scholarly journals AB0042 THREE HEMATOLOGIC/IMMUNE SYSTEM-SPECIFIC EXPRESSED GENES ARE CONSIDERED AS THE POTENTIAL BIOMARKERS FOR THE DIAGNOSIS OF EARLY RHEUMATOID ARTHRITIS THROUGH BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1053.1-1054
Author(s):  
Q. Cheng ◽  
X. Chen ◽  
H. Wu ◽  
Y. Du
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune System ◽  
Early Diagnosis ◽  
Disease Progression ◽  
Diagnosis And Treatment ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Early Ra ◽  
Organ Specific ◽  
Transcriptome Level

Background:Rheumatoid arthritis (RA) is a common chronic autoimmune connective tissue disease that mainly involves the joints. The incidence of RA is 5 to 10 per 1000 people[1]. Early diagnosis and treatment of RA can effectively prevent disease progression, joint damage, and other complications in 90% of patients[2]. At present, serum biomarkers used in the diagnosis of established RA are rheumatoid factor and anti-cyclic citrullinated peptide antibody[3]. However, early RA especially serum RF and anti-CCP antibody-negative is difficult to diagnose due to the lack of effective biomarkers. Therefore, it is vital to identify new and effective biomarkers for the early diagnosis and treatment of RA.Objectives: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 were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. 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 gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be biomarkers for diagnosis of early RA through combined the analysis of the verification results and the receiver operating characteristic (ROC) curve. 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.Conclusion: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.References:[1]Smolen JS, Aletaha D, McInnes IB: Rheumatoid arthritis.Lancet 2016, 388:2023-2038.[2]Aletaha D, Smolen JS: Diagnosis and Management of Rheumatoid Arthritis: A Review.Jama 2018, 320:1360-1372.[3]Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, 3rd, Birnbaum NS, Burmester GR, Bykerk VP, Cohen MD, et al: 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative.Arthritis Rheum 2010, 62:2569-2581.Disclosure of Interests:None declared

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

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qi Cheng ◽  
Xin Chen ◽  
Huaxiang Wu ◽  
Yan Du
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune System ◽  
Early Diagnosis ◽  
Disease Progression ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Early Ra ◽  
Organ Specific ◽  
Transcriptome Level ◽  
Potential Biomarkers

Abstract 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 were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. 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 gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be potential biomarkers for diagnosis of early RA. 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.

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

2020 ◽  
Author(s):  
Qi Cheng ◽  
Huaxiang Wu ◽  
Yan Du
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune System ◽  
Disease Progression ◽  
Diagnostic Value ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Early Ra ◽  
Organ Specific ◽  
Transcriptome Level ◽  
Potential Biomarkers

Abstract 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.

scholarly journals POS0392 PRESENCE OF FOUR SERUM AUTOANTIBODIES ASSOCIATES WITH THE ACPA STATUS IN EARLY RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 425.3-426
Author(s):  
L. Lourido ◽  
C. Ruiz-Romero ◽  
L. Collado ◽  
M. Hansson ◽  
L. Klareskog ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Early Diagnosis ◽  
Specific Antigen ◽  
Coiled Coil ◽  
Population Based ◽  
Mitogen Activated Protein Kinase ◽  
Absolute Deviation ◽  
Swedish Population ◽  
Early Ra ◽  
Acpa Status

