Gene Expression Profiling Analysis of Patients With Ankylosing Spondylitis: Retracted

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

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 acid-binding 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.

Downregulated miR-17, miR-29c, miR-92a and miR-214 may be related to BCL11B overexpression in T cell acute lymphoblastic leukemia

AIM

BCL11B overexpression is a characteristic of most T cell acute lymphoblastic leukemia (T-ALL) cases, and downregulated BCL11B in leukemic T cells inhibits cell proliferation and induces apoptosis. The purpose of this study was to analyze the miRNA expression pattern that may be related to BCL11B regulation in T-ALL.

METHODS

Quantitative real-time PCR was used to detect the miRNAs miR-17-3p, miR-17-5p, miR-29c-3p, miR-92a-3p, miR-214-3p and miR-214-5p, the BCL11B expression level in peripheral blood mononuclear cells which was obtained from 17 de novo and untreated T-ALL patients, and 15 healthy individuals (HIs) served as control. Correlations between the relative miRNA expression levels and BCL11B were analyzed.

RESULTS

Based on the computational prediction that certain miRNAs bind the BCL11B 3'-UTR, miR-17-3p, miR-17-5p, miR-29c-3p, miR-92a-3p, miR-214-3p and miR-214-5p were found to be candidates for regulating BCL11B. The expression levels of the six miRNAs were decreased compared with HIs, and with the exception of miR-17-5p, statistically significant differences in expression levels were found in the T-ALL group. Moreover, while significantly higher BCL11B expression was found in the T-ALL group, a negative trend in the correlation level for all six miRNAs could be found in all groups; however, statistical significance was only found for miR-214-3p in the T-ALL group.

CONCLUSION

miRNA downregulation together with BCL11B upregulation suggests that miR-17, miR-29c, miR-92a and miR-214 might be involved in BCL11B regulation. The therapeutic promise of regulating the expression of these miRNAs for T-ALL therapy may be considered in the future.

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

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.