Dataset Information

Comprehensive analyses of long non-coding RNA expression profiles by RNA sequencing and exploration of their potency as biomarkers in psoriatic arthritis patients.

ABSTRACT:

Background

The aim of the current study was to investigate the long non-coding RNA (lncRNA) expression profiles in psoriatic arthritis (PSA) patients by RNA sequencing, and to further explore potential biomarkers that were able to predict PSA risk and activity.

Methods

LncRNA and mRNA expression profiles in peripheral blood mononuclear cells (PBMC) of 4 PSA patients and 4 normal controls (NCs) were detected by RNA sequencing, followed by comprehensive bioinformatic analyses. Subsequently, 3 top upregulated and 2 top downregulated lncRNAs were chosen for further validation in 93 PSA patients and 93 NCs by quantitative polymerase chain reaction (qPCR) assay.

Results

Totally 76 upregulated and 54 downregulated lncRNAs, as well as 231 upregulated and 102 downregulated mRNAs were discovered in PSA patients compared with NCs. Enrichment analyses revealed that they were mostly associated with nucleosome, extracellular exosome and extracellular matrix, and the top enriched pathways were systemic lupus erythematosus (SLE), alcoholism and viral carcinogenesis. qPCR assay showed that lnc-RP11-701H24.7 and lnc-RNU12 were upregulated in PSA patients compared with NCs, and they could predict PSA risk with high area under curves. Besides, lnc-RP11-701H24.7 was positively associated with ESR, SJC, TJC and pain VAS score while lnc-RNU12 was positively correlated with PASI score, CRP and PGA score, implying that both of them were positively correlated with disease activity.

Conclusion

Our study facilitates comprehensive understanding of lncRNA expression profiles in PSA pathogenesis, and discovers that lnc-RP11-701H24.7 and lnc-RNU12 might be served as novel biomarkers for PSA risk and activity.

SUBMITTER: Yue T

PROVIDER: S-EPMC6686418 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Comprehensive analyses of long non-coding RNA expression profiles by RNA sequencing and exploration of their potency as biomarkers in psoriatic arthritis patients.

Yue Tao T Ji Mei M Qu Huanru H Guo Mengru M Bai Fengmin F Zhang Zhanming Z Wang Weifeng W Gong Xuming X Zhang Zhenghua Z

BMC immunology 20190807 1

<h4>Background</h4>The aim of the current study was to investigate the long non-coding RNA (lncRNA) expression profiles in psoriatic arthritis (PSA) patients by RNA sequencing, and to further explore potential biomarkers that were able to predict PSA risk and activity.<h4>Methods</h4>LncRNA and mRNA expression profiles in peripheral blood mononuclear cells (PBMC) of 4 PSA patients and 4 normal controls (NCs) were detected by RNA sequencing, followed by comprehensive bioinformatic analyses. Subse ...[more]

PMID: 31390976

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Project description:IntroductionPsoriatic arthritis (PsA) is a chronic inflammatory disease that frequently develops in patients with psoriasis (PsO) but can also occur spontaneously. As a result, PsA diagnosis and treatment is commonly delayed, or even missed outright due to the manifold of clinical presentations that patients often experience. This inevitably results in progressive articular damage to axial and peripheral joints and entheses. As such, patients with PsA frequently experience reduced expectancy and quality of life due to disability. More recently, research has aimed to improve PsA diagnosis and prognosis by identifying novel disease biomarkers.MethodsHere, we conducted a systematic review of the published literature on candidate biomarkers for PsA diagnosis and prognosis in MEDLINE(Pubmed), EMBase and the Cochrane library with the goal to identify clinically applicable PsA biomarkers. Meta-analyses were performed when a diagnostic bone and cartilage turnover biomarker was reported in 2 or moredifferent cohorts of PsA and control.ResultsWe identified 1444 publications and 124 studies met eligibility criteria. We highlighted bone and cartilage turnover biomarkers, genetic markers, and autoantibodies used for diagnostic purposes of PsA, as well as acute phase reactant markers and bone and cartilage turnover biomarkers for activity or prognostic severity purposes. Serum cartilage oligometrix metalloproteinase levels were significantly increased in the PsA sera compared to Healthy Control (HC) with a standardized mean difference (SMD) of 2.305 (95%CI 0.795-3.816, p=0.003) and compared to osteoarthritis (OA) with a SMD of 0.783 (95%CI 0.015-1.551, p=0.046). The pooled serum MMP-3 levels were significantly higher in PsA patients than in PsO patients with a SMD of 0.419 (95%CI 0.119-0.719; p=0.006), but no significant difference was highlighted when PsA were compared to HC. While we did not identify any new genetic biomarkers that would be useful in the diagnosis of PsA, recent data with autoantibodies appear to be promising in diagnosis, but no replication studies have been published.ConclusionIn summary, no specific diagnostic biomarkers for PsA were identified and further studies are needed to assess the performance of potential biomarkers that can distinguish PsA from OA and other chronic inflammatory diseases.

Project description:Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, which occurs in ~1.0% of the general population. Increasing studies have suggested that long non‑coding RNAs (lncRNAs) may serve important roles in various biological processes and may be associated with the pathogenesis of different types of disease, including RA. Astragalosides (AST) has been used as a traditional Chinese medicine for the treatment of RA. However, the mechanism underlying its therapeutic effect has remained unclear to date. Thus, there is an urgent need to elucidate the possible mechanism of AST in the treatment of RA from the perspective of lncRNAs. In the present study, the lncRNAs and mRNAs of a vehicle group, animal model group and AST treatment (control) group were determined by Arraystar Rat lncRNA/mRNA microarray. The differentially expressed genes with a fold change >1.5 and P<0.05 were selected and analyzed. Gene Ontology (GO) and pathway analysis was performed using the Database for Annotation, Visualization and Integration Discovery, and the coding‑non‑coding gene co‑expression network was drawn based on the correlation analysis between the differentially expressed lncRNAs and mRNAs. Based on node degree and the correlation between bioinformatics analysis and RA, the critical differentially expressed lncRNAs were selected, analyzed and verified by reverse transcription‑quantitative PCR (RT‑qPCR) analysis. The results showed that, following AST treatment, up to 75 lncRNAs and 247 mRNAs were found to be differentially expressed among the three groups. GO and pathway analysis manifested that 135 GO terms and 17 pathways were enriched by differentially expressed genes. Four lncRNAs (MRAK012530, MRAK132628, MRAK003448 and XR_006457) were selected as the critical lncRNAs and their trend in expression showed consistency between the RT‑qPCR and microarray data. In conclusion, AST had a regulatory effect on differentially expressed lncRNAs during the development of RA, and four lncRNAs could be selected as critical therapeutic targets of AST.

Dataset Information

Comprehensive analyses of long non-coding RNA expression profiles by RNA sequencing and exploration of their potency as biomarkers in psoriatic arthritis patients.

Background

Methods

Results

Conclusion

Publications

Comprehensive analyses of long non-coding RNA expression profiles by RNA sequencing and exploration of their potency as biomarkers in psoriatic arthritis patients.

Similar Datasets

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