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

Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment using bioinformatics analysis, with a focus on immune response

The immune system is involved in the development and progression of several diseases. Type 1 diabetes mellitus (T1DM), an autoimmune disorder, influences the progression of several other conditions; however, the link between T1DM and mild cognitive impairment (MCI) remains unclear. This study investigated the underlying immune response mechanisms that contribute to the development and progression of T1DM and MCI. Microarray datasets for MCI (GSE63060) and T1DM (GSE30208) were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package. To explore the functional implications of these DEGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using ClusterProfiler. Protein-protein interaction networks for the DEGs were constructed using the STRING database and visualized using Cytoscape. The Molecular Complex Detection algorithm was used to analyze DEGs. Immune cell infiltration in patients with T1DM and MCI was analyzed using the xCell method. Gene set enrichment analysis was used to gain in-depth insights into the functional characteristics of T1DM and MCI. Immune-related genes were obtained from the GeneCards and ImmPort databases. Machine learning algorithms were used to identify potential hub genes associated with immunity for T1DM and MCI diagnosis, and the diagnostic value was assessed using the receiver operating characteristic curve. The identified genes were validated using quantitative polymerase chain reaction. In the T1DM and MCI datasets, 610 DEGs showed consistent trends, of which 232 and 378 were upregulated and downregulated, respectively. Immune response analysis revealed significant changes in the immune cells associated with MCI and T1DM. Using immune-related genes, DEGs, and machine learning techniques, we identified CD3D in the MCI and T1DM groups. We observed a gradual decline in the cognitive function of mice with T1DM as the disease progressed. CD3D expression increased with increasing disease severity; CD3D primarily affected CD4+ T cells. This study revealed a complex interaction between T1DM and MCI, providing novel insights into the intricate relationship between immune dysregulation and cognitive impairment and expanding our understanding of these two interconnected disorders. These findings will facilitate the development of therapeutic interventions and identification of potential therapeutic targets.

An autoantibody profile identified by human genome-wide protein arrays in rheumatoid arthritis

Precise diagnostic biomarkers of anticitrullination protein antibody (ACPA)-negative and early-stage RA are still to be improved. We aimed to screen autoantibodies in ACPA-negative patients and evaluated their diagnostic performance. The human genome-wide protein arrays (HuProt arrays) were used to define specific autoantibodies from the sera of 182 RA patients and 261 disease and healthy controls. Statistical analysis was performed with SPSS 17.0. In Phase I study, 51 out of 19,275 recombinant proteins covering the whole human genome were selected. In Phase II validation study, anti-ANAPC15 (anaphase promoting complex subunit 15) exhibited 41.8% sensitivity and 91.5% specificity among total RA patients. There were five autoantibodies increased in ACPA-negative RA, including anti-ANAPC15, anti-LSP1, anti-APBB1, anti-parathymosin, and anti-UBL7. Anti-parathymosin showed the highest prevalence of 46.2% (p = 0.016) in ACPA-negative early stage (<2 years) RA. To further improve the diagnostic efficacy, a prediction model was constructed with 44 autoantibodies. With increased threshold for RA calling, the specificity of the model is 90.8%, while the sensitivity is 66.1% (87.8% in ACPA-positive RA and 23.8% in ACPA-negative RA) in independent testing patients. Therefore, HuProt arrays identified RA-associated autoantibodies that might become possible diagnostic markers, especially in early stage ACPA-negative RA.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis

Objective

Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy.

Methods

scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features.

Results

By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients.

Conclusion

Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

The shared circulating diagnostic biomarkers and molecular mechanisms of systemic lupus erythematosus and inflammatory bowel disease

Background

Systemic lupus erythematosus (SLE) is a multi-organ chronic autoimmune disease. Inflammatory bowel disease (IBD) is a common chronic inflammatory disease of the gastrointestinal tract. Previous studies have shown that SLE and IBD share common pathogenic pathways and genetic susceptibility, but the specific pathogenic mechanisms remain unclear.

