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

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.

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

Journal of Translational Medicine ◽

10.1186/s12967-020-02689-y ◽

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.

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

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.135 ◽

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

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The mRNA landscape profiling reveals potential biomarkers associated with acute kidney injury AKI after kidney transplantation

PeerJ ◽

10.7717/peerj.10441 ◽

2020 ◽

Vol 8 ◽

pp. e10441

Author(s):

Hui Bi ◽

Min Zhang ◽

Jialin Wang ◽

Gang Long

Keyword(s):

Acute Kidney Injury ◽

Kidney Transplantation ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Graft Function ◽

Kidney Injury ◽

Diagnostic Value ◽

Hub Genes ◽

Key Genes ◽

Potential Biomarkers

Background This study aims to identify potential biomarkers associated with acute kidney injury (AKI) post kidney transplantation. Material and Methods Two mRNA expression profiles from Gene Expression Omnibus repertory were downloaded, including 20 delayed graft function (DGF) and 68 immediate graft function (IGF) samples. Differentially expressed genes (DEGs) were identified between DGF and IGF group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed. Then, a protein-protein interaction analysis was performed to extract hub genes. The key genes were searched by literature retrieval and cross-validated based on the training dataset. An external dataset was used to validate the expression levels of key genes. Receiver operating characteristic curve analyses were performed to evaluate diagnostic performance of key genes for AKI. Results A total of 330 DEGs were identified between DGF and IGF samples, including 179 up-regulated and 151 down-regulated genes. Of these, OLIG3, EBF3 and ETV1 were transcription factor genes. Moreover, LEP, EIF4A3, WDR3, MC4R, PPP2CB, DDX21 and GPT served as hub genes in PPI network. EBF3 was significantly up-regulated in validation GSE139061 dataset, which was consistently with our initial gene differential expression analysis. Finally, we found that LEP had a great diagnostic value for AKI (AUC = 0.740). Conclusion EBF3 may be associated with the development of AKI following kidney transplantation. Furthermore, LEP had a good diagnostic value for AKI. These findings provide deeper insights into the diagnosis and management of AKI post renal transplantation.

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

Rheumatology ◽

10.1093/rheumatology/kez218 ◽

2019 ◽

Vol 58 (8) ◽

pp. 1331-1343 ◽

Cited By ~ 2

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.

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Identification of Key Genes and Competitive Endogenous RNA Network Construction in Osteonecrosis of the Femoral Head by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-1050652/v1 ◽

2021 ◽

Author(s):

Yang Dinglong ◽

Chen Shuai ◽

Chen Yujing ◽

Wang Beiyang ◽

Zhang Guohao ◽

...

Keyword(s):

Femoral Head ◽

Progressive Collapse ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Vital Role ◽

Hub Genes ◽

Regulatory Pathways ◽

Key Genes ◽

Normal Controls

Abstract Background: Despite cumulative evidence shows osteonecrosis of the femoral head (ONFH) could result in the progressive collapse of the femoral head. The pathogenesis of ONFH remains unclear. Early ONFH is difficult to diagnose due to the lack of effective biomarkers. Method: In Gene Expression Omnibus (GEO) database, we searched the Microarray datasets for serum (GSE123568) in ONFH and normal controls to identify differentially expressed genes (DEGs) by R software. The enrichment analyses were performed to enrich pathways of DEGs. Protein–protein interaction (PPI), miRNA-mRNA co-expression, ceRNA networks were constructed using Cytoscape to identity top15 hub genes, target miRNAs of hub genes and potential regulatory pathways. Furthermore, hub genes validated in GSE74089 with high diagnostic value for ONFH were selected as key genes. The Human Protein Atlas (HPA) and Bgee Database were used to find out the subcellular and tissue distribution of key genes.Results: A total of 568 DEGs were identified between 30 ONFH samples and 10 normal controls. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis showed that DEGs are mostly enriched in innate immune responses, thrombosis and signal transduction. Fifteen hub genes were identified by PPI network using Cytoscape. The 15 hub genes were almost all positively correlated with each other. The expression of PLEK (P＜0.001), TLR2 (P＜0.05), and TREM1 (P＜0.001) were validated in dataset GSE74089 and they had high diagnostic value (AUC＞0.8) for ONFH. MALAT1-miR-146b-5p-TLR2, MALAT1-miR-664b-3p-PLEK, NORAD-miR-106b-5p-TLR2, and MSMO1-miR-106b-5p-TLR2 might be potential RNA regulatory pathways in the disease progression of ONFH. PLEK mainly expressed in nucleus, TREM1 in dictyosome, and TLR2 in nucleoplasm and mitochondria.Conclusions: In this study, we found that PLEK, TLR2, and TREM1 might be potential biomarkers in diagnostic and play a vital role in the progression of ONFH.

