Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies

Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning

Ischemic stroke (IS) constitutes a severe neurological disorder with restricted treatment alternatives. Recent investigations have disclosed that glycosylation is closely associated with the occurrence and outcome of IS. Nevertheless, data on the transcriptomic dynamics of glycosylation in IS are lacking. The objective of this study was to undertake a comprehensive exploration of glycosylation-related genes (GRGs) in IS via bioinformatics and to assess their immune characteristics. In this study, through the intersection of genes from weighted gene co-expression network analysis, GRGs from five glycosylation pathways, and DEGs from differential expression analysis, 20 candidate GRGs were identified. Subsequently, through LASSO, Random Forest, and SVM-RFE, 3 hub GRGs (F5, PPP6C, and UBE2J1) were identified. Additional, a gene diagnostic model linked to glycosylation was developed and validated. The findings indicated that the diagnostic model could effectively distinguish between IS patients and healthy individuals in the training, validation, and merging datasets, indicating clinical relevance. Subsequently, by employing unsupervised clustering analysis, IS patients were classified into three clusters, and significant disparities were witnessed in immune cell infiltration among distinct clusters. In summary, this study successfully identified hub GRGs in IS and investigated the roles of these hub genes in the immune microenvironment, indicating potential clinical applications for IS.

A Multi-omics approach to identify and validate shared genetic architecture in rheumatoid arthritis, multiple sclerosis, and type 1 diabetes: integrating GWAS, GEO, MSigDB, and scRNA-seq data

The notable comorbidity among autoimmune diseases underscores their shared genetic underpinnings, particularly evident in rheumatoid arthritis (RA), type 1 diabetes (T1D), and multiple sclerosis (MS). However, the exact components and mechanisms of this shared genetic structure remain poorly understood. Here we show that ROMO1 is a key shared genetic component among RA, MS, and T1D. Using differential gene expression (DGE) and LASSO regression analyses of bulk RNA-seq data from whole blood tissues, we identified ROMO1 as a potential shared genetic factor. A multi-sample analysis with external Gene Expression Omnibus (GEO) data revealed ROMO1's consistent association with immune cell patterns across tissues in all three diseases. Single-gene Gene Set Enrichment Analysis (GSEA) suggested ROMO1's involvement in the reactive oxygen species (ROS) pathway, which was further substantiated by conjoint analysis with 256 ROS pathway-related genes(ROSGs) from Molecular Signatures Database (MSigDB). Single-gene Receiver Operating Characteristic (ROC) analysis highlighted ROMO1's potential as a disease biomarker. Single-cell RNA sequencing (scRNA-seq) analysis showed significantly altered ROMO1 expression in monocytes and other immune cells compared to healthy control (HC). Immune infiltration analysis revealed ROMO1's significant association with monocytes across all three diseases. Furthermore, two-sample Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) data demonstrated that ROMO1 could regulate epitopes on monocytes, potentially lowering autoimmune disease risk. Our findings clarify the importance of ROMO1 in the shared genetic architecture of RA, MS, and T1D, and its underlying mechanism in disease development.

Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

Neutrophils: a Central Point of Interaction Between Immune Cells and Nonimmune Cells in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease involving activation of the immune system and the infiltration of immune cells. As the first immune cells to reach the site of inflammation, neutrophils perform their biological functions by releasing many active substances and forming neutrophil extracellular traps (NETs). The overactivated neutrophils in patients with RA not only directly damage tissues but also, more importantly, interact with various other immune cells and broadly activate innate and adaptive immunity, leading to irreversible joint damage. However, owing to the pivotal role and complex influence of neutrophils in maintaining homoeostasis, the treatment of RA by targeting neutrophils is very difficult. Therefore, a comprehensive understanding of the interaction pathways between neutrophils and various other immune cells is crucial for the development of neutrophils as a new therapeutic target for RA. In this study, the important role of neutrophils in the pathogenesis of RA through their crosstalk with various other immune cells and nonimmune cells is highlighted. The potential of epigenetic modification of neutrophils for exploring the pathogenesis of RA and developing therapeutic approaches is also discussed. In addition, several models for studying cell‒cell interactions are summarized to support further studies of neutrophils in the context of RA.

Bioinformatics combining machine learning and single-cell sequencing analysis to identify common mechanisms and biomarkers of rheumatoid arthritis and ischemic heart failure

