Dataset Information

Network analysis and transcriptome profiling in peripheral blood mononuclear cells of patients with rheumatoid arthritis.

ABSTRACT: The present study aimed to investigate the differential expression of long non-coding RNAs (lncRNAs) in rheumatoid arthritis (RA). High-throughput gene sequencing technology was used to detect the expression of lncRNA and mRNA in three patients with RA (RA group) and normal controls (NC group). A Bioinformatics analysis was used to assess the effects of differentially expressed mRNAs on signaling pathways and biological functions. The selected dysregulated lncRNAs were verified by reverse transcription-quantitative (RT-q)PCR in the peripheral blood mononuclear cells (PBMCs) of patients with RA and age- and sex-matched controls. A correlation analysis was used to analyze the relationship between lncRNAs and clinical indexes. From the lncRNA sequencing data, significantly differentially expressed lncRNAs between the RA and NC groups were identified by a fold change ?2 and P<0.05. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis suggested that the differentially expressed mRNAs were mainly involved in organelle composition, intracellular regulation, signaling pathways, cancer, virus and inflammation. A total of four of these lncRNAs were confirmed by RT-qPCR to be significantly differentially expressed (LINC00304, MIR503HG, LINC01504 and FAM95B1). Through the correlation analysis, it was confirmed that there was a strong correlation between these lncRNAs and clinical laboratory indicators and indexes such as course of disease, arthrocele and joint tenderness. Overall, the present results suggested that the expression levels of LINC00304, MIR503HG, LINC01504 and FAM95B1 in PBMCs from patients with RA may serve as potential biomarkers for RA diagnosis, influencing the occurrence and progress of RA.

SUBMITTER: Long Y

PROVIDER: S-EPMC7792483 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Network analysis and transcriptome profiling in peripheral blood mononuclear cells of patients with rheumatoid arthritis.

Long Yan Y Liu Jian J Jiang Hui H Xin Ling L Wan Lei L Sun Yue Y Zhang Pingheng P Wen Jianting J Huang Dan D Sun Yanqiu Y Zhang Ying Y Bao Bingxi B Sun Guanghan G

Experimental and therapeutic medicine 20201225 2

The present study aimed to investigate the differential expression of long non-coding RNAs (lncRNAs) in rheumatoid arthritis (RA). High-throughput gene sequencing technology was used to detect the expression of lncRNA and mRNA in three patients with RA (RA group) and normal controls (NC group). A Bioinformatics analysis was used to assess the effects of differentially expressed mRNAs on signaling pathways and biological functions. The selected dysregulated lncRNAs were verified by reverse transc ...[more]

PMID: 33456537

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Project description:Background: Sepsis, as a clinical emergency, usually causes multiorgan dysfunction and can lead to high mortality. Establishment of specific and sensitive biomarkers for early diagnosis is critical to identify patients who would benefit from targeted therapy. In this study, we investigated this syndrome by analyzing the transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with sepsis and identified sepsis-specific biomarkers. Methods: In this study, a total of 87 patients with sepsis and 40 healthy controls from a prospective multicenter cohort were enrolled. Samples from 44 subjects (24 patients with sepsis and 20 healthy controls) were sequenced and the remaining patients were included in the validation group. Using high-throughput sequencing, a gene expression profile of PBMCs from patients with sepsis was generated to elucidate the pathophysiology of sepsis and identify sepsis-specific biomarkers. Results: Principal component analysis (PCA) and unsupervised hierarchical cluster analysis showed that patients with sepsis separated from healthy controls. A total of 1639 differentially expressed genes (DEGs) were identified (|log2 fold change|>2, adjusted P value <0.05) between these two groups, with 1278 (78.0%) upregulated and 361 (22.0%) downregulated in patients with sepsis. Gene Ontology (GO) analysis of the upregulated DEGs identified 194 GO terms that were clustered into 27 groups, and analysis of the downregulated DEGs identified 20 GO terms that were clustered into 4 groups. Four unique genes were identified that could be predictive of patients with sepsis. External validation of the four genes using quantitative real-time polymerase chain reaction (qRT-PCR) was consistent with the results of mRNA sequencing, revealing their potential in sepsis diagnosis. Conclusions: The transcriptome characteristics of PBMCs, which were significantly altered in sepsis patients, provide new insights into sepsis pathogenesis. The four identified gene expression changes differentiated patients with sepsis from healthy subjects, which could serve as a convenient tool contributing to sepsis diagnosis.

Dataset Information

Network analysis and transcriptome profiling in peripheral blood mononuclear cells of patients with rheumatoid arthritis.

Publications

Network analysis and transcriptome profiling in peripheral blood mononuclear cells of patients with rheumatoid arthritis.

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