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Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses

Bioscience Reports ◽

10.1042/bsr20201713 ◽

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<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.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.767654 ◽

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.

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Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

10.21203/rs.3.rs-700226/v1 ◽

2021 ◽

Author(s):

Li Tao ◽

ChaoLiang Xiong ◽

Li Xue

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Pathway Analysis ◽

Expression Profiles ◽

Diagnostic Value ◽

Functional Enrichment ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Gene Markers

Abstract Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and subsequent destruction of cartilage and bone. This study aimed to explore RA-related gene markers and the underlying molecular mechanism.Material and Methods: The expression profiles of GSE77298, GSE55235 and GSE12021 were obtained from the Gene Expression Omnibus database. Then, the differential gene expression analysis was conducted between GSE77298 and GSE55235 datasets. Limma package and a Venn diagram were utilized to screen the overlapping differentially expressed genes (DEGs), and Functional enrichment and pathway analysis were performed by using DAVID database. Subsequently, a protein-protein interaction (PPI) network was established, and candidate hub genes were recognized by using STRING and Cytoscape software. Finally, another dataset (GSE12021) was used for the validation of diagnostic value of the candidate hub genes and to identify real hub genes by using receiver operating characteristic (ROC) curves.Results: A total of 385 DEGs were detected, which include 19 downregulated genes and 366 upregulated genes. GO and KEGG pathway analysis showed that DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. The PPI network was composed of 374 nodes and 767 edges. A total of 8 real hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) which displayed an excellent diagnostic value for RA were identified.Conclusion: these findings may provide novel and reliable biomarkers for RA, which have some interesting implications for early diagnosis, prognosis and targeted therapy.

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Presence of anti-acetylated peptide antibodies (AAPA) in inflammatory arthritis and other rheumatic diseases suggests discriminative diagnostic capacity towards early rheumatoid arthritis

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x211022533 ◽

2021 ◽

Vol 13 ◽

pp. 1759720X2110225

Author(s):

Paul Studenic ◽

Alessia Alunno ◽

Daniela Sieghart ◽

Holger Bang ◽

Daniel Aletaha ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Rheumatic Diseases ◽

Diagnostic Value ◽

Enzyme Linked Immunosorbent Assay ◽

Inflammatory Rheumatic Diseases ◽

Operating Characteristics ◽

Likelihood Ratios ◽

Double Positive ◽

Early Ra ◽

Peptide Antibodies

Aims: To determine the diagnostic value of anti-acetylated peptide antibodies (AAPA) in patients with rheumatoid arthritis (RA). Methods: Three acetylated peptides (ac-lysine, ac-lysine.inv and ac-ornithine) derived from vimentin were employed to measure AAPA by enzyme-linked immunosorbent assay (ELISA) in sera of 120 patients with early RA (eRA), 195 patients with established RA (est RA), 99 healthy controls (HC), and 216 patients with other inflammatory rheumatic diseases. A carbamylated and a citrullinated version of the vimentin peptide were used additionally. Receiver operating characteristics and logistic regression analyses were used to assess the discriminative capacity of AAPA. Results: AAPA were detected in 60% of eRA and 68.7% of estRA patients, 22.2% of HC, and 7.1– 30.6% of patients with other rheumatic diseases. Importantly, AAPA were also present in 40% of seronegative RA patients, while antibodies to the carbamylated peptide were detected less frequently. Diagnostic sensitivity of individual peptides for eRA was 28.3%, 35.8%, and 34% for ac-lysine, ac-ornithine, and ac-lysine.inv, respectively. Positive likelihood ratios (LR+) for eRA versus HC were 14.0, 7.1, and 2.1. While the presence of a single AAPA showed varying specificity (range: 84–98%), the presence of two AAPA increased specificity considerably since 26.7% of eRA, as compared with 6% of disease controls, were double positive. Thus, double positivity discriminated eRA from axial spondyloarthritis with a LR+ of 18.3. Remarkably, triple positivity was 100% specific for RA, being observed in 10% of eRA and 21.5% of estRA patients, even in the absence of RF and ACPA. Conclusion: AAPA are highly prevalent in early RA and occur also independently of RF and ACPA, thereby reducing the gap of seronegativity. Furthermore, multiple AAPA reactivity increased the specificity for RA, suggesting high diagnostic value of AAPA testing.

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

Frontiers in Immunology ◽

10.3389/fimmu.2021.697157 ◽

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.

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