Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

Zhang, Runrun; Zhou, Xinpeng; Jin, Yehua; Chang, Cen; Wang, Rongsheng; Liu, Jia; Fan, Jun‐Yu; He, Dongyi

doi:10.1007/s10067-021-05825-1

Cited by 11 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ) is a public functional genome database that stores high-throughput gene expression data, chips, and microarrays. 13 Previous studies have obtained potential biomarkers in RA pathogenesis from the GSE1919 and GSE77298 datasets through bioinformatics analysis, 14 , 15 so we combined these two datasets for our analysis. 14 The GSE1919 and GSE77298 gene expression datasets were downloaded from the GEO database.…”

Section: Methodsmentioning

confidence: 99%

“… 13 Previous studies have obtained potential biomarkers in RA pathogenesis from the GSE1919 and GSE77298 datasets through bioinformatics analysis, 14 , 15 so we combined these two datasets for our analysis. 14 The GSE1919 and GSE77298 gene expression datasets were downloaded from the GEO database. GSE1919 contained five RA and five normal synovial tissue samples, and GSE77298 contained 16 RA and seven normal synovial tissue samples.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Bioinformatics Analysis Identified the Hub Genes, mRNA–miRNA–lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis

Yang¹,

Su²,

Xu³

et al. 2022

IJGM

View full text Add to dashboard Cite

Objective Rheumatoid arthritis (RA) is a nonspecific, chronic, systemic autoimmune disease characterized by symmetric polyarticular synovitis. Bioinformatics analysis of potential biomarkers, mRNA–miRNA–lncRNA axes, and signaling pathways in the pathogenesis of RA provides potential targets and theoretical basis for further research on RA. Methods The GSE1919 and GSE77298 datasets were downloaded from the Gene Expression Omnibus database ( http://www.ncbi.nlm.nih.gov/geo ). Perl was used to perform data merging, and R was used to perform batch correction. The “limma” package of R was used to screen differentially expressed genes, and the “clusterProfiler” package was used to perform enrichment analysis of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein–protein interaction network, Cytoscape was used for module analysis, and R was used to screen for hub genes. GraphPad Prism was used to plot the receiver operating characteristic curve of the hub genes. Gene set enrichment analysis and competitive endogenous RNA network analysis were performed on hub genes with the greatest diagnostic values. The hub gene with the greatest diagnostic value was verified using immunohistochemical staining. Results We obtained nine hub genes ( ITGB2, VAMP8, HLA-A, PTAFR, SYK, FCER1G, HLA-DPB1, LCP2 , and ACTR2 ) and four mRNA–miRNA–lncRNA axes (ITGB2-hsa-miR-486-3p-SNHG3, ITGB2-hsa-miR-338-5p-XIST, ITGB2-hsa-miR-5581-3p-XIST, and ITGB2-hsa-miR-1226-5p-XIST) related to the pathogenesis of RA. The nine hub genes were highly expressed, and ITGB2 had the highest diagnostic value for RA. We also identified signaling pathways related to the pathogenesis of RA: Fc epsilon Rl and chemokine signaling pathways. The immunohistochemical results showed that ITGB2 expression was significantly upregulated in RA. Conclusion The hub genes, mRNA–miRNA–lncRNA axes, and signaling pathways related to RA pathogenesis identified in this study provide a new research direction for the mechanism, diagnosis, and treatment of RA.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Bioinformatics Analysis Identified the Hub Genes, mRNA–miRNA–lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis

Yang¹,

Su²,

Xu³

et al. 2022

IJGM

View full text Add to dashboard Cite

show abstract

“…This risks the possibility of a significant delay in the diagnosis of RA, which is detrimental to the patient’s joint function rescue. Despite recent advances in the pathophysiology of RA and OA [ 2 , 4 , 5 ], early diagnosis and therapeutic intervention remain challenging. Although biopsy-based analysis [ 6 ] and advanced MRI protocols [ 7 ] may be helpful for diagnosis, these modalities are often time-consuming, less accessible, and more expensive, which limits their routine use in clinics.…”

Section: Introductionmentioning

confidence: 99%

Broken-fat pad sign: a characteristic radiographic finding to distinguish between knee rheumatoid arthritis and osteoarthritis

