Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of RA. The differentially expressed CRGs (DECRGs) between RA and normal control samples were screened from Datasets GSE12021 and GSE55235. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to explore the potential functional mechanisms of DECRGs in RA. Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator regression analysis were performed to identify hub CRGs of RA. CIBERSORT was conducted to compare the infiltration level of immune cells in RA and control synovial tissue and their relationship with hub genes. In addition, the diagnostic value of hub biomarkers was evaluated by the area under the receiver operator characteristic curve. Further, a nomogram prediction model was constructed and its significance for clinical decision-making was evaluated. The green module was identified as the hub module associated with RA. Four hub CRGs (EGR1, FOSL2, GADD45B, and NFIL3) were identified and showed that they had the highest specificity and sensitivity for RA diagnosis, respectively. The expression levels and diagnostic values of these genes were externally validated in the dataset GSE55457. A nomogram prediction model based on the four hub CRGs was constructed and proved to have a certain clinical decision value. Additionally, the correlation analysis of immune cells with hub genes showed that all hub genes were significantly positively correlated with activated mast cells, resting memory CD4+ T cells, and monocytes. Whereas, all hub genes were negatively correlated with plasma cells, CD8+ T cells, and activated memory CD4+ T cells. Meanwhile, FOSL2 and GADD45B were negatively correlated with Tfh cells. Four hub CRGs were identified and showed excellent diagnostic value for RA. These genes may be involved in the pathological process of RA by disrupting the rhythmic oscillations of cytokines through immune-related pathways and could be considered molecular targets for future chronotherapy against RA.

Wen P ，Ma T ，Zhang B ，Hao L ，Wang Y ，Guo J ，Song W ，Wang J ，Zhang Y ... -  《Frontiers in Immunology》

Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis. Five microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by "Batch correction" and "RobustRankAggreg" methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman's rank correlation analysis in R software. Ten significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package "UpSet". CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells. CCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.

Zhou S ，Lu H ，Xiong M 《Frontiers in Immunology》

Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis.

Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive. This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion. Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the "LIMMA" package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA. We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion (P < 0.05). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA. We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

Sun F ，Zhou JL ，Peng PJ ，Qiu C ，Cao JR ，Peng H ... -  《-》

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

Wu YK ，Liu CD ，Liu C ，Wu J ，Xie ZG ... -  《Frontiers in Immunology》

Screening and identification of potential hub genes and immune cell infiltration in the synovial tissue of rheumatoid arthritis by bioinformatic approach.

Rheumatoid arthritis (RA) is an autoimmune disease that affects individuals of all ages. The basic pathological manifestations are synovial inflammation, pannus formation, and erosion of articular cartilage, bone destruction will eventually lead to joint deformities and loss of function. However, the specific molecular mechanisms of synovitis tissue in RA are still unclear. Therefore, this study aimed to screen and explore the potential hub genes and immune cell infiltration in RA. Three microarray datasets (GSE12021, GSE55457, and GSE55235), from the Gene Expression Omnibus (GEO) database, have been analyzed to explore the potential hub genes and immune cell infiltration in RA. First, the LIMMA package was used to screen the differentially expression genes (DEGs) after removing the batch effect. Then the clusterProfiler package was used to perform functional enrichment analyses. Second, through weighted coexpression network analysis (WGCNA), the key module was identified in the coexpression network of the gene set. Third, the protein-protein interaction (PPI) network was constructed through STRING website and the module analysis was performed using Cytoscape software. Fourth, the CIBERSORT and ssGSEA algorithm were used to analyze the immune status of RA and healthy synovial tissue, and the associations between immune cell infiltration and RA-related diagnostic biomarkers were evaluated. Fifth, we used the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) to validate the expression levels of the hub genes, and ROC curve analysis of hub genes for discriminating between RA and healthy tissue. Finally, the gene-drug interaction network was constructed using DrugCentral database, and identification of drug molecules based on hub genes using the Drug Signature Database (DSigDB) by Enrichr. A total of 679 DEGs were identified, containing 270 downregulated genes and 409 upregulated genes. DEGs were primarily enriched in immune response and chemokine signaling pathways, according to functional enrichment analysis of DEGs. WGCNA explored the co-expression network of the gene set and identified key modules, the blue module was selected as the key module associated with RA. Seven hub genes are identified when PPI network and WGCNA core modules are intersected. Immune infiltration analysis using CIBERSORT and ssGSEA algorithms revealed that multiple types of immune infiltration were found to be upregulated in RA tissue compared to normal tissue. Furthermore, the levels of 7 hub genes were closely related to the relative proportions of multiple immune cells in RA. The results of the qRT-PCR demonstrated that the relative expression levels of 6 hub genes (CD27, LCK, CD2, GZMB, IL7R, and IL2RG) were up-regulated in RA synovial tissue, compared with normal tissue. Simultaneously, ROC curves indicated that the above 6 hub genes had strong biomarker potential for RA (AUC >0.8). Through bioinformatics analysis and qRT-PCR experiment, our study ultimately discovered 6 hub genes (CD27, LCK, CD2, GZMB, IL7R, and IL2RG) that closely related to RA. These findings may provide valuable direction for future RA clinical diagnosis, treatment, and associated research.

Feng ZW ，Tang YC ，Sheng XY ，Wang SH ，Wang YB ，Liu ZC ，Liu JM ，Geng B ，Xia YY ... -  《Heliyon》