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 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 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

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 biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses

2020 ◽  
Vol 40 (9) ◽  
Author(s):  
Zhaoyan Li ◽  
Meng Xu ◽  
Ronghang Li ◽  
Zhengqing Zhu ◽  
Yuzhe Liu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Epidermal Growth Factor Receptor ◽  
Growth Factor ◽  
Epidermal Growth Factor ◽  
Growth Factor Receptor ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Ppi Networks ◽  
Epidermal Growth ◽  
Potential Biomarkers

Abstract Objectives: Rheumatoid arthritis (RA) is the most common inflammatory arthritis in the world, but its underlying mechanism is still unclear. The present study aims to screen and verify the potential biomarkers of RA. Methods: We searched the Gene Expression Omnibus (GEO) database for synovial expression profiling from different RA microarray studies to perform a systematic analysis. Functional annotation of differentially expressed genes (DEGs) was conducted, including GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The protein–protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The expression levels of the hub genes in normal membranes and RA synovium were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot system. Results: A total of 444 differential expression genes were identified, including 172 up-regulated and 272 down-regulated genes in RA synovium compared with normal controls. The top ten hub genes; protein tyrosine phosphatase receptor type C (PTPRC), LCK proto-oncogene (LCK), cell division cycle 20 (CDC20), Jun proto-oncogene (JUN), cyclin-dependent kinase 1 (CDK1), kinesin family member 11 (KIF11), epidermal growth factor receptor (epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGFA), mitotic arrest deficient 2 like 1 (MAD2L1), and signal transducer and activator of transcription 1 (STAT1) were identified from the PPI network, and the expression level of VEGFA and EGFR was significantly increased in RA membranes (P&lt;0.05). Conclusion: Our results indicate that the hub genes VEGFA and EGFR may have essential effects during the development of RA and can be used as potential biomarkers of RA.

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 Serum Biomarker Panel of exomiR-451a, exomiR-25-3p and Soluble TWEAK for Early Diagnosis of Rheumatoid Arthritis

2021 ◽  
Vol 12 ◽  
Author(s):  
Samantha Rodríguez-Muguruza ◽  
Antonio Altuna-Coy ◽  
Sonia Castro-Oreiro ◽  
Maria José Poveda-Elices ◽  
Ramon Fontova-Garrofé ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Pilot Study ◽  
Early Diagnosis ◽  
Serum Levels ◽  
Serum Biomarker ◽  
Enzyme Linked Immunosorbent Assay ◽  
Biomarker Panel ◽  
Protein Protein Interaction ◽  
Early Ra ◽  
Relevant Role

BackgroundThe etiology of rheumatoid arthritis (RA) remains poorly understood. Early and accurate diagnosis still difficult to achieve. Inflammatory related molecules released into the circulation such cytokines and exosome-derived microRNAs (exomiRNAs) could be good candidates for early diagnosis of autoimmune diseases. We sought to discover a serum biomarker panel for the early detection of RA based on exomiRNAs and inflammatory markers.MethodsA 179 miRNAs-microarray panel was analyzed in a pilot study (4 early RA and 4 controls). Validation of deregulated exomiRNAs was performed in a larger cohort (24 patients with early RA and 24 controls). miRNet software was used to predict exomiRNA gene-targets interactions. Potentially altered pathways were analyzed by Reactome pathway database search. STRING database was used to predict protein-protein interaction networks. Enzyme-linked immunosorbent assay was used to measure serum levels of sTWEAK and sCD163. Signature biomarker candidates were statistical analyzed.ResultsWe detected 11 differentially expressed exomiRNAs in early RA pilot study. Validation analysis revealed that 6/11 exomiRNAs showed strong agreement with the pilot microarray data (exomiR-144-3p, -25-3p, -15a-5p, -451a, -107 and -185-5p). sTWEAK and sCD163 biomarkers were significantly elevated in the serum of patients with early RA. Receiver operating characteristic (ROC) analysis showed that the best panel to diagnose early RA contained exomiR-451a, exomiR-25-3p and sTWEAK, and could correctly classify 95.6% of patients, with an area under the ROC curve of 0.983 and with 100% specificity and 85.7% sensitivity. The YWHAB gene was identified as a common target of the putative miRNA-regulated pathways.ConclusionA novel serum biomarker panel composed of exomiR-451a, exomiR-25-3p and serum levels of sTWEAK may have use in the early clinical diagnosis of RA. A new predicted exomiRNA-target gene YHWAB has been identified and may have a relevant role in the development of RA.

