Research On The Immunogenetic Mechanism And Risk Prediction For Ankylosing Spondylitis Onset

Ankylosing spondylitis is a progressive chronic disease characterized by inflammatory lower back pain, frequently accompanied by peripheral arthritis, enthesis and iritis, and even spinal deformity and ankylosis. Similar to the incidence in populations of European ancestry, the prevalence of ankylosing spondylitis is0.24%in the Chinese population. Individuals with ankylosing spondylitis have a high prevalence of work-related disabilities, ranging from4%at5years after disease diagnosis to50%at45years after diagnosis.AS is a polygenic disease with complex genetic background. In order to get a more comprehensive understanding of the etiology, risk of developing and progress mechanism, AS need to be researched in the different levels such as gene, transcription and translation and so on.This study is aimed to explore the immunogenetic pathogenesis of AS in transcriptome (gene expression profile) and risk prediction of the onset respectively.Part Ⅰ:The study of ankylosing spondylitis susceptible gene validation in Chinese Han patientsObjective:To compare the differential susceptible gene expression on Han AS patients and healthy controls peripheral blood in Chinese, observe the biological function the meachanism of the Chinese Han patients with AS.Method:Through the search and summary reports in recent years and the prevalence of AS susceptibility genes, which associated with the first part, selected47susceptible gene as candidate gene, the expression analysis by real-time fluorescence quantitative PCR technology, identify differentially expressed genes. And the difference between GO and KEGG gene by bioinformatics tools for the analysis of its enrichment of metabolic pathways, in order to understand the mechanism of AS.Results:Trough comparative analysis of peripheral blood gene expression profiling of AS patients and normal controls,35genes were found differentially expressed, biological function analysis of the19up-regulated genes and16down-regulated genes, the inflammatory reaction mediated gene high expression, mediated immune response genes with low expression genes, which prompting immune disorders and inflammatory reaction in the up-regulation of AS in disease pathogenesis in the role of concern and further study. Conclusion:Immunodeficiency or immune deficiency may also exist in AS patients, which may have beneficial to environmental factors trigger disease; inflammatory pathway upregulation is the direct cause of disease development.Part Ⅱ The AS risk predictionObjective:The combination of gene polymorphism and gene expression of AS risk prediction model spectrum data, and in the Chinese Han population and verify that the forecast rate.Methods:Gene construction model of a five differential expression using the RNA sequencing and test, discriminant analysis was performed using SPSS16.0, building ROC curve (receiver operating curve), detection prediction rate in the first partial validation of the crowd and get the area under the curve. In a model on the basis of the last SNP site construction of disease prediction model two, logistical regression was used for the6covariate analysis and verification, and calculating the area under the curve by ROC curve. Establishment of prediction model of the three most prominent three differences in gene expression by real-time fluorescence quantitative PCR in second parts to the, use discriminant analysis and verification, ROC (receiver operating curve) curve and the area under the curve.Results:The model a (5differentially expressed gene data) effectiveness evaluation prediction, the minimum level at B range, model three (3differentially expressed gene data) forecast performance in B (0.80～0.89Good); model two (when combined with1SNP loci were modeling) evaluation of prediction effect promotion to class A (0.90～1Excellent) range; that when the SNP data of gene expression using the difference data corresponding to model, volatility of smaller, more stable, have better predictive value. The model can be used for AS risk assessment, is a ratio of phenotypic selection criterion is more accurate and can achieve good prediction effect. Conclusion:In Chinese Han population, expression profiling data to establish the risk model by gene, which has high forecast rate; spectrum combined with gene polymorphism data can greatly improve the prediction rate of gene expression.