Patients with rheumatoid arthritis (RA) have an increased risk of ischemic heart failure (IHF), but the shared mechanisms are unclear. This study analyzed RNA sequencing data from five RA and IHF datasets to identify common biological mechanisms and significant biomarkers. One hundred and seventy-seven common differentially expressed genes (CDEGs) were identified, with enrichment analysis highlighting pathways related to sarcomere organization, ventricular myocardial tissue morphogenesis, chondrocyte differentiation, prolactin signaling, hematopoietic cell lineage, and protein methyltransferases. Five hub genes (CD2, CD3D, CCL5, IL7R, and SPATA18) were identified through protein-protein interaction (PPI) network analysis and machine learning. Co-expression and immune cell infiltration analyses underscored the importance of the inflammatory immune response, with hub genes showing significant correlations with plasma cells, activated CD4+ T memory cells, monocytes, and T regulatory cells. Single-cell RNA sequencing (scRNA-seq) confirmed hub gene expression primarily in T cells, activated T cells, monocytes, and NK cells. The findings underscore the critical roles of sarcomere organization, prolactin signaling, protein methyltransferase activity, and immune responses in the progression of IHF in RA patients. These insights not only identify valuable biomarkers and therapeutic targets but also offer promising directions for early diagnosis, personalized treatments, and preventive strategies for IHF in the context of RA. Moreover, the results highlight opportunities for repurposing existing drugs and developing new therapeutic interventions, which could reduce the risk of IHF in RA patients and improve their overall prognosis.

Preliminary evidence of immune infiltration and neutrophil degranulation in peripheral blood of non-obese OSA patients related to cognitive decline

Obstructive sleep apnea (OSA) patients have varying degrees of cognitive impairment, but the specific pathogenic mechanism is still unclear. Meanwhile, poor compliance with continuous positive airway pressure (CPAP) in OSA prompts better solutions. This study aimed to identify differentially expressed genes between the non-obese OSA patients and healthy controls, and to explore potential biomarkers associated with cognitive impairment. Cohorts of healthy control (n = 20) and non-obese, treatment-naïve OSA patients (n = 20) were recruited. We collected their peripheral blood mononuclear cells and neutrophils, and their cognitive performances were evaluated by the Montreal Cognitive Assessment (MoCA). The differentially expressed genes were identified by bioinformatic analysis and confirmed by PCR. Imbalanced immune cell proportions were assessed by Cibersort. Biomarkers related to enriched cellular pathways were measured by ELISA. OSA patients showed a significant decline in overall cognitive function and were associated with higher daytime sleepiness scores. Multiple signaling pathways were enriched in the non-obese OSA cohort, including upregulation of neutrophil-degranulation. Increased monocyte proportion and decreased NK cell proportion were figured out. The relevant genes, including upregulated defensin alpha 4 (DEFA4), haptoglobin (HP), survivin (BIRC5), and suppressed interferon gamma (IFNG) expression were detected. The relative expression of DEFA4 was significantly correlated with the MoCA score and sleep parameters. Biomarkers such as myeloperoxidase (MPO), H2O2, and lipocalin-2, as representatives of neutrophils' activation, elevated significantly in the OSA group. The data demonstrated a positive correlation between MPO and oxygen desaturation index (ODI) and a negative correlation between MPO and lowest oxygen saturation (LSaO2). The level of Lipocalin-2 was positively correlated with apnea-hypopnea index (AHI) and ODI and negatively correlated with LSaO2 and MoCA score. We also observed a negative correlation between H2O2 and mean oxygen saturation (MSaO2). Degranulation of neutrophils was activated in non-obese OSA patients without other complications. The process is related to OSA severity and cognitive impairment, implying its role in pathogenesis.

Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning

Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses. Bioinformatics technologies based on high-throughput data provide a crucial foundation for comprehensively understanding the biological characteristics of ischemic stroke and discovering effective predictive targets. In this study, we evaluated gene expression data from ischemic stroke patients retrieved from the Gene Expression Omnibus (GEO) database, conducting differential expression analysis and functional analysis. Through weighted gene co-expression network analysis (WGCNA), we characterized gene modules associated with ischemic stroke. To screen candidate core genes, three machine learning algorithms were applied, including Least Absolute Shrinkage and Selection Operator (LASSO), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE), ultimately identifying five candidate core genes: MBOAT2, CKAP4, FAF1, CLEC4D, and VIM. Subsequent validation was performed using an external dataset. Additionally, the immune infiltration landscape of ischemic stroke was mapped using the CIBERSORT method, investigating the relationship between candidate core genes and immune cells in the pathogenesis of ischemic stroke, as well as the key pathways associated with the core genes. Finally, the key gene VIM was further identified and preliminarily validated through four machine learning algorithms, including generalized linear model (GLM), Extreme Gradient Boosting (XGBoost), RF, and SVM-RFE. This study contributes to advancing our understanding of biomarkers for ischemic stroke and provides a reference for the prediction and diagnosis of ischemic stroke.

