Integrated Analyses of Gene Expression Profiles Digs out Common Markers for Rheumatic Diseases

Identification of Key Biomarkers in Systemic Lupus Erythematosus by a Multi-Cohort Analysis

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple body systems with heterogeneous clinical manifestations. Since gene expression analyses have been accomplished on diverse types of samples to specify SLE-related genes, single-cohort transcriptomics have not produced reliable results. Using an integrated multi-cohort analysis framework, we analyzed whole blood cells from SLE patients from three transcriptomics cohorts (n=1222) and identified a five-gene signature that distinguished SLE patients from controls. We validated the diagnostic performance of this five-gene signature in six independent validation cohorts (n= 469), with an area under the receiver operating characteristic curve of 0.88 [95% CI 0.7 − 0.96]. This five-gene signature may be associated with the proportion of SLE immune cells, and generalizable across ages and sample types with real diagnostic value for clinical application.

Identification of immune related cells and crucial genes in the peripheral blood of ankylosing spondylitis by integrated bioinformatics analysis

Background

Ankylosing spondylitis (AS) is a progressive rheumatic disease and studies reveal that the immune system is critical for the pathogenesis of AS. In the present study, various bioinformatics analysis methods were comprehensively applied, designed to identify potential key genes and inflammation states of AS.

Methods

The transcriptome profiles of GSE25101 and GSE73754 obtained from the Gene Expression Omnibus (GEO) database were merged for subsequent analyses. The differentially expressed genes (DEGs) were identified using the Bioconductor package Limma and threshold values. Functional enrichment and pathway enrichment analyses were performed using the clusterProfiler package and Gene Set Enrichment Analysis (GSEA). Next, protein-protein interaction (PPI) network of the identified DEGs was constructed by the online database, the Search Tool for the Retrieval of Interacting Genes (STRING), visualization and analysis were performed through Cytoscape software. Subsequently, we applied CIBERSORT algorithm to identify subpopulation proportions of immune cells in peripheral blood samples. Finally, we validated the hub genes with the GSE18781 dataset. Samples were collected from patients to validate gene and protein expression using qRT-PCR and ELISA.

Results

A total of 334 DEGs were identified, including 182 upregulated and 152 downregulated DEGs, between AS patients and normal human controls, which were primarily involved in immune response, autophagy, and natural killer cell-mediated cytotoxicity. The most prominent module and candidate biomarkers were identified from the PPI network. Biomarkers were selected for validation and their expressions were significantly decreased in peripheral blood samples which was consistent with transcriptome sequencing results. Nine genes with AUC > 0.70 were considered to be AS hub genes for ROC curve analysis, including GZMA, GZMK, PRF1, GNLY, NKG7, KLRB1, KLRD1, IL2RB and CD247. Furthermore, CIBERSORT results suggest that AS contained a higher proportion of CD8+ T cells, naive CD4+ T cells, neutrophils, and lower levels of gamma delta T cells compared with the normal controls.

Conclusion

In this study, we identified DEGs combined with their closely related biological functions and propose that granule-associated proteins and immune infiltration maybe involved in the progression of ankylosing spondylitis. These validated hub genes may provide new perspectives for understanding the molecular mechanisms of ankylosing spondylitis.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

Subcutaneous panniculitis-like T-cell lymphoma, lupus erythematosus profundus, and overlapping cases: molecular characterization through the study of 208 genes

Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a rare cytotoxic cutaneous lymphoma. Differential diagnosis with lupus erythematosus panniculitis (LEP) can be challenging and overlapping cases have been described. In this study, we investigate whether gene expression profiling may or not identify markers that can be used to improve our understanding of the disease and to make a precise differential diagnosis. SPTCL, LEP, and overlapping cases were analyzed using a customized NanoString platform including 208 genes related to T-cell differentiation, stromal signatures, oncogenes, and tumor suppressor genes. Gene expression unsupervised analysis of the samples differentiated SPTCL from LEP samples. Most overlapping cases were clustered with LEP cases. Differentially expressed genes were observed when comparing SPTCL with LEP cases; and overlapping with LEP cases. Gene set enrichment analysis recognized gene sets defining each group. In conclusion, SPTCL and LEP have distinctive molecular profiles and the molecular background of overlapping cases more closely resembles LEP.

