Marker Genes Change of Synovial Fibroblasts in Rheumatoid Arthritis Patients

Liao, Lifen; Liang, Ke; Lan, Lan; Wang, Jinheng; Guo, Jun

doi:10.1155/2021/5544264

Cited by 7 publications

(6 citation statements)

References 62 publications

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“…Compared with normal samples, RA samples had distinctly higher levels of Activated.CD4.T.cell, Activated.CD8.T.cell, Activated.dendritic.cell, Immature.dendritic.cell, Gamma.delta.T.cell, Eosinophil, CD56dim.natural.killer.cell, MDSC, Macrophage, Mast.cell, Neutrophil, Regulatory.T.cell, Type.17.T.helper.cell, Type.2.T.helper.cell, Memory.B.cell, Central.memory.CD4.T.cell. CD8 infiltration in synovial tissues was revealed to be a predictor of RA progression and the existence of antibodies against citrullinated peptides by one investigation ( 38 , 39 ). Moreover, our group found that the expressions of CKS2, CSTA and LY96 were related to the levels of many immune cells, highlighting their potential used as therapeutic targets for RA.…”

Section: Discussionmentioning

confidence: 99%

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

2022

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Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients’ synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes’ expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA‐HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA‐HFLS cells. Overall, to help diagnose and treat RA, it’s expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

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Section: Discussionmentioning

confidence: 99%

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

2022

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“…However, Cluster 3, which was involved in focal adhesion, ECM receptor interactions, and phagosomes, was enriched in the RA group. 86 Liao et al 72 also discovered significantly fewer B cells in the RA group, while the numbers of CD8 + T cells and neutrophils were substantially higher. Cai et al 87 reported similar potential regulatory effects of the Wnt signaling pathway, the TGF signaling pathway, the FcRI signaling pathway, and the ERBB signaling pathway on synovial fibroblasts from individuals with RA and OA, indicating potentially overlapping pathogenic mechanisms in these two diseases and potentially revealing new therapeutic targets to attenuate disease progression.…”

Section: Introductionmentioning

confidence: 92%

“…Similarly, according to the results of a receiver operating characteristic curve analysis, CCL2 and MMP13 were good predictive and diagnostic markers of RA. 72 …”

Section: Introductionmentioning

confidence: 99%

“…Additionally, several studies have revealed the differences between OA and RA using scRNA-seq. Liao et al 72 found that fibroblasts (Cluster 0), enriched in the OA group, were related to protein processing in the endoplasmic reticulum, glycolysis/gluconeogenesis, and proteoglycans in cancer. However, Cluster 3, which was involved in focal adhesion, ECM receptor interactions, and phagosomes, was enriched in the RA group.…”

Section: Introductionmentioning

confidence: 99%

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Single-cell RNA sequencing in orthopedic research

et al. 2023

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Although previous RNA sequencing methods have been widely used in orthopedic research and have provided ideas for therapeutic strategies, the specific mechanisms of some orthopedic disorders, including osteoarthritis, lumbar disc herniation, rheumatoid arthritis, fractures, tendon injuries, spinal cord injury, heterotopic ossification, and osteosarcoma, require further elucidation. The emergence of the single-cell RNA sequencing (scRNA-seq) technique has introduced a new era of research on these topics, as this method provides information regarding cellular heterogeneity, new cell subtypes, functions of novel subclusters, potential molecular mechanisms, cell-fate transitions, and cell‒cell interactions that are involved in the development of orthopedic diseases. Here, we summarize the cell subpopulations, genes, and underlying mechanisms involved in the development of orthopedic diseases identified by scRNA-seq, improving our understanding of the pathology of these diseases and providing new insights into therapeutic approaches.

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“…Molecular classification of OA which is popular nowadays allows for the prediction of high-risk OA individual, the diagnosis of early OA, and the assessment of individual-based therapy 12 , 13 . Single cell RNA sequencing of OA synovial tissues also revealed distinct cell subtypes with different dominant function 14 , 15 . Nonetheless, study analyzing classification of distinct senescence clusters and analyzing its connection with immune infiltration was missing up to now.…”

Section: Introductionmentioning

confidence: 98%

Identification and validation of BCL6 and VEGFA as biomarkers and ageing patterns correlating with immune infiltrates in OA progression

Chen

Wang²,

Hua³

2023

Sci Rep

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Osteoarthritis (OA), the most common type of arthritis, is a complex biological response caused by cartilage wear and synovial inflammation that links biomechanics and inflammation. The progression of OA correlates with a rise in the number of senescent cells in multiple joint tissues. However, the mechanisms by which senescent cells and their involvement with immune infiltration promote OA progression are not fully understood. The gene expression profiles and clinical information of OA and healthy control synovial tissue samples were retrieved from the Gene Expression Omnibus database, and then differential analysis of senescence regulators between OA and normal samples was performed. The random forest (RF) was used to screen candidate senescence regulators to predict the occurrence of OA. The reverse transcription quantitative real-time PCR experiments at tissue’s level was performed to confirm these biomarkers. Moreover, two distinct senescence patterns were identified and systematic correlation between these senescence patterns and immune cell infiltration was analyzed. The senescence score and senescence gene clusters were constructed to quantify senescence patterns together with immune infiltration of individual OA patient. 73 senescence differentially expressed genes were identified between OA patients and normal controls. The RF method was utilized to build an OA risk model based on two senescence related genes: BCL6 and VEGFA. Next, two distinct aging patterns were determined in OA synovial samples. Most patients from senescence cluster A were further classified into gene cluster B and high senescence score group correlated with a non-inflamed phenotype, whereas senescence cluster B were classified into gene cluster A and low senescence score group correlated with an inflamed phenotype. Our study revealed that senescence played an important role in in OA synovial inflammation. Evaluating the senescence patterns of individuals with OA will contribute to enhancing our cognition of immune infiltration characterization, providing novel diagnostic and prognostic biomarkers, and guiding more effective immunotherapy strategies.

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Marker Genes Change of Synovial Fibroblasts in Rheumatoid Arthritis Patients

Cited by 7 publications

References 62 publications

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Single-cell RNA sequencing in orthopedic research

Identification and validation of BCL6 and VEGFA as biomarkers and ageing patterns correlating with immune infiltrates in OA progression

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