Methods

The datasets of SLE and IBD were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package. Weighted gene coexpression network analysis (WGCNA) was used to determine co-expression modules related to SLE and IBD. Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for co-driver genes. Using the Least AbsoluteShrinkage and Selection Operator (Lasso) regressionand Support Vector Machine-Recursive Feature Elimination (SVM-RFE), common diagnostic markers for both diseases were further evaluated. Then, we utilizedthe CIBERSORT method to assess the abundance of immune cell infiltration. Finally,we used the single-cell analysis to obtain the location of common diagnostic markers.

Results

71 common driver genes were identified in the SLE and IBD cohorts based on the DEGs and module genes. KEGG and GO enrichment results showed that these genes were closely associated with positive regulation of programmed cell death and inflammatory responses. By using LASSO regression and SVM, five hub genes (KLRF1, GZMK, KLRB1, CD40LG, and IL-7R) were ultimately determined as common diagnostic markers for SLE and IBD. ROC curve analysis also showed good diagnostic performance. The outcomes of immune cell infiltration demonstrated that SLE and IBD shared almost identical immune infiltration patterns. Furthermore, the majority of the hub genes were commonly expressed in NK cells by single-cell analysis.

Conclusion

This study demonstrates that SLE and IBD share common diagnostic markers and pathogenic pathways. In addition, SLE and IBD show similar immune cellinfiltration microenvironments which provides newperspectives for future treatment.

A comprehensive analysis of the KLRB1 expression and its clinical implication in testicular germ cell tumors: A review

Testicular germ cell tumors (TGCT) are the most common testicular malignancies. KLRB1 is considered to influence the development and progression of a number of cancers. However, it is unclear how the KLRB1 gene functions in TGCT. First, it was determined the expression level of KLRB1 in TGCT using The Cancer Genome Atlas (TCGA) (The Cancer Genome Atlas) dataset and GTEx (Genotype-Tissue Expression) dataset. The clinical significance and biological functions of KLRB1 were explored using the TCGA dataset, and we analyzed the correlation of the KLRB1 gene with tumor immunity and infiltrating immune cells using gene set variation analysis and the TIMER database. We found that the expression level of KLRB1 was upregulated in TGCT malignant tissues with the corresponding normal tissues as controls, and KLRB1 expression correlated with clinicopathologic features of TGCT. Functional enrichment analysis suggested that KLRB1 might be involved in immune response and inflammatory response. KLRB1 was highly positively correlated with natural killer cell activation in immune response and positively correlated with tumor-infiltrating immune cells. This study demonstrated for the first time the role of KLRB1 in TGCT, which may serve as a new biomarker associated with immune infiltration and provide a potential therapeutic target for the treatment of TGCT.

Comparison of inflammatory molecular mechanisms between osteoarthritis and rheumatoid arthritis via gene microarrays

Osteoarthritis (OA) and rheumatoid arthritis (RA) treatment requires exact arthritis type diagnosis. We compared inflammatory molecular mechanisms between OA and RA to introduce reliable molecular biomarkers. The GSE55235 and GSE100786 microarray datasets were acquired from the GEO. Data preprocessing and differential expression analysis were conducted in OA and RA groups and their control groups applying GEO2R. Differentially expressed genes (DEGs) with a |LogFC|>1 and adj. p<0.05 were determined. Gene ontology (GO) and signaling pathway analysis were done utilizing PANTHER and Enrichr. The suitability of gene expression alterations as biomarkers was tested using the receiver operating characteristic (ROC) curve analysis. We found 2129 DEGs between the OA and control groups and 2494 DEGs between the RA and control groups. GO on the DEGs showed enrichment in binding, cellular processes, and cellular anatomical entities in molecular functions, biological processes, and cellular components, respectively. Enrichr found the cell differentiation pathways of Th1 and Th2 only in RA. The ROC curve analysis indicated HLA-DQA1 downregulation and MAPK8IP3 upregulation as reliable biomarkers to discriminate RA from OA in peripheral blood and bone marrow samples, respectively. We found more DEGs in patients with OA than those with RA and determined inflammatory pathways and genes unique to RA as reliable biomarkers to discriminate RA from OA. Gene expression alterations associated with Th1 and Th2 cell differentiation pathways, including HLA-DQA1 downregulation and MAPK8IP3 upregulation, could be novel molecular biomarkers to diagnose RA.

