Exploration and validation of therapeutic molecules for rheumatoid arthritis based on ferroptosis-related genes.

AIMS: This study aimed to identify hub ferroptosis-related genes (FRGs) and investigate potential therapy for RA based on FRGs. MAIN METHODS: The differentially expressed FRGs in synovial tissue of RA patients were obtained from the dataset GSE12021 (GPL96). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted to investigate the potential signaling pathways associated with FRGs. Hub genes were identified through topological analysis. The expression levels of these hub genes as well as their diagnostic accuracies were further evaluated. Connectivity Map (CMap) database was utilized to analyze the top 10 FRGs-guided potential drugs for RA. In vitro and in vivo experiments were carried out for further validation. KEY FINDINGS: 2 hub genes among 58 FRGs were identified (EGR1 and CDKN1A), and both were down regulated in RA synovial tissue. GPx4 expression was also decreased in the RA synovial tissue. The natural compound withaferin-a exhibited the highest negative CMap score. In-vitro and in-vivo experiments demonstrated anti-arthritic effects of withaferin-a. SIGNIFICANCE: Ferroptosis participates in pathogenesis of RA, ferroptosis-related genes EGR1 and CDKN1A can be used as diagnostic and therapeutic targets for RA. Withaferin-a can be used as potential anti-arthritic treatment.

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures.

Objective: To study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS. Methods: We retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes. Results: We obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores. Conclusion: The study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

The m6A/m1A/m5C-Related Methylation Modification Patterns and Immune Landscapes in Rheumatoid Arthritis and Osteoarthritis Revealed by Microarray and Single-Cell Transcriptome.

Purpose: The goal of this study was to explore the expression characteristics of RNA modification-related genes, reveal immune landscapes and identify novel potential diagnostic biomarkers in osteoarthritis (OA) and rheumatoid arthritis (RA) patients. Patients and Methods: RNA microarray and single-cell sequencing (scRNA-seq) data were downloaded from gene expression omnibus (GEO) database. Differentially expressed RNA modification-related genes were identified and then functionally annotated. Univariate logistic regression and lasso regression analysis were used to identify primary disease genes for OA and RA. Validation was done using scRNA-seq analysis and immunohistochemistry (IHC) in human knee synovial tissues and a murine destabilization of the medial meniscus (DMM) model. Through WGCNA analysis, genes associated with cell pyroptosis or autophagy in OA and RA were identified, which were then combined with differentially expressed RNA modification-related genes to construct a PPI interaction network. Furthermore, hub genes were selected for ceRNA interaction network analysis, correlation analysis with OA and RA molecular subtypes, as well as correlation analysis with 22 immune cells. Results: Six RNA modification-related genes (ADAMDEC1, IGHM, OGN, TNFRSF11B, SCARA3 and PTN) were identified as potential OA and RA pathogenesis biomarkers. Their expression was validated in human knee synovial tissues and a murine DMM model. Functional enrichment of differentially expressed RNA modification-related genes between RA and OA was analyzed using GO, KEGG, GSEA, and GSVA. Based on WGCNA and PPI analysis, the six hub genes related to pyroptosis and RNA modification (CXCL10, CXCL9, CCR7, CCL5, CXCL1, and CCR2) were identified as central nodes for ceRNA interaction, correlation with OA and RA molecular subtypes, and association with 22 immune cells. Conclusion: Our research revealed the significance of RNA modification-related genes in the development of OA and RA pathogenesis, thereby providing a novel research direction for understanding the mechanisms, diagnosis, and treatment of OA and RA.

Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment.

OBJECTIVE: To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management. METHODS: Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group. RESULTS: We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4. CONCLUSION: The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks.

OBJECTIVE: This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA. METHODS: Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA. RESULTS: The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions. CONCLUSION: The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

Bioinformatics Analysis Identified the Hub Genes, mRNA-miRNA-lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis.

