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

Long,Yan; Liu,Jian; Jiang,Hui; Xin,Ling; Wan,Lei; Sun,Yue; Zhang,Pingheng; Wen,Jianting; Huang,Dan; Sun,Yanqiu; Zhang,Ying; Bao,Bingxi; Sun,Guanghan

doi:10.3892/etm.2020.9601

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

Authors:
- Yan Long
- Jian Liu
- Hui Jiang
- Ling Xin
- Lei Wan
- Yue Sun
- Pingheng Zhang
- Jianting Wen
- Dan Huang
- Yanqiu Sun
- Ying Zhang
- Bingxi Bao
- Guanghan Sun
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Affiliations: Department of Graduate, Anhui University of Chinese Medicine, Hefei, Anhui 230011, P.R. China, Laboratory for Rheumatism, Institute of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui 230011, P.R. China, Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui 230038, P.R. China, Department of Chinese Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
Published online on: December 25, 2020 https://doi.org/10.3892/etm.2020.9601
Article Number: 170
Copyright: © Long et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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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.

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