PRKCH polymorphism is associated with rheumatoid arthritis in a Chinese population

Genetic factors have been widely considered to have a substantial effect on the susceptibility to rheumatoid arthritis (RA). The purpose of this study was to determine whether the four newly discovered polymorphisms in a genome-wide association study (GWAS) meta-analysis confer susceptibility to RA in a Chinese Han population. We conducted a case-control study involving 359 RA cases and 873 age-and gender-matched controls and performed genotyping of four single nucleotide polymorphisms (SNPs), rs227163, rs726288, rs3783782 and rs2469434, using the dye terminator-based SNaPshot method. Consequently, we detected significant differences of genotype distribution of rs3783782 in PRKCH between RA and controls. The minor allele frequencies (MAFs) of rs3783782 were significantly higher in RA patients compared to control subjects. Moreover, the rs227163 in TNFRSF9 had higher MAFs in male RA compared with male controls. In addition, the polymorphism of rs3783782 in PRKCH was significantly associated with RA susceptibility (OR = 1.67, 95% CI = 1.32-2.11, p = 1.32 × 10^-5). After stratification by gender, the minor (A) allele was strongly associated with increased risk for RA in males (OR = 1.87, 95% CI = 1.34-2.60; p = 1.62 × 10^-4) and in females (OR = 1.51, 95% CI = 1.08-2.10; p = 0.014). For rs227163, the minor (C) allele was found to be associated with RA risk only in males (OR = 1.34, 95% CI = 1.02-1.75; p = 0.036). These findings for the first time confirmed that rs3783782 in PRKCH was associated with RA susceptibility in a Chinese population, and rs227163 in TNFRSF9 was associated with RA risk in Chinese males; these SNPs may serve as genetic markers for RA.

Preliminary Exploration of hsa_circ_0032131 Levels in Peripheral Blood as a Potential Diagnostic Biomarker of Osteoarthritis

Background: Osteoarthritis (OA) is a common chronic degenerative joint disease characterized by articular cartilage degeneration and synovitis. CircRNAs are increasingly being recognized as functional endogenous RNAs with a stable structure and high tissue specificity. Recent studies have shown that some circRNAs may be involved in the initiation and progression of OA and that there is differential expression of circRNAs in chondrocytes in vitro isolated from patients with OA. Purpose: In this study, we aimed to determine if circRNA levels in the peripheral blood of Chinese Han patients with OA would be diagnostic based on the previous in vitro studies. Methods: We collected peripheral blood samples from 25 patients suffering from OA and 25 healthy controls and measured hsa_circ_0032131_CBC1 RNA levels through quantitative RT-PCR (qRT-PCR). The statistical basis for evaluating the diagnostic value was to calculate the area under the receiver operator characteristic (ROC) curve. Results: The results of the qRT-PCR for hsa_circ_0032131_CBC1 were consistent with those of the microarray analysis. The ROC curve shows that hsa_circ_0032131 holds diagnostic value for OA (0.8455, p < 0.01). Conclusions: Our research indicates that differentially expressed circRNAs may be involved in the development of OA and could be used diagnostically.

Screening for Differentially Expressed Circular RNAs in the Cartilage of Osteoarthritis Patients for Their Diagnostic Value

Background: Osteoarthritis (OA) is the most prevalent osteoarticular disease, which typically involves chronic cartilage degeneration and synovitis. The latest research shows that circular RNAs (circRNAs) play a role in the development of a variety of diseases, including osteoarthrosis. Purposes: The aim of this study was to explore the expression of circRNAs in OA chondrocytes and predict biomarkers for diagnosis. Materials and Methods: The circRNA expression profile was analyzed through use of the Gene Spring software V13.0; differentially expressed circRNAs were screened by comparing OA chondrocytes and normal articular chondrocytes. We validated the microarray data by quantitative real-time polymerase chain reaction analyses of OA chondrocytes and chondrocytes from normal controls. TargetScan software and miRanda software were used to predict networks of circRNA-miRNA interactions in cartilage. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses were applied to predict the functions of differentially expressed circRNAs. Results: Overall, 1380 circRNAs were differentially expressed between OA chondrocytes and normal articular chondrocytes (fold-change ≥2, p ≤ 0.05), including 215 that were upregulated and 1165 that were downregulated circRNAs. After analyzing the differentially expressed circRNA genes, the top 20 enriched GO entries and KEGG pathways were annotated. The hsa_circrna_0032131 was identified for further analysis. A circRNA-miRNA network was constructed to represent the 10 most likely target genes associated with the validated circRNA. Conclusions: Our research suggests that some of the differentially expressed circRNAs in OA chondrocytes compared to normal chondrocytes are etiologically associated with the pathological process of OA. It was found that hsa_circRNA_0032131 likely participates in the initiation and progression of OA and has potential as a diagnostic marker. Clinical Relevance: To analyze the difference of circRNA expression profiles between OA and normal controls and explore biomarkers for diagnosis.