Objective: Rheumatoid arthritis (RA) is a nonspecific, chronic, systemic autoimmune disease characterized by symmetric polyarticular synovitis. Bioinformatics analysis of potential biomarkers, mRNA-miRNA-lncRNA axes, and signaling pathways in the pathogenesis of RA provides potential targets and theoretical basis for further research on RA. Methods: The GSE1919 and GSE77298 datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo). Perl was used to perform data merging, and R was used to perform batch correction. The "limma" package of R was used to screen differentially expressed genes, and the "clusterProfiler" package was used to perform enrichment analysis of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein-protein interaction network, Cytoscape was used for module analysis, and R was used to screen for hub genes. GraphPad Prism was used to plot the receiver operating characteristic curve of the hub genes. Gene set enrichment analysis and competitive endogenous RNA network analysis were performed on hub genes with the greatest diagnostic values. The hub gene with the greatest diagnostic value was verified using immunohistochemical staining. Results: We obtained nine hub genes (ITGB2, VAMP8, HLA-A, PTAFR, SYK, FCER1G, HLA-DPB1, LCP2, and ACTR2) and four mRNA-miRNA-lncRNA axes (ITGB2-hsa-miR-486-3p-SNHG3, ITGB2-hsa-miR-338-5p-XIST, ITGB2-hsa-miR-5581-3p-XIST, and ITGB2-hsa-miR-1226-5p-XIST) related to the pathogenesis of RA. The nine hub genes were highly expressed, and ITGB2 had the highest diagnostic value for RA. We also identified signaling pathways related to the pathogenesis of RA: Fc epsilon Rl and chemokine signaling pathways. The immunohistochemical results showed that ITGB2 expression was significantly upregulated in RA. Conclusion: The hub genes, mRNA-miRNA-lncRNA axes, and signaling pathways related to RA pathogenesis identified in this study provide a new research direction for the mechanism, diagnosis, and treatment of RA.

Peroxiredoxin 4 regulates tumor-cell-like characteristics of fibroblast-like synoviocytes in rheumatoid arthritis through PI3k/Akt signaling pathway.

Peroxiredoxin-4 (PRDX4), a member of PRDX family, which played an important role in scavenging reactive oxygen species (ROS). The up-regulation of PRDX4 in synovial tissue and synovial fluid from rheumatoid arthritis (RA) patients has been reported. However, the biological functions of PRDX4 in fibroblast-like synoviocytes (RA-FLS) remains unclear. In this research, we reveal that expression of PRDX4 was notably increased in RA synovial tissue, especially in hyperplastic synovial tissue. PRDX4 silencing significantly inhibited the tumor cell-like behaviors and mRNA expression of matrix metalloproteinases (MMPs) in RA-FLS. Furthermore, overexpression of PRDX4 markedly activated PI3K/Akt signaling pathway, which can be reverted by Akt inhibitor MK-2206. These observations identified elevated PRDX4 may regulates the tumor cell-like biological characteristic of RA-FLS via Pi3k/Akt pathway. Targeting PRDX4 and its downstream signaling pathway might provide a potential diagnostic markers and therapeutic target for RA.

Bioinformatics analysis to investigate immune cell infiltration in synovial lesions of rheumatoid arthritis patients / 中华风湿病学杂志

Objective:To explore the immune infiltration cells in rheumatoid arthritis (RA) synovial lesions, and to provide new research directions and therapeutic targets for the pathogenesis and treatment of RA.Methods:The three gene expression data sets GSE77298, GSE55457 and GSE1919 were downloaded from gene expression omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo), and the data were merged with Perl. The "limma" package was used to adjust batch differences. In R, "CIBERSORT" software was used to obtain the expression matrix of 22 kinds of immune cells corresponding to RA synovial tissue samples and normal synovial tissue samples were analyzed with the three packages of "e1071", "parallel" and "preprocessCore". Perl was used to screen samples with P<0.05 in the immune cell matrix. R's "barplot" function was analyzed by the percentage of 22 immune cells in samples with P<0.05. The "pheatmap" package of R was used to visualize heatmaps, and "corrplot" package was used to draw correlation heatmaps. The "vioplot" package of R was used to draw violin plots of differences via the wilcox test. Results:The results of immune cell infiltration analysis showed that in RA synovial tissue samples and normal synovial tissue samples at P<0.05, B cells naive and natural killer cells resting were under-expressed in RA synovial tissue, and plasma cells, mast cells resting, macrophages M1, B cells memory and T cells regulatory were highly expressed in RA synovial tissue. This study also found that in the same sample, the correlation coefficient between natural killer cells resting and neutrophils ( r=0.91) was the highest, indicating synergistic effect between the two. In the same sample, the correlation coefficient between macrophages M0 and plasma cells ( r=-0.88) was the lowest, indicating antagonistic effect between the two. Conclusion:The immune infiltrating cells in RA synovial lesions discovered in this study provide a certain theoretical basis and research direction for the research on the disease mechanism and treatment of RA.

A Pan-Cancer Analysis of the Role of Selenoprotein P mRNA in Tumorigenesis.