Background:The presence of anti-citrullinated protein antibodies (ACPAs) is a hallmark of rheumatoid arthritis (RA) that precede the development of the disease by years and is used for its clinical diagnosis. However, there are RA subjects that test negative for ACPA and thus the early diagnosis on these patients may be delayed. Furthermore, the presence or absence of ACPA in RA supports the hypothesis that on these two subsets of patients underlie different pathogenesis and clinical outcomes.Objectives:In this work, we searched for serum autoantibodies useful to assist the early diagnosis of ACPA-seronegative RA and its management.Methods:We profiled the serum autoantibody repertoire of 80 ACPA-seronegative and 80 ACPA-seropositive RA subjects from the Swedish population-based Epidemiological Investigation of RA (EIRA) cohort. A suspension bead array platform built on protein fragments within Human Protein Atlas and selected from an initial untargeted screening using arrays containing 2660 total antigens was employed to identify IgG and IgA serum autoantibodies. A validation phase on antigen suspension bead arrays was carried out on another set of samples from EIRA containing 386 ACPA-seropositive, 358 ACPA-seronegative and 372 randomly selected control subjects of the same age and sex. A sample-specific threshold based on 20 times the median absolute deviation plus the median of all signals was selected to determine the reactivity of samples. The Wilcoxon rank sum test and Fisher’s test were applied for the comparison of autoantibody levels and reactivity frequencies between the groups.Results:Our data revealed four antigens associated with the ACPA status (Table 1). Testis-specific Y-encoded-like protein 4 (TSPYL4) showed significantly higher IgG reactivity frequency in ACPA-seronegative subjects compared to ACPA-seropositive (8% vs. 3%; P<0.05). Significant differences at IgG autoantibody levels (P<0.05) were also observed between ACPA-seronegative subjects and controls for this specific antigen. Significantly higher IgG autoantibody levels (P<0.05) towards another antigen, dual specificity mitogen-activated protein kinase kinase 6 (MAP2K6), were also observed in ACPA-seronegative subjects compared to ACPA-seropositive and controls. In contrast, we found significantly higher IgG autoantibody levels (P<0.05) in ACPA-seropositive individuals compared to ACPA-seronegative and controls towards two antigens, anosmin-1 (ANOS-1) and muscle related coiled-coil protein (MURC). ANOS-1 shows also significantly higher IgG reactivity frequency in ACPA-seropositive individuals compared to ACPA-seronegative and controls (22%, 9% and 6% respectively; P<0.05). Interestingly, three out of the four antigens discovered to be associated with the ACPA status in early RA are highly expressed in lungs and heart, two of the main extraarticular sites affected in RA. No significant differences were observed at IgA levels for any of the antigens analyzed.Table 1.Scheme of the different phases of the study, the features within each phase and the results. The reactivity to four antigens allows to distinguish ACPA-seronegative (ACPA-), ACPA seropositive (ACPA+) and controls.PhasesUntargeteddiscoveryTargeteddiscoveryTargetedvalidationNumber of samples80 ACPA-80 ACPA-358 ACPA-372 Controls80 ACPA+80 ACPA+386 ACPA+Antigen arrayplatformPlanararraysSuspensionbead array 1Suspensionbead array 2Number of antigens26606227Number of candidatebiomarkers6227 4 (TSPYL4,MAP2K6,ANOS1,MURC)Conclusion:Upon further validation in other early RA sample cohorts, our data suggest the measurement of these four autoantibodies may be useful for the early diagnosis of ACPA-seronegative RA and give insight into the pathogenesis of the different RA subsets.Characters from table content including title and footnotes:Disclosure of Interests:None declared

Early Diagnosis and Treatment of Rheumatoid Arthritis

2018 ◽  
Vol 45 (2) ◽  
pp. 237-255 ◽  
Author(s):  
Emily A. Littlejohn ◽  
Seetha U. Monrad
Keyword(s):  
Rheumatoid Arthritis ◽  
Early Diagnosis ◽  
Diagnosis And Treatment

scholarly journals Fc-gamma receptors and S100A8/A9 cause bone erosion during rheumatoid arthritis. Do they act as partners in crime?

Rheumatology ◽  
2019 ◽  
Vol 58 (8) ◽  
pp. 1331-1343 ◽  
Author(s):  
Irene Di Ceglie ◽  
Nik N L Kruisbergen ◽  
Martijn H J van den Bosch ◽  
Peter L E M van Lent
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune System ◽  
Disease Progression ◽  
Immune Cells ◽  
Bone Erosion ◽  
Fcγ Receptor ◽  
Receptor Signalling ◽  
Radiographic Disease ◽  
Osteoclast Function ◽  
And Function