[Upcoming value of gene expression analysis in rheumatology]

Rheumatoid arthritis (RA) is a chronic inflammatory disease of unknown etiology, which involves disturbance in immune system signaling pathway functions, damage of other tissues, pain and joint destruction. Modern treatment attempts to improve pathophysiological and biochemical mechanisms damaged by the disease. However, due to the RA patient heterogeneity personalized approach to treatment is required; the choice of personalized treatment is complicated by the variability of patient's response to treatment. Gene expression analysis might serve a tool for the disease control and therapy personification for inhibition of inflammation and pain as well as for prevention of joint destruction.

The Role of Long Non-coding RNAs in the Pathogenesis of RA, SLE, and SS

Rheumatoid diseases are a group of systemic autoimmune diseases which affect multiple organs with largely unknown etiology. In the past decade, long non-coding RNAs (lncRNAs) have emerged as important regulators of biological processes and contribute deeply to immune cell development and immune responses. Substantial evidences have been accumulated showing that LncRNAs involved in the pathogenesis of the rheumatoid diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and Sjögren's syndrome (SS). In this review, we summarize literature combined with bioinformatics methods to analyze the unique and common lncRNAs patterns in rheumatoid diseases and try to reveal the important function of lncRNAs in RA, SLE and SS.

Integrated analyses for genetic markers of polycystic ovary syndrome with 9 case-control studies of gene expression profiles

Due to genetic heterogeneity and variable diagnostic criteria, genetic studies of polycystic ovary syndrome are particularly challenging. Furthermore, lack of sufficiently large cohorts limits the identification of susceptibility genes contributing to polycystic ovary syndrome. Here, we carried out a systematic search of studies deposited in the Gene Expression Omnibus database through August 31, 2016. The present analyses included studies with: 1) patients with polycystic ovary syndrome and normal controls, 2) gene expression profiling of messenger RNA, and 3) sufficient data for our analysis. Ultimately, a total of 9 studies with 13 datasets met the inclusion criteria and were performed for the subsequent integrated analyses. Through comprehensive analyses, there were 13 genetic factors overlapped in all datasets and identified as significant specific genes for polycystic ovary syndrome. After quality control assessment, there were six datasets remained. Further gene ontology enrichment and pathway analyses suggested that differentially expressed genes mainly enriched in oocyte pathways. These findings provide potential molecular markers for diagnosis and prognosis of polycystic ovary syndrome, and need in-depth studies on the exact function and mechanism in polycystic ovary syndrome.

Cell-type-specific gene expression patterns in the knee cartilage in an osteoarthritis rat model

Osteoarthritis (OA) is a chronic degenerative disease that leads to joint failure, pain, and disability. Gene regulation is implicated as a driver of the imbalance between the expression of catabolic and anabolic factors that eventually leads to the degeneration of osteoarthritic cartilage. In our model, knee-joint OA was induced in male Wistar rats by intra-articular sodium monoiodoacetate (MIA) injections. Whole-genome microarrays were used to analyse the alterations in gene expression during the time-course of OA development (at 2, 14, and 28 days post-injection) in rat knee joints. The identified co-expressed groups of genes were analysed for enriched regulatory mechanisms, functional classes, and cell-type-specific expression. This analysis revealed 272 regulated transcripts (ANOVA FDR < 0.1% and fold > 2). Functionally, the five major gene expression patterns (A–E) were connected to PPAR signalling and adipogenesis (in cluster A), WNT signalling (in cluster B), endochondral ossification (in cluster C), matrix metalloproteinases and the ACE/RAGE pathway (in cluster D), and the Toll-like receptor, and IL-1 signalling pathways (in cluster E). Moreover, the dynamic profiles of these transcriptional changes were assigned to cellular compartments of the knee joint. Classifying the molecular processes associated with the development of cartilage degeneration provides novel insight into the OA disease process. Our study identified groups of co-regulated genes that share functional relationships and that may play an important role in the early and intermediate stages of OA.

The clinical utility of gene expression examination in rheumatology

Rheumatoid arthritis (RA) is a chronic inflammatory disease with unknown etiology that affects various pathways within the immune system, involves many other tissues and is associated with pain and joint destruction. Current treatments fail to address pathophysiological and biochemical mechanisms involved in joint degeneration and the induction of pain. Moreover, RA patients are extremely heterogeneous and require specific treatments, the choice of which is complicated by the fact that not all patients equally respond to therapy. Gene expression analysis offer tools for patient management and personalization of patient’s care to meet individual needs in controlling inflammation and pain and delaying joint destruction.