Lycium ruthenicum Murr. anthocyanins inhibit hyperproliferation of synovial fibroblasts from rheumatoid patients and the mechanism study powered by network pharmacology

BACKGROUND

Rheumatoid arthritis (RA), is a typical autoimmune disease affecting nearly 1% of the world's population. The dysfunctional hyperproliferation of synovial fibroblast (SF) in articular cartilage of RA patients is considered as the essential etiology. Traditional chemotherapeutic agents for RA treatment are imperfect for their high cost and unpredictable side-effects. L. ruthenicum anthocyanins (LRAC) is a natural product that of potential for therapeutic application against RA.

METHODS

LRAC was characterized by UPLC-MS/MS. Bioinformatics analyses based on network pharmacology were applied to predict the potential targets of LRAC, and to select DEGs (differentially expressed genes) caused by RA pathogenesis from GSE77298. Interactions between LRAC and the predicted targets were evaluated by molecular docking. Effects of LRAC on SFs from RA patients were examined by in vitro assays, which were analyzed by flow cytometry and western blotting (WB).

RESULTS

LRAC was able to inhibit the abnormal proliferation and aggressive invasion of SFs from RA patients. LRAC was mainly constituted by petunidin (82.7%), with small amount of delphinidin (12.9%) and malvidin (4.4%) in terms of anthocyanidin. Bioinformatics analyses showed that in 3738 RA-related DEGs, 58 of them were collectively targeted by delphinidin, malvidin and delphinidin. AR, CDK2, CHEK1, HIF1A, CXCR4, MMP2 and MMP9, the seven hub genes constructed a central network mediating the signal transduction. Molecular docking confirmed the high affinities between the LRAC ligands and the protein receptors encoded by the hub genes. The in vitro assays validated that LRAC repressed the growth of RASF by cell cycle arresting and cell invasion paralyzing (c-Myc/p21/CDK2), initiating cell apoptosis (HIF-1α/CXCR4/Bax/Bcl-2), and inducing pyroptosis via ROS-dependent pathway (NOX4/ROS/NLRP3/IL-1β/Caspase-1).

CONCLUSION

LRAC can selectively inhibit the proliferation of RASFs, without side-effecting immunosuppression that usually occurred for RA treatment using MTX (methotrexate). These findings demonstrate the potential application of LRAC as a phytomedicine for RA treatment, and provide a valid approach for exploring natural remedies against autoimmune diseases.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy

Background

Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN.

Methods

Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated.

Results

A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb).

Conclusion

This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.

A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity

Background

The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model with bioinformatic methods and the Gene Expression Omnibus database (GEO) to improve diagnostic and therapeutic efficiency.

Methods

To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA). We used the GSVA, GSEABase, limma, ggpubr, and reshape2 packages to score immune data and investigated the links between immune cells and immunological functions by using single-sample gene set enrichment analysis (ssGSEA). The value of the core gene set and constructed model for early AS diagnosis was investigated by using receiver operating characteristic (ROC) curve analysis.

Results

Biological function and immune score analyses identified central genes related to immunity, key immune cells, key related pathways, gene modules, and the coexpression network in AS. Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8⁺ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression. Furthermore, the T cell coinhibitory pathway may be involved in AS pathogenesis.

Conclusion

The AS disease risk model constructed based on immune-related genes can guide clinical diagnosis and treatment and may help in the development of personalized immunotherapy.

Role of the granzyme family in rheumatoid arthritis: Current Insights and future perspectives

Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic inflammation that affects synovial tissues of multiple joints. Granzymes (Gzms) are serine proteases that are released into the immune synapse between cytotoxic lymphocytes and target cells. They enter target cells with the help of perforin to induce programmed cell death in inflammatory and tumor cells. Gzms may have a connection with RA. First, increased levels of Gzms have been found in the serum (GzmB), plasma (GzmA, GzmB), synovial fluid (GzmB, GzmM), and synovial tissue (GzmK) of patients with RA. Moreover, Gzms may contribute to inflammation by degrading the extracellular matrix and promoting cytokine release. They are thought to be involved in RA pathogenesis and have the potential to be used as biomarkers for RA diagnosis, although their exact role is yet to be fully elucidated. The purpose of this review was to summarize the current knowledge regarding the possible role of the granzyme family in RA, with the aim of providing a reference for future research on the mechanisms of RA and the development of new therapies.