A synergistic effect of triptolide and curcumin on rheumatoid arthritis by improving cell proliferation and inducing cell apoptosis via inhibition of the IL-17/NF-κB signaling pathway

Rheumatoid arthritis (RA) is a chronic, progressive, systemic autoimmune disease. While triptolide (TPL) and curcumin (CUR) are known to have multiple beneficial effects on RA, the combined effect of TPL and CUR remains unexplored. This study aimed to investigate their synergistic effect on cell proliferation and apoptosis via the IL-17/NF-κB signaling pathway. The collagen-induced arthritis (CIA) rat model was established, showing severe joint and synovial damage compared to normal rats. Treatment with TPL and CUR reduced the severity of RA in the CIA rat model and alleviated serum inflammatory cytokines, such as rheumatoid factor, IL-17, TNF-α, IL-1β, and IL-6. The elevated levels of IL-17 and NF-κB in CIA rats were also inhibited, and the resistant apoptosis was aggravated by TPL and CUR. In vitro, the improvement of cell proliferation and induction of apoptosis were observed in LPS-stimulated MH7A cells treated with TPL and CUR, associated with the inhibition of the IL-17/NF-κB signaling pathway. Taken together, a synergistic effect of TPL and CUR on RA may involve relieving symptoms, improving excessive proliferation, inducing apoptosis resistance, and inhibiting the IL-17/NF-κB signaling pathway.

Innovative biomarkers TCN2 and LY6E can significantly inhibit respiratory syncytial virus infection

BACKGROUND

Respiratory syncytial virus (RSV) is a prominent etiological agent of lower respiratory tract infections in children, responsible for approximately 80% of cases of pediatric bronchiolitis and 50% of cases of infant pneumonia. Despite notable progress in the diagnosis and management of pediatric RSV infection, the current biomarkers for early-stage detection remain insufficient to meet clinical needs. Therefore, the development of more effective biomarkers for early-stage pediatric respiratory syncytial virus infection (EPR) is imperative.

METHODS

The datasets used in this study were derived from the Gene Expression Omnibus (GEO) database. We used GSE188427 dataset as the training set to screen for biomarkers. Biomarkers of EPR were screened by Weighted Gene Co-expression Network Analysis (WGCNA), three machine-learning algorithms (LASSO regression, Random Forest, XGBoost), and other comprehensive bioinformatics analysis techniques. To evaluate the diagnostic value of these biomarkers, multiple external and internal datasets were employed as validation sets. Next, an examination was performed to investigate the relationship between the screened biomarkers and the infiltration of immune cells. Furthermore, an investigation was carried out to identify potential small molecule compounds that interact with selected diagnostic markers. Finally, we confirmed that the expression levels of the selected biomarkers exhibited a significant increase following RSV infection, and they were further identified as having antiviral properties.

RESULTS

The study found that lymphocyte antigen 6E (LY6E) and Transcobalamin-2 (TCN2) are two biomarkers with diagnostic significance in EPR. Analysis of immune cell infiltration showed that they were associated with activation of multiple immune cells. Furthermore, our analysis demonstrated that small molecules, 3'-azido-3'-deoxythymine, methotrexate, and theophylline, have the potential to bind to TCN2 and exhibit antiviral properties. These compounds may serve as promising therapeutic agents for the management of pediatric RSV infections. Additionally, our data revealed an upregulation of LY6E and TCN2 expression in PBMCs from patients with RSV infection. ROC analysis indicated that LY6E and TCN2 possessed diagnostic value for RSV infection. Finally, we confirmed that LY6E and TCN2 expression increased after RSV infection and further inhibited RSV infection in A549 and BEAS-2B cell lines. Importantly, based on TCN2, our findings revealed the antiviral properties of a potentially efficacious compound, vitamin B12.

CONCLUSION

LY6E and TCN2 are potential peripheral blood diagnostic biomarkers for pediatric RSV infection. LY6E and TCN2 inhibit RSV infection, indicating that LY6E and TCN2 are potential therapeutic target for RSV infection.

The Effectiveness and Safety of Wu Tou Decoction on Rheumatoid Arthritis-A Systematic Review and Meta-Analysis

Rheumatoid arthritis (RA) is an autoimmune disease primarily affecting the joints and requires various treatments, including medication, injection, and physiotherapy. Wu tou decoction (WTD) is a traditional Chinese medicine prescribed for RA, with several articles documenting its effectiveness in RA treatment. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of WTD for RA. We searched for randomized controlled trials (RCTs) comparing WTD with conventional treatments (including medication, injection, and physiotherapy) from its inception to May 2024. Primary outcomes were disease activity scores, including effective rate, tender joint count, and morning stiffness. Secondary outcomes comprised blood test results (erythrocyte sedimentation rate, C-reactive protein, and rheumatoid factor) and adverse events. Nineteen RCTs involving 1794 patients were included. Statistically, WTD demonstrated better improvement than conventional treatments (18 medications and 1 injection) across the effective rate, joint scale, and blood tests, regardless of the treatment type (monotherapy or combination therapy). Adverse events were reported in 11 studies, with no statistical differences observed between them. The numerical results showed that WTD may offer potential benefits for managing RA. However, the significant discrepancy between clinical practice and the low quality of the RCTs remains a limitation. Therefore, further well-designed studies with larger patient cohorts are needed to draw definitive conclusions.

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.

Identification of diagnostic genes and drug prediction in metabolic syndrome-associated rheumatoid arthritis by integrated bioinformatics analysis, machine learning, and molecular docking

Background

Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS.

Methods

Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA.

