and SAA1. The area under the receiver operating characteristic (ROC) curve was nearly 0.900, the sensitivity was 0.970%, and the specificity was 0.805% at a 95% confidence interval from 0.811 to 0.977. Using 0.387 as the cutoff value, the predictive values reached 92.00% in the internal validation set (62 with active AS vs 114 healthy subjects) and 97.50% in the external validation phase (40 with active AS vs 40 healthy subjects). From the cohort 2 study, a panel of CRP and SAA1 can differentiate well among active AS, stable AS, and healthy subjects. For active AS vs stable AS, the area under the ROC curve was 0.951, the sensitivity was 96.43%, the specificity was 88.46% at a 95% confidence interval from 0.891 to 1, and the coincidence rate was 92.30%. For stable AS vs healthy humans, the area under the ROC curve was 0.908, the sensitivity was 89.29%, the specificity was 78.57% at a 95% confidence interval from 0.836 to 0.980, and the coincidence rate was 83.93%. For active AS vs healthy subjects, the predictive value was 94.44%. The results indicated that the CRP and SAA1 combination can potentially diagnose disease status, especially for active or stable AS, which will be conducive to treatment recommendation for patients with AS.

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Section: Introductionmentioning

confidence: 99%

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics

Liu

et al. 2020

J. Proteome Res.

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“…In 2015, Zhao et al ( 8 ) used the same microarray profile of GSE25101 to predict the potential AS-related genes, such as RPL17 , MRPL22 , PSMA6 and PSMA4 . In 2015, also using the same data, Shi et al ( 9 ) identified 284 DEGs correlated with AS (such as MYH9 , BCL11B and CD4 ), and detected the pathway for immune response regulation. Nevertheless, so far, most studies that assessed the genetics of AS focused on a single gene or a single pathway.…”

Section: Introductionmentioning

confidence: 98%

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways

Liu

Wei

2018

Braz J Med Biol Res

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Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

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Association of Non-HLA Genes with Ankylosing Spondylitis

Rahmati

Hakemi

2021

Ankylosing Spondylitis - Axial Spondyloarthritis

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Gene Expression Profiling Analysis of Patients With Ankylosing Spondylitis

Abstract: MYH9, BCL11B, PTPRC, CD3G, CD247, CD4, IL7R, and PRF1 might have a correlation with AS.

Cited by 6 publications

References 49 publications

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways

Association of Non-HLA Genes with Ankylosing Spondylitis

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