BACKGROUND: Selenium (Se) exhibits its anti-carcinogenic properties by regulating the redox system. However, the relationship between selenoprotein P (SeP), mRNA (SELENOP mRNA) and tumorigenesis remains unclear. Plasma SeP transports Se to various target tissues and has antioxidant characteristics. The present study aimed to explore the multifaceted pan-cancer properties of SELENOP in terms of its tissue-specific expression, prognostic value, immune function, and signaling pathway enrichment. PATIENTS AND METHODS: The expression profile of SELENOP was determined in 33 tumor types and survival, pathway enrichment, and correlation analyses were conducted based on TCGA database. The relationship between SELENOP expression and immune infiltration and macrophage subtype gene markers was investigated using the TIMER and GEPIA. RESULTS: SELENOP gene expression was decreased in many cancer tissues, but was upregulated in brain lower grade glioma (LGG). Furthermore, SELENOP expression was associated with a better prognosis in most cancers, but a poorer prognosis in LGG and uterine corpus endometrioid carcinoma (UCEC). Our results showed that SELENOP was correlated with infiltration level of six immune cell types, where SELENOP also showed a strong correlation with macrophages in some cancer types. However, we failed to determine macrophage polarization in 33 tumor types. SELENOP negatively regulated vascular endothelial cell proliferation in LGG and UCEC and epidermal cell differentiation in six tumor types. In contrast, upregulation was related to immune function, including T cell activation, B cell-mediated immunity, adaptive immune response and immune response regulation cell surface receptor signaling pathways in another six tumor types. CONCLUSION: These findings highlighted the tissue-specific expression, prognostic value and immune characteristics of SELENOP in pan-cancer, and provided insights for illustrating the role of SELENOP in tumorigenesis.

Quantitative Proteomic Analysis of Synovial Tissue Reveals That Upregulated OLFM4 Aggravates Inflammation in Rheumatoid Arthritis.

Tandem mass tag (TMT)-coupled liquid chromatography coupled with tandem mass spectrometry is a powerful method to investigate synovial tissue protein profiles in patients with rheumatoid arthritis (RA) and osteoarthritis (OA). Protein was isolated from synovial tissue samples of 22 patients and labeled with a TMT kit. Over 500 proteins were identified as the differential expression protein on comparing RA and OA synovial tissue, including 239 upregulated and 271 downregulated proteins. Data are available via ProteomeXchange with identifier PXD027703. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that the majority participated in the developmental processes and protein processing in the endoplasmic reticulum. Olfactomedin 4 (OLFM4), a secreted glycoprotein, in joint inflammation of RA was explored. OLFM4 was upregulated in RA synovial tissue samples. In fibroblast-like synoviocytes (FLS), inflammation cytokines, TNF-α, interleukin (IL)-1ß, and LPS can upregulate OLFM4. After OLFM4 knockdown under TNF-α stimulation, RA FLS proliferation was inhibited and the expression of CXCL9, CXCL11, and MMP-1 was decreased. Overall, the RA synovial tissue protein expression profile by proteomic analysis shows some unique targets in RA pathophysiology, and OLFM4 in FLS plays an important role in RA joint inflammation. OLFM4 can be a promising therapeutic target in RA synovial tissue.

Therapeutic Effects of Naringin in Rheumatoid Arthritis: Network Pharmacology and Experimental Validation.

Rheumatoid arthritis is a chronic autoimmune disease characterized by persistent hyperplasia of the synovial membrane and progressive erosion of articular cartilage. Disequilibrium between the proliferation and death of RA fibroblast-like synoviocytes (RA-FLSs) is the critical factor in progression of RA. Naringin has been reported to exert anti-inflammatory and antioxidant effect in acute and chronic animal models of RA. However, the therapeutic effect and underlying mechanisms of naringin in human RA-FLS remain unclear. Based on network pharmacology, the corresponding targets of naringin were identified using SwissTargetPrediction database, STITCH database, and Comparative Toxicogenomics Database. Deferentially expressed genes (DEGs) in RA were obtained from the GEO database. The protein-protein interaction (PPI) networks of intersected targets were constructed using the STRING database and visualized using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the pathways directly related to pathogenesis of RA were integrated manually. Further, in vitro studies were carried out based on network pharmacology. 99 target genes were intersected between targets of naringin and DEGs. The PPI network and topological analysis indicated that IL-6, MAPK8, MMP-9, TNF, and MAPK1 shared the highest centrality among all. GO analysis and KEGG analysis indicated that target genes were mostly enriched in (hsa05200) pathways in cancer, (hsa05161) hepatitis B, (hsa04380) osteoclast differentiation, (hsa04151) PI3K-Akt signaling pathway, and (hsa05142) Chagas disease (American trypanosomiasis). In vitro studies revealed that naringin exposure was found to promote apoptosis of RA-FLS, increased the activation of caspase-3, and increased the ratio of Bax/Bcl-2 in a dose-dependent manner. Furthermore, treatment of naringin attenuated the production of inflammatory cytokines and matrix metalloproteinases (MMPs) in TNF-ɑ-induced RA-FLS. Moreover, treatment of naringin inhibited the phosphorylation of Akt and ERK in RA-FLS. Network pharmacology provides a predicative strategy to investigate the therapeutic effects and mechanisms of herbs and compounds. Naringin inhibits inflammation and MMPs production and promotes apoptosis in RA-FLS via PI3K/Akt and MAPK/ERK signaling pathways.