scholarly journals Integrated Bioinformatics and Validation Reveal Potential Biomarkers Associated With Progression of Primary Sjögren’s Syndrome

2021 ◽  
Vol 12 ◽  
Author(s):  
Ning Li ◽  
Lei Li ◽  
Mengyao Wu ◽  
Yusi Li ◽  
Jie Yang ◽  
...  
Keyword(s):  
Salivary Glands ◽  
Disease Progression ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Primary Sjögren’S Syndrome ◽  
Morphological Features ◽  
Healthy Controls ◽  
Hub Genes ◽  
Key Genes ◽  
Potential Biomarkers

BackgroundPrimary Sjögren’s syndrome (pSS) is a chronic systemic autoimmune disease of the exocrine glands characterized by specific pathological features. Previous studies have pointed out that salivary glands from pSS patients express a unique profile of cytokines, adhesion molecules, and chemokines compared to those from healthy controls. However, there is limited evidence supporting the utility of individual markers for different stages of pSS. This study aimed to explore potential biomarkers associated with pSS disease progression and analyze the associations between key genes and immune cells.MethodsWe combined our own RNA sequencing data with pSS datasets from the NCBI Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) via bioinformatics analysis. Salivary gland biopsies were collected from 14 pSS patients, 6 non-pSS patients, and 6 controls. Histochemical staining and scanning electron micrographs (SEM) were performed to macroscopically and microscopically characterize morphological features of labial salivary glands in different disease stages. Then, we performed quantitative PCR to validate hub genes. Finally, we analyzed correlations between selected hub genes and immune cells using the CIBERSORT algorithm.ResultsWe identified twenty-eight DEGs that were upregulated in pSS patients compared to healthy controls. These were mainly involved in immune-related pathways and infection-related pathways. According to the morphological features of minor salivary glands, severe interlobular and periductal lymphocytic infiltrates, acinar atrophy and collagen in the interstitium, nuclear shrinkage, and microscopic organelle swelling were observed with pSS disease progression. Hub genes based on above twenty-eight DEGs, including MS4A1, CD19, TCL1A, CCL19, CXCL9, CD3G, and CD3D, were selected as potential biomarkers and verified by RT-PCR. Expression of these genes was correlated with T follicular helper cells, memory B cells and M1 macrophages.ConclusionUsing transcriptome sequencing and bioinformatics analysis combined with our clinical data, we identified seven key genes that have potential value for evaluating pSS severity.

scholarly journals CD3D, GZMK, and KLRB1 Are Potential Markers for Early Diagnosis of Rheumatoid Arthritis, Especially in Anti-Citrullinated Protein Antibody-Negative Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Junqin Lu ◽  
Yihui Bi ◽  
Yapeng Zhu ◽  
Shi Huipeng ◽  
Wenxiu Duan ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Early Diagnosis ◽  
Bioinformatics Analysis ◽  
High Sensitivity ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Construction ◽  
Protein Protein Interaction ◽  
Citrullinated Protein ◽  
Coexpression Network Analysis

Early diagnosis and monitoring of rheumatoid arthritis (RA) progress are critical for effective treatment. In clinic, the detection of rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) are usually combined to diagnose early RA. However, the poor specificity of RF and high heterogeneity of ACPA make the early diagnosis of RA still challenging. Bioinformatics analysis based on high-throughput omics is an emerging method to identify novel and effective biomarkers, which has been widely used in many diseases. Herein, utilizing an integrated strategy based on expression correlation analysis and weighted gene coexpression network analysis (WGCNA), we identified 76 RA-trait different expression genes (DEGs). Combined with protein-protein interaction (PPI) network construction and clustering, new hub genes associated in RA synovia, CD3D, GZMK, and KLRB1, were identified. We verified the specificity of these genes in the synovium of RA patients through three external datasets. We also observed high sensitivity and specificity of them for ACPA-negative patients. CD3D, GZMK, and KLRB1 are potentially key mediators of RA pathogenesis and markers for RA diagnosis.

Immune system and bone cells in early rheumatoid arthritis

2016 ◽  
Author(s):  
Ilaria Buondonno ◽  
Francesca Sassi ◽  
Micol Rigoni ◽  
Guido Rovera ◽  
Giovanni Carlo Isaia ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune System ◽  
Early Rheumatoid Arthritis ◽  
Bone Cells
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