Results

MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA.

Conclusion

Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.

[Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database]

OBJECTIVE

To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms.

METHODS

The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA).

RESULTS

We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.

CONCLUSION

CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Sinomenine treats rheumatoid arthritis by inhibiting MMP9 and inflammatory cytokines expression: bioinformatics analysis and experimental validation

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease marked by inflammatory cell infiltration and joint damage. The Chinese government has approved the prescription medication sinomenine (SIN), an effective anti-inflammation drug, for treating RA. This study evaluated the possible anti-inflammatory actions of SIN in RA based on bioinformatics analysis and experiments. Six microarray datasets were acquired from the gene expression omnibus (GEO) database. We used R software to identify differentially expressed genes (DEGs) and perform function evaluations. The CIBERSORT was used to calculate the abundance of 22 infiltrating immune cells. The weighted gene co-expression network analysis (WGCNA) was used to discover genes associated with M1 macrophages. Four public datasets were used to predict the genes of SIN. Following that, function enrichment analysis for hub genes was performed. The cytoHubba and least absolute shrinkage and selection operator (LASSO) were employed to select hub genes, and their diagnostic effectiveness was predicted using the receiver operator characteristic (ROC) curve. Molecular docking was undertaken to confirm the affinity between the SIN and hub gene. Furthermore, the therapeutic efficacy of SIN was validated in LPS-induced RAW264.7 cells line using Western blot and Enzyme-linked immunosorbent assay (ELISA). The matrix metalloproteinase 9 (MMP9) was identified as the hub M1 macrophages-related biomarker in RA using bioinformatic analysis and molecular docking. Our study indicated that MMP9 took part in IL-17 and TNF signaling pathways. Furthermore, we found that SIN suppresses the MMP9 protein overexpression and pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the LPS-induced RAW264.7 cell line. In conclusion, our work sheds new light on the pathophysiology of RA and identifies MMP9 as a possible RA key gene. In conclusion, the above findings demonstrate that SIN, from an emerging research perspective, might be a potential cost-effective anti-inflammatory medication for treating RA.

New Classification of Rheumatoid Arthritis Based on Immune Cells and Clinical Characteristics

Background

Rheumatoid arthritis (RA) is a chronic systemic immune disease characterized by joint synovitis, but there are differences in clinical manifestations and serum test results among different patients.

Methods

This is a bioinformatics study. We first obtained the gene expression profile of RA and normal synovium from the database, and screened the differentially expressed immune related genes for enrichment analysis. Subsequently, we classified RA into three subtypes by unsupervised clustering of serum gene expression profiles based on immune enrichment scores. Then, the enrichment and clinical characteristics of different subtypes were analyzed. Finally, according to the infiltration of different subtypes of immune cells, diagnostic markers were screened and verified by qRT-PCR.

Results

C1 subtype is related to the increase of neutrophils, C-reactive protein and erythrocyte sedimentation rate, and joint pain is more significant in patients. C2 subtype is related to the expression of CD8+T cells and Tregs, and patients have mild joint pain symptoms. The RF value of C3 subtype is higher, and the expression of various immune cells is increased. CD4 T cells, NK cells activated, macrophages M1 and neutrophils are immune cells significantly infiltrated in synovium and serum of RA patients. IFNGR1, TRAC, IFITM1 can be used as diagnostic markers of different subtypes.

Conclusion

In this study, RA patients were divided into different immune molecular subtypes based on gene expression profile, and immune diagnostic markers were screened, which provided a new idea for the diagnosis and treatment of RA.

Wogonin inhibits the migration and invasion of fibroblast-like synoviocytes by targeting PI3K/AKT/NF-κB pathway in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is currently an autoimmune inflammatory disease with an unclear pathogenesis. Fibroblast-like synoviocytes (FLSs) have tumor-like properties, and their activation and secretion of pro-inflammatory factors are important factors in joint destruction. Wogonin (5,7-dihydroxy-8-methoxyflavone), a natural flavonoid isolated from Scutellaria baicalensis root, has been shown to have significant anti-inflammatory, anti-oxidative stress, and anti-tumor effects in a variety of diseases. However, the role of wogonin in RA has not yet been demonstrated.

PURPOSE

To investigate the inhibitory effect of wogonin on the invasive behavior of fibroblast-like synoviocytes and to explore the mechanism of action of wogonin in RA.

METHODS

CCK-8, EdU, cell migration and invasion, immunofluorescence staining, RT-qPCR, and protein blot analysis were used to study the inhibitory effects of wogonin on migration, invasion, and pro-inflammatory cytokine overexpression in the immortalized rheumatoid synovial cell line MH7A. The therapeutic effects of wogonin were validated in vivo using arthritis scores and histopathological evaluation of collagen-induced arthritis mice.