AbstractBone erosion is one of the central hallmarks of RA and is caused by excessive differentiation and activation of osteoclasts. Presence of autoantibodies in seropositive arthritis is associated with radiographic disease progression. ICs, formed by autoantibodies and their antigens, activate Fcγ-receptor signalling in immune cells, and as such stimulate inflammation-mediated bone erosion. Interestingly, ICs can also directly activate osteoclasts by binding to FcγRs on their surface. Next to autoantibodies, high levels of alarmins, among which is S100A8/A9, are typical for RA and they can further activate the immune system but also directly promote osteoclast function. Therefore, IC-activated FcγRs and S100A8/A9 might act as partners in crime to stimulate inflammation and osteoclasts differentiation and function, thereby stimulating bone erosion. This review discusses the separate roles of ICs, FcγRs and alarmins in bone erosion and sheds new light on the possible interplay between them, which could fuel bone erosion.

scholarly journals Identification of Hub Genes and Potential ceRNA Networks of Diabetic Nephropathy by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Guoqing Li ◽  
Jun Zhang ◽  
Dechen Liu ◽  
Qiong Wei ◽  
Hui Wang ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Disease Progression ◽  
Signaling Pathway ◽  
Microvascular Complications ◽  
Pearson Correlation ◽  
Mapk Signaling ◽  
Diabetic Patients ◽  
Hub Genes ◽  
Regulatory Pathways

Diabetic nephropathy (DN) is one of the most common microvascular complications in diabetic patients, and is the main cause of end-stage renal disease. The exact molecular mechanism of DN is not fully understood. The aim of this study was to identify novel biomarkers and mechanisms for DN disease progression by weighted gene co-expression network analysis (WGCNA). From the GSE142153 dataset based on the peripheral blood monouclear cells (PBMC) of DN, we identified 234 genes through WGCNA and differential expression analysis. Gene Ontology (GO) annotations mainly included inflammatory response, leukocyte cell-cell adhesion, and positive regulation of proteolysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways mostly included IL-17 signaling pathway, MAPK signaling pathway, and PPAR signaling pathway in DN. A total of four hub genes (IL6, CXCL8, MMP9 and ATF3) were identified by cytoscape, and the relative expression levels of hub genes were also confirmed by RT-qPCR. ROC curve analysis determined that the expression of the four genes could distinguish DN from controls (the area under the curve is all greater than 0.8), and Pearson correlation coefficient analysis suggested that the expression of the four genes was related to estimated glomerular filtration rate (eGFR) of DN. Finally, through database prediction and literature screening, we constructed lncRNA-miRNA-mRNA network. We propose that NEAT1/XIST/KCNQ1T1-let-7b-5p-IL6, NEAT1/XIST-miR-93-5p-CXCL8 and NEAT1/XIST/KCNQ1T1-miR-27a-3p/miR-16-5p-ATF3 might be potential RNA regulatory pathways to regulate the disease progression of early DN. In conclusion, we identified four hub genes, namely, IL6, CXCL8, MMP9, and ATF3, as markers for early diagnosis of DN, and provided insight into the mechanisms of disease development in DN at the transcriptome level.

scholarly journals A NEVER-ENDING STORY OF RHEUMATOID ARTHRITIS

2020 ◽  
pp. 10-17
Author(s):  
BALJIT K. ◽  
QADRIE Z. L. ◽  
AMIT B. ◽  
GAUTAM S. P.
Keyword(s):  
Rheumatoid Arthritis ◽  
Early Diagnosis ◽  
Clinical Manifestations ◽  
Joint Stiffness ◽  
Review Article ◽  
Diagnosis And Treatment ◽  
Treat To Target ◽  
The Novel ◽  
Joint Tenderness ◽  
Loss Of Motion

There are distinct Rheumatic disorders, still Rheumatoid arthritis (RA) is believed to be very prevailing. RA is an empathic disorder described over integral redness, constant inflammation, and the existence of auto-antibodies. In RA, inflammation in joints, loss of motion of joint stiffness, joint tenderness are most common in patients. Deformity of joints can be prevented by early diagnosis and treatment. The extremity of the disease can be reduced by combining the drugs and improved weight more profiled than single medication. Treat-to-target progress results in a superior-conclusion in RA, and the ACR, EULAR, and other specialized systems have supported treat-to-target as a basic curative strategy for RA. The novel methods used in RA have upgraded the development of the disorder and maximum people helpful in cancellation of clinical manifestations if the identification of disorder takes place before time. This review article is written after studying most of the journal’s articles, which were published between 1997-2019.

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