Abstract
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.
Highlights
Ankylosing spondylitis (AS) is a chronic inflammation disorder that attacks sacroiliac joints and spine [1] with a 0.3% incidence rate in the Asian population [2]
GSE25101 is one of the microarray profiles of AS that was reported by Pimentel-Santos et al [7], who identified a set of differentially expressed genes (DEGs), which were highly connected with AS, partially regulating the inflammatory process and joint destruction
DEGs identification and pathway enrichment analysis After robust microarray analysis, 11,586 genes were identified for differential expression analysis
Summary
Ankylosing spondylitis (AS) is a chronic inflammation disorder that attacks sacroiliac joints and spine [1] with a 0.3% incidence rate in the Asian population [2]. Genetic-associated research has detected several new genes related to AS. Some of these genes seem specific for AS, but others have pleiotropic associations [5,6]. These studies offer little information concerning changes in gene activity during the progression of the disease. Gene expression profiling provides a ‘‘snapshot’’ of cellular activity, supplying information on molecular mechanisms mediating disease changes, and can produce diagnostic gene sets. GSE25101 is one of the microarray profiles of AS that was reported by Pimentel-Santos et al [7], who identified a set of differentially expressed genes (DEGs), which were highly connected with AS, partially regulating the inflammatory process and joint destruction.
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