Derlin-1, as a Potential Early Predictive Biomarker for Nonresponse to Infliximab Treatment in Rheumatoid Arthritis, Is Related to Autophagy.

Background: The goal of this study was to identify potential predictive biomarkers for the therapeutic effect of infliximab (IFX) in Rheumatoid arthritis (RA) and explore the potential molecular mechanism of nonresponse to IFX treatment to achieve individualized treatment of RA. Methods: Differential gene expression between IFX responders and nonresponders in the GSE58795 and GSE78068 datasets was identified. Coexpression analysis was used to identify the modules associated with nonresponse to IFX therapy for RA, and enrichment analysis was conducted on module genes. Least absolute shrink and selection operator (LASSO) regression was used to develop a gene signature for predicting the therapeutic effect of IFX in RA, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive value of the signature. Correlation analysis and single-sample gene set enrichment analysis (ssGSEA) were used to explore the potential role of the hub genes. Experimental validation was conducted in synovial tissue and RA fibroblast-like synoviocytes (RA-FLSs). Results: A total of 46 common genes were obtained among the two datasets. The yellow-green module was identified as the key module associated with nonresponse to IFX therapy for RA. We identified a 25-gene signature in GSE78068, and the AUC for the signature was 0.831 in the internal validation set and 0.924 in the GSE58795 dataset(external validation set). Derlin-1 (DERL1) was identified as the hub gene and demonstrated to be involved in the immune response and autophagy regulation. DERL1 expression was increased in RA synovial tissue compared with OA synovial tissue, and DERL1-siRNA partially inhibited autophagosome formation in RA-FLSs. Conclusion: The 25-gene signature may have potential predictive value for the therapeutic effect of IFX in RA at the beginning of IFX treatment, and autophagy may be involved in nonresponse to IFX treatment. In particular, DERL1 may be associated with the regulation of autophagy.

MiR-103a-3p and miR-107: potential biomarkers for the progression of osteoarthritis / 中华风湿病学杂志

Objective:To explore the potential Hub genes, key miRNAs, biological processes and related signaling pathways in the pathogenesis of osteoarthritis (OA), and provide bioinformatics basis for the pathogenesis and treatment of OA.Methods:The expression profiling chip of OA synovial tissue sample from Gene Expression Omnibus (GEO) were downloaded, differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. A protein-protein interaction network (PPI) was constructed. STRING and Cytoscape was used for module analysis, and the Hub gene was further identified, and further miRNAs mining of the Hub gene was carried out.Results:Finally, 9 Hub genes (SOCS3, BTRC, FBXO32, KLHL22, UBE3A, HUWE1, UBR4, ANAPC5, TRIM50) and 2 key miRNAs (hsa-miR-103a-3P, hsa-miR-107) related to the progression of OA were identified .They might be potential biomarkers for the pathogenesis of OA. We also found that signal transduction, the transcriptional positive regulation of RNA polymerase Ⅱ promoter, and protein serine/threoninase activity had a certain correlation with the pathogenesis of OA. In addition, our analysis results showed that cAMP signaling pathway and Rap1 signaling pathway were also involved in the progression of OA.Conclusion:The potential biological molecules, biological processes and related pathways identified in this study may guide us for the further research on the etiology and treatment of OA.

Clinical markers combined with HMGB1 polymorphisms to predict efficacy of conventional DMARDs in rheumatoid arthritis patients.