RESULTS

Wogonin inhibited the migration and invasion of MH7A cells, reduced the production of TNF-α, IL-1β, IL-6, MMP-3 and MMP-9, and increased the expression of IL-10. Moreover, wogonin also inhibited the myofibrillar differentiation of MH7A cells, increased the expression of E-cadherin (E-Cad) and decreased the expression of α-smooth muscle actin (α-SMA). In addition, wogonin treatment effectively ameliorated joint destruction in CIA mice. Further molecular mechanism studies showed that wogonin treatment significantly inhibited the activation of PI3K/AKT/NF-κB signaling pathway in TNF-α-induced arthritic FLSs.

CONCLUSION

Wogonin effectively inhibits migration, invasion and pro-inflammatory cytokine production of RA fibroblast-like synoviocytes through the PI3K/AKT/NF-κB pathway, and thus wogonin, as a natural flavonoid, has great potential for treating RA.

Weighted Gene Co-expression Network Analysis and Machine Learning Validation for Identifying Major Genes Related to Sjogren's Syndrome

Sjogren's syndrome (SS) is an autoimmune disorder characterized by dry mouth and dry eyes. Its pathogenic mechanism is currently unclear. This study aims to integrate weighted gene co-expression network analysis (WGCNA) and machine learning to identify key genes associated with SS. We downloaded 3 publicly available datasets from the GEO database comprising the gene expression data of 231 SS and 78 control cases, including GSE84844, GSE48378 and GSE51092, and carried out WGCNA to elucidate differences in the abundant genes. Candidate biomarkers for SS were then identified using a LASSO regression model. Totally 6 machine-learning models were subsequently utilized for validating the biological significance of major genes according to their expression. Finally, immune cell infiltration of the SS tissue was assessed using the CIBERSORT algorithm. A weighted gene co-expression network was built to divide genes into 10 modules. Among them, blue and red modules were most closely associated with SS, and showed significant enrichment in type I interferon signaling, cellular response to type I interferon and response to virus, etc. Combined machine learning identified 5 hub genes, including OAS1, EIF2AK2, IFITM3, TOP2A and STAT1. Immune cell infiltration analysis showed that SS was associated with CD8+ T cell, CD4+ T cell, gamma delta T cell, NK cell and dendritic cell activation. WGCNA was combined with machine learning to uncover genes that may be involved in SS pathogenesis, which can be utilized for developing SS biomarkers and appropriate therapeutic targets.

Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning

Rheumatoid arthritis (RA) is a persistent autoimmune condition characterized by synovitis and joint damage. Recent findings suggest a potential link to abnormal lactate metabolism. This study aims to identify lactate metabolism-related genes (LMRGs) in RA and investigate their correlation with the molecular mechanisms of RA immunity. Data on the gene expression profiles of RA synovial tissue samples were acquired from the gene expression omnibus (GEO) database. The RA database was acquired by obtaining the common LMRDEGs, and selecting the gene collection through an SVM model. Conducting the functional enrichment analysis, followed by immuno-infiltration analysis and protein-protein interaction networks. The results revealed that as possible markers associated with lactate metabolism in RA, KCNN4 and SLC25A4 may be involved in regulating macrophage function in the immune response to RA, whereas GATA2 is involved in the immune mechanism of DC cells. In conclusion, this study utilized bioinformatics analysis and machine learning to identify biomarkers associated with lactate metabolism in RA and examined their relationship with immune cell infiltration. These findings offer novel perspectives on potential diagnostic and therapeutic targets for RA.

Integrative analysis and validation of necroptosis-related molecular signature for evaluating diagnosis and immune features in Rheumatoid arthritis

OBJECTIVES

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that is characterized by persistent morning stiffness, joint pain, and swelling. However, there is a lack of reliable diagnostic markers and therapeutic targets that are both effective and trustworthy.

METHODS

In this study, gene expression profiles (GSE89408, GSE55235, GSE55457, and GSE77298) were obtained from the Gene Expression Omnibus database. Differentially expressed necroptosis-related genes were attained from intersection of necroptosis-related gene set, differentially expressed genes, and weighted gene co-expression network analysis. The LASSO, random forest, and SVM-RFE machine learning algorithms were utilized to further screen potential diagnostic genes for RA. Immune cell infiltration was analyzed using the CIBERSORT method. The expressions of diagnostic genes were validated through quantitative real-time PCR, western blotting, immunohistochemistry, and immunofluorescence staining in synovial tissues collected from three trauma controls and three RA patients.

RESULTS

Five core necroptosis-related genes (FAS, CYBB, TNFSF10, EIF2AK2, and BIRC2) were identified as potential biomarkers for RA. Two different necroptosis patterns based on these five genes were confirmed to significantly correlated with immune cells (especially macrophages). In vitro experiments showed significantly higher mRNA and protein expression levels of CYBB and EIF2AK2 in RA patients compared to normal controls, consistent with the bioinformatics analysis results.

CONCLUSION

Our study identified a novel necroptosis-related subtype and five diagnostic biomarkers of RA, revealed vital roles in the development and occurrence of RA, and offered potential targets for clinical diagnosis and immunotherapy.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence

BACKGROUND

People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic features and clinical value of peripheral blood biomarkers for osteoarthritis.