Wang,

Zhao,

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

Objectives Diagnostic imaging plays an important role in the pre-treatment workup of knee osteoarthritis (OA) and rheumatoid arthritis (RA). Herein, we identified a useful MRI sign of infrapatellar fat pad (IPFP) to improve diagnosis. Methods Eighty-one age- and sex-matched RA and OA patients each, with pathological diagnosis and pre-treatment MRI were retrospectively evaluated. All randomized MR images were blinded and independently reviewed by two radiologists. The assessment process included initial diagnosis, sign evaluation, and final diagnosis, with a 3-week interval between each assessment. Broken-fat pad (BFP) sign was assessed on sagittal T2-weighted-imaging in routine MRI. The area under the curve and Cohen’s kappa (κ) were used to assess the classification performance. Two shape features were extracted from IPFP for quantitative interpretation. Results The median age of the study population was 57.6 years (range: 31.0–78.0 years). The BFP sign was detected more frequently in patients with RA (72.8%) than those with OA (21.0%). Both radiologists achieved better performance by referring to the BFP sign, with accuracies increasing from 58.0 to 75.9% and 72.8 to 79.6%, respectively. The inter-reader correlation coefficient showed an increase from fair (κ = 0.30) to substantial (κ = 0.75) upon the consideration of the BFP sign. For quantitative analysis, the IPFP of RA had significantly lower sphericity (0.54 ± 0.04 vs. 0.59 ± 0.03, p < 0.01). Despite larger surface-volume-ratio of RA (0.38 ± 0.05 vs. 0.37 ± 0.04, p = 0.25) than that of OA, there was no statistical difference. Conclusions The BFP sign is a potentially important diagnostic clue for differentiating RA from OA with routine MRI and reducing misdiagnosis. Critical relevance statement With the simple and feasible broken-fat pad sign, clinicians can help more patients with early accurate diagnosis and proper treatment, which may be a valuable addition to the diagnostic workup of knee MRI assessment. Key points • Detailed identification of infrapatellar fat pad alterations of patients may be currently ignored in routine evaluation. • Broken-fat pad sign is helpful for differentiating rheumatoid arthritis and osteoarthritis. • The quantitative shape features of the infrapatellar fat pad may provide a possible explanation of the signs. • This sign has good inter-reader agreements and is feasible for clinical application. Graphical Abstract

show abstract

Section: Introductionmentioning

confidence: 99%

“…Bioinformatics analysis can screen out promising biomarkers and analysis pathways and immune infiltration patterns in RA, which may shed light on the understanding of the pathogenesis, occurrence, development, and consequently provide new strategies for its diagnosis, prevention, and treatment. 14 Until now, as a powerful tool, single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) analysis has been used to reveal cell diversity and cell-to-cell communication at single-cell level. This scRNA-seq analysis approach makes an improvement in understanding cellular heterogeneity and easy to screen out promising biomarkers for diagnosis, prognosis, and therapeutic response of RA.…”

mentioning

confidence: 99%

Identification of the candidate genes of diagnosing rheumatoid arthritis using the single-cell sequencing technology and T cell subclusters analysis of patients with rheumatoid arthritis

2022

View full text Add to dashboard Cite

Objectives: This study aims to analyze the heterogeneity among different cell types in peripheral blood mononuclear cells (PBMC) in rheumatoid arthritis (RA) patients and to analyze T cell subsets to obtain key genes that may lead to RA. Materials and methods: The sequencing data of 10,483 cells were obtained from the GEO data platform. The data were filtered and normalized initially and, then, principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (TSNE) cluster analysis were performed using the Seurat package in R language to group the cells, thereby obtaining the T cells. The T cells were subjected to subcluster analysis. The differentially expressed genes (DEGs) in T cell subclusters were obtained, and the hub genes were determined by Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network construction. Finally, the hub genes were validated using other datasets in the GEO data platform. Results: The PBMC of RA patients were mainly divided into T cells, natural killer (NK) cells, B cells, and monocyte cells. The number of T cells was 4,483, which were further divided into seven clusters. The pseudotime trajectory analysis showed that the differentiation of T cells developed from cluster 0 and cluster 1 to cluster 5 and cluster 6. Through GO, KEGG and PPI analysis, the hub genes were identified. After validation by external data sets, nine genes were identified as candidate genes highly associated with the occurrence of RA, including CD8A, CCL5, GZMB, NKG7, PRF1, GZMH, CCR7, GZMK, and GZMA. Conclusion: Based on single-cell sequencing analysis, we identified nine candidate genes for diagnosing RA, and validated their diagnostic value for RA patients. Our findings may provide new sights for the diagnosis and treatment of RA.

show abstract

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

Cited by 11 publications

References 31 publications

Bioinformatics Analysis Identified the Hub Genes, mRNA–miRNA–lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis

Bioinformatics Analysis Identified the Hub Genes, mRNA–miRNA–lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis

Broken-fat pad sign: a characteristic radiographic finding to distinguish between knee rheumatoid arthritis and osteoarthritis

Identification of the candidate genes of diagnosing rheumatoid arthritis using the single-cell sequencing technology and T cell subclusters analysis of patients with rheumatoid arthritis

Contact Info

Product

Resources

About