The efficacy of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) for rheumatoid arthritis (RA) patients was limited. However, there were no predictive markers for poor csDMARDs outcome. Clinical information of RA patients was collected and the high-mobility group box 1 (HMGB1) polymorphisms (rs4145277, rs2249825, rs1412125 and rs1045411) were examined. Among the 252 patients, 31.0% had no response of csDMARDs. Anti-citrullinated protein antibody (ACPA)-positive, C-reactive protein (CRP) and Disease Activity Score (DAS) 28- erythrocyte sedimentation rate (ESR) were the associated factors, which (DAC:DAS 28 > 4.7 and ACPA-positive and CRP > 7.1 mg/L) was used to predict poor csDMARDs outcome, the sensitivity and specificity was 87.2% and 60.9%, respectively. Among those DAC patients, the refractory RA rate in the rs2249825 GG genotype patients was 83.3%, the specificity was 98.5%. The clinical markers (DAC) and rs2249825 GG genotype can be used to predict poor csDMARDs outcome.

Identification of pivotal genes and pathways in the synovial tissue of patients with rheumatoid arthritis and osteoarthritis through integrated bioinformatic analysis.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the two most common debilitating joint disorders and although both share similar clinical manifestations, the pathogenesis of each is different and remains relatively unclear. The present study aimed to use bioinformatic analysis to identify pivotal genes and pathways involved in the pathogenesis of RA. Microarray datasets from patients with RA and OA were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified using GEO2R software; Gene Ontology analysis and pathway enrichment were analyzed using the Database for Annotation, Visualization and Integrated Discovery and the Kyoto Encylopedia for Genes and Genomes, respectively; and protein­protein interaction networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes database, and module analysis and pathway crosstalk of the PPI network was visualized using plugins of Cytoscape. In addition, the prediction of target mRNAs for differentially expressed microRNAs (DEMs) was performing using the starBase database and the identified pivotal genes were verified using reverse­transcription quantitative PCR in synovial tissue from patients with RA. A total of 566 DEGs were identified in GSE55457, GSE55235 while 23 DEMs were identified in the GSE72564 dataset. Upregulated DEGs were found to be mostly enriched in the 'Cytokine­cytokine receptor interaction' pathway, whereas downregulated DEGs were discovered to be enriched in the 'PPAR signaling pathway'. The top 25 DEGs were mostly enriched in the 'Chemokine signaling pathway'. In addition, six of the miRNA target genes were selected as potential biomarkers and a total of 24 genes were selected as potential hub genes. Experimental validation demonstrated that the expression levels of Cytotoxic T­Lymphocyte Associated Protein 4 (CTLA4), Zeta­chain­associated protein kinase 70 (ZAP70) and LCK proto­oncogene (LCK) were significantly increased, whereas HGF expression levels were decreased in RA synovial tissue. In conclusion, these findings suggest that the identified DEGs and pivotal genes in the present study may further enhance our knowledge of the underlying pathways in the pathogenesis of RA. These genes may also serve as diagnostic biomarkers and therapeutic targets for RA; however, further experimental validation is necessary following the bioinformatic analysis to determine our conclusions.

Effects of ginsenoside Rb1 on spinal cord ischemia-reperfusion injury in rats.

BACKGROUND: The aim of this study was to evaluate the effects of different doses of ginsenoside Rb1 (GRb1) pretreatment on spinal cord ischemia-reperfusion (SCII) in rats and explore the potential mechanisms about the expression of survivin protein after the intervention. METHODS: A total of 90 healthy adult Sprague-Dawley (SD) rats were randomly divided into six groups: sham-operated (n = 15), SCII model (n = 15), and GRb1-treated groups (n = 60). The GRb1-treated group was divided into four subgroups: 10 mg/kg, 20 mg/kg, 40 mg/kg, and 80 mg/kg (n = 15). The corresponding dose of GRb1 was injected intraperitoneally 30 min before operation and every day after operation. Forty-eight hours after model establishment, the neurological function of hind limbs was measured with Basso, Beattie, and Bresnahan (BBB) scale. The superoxide dismutase (SOD) and malondialdehyde (MDA) levels in serum and spinal cord tissue were detected respectively. The expression of survivin protein was observed by immunofluorescence staining. HE and TUNEL staining were used to observe neural cell injury and apoptosis, respectively, in the spinal cord of rats with SCII. RESULTS: The intervention of different doses of GRb1 could increase SOD activity and decrease MDA content in serum and spinal cord tissue, increase survivin protein expression, and decrease neuronal apoptosis. It was dose-dependent, but there was no significant change between 40 mg/kg and 80 mg/kg. CONCLUSIONS: GRb1 could reduce the cell apoptosis induced by SCII through inhibiting oxidative stress. It can also inhibit apoptosis by promoting the expression of Survivin protein. Ginsenoside Rb1 had a dose-dependent protective effect on SCII in the dose range of 10 mg/kg-40 mg/kg.