OBJECTIVE

The goal of this project was to investigate the diagnostic features of peripheral blood and immune cell infiltration in osteoarthritis (OA).

METHODS

Two eligible datasets (GSE63359 and GSE48556) were obtained from the GEO database to discern differentially expressed genes (DEGs). The machine learning strategy was employed to filtrate diagnostic biomarkers for OA. Additional verification was implemented by collecting clinical samples of OA. The CIBERSORT website estimated relative subsets of RNA transcripts to evaluate the immune-inflammatory states of OA. The link between specific DEGs and clinical immune-inflammatory markers was found by correlation analysis.

RESULTS

Overall, 67 robust DEGs were identified. The nuclear receptor subfamily 2 group C member 2 (NR2C2), transcription factor 4 (TCF4), stromal antigen 1 (STAG1), and interleukin 18 receptor accessory protein (IL18RAP) were identified as effective diagnostic markers of OA in peripheral blood. All four diagnostic markers showed significant increases in expression in OA. Analysis of immune cell infiltration revealed that macrophages are involved in the occurrence of OA. Candidate diagnostic markers were correlated with clinical immune-inflammatory indicators of OA patients.

CONCLUSION

We highlight that DEGs associated with immune inflammation (NR2C2, TCF4, STAG1, and IL18RAP) may be potential biomarkers for peripheral blood in OA, which are also associated with clinical immune-inflammatory indicators.

Mast Cell Involvement in the Pathogenesis of Selected Musculoskeletal Diseases

In recent years, there has been a noteworthy revival of interest in the function of mast cells (MCs) in the human body. It is now acknowledged that MCs impact a wide array of processes beyond just allergies, leading to a shift in research direction. Unfortunately, some earlier conclusions were drawn from animal models with flawed designs, particularly centered around the receptor tyrosine kinase (Kit) pathway. Consequently, several subsequent findings may have been unreliable. Thus, what is now required is a re-examination of these earlier findings. Nevertheless, the remaining data are fascinating and hold promise for a better comprehension of numerous diseases and the development of more effective therapies. As the field continues to progress, many intriguing issues warrant further investigation and analysis. For instance, exploring the bidirectional action of MCs in rheumatoid arthritis, understanding the extent of MCs' impact on symptoms associated with Ehlers-Danlos syndrome, and unraveling the exact role of the myofibroblast-mast cell-neuropeptides axis in the joint capsule during post-traumatic contractures are all captivating areas for exploration. Hence, in this review, we summarize current knowledge regarding the influence of MCs on the pathogenesis of selected musculoskeletal diseases, including rheumatoid arthritis, spondyloarthritis, psoriatic arthritis, gout, muscle and joint injuries, tendinopathy, heterotopic ossification, and Ehlers-Danlos syndrome. We believe that this review will provide in-depth information that can guide and inspire further research in this area.

Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning

Background and aim

Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers.

Methods

We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC).

Results

The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.

Conclusion

We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.

Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer

Background

Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways.

Methods

Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects' working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR.

Results

A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells.

Conclusion

The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention.

A survey of artificial intelligence in rheumatoid arthritis

The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.

Identification of ribosomal protein family as immune-cell-related biomarkers of NAFLD by bioinformatics and experimental analyses

BACKGROUND

Immune cells play an integral role in the development and progression of non-alcoholic fatty liver disease (NAFLD). This study was to identify immune-cell-related biomarkers for the diagnosis and treatment of NAFLD.

METHODS AND FINDINGS

First, we introduced human liver transcriptome data from the GEO database (GSE48452 and GSE126848) and performed a weighted gene co-expression network analysis (WGCNA) to screen out the modules related to immune cell infiltration and to identify immune-cell-related differentially expressed genes (ICR-DEGs) associated with NAFLD progression. Further, the protein-protein interaction (PPI) network of ICR-DEGs was established to obtain hub genes and subsequently, the expression trend analysis was conducted to identify immune-cell-related biomarkers of NAFLD. Finally, the mRNA expression of biomarkers was validated in a NAFLD mouse model induced by high-fat diet (HFD) feeding. In total, we identified 66 ICR-DEGs and 13 hub genes associated with NAFLD. Among them, 9 hub genes (CD247, CD74, FCGR2B, IL2RB, INPP5D, MRPL16, RPL35, RPS3A, RPS8) were correlated with the infiltrating immune cells by the Pearson correlation analysis. Subsequently, 4 immune-cell-related biomarkers (RPL35, RPS3A, RPS8, and MRPL16) with the same expression trends in GSE48452 and GSE126848 datasets were identified. These biomarkers were enriched in immune-related pathways and had a good ability to distinguish between NASH and healthy samples. Moreover, we constructed a competing endogenous RNA (ceRNA) network of biomarkers and predicted twenty potential therapeutic drugs targeting RPS3A such as taxifolin and sitagliptin. Finally, experimental validation indicated that the hepatic mRNA expression of Rpl35, Rps3A, and Rps8 was significantly decreased in NAFLD mice.