The commonness in immune infiltration of rheumatoid arthritis and atherosclerosis: Screening for central targets via microarray data analysis

Background

Although increasing evidence has reported an increased risk of atherosclerosis (AS) in rheumatoid arthritis (RA), the communal molecular mechanism of this phenomenon is still far from being fully elucidated. Hence, this article aimed to explore the pathogenesis of RA complicated with AS.

Methods

Based on the strict inclusion/exclusion criteria, four gene datasets were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the communal differentially expressed genes (DEGs) and hub genes, comprehensive bioinformatics analysis, including functional annotation, co-expression analysis, expression validation, drug-gene prediction, and TF-mRNA-miRNA regulatory network construction, was conducted. Moreover, the immune infiltration of RA and AS was analyzed and compared based on the CIBERSORT algorithm, and the correlation between hub genes and infiltrating immune cells was evaluated in RA and AS respectively.

Results

A total of 54 upregulated and 12 downregulated communal DEGs were screened between GSE100927 and GSE55457, and functional analysis of these genes indicated that the potential pathogenesis lies in immune terms. After the protein-protein interaction (PPI) network construction, a total of six hub genes (CCR5, CCR7, IL7R, PTPRC, CD2, and CD3D) were determined as hub genes, and the subsequent comprehensive bioinformatics analysis of the hub genes re-emphasized the importance of the immune system in RA and AS. Additionally, three overlapping infiltrating immune cells were found between RA and AS based on the CIBERSORT algorithm, including upregulated memory B cells, follicular helper T cells and γδT cells.

Conclusions

Our study uncover the communal central genes and commonness in immune infiltration between RA and AS, and the analysis of six hub genes and three immune cells profile might provide new insights into potential pathogenesis therapeutic direction of RA complicated with AS.

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

Methods

Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes.

Results

A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis.

Conclusion

15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.

Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study

Type 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were obtained, and whole transcriptome sequencing (RNA-seq) was performed. Samples were divided into two groups: diabetes mellitus (DM) versus non-DM groups and unstable versus stable groups. The Limma package in R was used to identify lncRNAs, circRNAs, and mRNAs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) network creation, and module generation were performed for differentially expressed mRNAs. Cytoscape was used to create a transcription factor (TF)-mRNA regulatory network, lncRNA/circRNA-mRNA co-expression network, and a competitive endogenous RNA (ceRNA) network. The GSE118481 dataset and RT-qPCR were used to verify potential mRNAs.The regulatory network was constructed based on the verified core genes and the relationships were extracted from the above network. In total, 180 differentially expressed lncRNAs, 343 circRNAs, and 1092 mRNAs were identified in the DM versus non-DM group; 240 differentially expressed lncRNAs, 390 circRNAs, and 677 mRNAs were identified in the unstable versus stable group. Five circRNAs, 14 lncRNAs, and 171 mRNAs that were common among all four groups changed in the same direction. GO/KEGG functional enrichment analysis showed that 171 mRNAs were mainly related to biological processes, such as immune responses, inflammatory responses, and cell adhesion. Five circRNAs, 14 lncRNAs, 46 miRNAs, and 54 mRNAs in the ceRNA network formed a regulatory relationship. C22orf34-hsa-miR-6785-5p-RAB37, hsacirc_013887-hsa-miR-6785-5p/hsa-miR-4763-5p/hsa-miR-30b-3p-RAB37, MIR4435-1HG-hsa-miR-30b-3p-RAB37, and GAS5-hsa-miR-30b-3p-RAB37 may be potential RNA regulatory pathways. Seven upregulated mRNAs were verified using the GSE118481 dataset and RT-qPCR. The regulatory network included seven mRNAs, five circRNAs, six lncRNAs, and 14 TFs. We propose five circRNAs (hsacirc_028744, hsacirc_037219, hsacirc_006308, hsacirc_013887, and hsacirc_045622), six lncRNAs (EPB41L4A-AS1, LINC00969, GAS5, MIR4435-1HG, MIR503HG, and SNHG16), and seven mRNAs (RAB37, CCR7, CD3D, TRAT1, VWF, ICAM2, and TMEM244) as potential biomarkers related to the progression of T2DM complicated with CAP. The constructed ceRNA network has important implications for potential RNA regulatory pathways.