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Screening of underlying genetic biomarkers for ankylosing spondylitis.

ABSTRACT: Genetic biomarkers for the diagnosis of ankylosing spondylitis (AS) remain unreported except for human leukocyte antigen B27 (HLA?B27). Therefore, the aim of the present study was to screen the differentially expressed genes (DEGs), and those that also possess differential single nucleotide polymorphism (SNP) loci in the whole blood of AS patients compared with healthy controls by integrating two mRNA expression profiles (GSE73754 and GSE25101) and SNP microarray data (GSE39428) collected from the Gene Expression Omnibus (GEO). Using the t?test, 1,056 and 1,073 DEGs were identified in the GSE73754 and GSE25101 datasets, respectively. Among them, 234 DEGs were found to be shared in both datasets, which were subsequently overlapped with 122 differential SNPs of genes in the GSE39428 dataset, resulting in identification of two common genes [eukaryotic translation elongation factor 1 epsilon 1 (EEF1E1) and serpin family A member 1 (SERPINA1)]. Their expression levels were significantly upregulated and the average expression log R ratios of SNP sites in these genes were significantly higher in AS patients than those in controls. Function enrichment analysis revealed that EEF1E1 was involved in AS by influencing the aminoacyl?tRNA biosynthesis, while SERPINA1 may be associated with AS by participating in platelet degranulation. However, only the genotype and allele frequencies of SNPs (rs7763907 and rs7751386) in EEF1E1 between AS and controls were significantly different between AS and the controls, but not SERPINA1. These findings suggest that EEF1E1 may be an underlying genetic biomarker for the diagnosis of AS.

SUBMITTER: Fan X

PROVIDER: S-EPMC6522869 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Screening of underlying genetic biomarkers for ankylosing spondylitis.

Fan Xutao X Qi Bao B Ma Longfei L Ma Fengyu F

Molecular medicine reports 20190424 6

Genetic biomarkers for the diagnosis of ankylosing spondylitis (AS) remain unreported except for human leukocyte antigen B27 (HLA‑B27). Therefore, the aim of the present study was to screen the differentially expressed genes (DEGs), and those that also possess differential single nucleotide polymorphism (SNP) loci in the whole blood of AS patients compared with healthy controls by integrating two mRNA expression profiles (GSE73754 and GSE25101) and SNP microarray data (GSE39428) collected from t ...[more]

PMID: 31059041

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Project description:BackgroundWe sought to discover serum biomarkers of ankylosing spondylitis (AS) for diagnosis and monitoring disease activity.MethodsWe studied biologic-treatment-naïve AS and healthy control (HC) patients' sera. Eighty samples matched by age, gender, and race (1:1:1 ratio) for AS patients with active disease, inactive disease, and HC were analyzed with SOMAscan™, an aptamer-based discovery platform. T-tests tests were performed for high/low-disease activity AS patients versus HCs (diagnosis) and high versus low disease activity (Monitoring) in a 2:1 and 1:1 ratio, respectively, to identify differentially expressed proteins (DEPs). We used the Cytoscape Molecular Complex Detection (MCODE) plugin to find clusters in protein-protein interaction networks and Ingenuity Pathway Analysis (IPA) for upstream regulators. Lasso regression analysis was performed for diagnosis.ResultsOf the 1317 proteins detected in our diagnosis and monitoring analyses, 367 and 167 (317 and 59, FDR-corrected q < .05) DEPs, respectively, were detected. MCODE identified complement, IL-10 signaling, and immune/interleukin signaling as the top 3 diagnosis PPI clusters. Complement, extracellular matrix organization/proteoglycans, and MAPK/RAS signaling were the top 3 monitoring PPI clusters. IPA showed interleukin 23/17 (interleukin 22, interleukin 23A), TNF (TNF receptor-associated factor 3), cGAS-STING (cyclic GMP-AMP synthase, Stimulator of Interferon Gene 1), and Jak/Stat (Signal transducer and activator of transcription 1), signaling in predicted upstream regulators. Lasso regression identified a Diagnostic 13-protein model predictive of AS. This model had a sensitivity of 0.75, specificity of 0.90, a kappa of 0.59, and overall accuracy of 0.80 (95% CI: 0.61-0.92). The AS vs HC ROC curve was 0.79 (95% CI: 0.61-0.96).ConclusionWe identified multiple candidate AS diagnostic and disease activity monitoring serum biomarkers using a comprehensive proteomic screen. Enrichment analysis identified key pathways in AS diagnosis and monitoring. Lasso regression identified a multi-protein panel with modest predictive ability.