CONCLUSIONS

This study identified four ribosomal protein genes (RPL35, RPS3A, RPS8, and MRPL16) as immune-cell-related biomarkers of NAFLD, which may actively participate in the immune processes during NAFLD progression and could serve as potential targets for the diagnosis and treatment of NAFLD.

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.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS.

METHODS

Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores.

RESULTS

Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression.

CONCLUSION

Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

Construction of a ferroptosis-related signature based on seven lncRNAs for prognosis and immune landscape in clear cell renal cell carcinoma

BACKGROUND

Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC).

METHODS

In the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups.

RESULTS

A 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups.

CONCLUSION

We established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

Background

Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis.

Methods

We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the “limma” and “sva” packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells.

Results

CRTAM, CXCL13, and LRRC15 were identified as RA's potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA.

Conclusions

In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA.

Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF.

Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells.

Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen production-associated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells.

Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF.

FAM171B as a Novel Biomarker Mediates Tissue Immune Microenvironment in Pulmonary Arterial Hypertension

The purpose of this study was to uncover potential diagnostic indicators of pulmonary arterial hypertension (PAH), evaluate the function of immune cells in the pathogenesis of the disease, and find innovative treatment targets and medicines with the potential to enhance prognosis. Gene Expression Omnibus was utilized to acquire the PAH datasets. We recognized differentially expressed genes (DEGs) and investigated their functions utilizing R software. Weighted gene coexpression network analysis, least absolute shrinkage and selection operators, and support vector machines were used to identify biomarkers. The extent of immune cell infiltration in the normal and PAH tissues was determined using CIBERSORT. Additionally, the association between diagnostic markers and immune cells was analyzed. In this study, 258DEGs were used to analyze the disease ontology. Most DEGs were linked with atherosclerosis, arteriosclerotic cardiovascular disease, and lung disease, including obstructive lung disease. Gene set enrichment analysis revealed that compared to normal samples, results from PAH patients were mostly associated with ECM-receptor interaction, arrhythmogenic right ventricular cardiomyopathy, the Wnt signaling pathway, and focal adhesion. FAM171B was identified as a biomarker for PAH (area under the curve = 0.873). The mechanism underlying PAH may be mediated by nave CD4 T cells, resting memory CD4 T cells, resting NK cells, monocytes, activated dendritic cells, resting mast cells, and neutrophils, according to an investigation of immune cell infiltration. FAM171B expression was also associated with resting mast cells, monocytes, and CD8 T cells. The results suggest that PAH may be closely related to FAM171B with high diagnostic performance and associated with immune cell infiltration, suggesting that FAM171B may promote the progression of PAH by stimulating immune infiltration and immune response. This study provides valuable insights into the pathogenesis and treatment of PAH.

Single-Cell Transcriptomics Reveals Peripheral Immune Responses in Anti-Synthetase Syndrome-Associated Interstitial Lung Disease

OBJECTIVE

Interstitial lung diseases (ILDs) secondary to anti-synthetase syndrome (ASS) greatly influence the prognoses of patients with ASS. Here we aimed to investigate the peripheral immune responses to understand the pathogenesis of this condition.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from 5 patients with ASS-ILD and 3 healthy donors (HDs). Flow cytometry of PBMCs was performed to replenish the results of scRNA-seq.

RESULTS

We used scRNA-seq to depict a high-resolution visualization of cellular landscape in PBMCs from patients with ASS-ILD. Patients showed upregulated interferon responses among NK cells, monocytes, T cells, and B cells. And the ratio of effector memory CD8 T cells to naïve CD8 T cells was significantly higher in patients than that in HDs. Additionally, Th1, Th2, and Th17 cell differentiation signaling pathways were enriched in T cells. Flow cytometry analyses showed increased proportions of Th17 cells and Th2 cells, and decreased proportion of Th1 cells in patients with ASS-ILD when compared with HDs, evaluated by the expression patterns of chemokine receptors.

CONCLUSIONS

The scRNA-seq data analyses reveal that ASS-ILD is characterized by upregulated interferon responses, altered CD8 T cell homeostasis, and involvement of differentiation signaling pathways of CD4 T cells. The flow cytometry analyses show that the proportions of Th17 cells and Th2 cells are increased and the proportion of Th1 cells is decreased in patients with ASS-ILD. These findings may provide foundations of novel therapeutic targets for patients with this condition.

Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis

Aims

This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis.

Methods

Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined.

Results

A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways.

