Dataset Information

Identification of pathways significantly associated with spondyloarthropathy/ankylosing spondylitis using the sub?pathway method.

ABSTRACT: The aim of the present study was to extract potential sub?pathway biomarkers for spondyloarthropathy (SpA)/ankylosing spondylitis (AS) using a sub?pathway strategy. SpA/AS?relevant data, reference pathways and long non?coding (lnc)RNA?micro (mi)RNA?mRNA interactions were downloaded. The seed pathways based on Kyoto Encyclopedia of Genes and Genomes pathways and the mRNAs in the co?expressed lncRNA?mRNA interactions were extracted. Sub?pathways regulated by lncRNA were selected after establishing condition?specific lncRNA competitively regulated pathways (LCRP) network. Significant sub?pathways were further identified using the attract method. These significant sub?pathways were evaluated in the other independent published AS microarray data (E?GEOD?25101) using in silico validation. In addition, to uncover SpA/AS?relevant lncRNAs, the degree analysis for all nodes in the LCRP network was conducted. A total of 35 lncRNAs, 131 mRNAs and 145 co?expressed interactions were identified. When entering these 131 mRNAs into the reference pathways, 82 seed pathways were extracted, which were transformed into undirected graphs, and the 35 lncRNAs were mapped to the pathway graphs to further establish the condition?specific LCRP network. Based on degree analysis, four hub lncRNAs were selected, including C14orf169, LINC00242, LINC00116 and LINC00482. It was identified that 35 lncRNAs competitively regulating sub?pathways were involved in 56 complete pathways. Among these, the top three sub?pathways were path: 04010_1, which was a subregion of the mitogen?activated protein kinase (MAPK) signaling pathway; path: 04062?1, an important subregion in the chemokine signaling pathway; and path: 04066_2, was a part of HIF?1 signaling pathway. Furthermore, it was validated consistently in the separate microarray data set E?GEOD?25101. Cancer?associated pathways and hub node C14orf169 were identified in validation. Sub?pathways, including the MAPK signaling pathway and chemokine signaling pathway, and hub lncRNA (C14orf169) may serve important roles in SpA/AS.

SUBMITTER: Ding M

PROVIDER: S-EPMC6131564 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Identification of pathways significantly associated with spondyloarthropathy/ankylosing spondylitis using the sub‑pathway method.

Ding Ming M Guan Ting-Jin TJ Wei Chuan-Yin CY Chen Bo-Hua BH

Molecular medicine reports 20180817 4

The aim of the present study was to extract potential sub‑pathway biomarkers for spondyloarthropathy (SpA)/ankylosing spondylitis (AS) using a sub‑pathway strategy. SpA/AS‑relevant data, reference pathways and long non‑coding (lnc)RNA‑micro (mi)RNA‑mRNA interactions were downloaded. The seed pathways based on Kyoto Encyclopedia of Genes and Genomes pathways and the mRNAs in the co‑expressed lncRNA‑mRNA interactions were extracted. Sub‑pathways regulated by lncRNA were selected after establishing ...[more]

PMID: 30132545

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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:We have compared synovial biopsies from ankylosing spondylitis and undifferentiated spondylitis patients with healthy controls and osteoarthritis patients Objective: In spondylarthropies, whole-genome gene expression profiling studies have been limited to peripheral blood to date. By undertaking a study in knee synovial biopsies from spondylarthropy (SpA) and ankylosing spondylitis (AS) patients we aimed to identified joint-specific candidate genes and pathways. These pathways may mediate systemic inflammation driven joint damaging processes and more specifically, the osteoproliferation that is characteristic of these conditions. Methods: RNA was extracted from six seronegative SpA, two AS, three osteoarthritis (OA) and four normal control knee synovial biopsies. Whole genome expression profiling was undertaken using the Illumina DASL system, which assays 24000 cDNA probes. Differentially expressed candidate genes were then validated using quantitative PCR and immunohistochemistry. Results: 416 differentially expressed genes were identified that clearly delineated between AS/SpA and control groups. Pathway analysis showed altered gene-expression in oxidoreductase activity, osteoblast activity, B-cell associated, matrix catabolic, and metabolic pathways. The inflammatory mediator, MMP3, was strongly upregulated in AS/SpA samples and the Wnt pathway inhibitors DKK3 and Kremen1 were downregulated. Conclusion: Pathways mediating both systemic inflammation as well as local tissue changes were identified. This suggests initial systemic inflammation in spondylarthropies transfers to and persists in the local joint environment, subsequently mediating changes in genes directly involved in the destructive tissue remodelling. Fifteen knee synovial biopsy tissue samples consisting of six seronegative spondyloarthropy (SpA), two ankylosing spondylitis (AS), three osteoarthritis (OA) and four normal control biopsies were obtained from the Synovial Tissue Bank at the Repatriation General Hospital in Adelaide, South Australia with the appropriate ethical approval (Supplementary Table 1). All patients provided informed written consent.

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.

Dataset Information

Identification of pathways significantly associated with spondyloarthropathy/ankylosing spondylitis using the sub?pathway method.

Publications

Identification of pathways significantly associated with spondyloarthropathy/ankylosing spondylitis using the sub‑pathway method.

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