Project description:Genotyping was performed using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina) to determine possible associations of genes encoding PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR,SERPNA1, PCSK6, DNAH9 to rheumatoid arthrities (RA) and ankylosing spondylitis (AS). We previously found that encoding genes of PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR, SERPNA1, PCSK6 and DNAH9 could be associated with rheumatoid arthritis (RA) and ankylosing spondylitis (AS). We used illumina customer designed microarray to verify the above finding with an increased numbers of samples. Genotyping was performed using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina) to determine possible associations of genes encoding PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR, SERPNA1, PCSK6, DNAH9 to RA and AS. A case-control study was performed with 51 AS patients, 266 rheumatoid arthritis (RA) and 163 health controls. Tag single nucleotide polymorphisms (tag SNPs) across the above genes were determined by searching the HapMap database. The tag SNPs were selected on the basis of linkage disequilibrium patterns observed in the Han Chinese in Beijing (CHB) samples that were genotyped as a part of the International HapMap Project. Only SNPs with a minor allele frequency of greater than 5% with a pair-wise r2 ≥ 0.8 in the HapMap were considered. SNPs that were in exons and untranslated regions (UTR), as well as promoters and introns within 500 bp of exons were also selected. Candidate SNPs were submitted to Illumina for a design score. The Illumina Assay Design Tool filtered out SNPs that were not suitable for the Illumina platform. Finally, 382 SNPs with a design score of “1” were selected. 382 SNPs within PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR,SERPNA1, PCSK6, DNAH9 encoding genes were selected. Genotyping was performed using Illumina VeraCode microarray in a case-control study including 51 AS patients, 266 rheumatoid arthritis (RA) and 163 health controls We performed genotyping using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina). Peripheral blood samples were collected from patients with RA (n=266, 183 female) and AS (n=51, 10 female). RA patients had a mean age of 51.7 years, while AS patients had a mean age of 35.9 years. A total of 163 (60 female) healthy individuals with a mean age of 48.0 years were blood donors. Blood samples were put into Monovette tubes containing 3.8% sodium citrate. The genotyping was conducted with the BeadXpress Reader using the Illumina VeraCode GoldenGate Assay kit. 500 ng of sample DNA were used per assay. Genotype clustering and calling were performed using BeadStudio software (Genomestudio V2010.2 Genotyping module V1.6) (Illumina). This work was completed at the Beijing Institute of Genomics.

Project description:Background Long non-coding RNAs (lncRNAs) are important regulators in ankylosing spondylitis (AS). Few studies have examined the lncRNA-RNA binding protein (RBP) interaction in AS. This study performed bioinformatics analysis and clinical verification to identify key lncRNAs and propose their RBP interaction. Methods Three GEO datasets of AS were analyzed by differential expression analysis. The differentially expressed lncRNAs between the AS and control groups were screened out, and the intersecting lncRNAs were regarded as target lncRNAs. Functional was performed to identify target lncRNAs by enrichment analysis, co-expressed RNA analysis, and lncRNA-RBP interaction analysis. Finally, this study analyzed the differential expression level and clinical value of lncRNAs between the AS and control groups. Results Linc00304, linc00926, and MIAT were differentially expressed and upregulated. Enrichment analysis indicated that the key KEGG terms were the T-cell receptor signaling pathway and B-cell receptor signaling pathway. The key molecular function term was protein binding, and the key biological process term was adaptive immune response. In qRT-PCR results, 44 samples were validated. linc00304 expression was positively correlated with bath ankylosing spondylitis disease activity index (BASDAI), bath ankylosing spondylitis functional index (BASFI), erythrocyte sedimentation rate (ESR), and c-reactive protein (CRP). linc00926 expression was only positively correlated with ESR, whereas MIAT expression was positively correlated with BASFI, ESR, and CRP. Logistic regression revealed that linc00304, ESR, and CRP were the independent risk factors for BASDAI activation. The area under the curve (AUC) of serum linc00304 level in the diagnosis of AS was 0.687 (cutoff value: 0.413, specificity: 0.423, sensitivity: 0.900). AUC of linc00926 was 0.664 (cutoff value: 0.299, sensitivity: 0.882, specificity: 0.417). AUC of MIAT was 0.623 (cutoff value: 0.432, specificity: 0.443, sensitivity: 0.890) (all P <0.05). Conclusion Overall, this study uncovered three novel lncRNAs, which were upregulated in AS, and proposed a new lncRNA-RBP-mRNA interaction that might regulate adaptive immune response.

Project description:ObjectiveThe 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.MethodsGene 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.ResultsA 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.ConclusionThe 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.

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Screening of underlying genetic biomarkers for ankylosing spondylitis.

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Screening of underlying genetic biomarkers for ankylosing spondylitis.

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