Conclusion

This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

Identification of hub genes for adult patients with sepsis via RNA sequencing

To screen out potential prognostic hub genes for adult patients with sepsis via RNA sequencing and construction of a microRNA-mRNA-PPI network and investigate the localization of these hub genes in peripheral blood monocytes. The peripheral blood of 33 subjects was subjected to microRNA and mRNA sequencing using high-throughput sequencing, and differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs) were identified by bioinformatics. Single-cell transcriptome sequencing (10 × Genomics) was further conducted. Among the samples from 23 adult septic patients and 10 healthy individuals, 20,391 genes and 1633 microRNAs were detected by RNA sequencing. In total, 1114 preliminary DEGs and 76 DEMs were obtained using DESeq2, and 454 DEGs were ultimately distinguished. A microRNA-mRNA-PPI network was constructed based on the DEGs and the top 20 DEMs, which included 10 upregulated and 10 downregulated microRNAs. Furthermore, the hub genes TLR5, FCGR1A, ELANE, GNLY, IL2RB and TGFBR3, which may be associated with the prognosis of sepsis, and their negatively correlated microRNAs, were analysed. The genes TLR5, FCGR1A and ELANE were mainly expressed in macrophages, and the genes GNLY, IL2RB and TGFBR3 were expressed specifically in T cells and natural killer cells. Parallel analysis of mRNAs and microRNAs in patients with sepsis was demonstrated to be feasible using RNA-seq. Potential hub genes and microRNAs that may be related to sepsis prognosis were identified, providing new prospects for sepsis treatment. However, further experiments are needed.

Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification

Aim

The purpose of this study was to identify potential diagnostic markers for aortic valve calcification (AVC) and to investigate the function of immune cell infiltration in this disease.

Methods

The AVC data sets were obtained from the Gene Expression Omnibus. The identification of differentially expressed genes (DEGs) and the performance of functional correlation analysis were carried out using the R software. To explore hub genes related to AVC, a protein–protein interaction network was created. Diagnostic markers for AVC were then screened and verified using the least absolute shrinkage and selection operator, logistic regression, support vector machine-recursive feature elimination algorithms, and hub genes. The infiltration of immune cells into AVC tissues was evaluated using CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. Finally, the Connectivity Map database was used to forecast the candidate small molecule drugs that might be used as prospective medications to treat AVC.

Results

A total of 337 DEGs were screened. The DEGs that were discovered were mostly related with atherosclerosis and arteriosclerotic cardiovascular disease, according to the analyses. Gene sets involved in the chemokine signaling pathway and cytokine–cytokine receptor interaction were differently active in AVC compared with control. As the diagnostic marker for AVC, fibronectin 1 (FN1) (area the curve = 0.958) was discovered. Immune cell infiltration analysis revealed that the AVC process may be mediated by naïve B cells, memory B cells, plasma cells, activated natural killer cells, monocytes, and macrophages M0. Additionally, FN1 expression was associated with memory B cells, M0 macrophages, activated mast cells, resting mast cells, monocytes, and activated natural killer cells. AVC may be reversed with the use of yohimbic acid, the most promising small molecule discovered so far.

Conclusion

FN1 can be used as a diagnostic marker for AVC. It has been shown that immune cell infiltration is important in the onset and progression of AVC, which may benefit in the improvement of AVC diagnosis and treatment.

Identification of PSMB9 and CXCL13 as Immune-related Diagnostic Markers for Rheumatoid Arthritis by Machine Learning

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory disease that causes significant physical and psychological damage. Although researchers have gained a better understanding of the mechanisms of RA, there are still difficulties in diagnosing and treating RA. We applied a data mining approach based on machine learning algorithms to explore new RA biomarkers and local immune cell status.

METHODS

We extracted six RA synovial microarray datasets from the GEO database and used bioinformatics to obtain differentially expressed genes (DEGs) and associated functional enrichment pathways. In addition, we identified potential RA diagnostic markers by machine learning strategies and validated their diagnostic ability for early RA and established RA, respectively. Next, CIBERSORT and ssGSEA analyses explored alterations in synovium-infiltrating immune cell subpopulations and immune cell functions in the RA synovium. Moreover, we examined the correlation between biomarkers and immune cells to understand their immune-related molecular mechanisms in the pathogenesis of RA.

RESULTS

We obtained 373 DEGs (232 upregulated and 141 downregulated genes) between RA and healthy controls. Enrichment analysis revealed a robust correlation between RA and immune response. Comprehensive analysis indicated PSMB9, CXCL13, and LRRC15 were possible potential markers. PSMB9 (AUC: 0.908, 95% CI: 0.853-0.954) and CXCL13 (AUC: 0.890, 95% CI: 0.836-0.937) also showed great diagnostic ability in validation dataset. Infiltrations of 16 kinds of the immune cell were changed, with macrophages being the predominant infiltrating cell type. Most proinflammatory pathways in immune cell function were activated in RA. The correlation analysis found the strongest positive correlation between CXCL13 and plasma cells, PSMB9, and macrophage M1.

CONCLUSION

There is a robust correlation between RA and local immune response. The immune-related CXCL13 and PSMB9 were identified as potential diagnostic markers for RA based on a machine learning approach. Further in-depth exploration of the target genes and associated immune cells can deepen the understanding of RA pathophysiological processes and provide new insights into diagnosing and treating RA.