Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Li, Wen Chao; Bai, De Lei; Xu, Yang; Chen, Hui; Ma, Rui; Hou, Wen Bo; Xu, Rui

doi:10.1002/jcb.27741

Cited by 45 publications

(39 citation statements)

References 40 publications

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“…The expression levels of genes related to the activation of dendritic cells, BATF2, LAMP3, and CD83, showed significant differences between responders and nonresponders at baseline. LAMP3 was one of the differentially expressed genes between RA and osteoarthritis patients [39], and CD83 was expressed in more than 20% of pDCs in the RA synovium [40]. In addition, early-stage RA patients had elevated levels of soluble CD83 in plasma [41].…”

Section: Discussionmentioning

confidence: 99%

Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis

et al. 2020

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Background: Abatacept (ABA) is a biological disease-modifying antirheumatic drug (bDMARD) for rheumatoid arthritis (RA). The aim of this study was to identify molecules that are associated with therapeutic responses to ABA in patients with RA. Methods: Peripheral blood was collected using a PAX gene Blood RNA kit from 45 bDMARD-naïve patients with RA at baseline and at 6 months after the initiation of ABA treatment. Gene expression levels of responders (n = 27) and non-responders (n = 8) to ABA treatment among patients with RA at baseline were compared using a microarray. The gene expression levels were confirmed using real-time quantitative polymerase chain reaction (RT-qPCR). Results: Gene expression analysis revealed that the expression levels of 218 genes were significantly higher and those of 392 genes were significantly lower in the responders compared to the non-responders. Gene ontology analysis of the 218 genes identified "response to type I interferon (IFN)" with 24 type I IFN-related genes. RT-qPCR confirmed that there was a strong correlation between the score calculated using the 24 genes and that using OAS3, MX1, and IFIT3 (type I IFN score) (rho with the type I IFN score 0.981); the type I IFN score was significantly decreased after treatment with ABA in the responders (p < 0.05), but not in the non-responders. The receiver operating characteristic curve analysis of the type I IFN score showed that sensitivity, specificity, and AUC (95% confidence interval) for the responders were 0.82, 1.00, and 0.92 (0.82-1.00), respectively. Further, RT-qPCR demonstrated higher expression levels of BATF2, LAMP3, CD83, CLEC4A, IDO1, IRF7, STAT1, STAT2, and TNFSF10 in the responders, all of which are dendritic cell-related genes or type I IFN-related genes with significant biological implications. Conclusion: Type I IFN score and expression levels of the nine genes may serve as novel biomarkers associated with a clinical response to ABA in patients with RA.

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Section: Discussionmentioning

confidence: 99%

Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis

et al. 2020

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“…The raw data (.CEL files) of these three microarray data sets were downloaded. R software (Version 3.5.1; https://www.r-project.org/), affy package (https://bioconductor.org/packages/release/bioc/html/ affy.html), and affyPLM (http://bioconductor.org/packages/ release/bioc/html/affyPLM.html) package were used for raw data analysis (Li et al, 2019). The raw data in CEL format were converted into expression measures.…”

Section: Data Set Collection and Identification Of Degsmentioning

confidence: 99%

“…The raw data in CEL format were converted into expression measures. Log scale robust multiarray analysis (RMA) background correction, quantile normalization, pmonly (perfect match correction), and median polish were performed in the R software (Li et al, 2019). RMA functions are provided by affy package (Irizarry et al, 2003).…”

Section: Data Set Collection and Identification Of Degsmentioning

confidence: 99%

Identification of Key Candidate Genes in Dairy Cow in Response to Escherichia coli Mastitis by Bioinformatical Analysis

Chen²,

Chen

2019

Front. Genet.

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At present, bovine mastitis is one of the most costly diseases affecting animal health and welfare. Escherichia coli (E. coli) is considered to be one of the main pathogens causing mastitis with clinical signs in dairy cattle. However, the cure rate of E. coli mastitis is low, and the pathogenesis of E. coli mastitis is not completely known. In order to develop new strategies for the rapid detection of E. coli mastitis, a comprehensive molecular investigation of E. coli mastitis is necessary. Hence, this study integrated three microarray data sets to identify the potential key candidate genes in dairy cow in response to E. coli mastitis. Differentially expressed genes (DEGs) were screened in mammary gland tissues with live E. coli infection. Furthermore, the pathways enrichment of DEGs were analyzed, and the protein-protein interaction (PPI) network was performed. In total, 105 shared DEGs were identified from the three data sets. The DEGs were significantly enriched in biological processes mainly involved in immunity. The PPI network of DEGs was constructed with 102 nodes and 546 edges. The module with the highest score through MCODE analysis was filtered from PPI; 18 central node genes were identified. However, in addition to immunerelated pathways, some of the 18 DEGs were involved in signaling pathways triggered by other diseases. Considering the specificity of biomarkers for rapid detection, IL8RB, CXCL6, and MMP9 were identified as the most potential biomarker for E. coli mastitis. In conclusion, the novel DEGs and pathways identified in this study can help to improve the diagnosis and treatment strategies for E. coli mastitis in cattle.

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“…Currently, transcriptomic and microarray analyses have been widely used in a variety of diseases, including RA, to identify new biomarkers to improve diagnosis and treatment [8][9][10]. In addition, competitive endogenous RNA (ceRNA) networks can elucidate a new mechanism for promoting the development of the disease in a transcriptional regulatory network [11].…”

Section: Introductionmentioning

confidence: 99%

Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis

Cheng

2020

Preprint

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Background Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA is difficult to diagnose due to the lack of effective biomarkers. This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses.Methods Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein-protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student’s t-test.Results A total of 275 DEGs, including 197 downregulated genes and 78 upregulated genes, were identified between the samples from RA and OA. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and 4 gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets, and three genes (granzyme A (GZMA), protein regulator of cytokinesis 1 (PRC1), and threonine/tyrosine kinase protein kinase (TTK)) that have a high diagnostic value for early RA were identified. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA.Conclusions This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.

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Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Cited by 45 publications

References 40 publications

Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis

Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis

Identification of Key Candidate Genes in Dairy Cow in Response to Escherichia coli Mastitis by Bioinformatical Analysis

Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis

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