Biosemantics guided gene expression profiling of Sjögren's syndrome: a comparative analysis with systemic lupus erythematosus and rheumatoid arthritis

BACKGROUND

Sjögren's syndrome (SS) shares many clinical and pathological similarities with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). These autoimmune diseases mostly affect women. In this study, concept profile analysis (CPA) and gene expression meta-analysis were used to identify genes potentially involved in SS pathogenesis.

METHODS

Human genes associated with SS, SLE, and RA were identified using the CPA tool, Anni 2.1. The differential mRNA expression of genes common to SS and SLE (SS-SLE) was determined in female peripheral blood mononuclear cells (PBMCs) using NCBI-GEO2R. Differentially expressed (DE) SS-SLE PBMC genes in common with the SS-SLE CPA-identified genes were analyzed for differential expression in salivary glands or synovial biopsies, and for genes common to SS and RA and SLE and RA, analyzing differential expression in salivary glands in SS, synovial fibroblasts in RA, and synovial fluid in SLE. Among common genes, DE genes found in salivary gland mRNA expression in patients with SS were used for gene enrichment and SS molecular network construction. Secondary analysis was performed to identify DE genes unique to the disease site tissues, by excluding PBMC and CPA common DE genes to complement the SS network.

RESULTS

We identified 22 DE genes in salivary gland datasets in SS that have not previously been clearly associated with SS pathogenesis. Among these, higher levels of checkpoint kinase 1 (CHEK1), V-Ets avian erythroblastosis virus E26 oncogene homolog 1 (ETS1), and lymphoid enhancer binding factor 1 (LEF1) were significantly correlated with higher matrix metalloproteinase 9 (MMP9) levels. Higher MMP9 levels have been implicated in degradation of salivary gland structural integrity, leading to hypo-salivation in patients with SS. Salivary gland mRNA expression of MMP9 and the expression of cytokine CXCL10 were higher in patients with SS. CXCL10 has been shown to increase MMP9 expression and therefore may also play an important role in SS pathogenesis.

CONCLUSION

Using CPA and gene expression analysis, we identified factors targeting MMP9 expression and/or function, namely CHEK1, CXCL10, ETS1, LEF1, and tissue inhibitor of metalloproteinase 1; altered mRNA expression of these could increase expression/activity of MMP9 in a concerted manner, thereby potentially impacting SS pathogenesis.

NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients

The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using ¹H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.

Long noncoding RNA expression profile of infantile hemangioma identified by microarray analysis

Infantile hemangioma (IH) is one of the most common vascular tumors of childhood. Long noncoding RNAs (lncRNAs) play a critical role in angiogenesis, but their involvement in hemangioma remains unknown. This study aimed to assess the expression profiles of lncRNAs in IH and adjacent normal tissue samples, exploring the biological functions of lncRNAs as well as their involvement in IH pathogenesis. The lncRNA expression profiles were determined by lncRNA microarrays. A total of 1259 and 857 lncRNAs were upregulated and downregulated in IH, respectively, at a fold change cutoff of 2.0 (p < 0.05); in addition, 1469 and 1184 messenger RNAs (mRNAs) were upregulated and downregulated, respectively (fold change cutoff of 2.0; p < 0.05). A total of 292 differentially expressed mRNAs were targeted by the lncRNAs with altered expression in hemangioma, including 228 and 64 upregulated and downregulated, respectively (cutoff of 2.0, p < 0.05). Gene ontology (GO) analyses revealed several angiogenesis-related pathways. An lncRNA-mRNA co-expression network for differentially expressed lncRNAs revealed significant associations of the lncRNAs MEG3, MEG8, FENDRR, and Linc00152 with their related mRNAs. The validation results of nine differentially expressed lncRNAs (MALAT1, MEG3, MEG8, p29066, p33867, FENDRR, Linc00152, p44557_v4, p8683) as well as two mRNAs (FOXF1, EGFL7) indicated that the microarray data correlated well with the QPCR results. Interestingly, MALAT1 knockdown induced apoptosis and S-phase cell cycle arrest in human umbilical vein endothelial cells (HUVECs). Overall, this study revealed the lncRNA expression profile of IH and that lncRNAs likely regulate several genes with important roles in angiogenesis.