Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

DUOX1 inhibits the progression of rheumatoid arthritis by regulating the NF-κB pathway in vitro

BACKGROUND

This study investigates the role of dual oxidase 2 (DUOX1) in fibroblast-like synoviocytes associated with rheumatoid arthritis (RA) and to elucidate its potential mechanism of action.

METHOD

The anti-inflammatory effects of DUOX1 were assessed using IL-1β (interleukin-1 beta)-stimulated synovial fibroblasts (MH7A). Cell viability and migration were evaluated using the Cell Counting Kit-8 and Transwell assays, respectively. Enzyme-linked immunosorbent assay (ELISA) was performed to measure cellular inflammatory factor levels, and immunofluorescence and specific kits were used to assess reactive oxygen species (ROS) production and redox indicators. Western blotting was performed to confirm the antiarthritic mechanism of DUOX1.

RESULT

The findings revealed that the stimulation if IL-1β downregulates DUOX1 expression in MH7A cells, leading to increased proliferation, migration, inflammatory responses, and oxidative stress. Conversely, DUOX1 overexpression increased the production of IL-1β inducing excessive proliferation, migration, inflammation, and oxidative stress in MH7A cells, and inhibited the activation of the nuclear factor kappa B (NF-κB) inflammatory pathway.

CONCLUSION

DUOX1 significantly suppresses the proliferation, migration, inflammation, and oxidative stress of RA synovial cells through the inhibition of the NF-κB signaling pathway.

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Identification of cancer-associated fibroblast subtypes and prognostic model development in breast cancer: role of the RUNX1/SDC1 axis in promoting invasion and metastasis

In this study, we identified cancer-associated fibroblast (CAF) molecular subtypes and developed a CAF-based prognostic model for breast cancer (BRCA). The heterogeneity of cancer-associated fibroblasts (CAFs) and their significant involvement in the advancement of BRCA were discovered employing single-cell RNA sequencing. Notably, we discovered that the RUNX1/SDC1 axis enhances BRCA cell invasion and metastasis. RUNX1 transcriptionally upregulates SDC1, which facilitates extracellular matrix remodeling and promotes tumor cell migration. This finding highlights the vital contribution of CAFs to the tumor microenvironment and provides new potential targets for therapeutic intervention. The predictive model showcased remarkable precision in anticipating patient outcomes and could guide personalized treatment strategies.

Identification and Experimental Verification of PDK4 as a Potential Biomarker for Diagnosis and Treatment in Rheumatoid Arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by sustained joint inflammation, with an etiology that remains elusive. Achieving an early and precise diagnosis poses significant challenges. This study aims to elucidate the molecular pathways involved in RA pathogenesis by screening genes associated with its occurrence, analyzing the related molecular activities, and ultimately developing more effective molecular-level treatments for RA.

METHODS

Microarray expression profiling datasets GSE1919, GSE10500, GSE15573, GSE77298, GSE206848, and GSE236924 were sourced from the Gene Expression Omnibus (GEO) database. Samples were divided into experimental (RA) and control (normal) groups. Differentially expressed genes (DEGs) were identified using R software packages such as limma, glmnet, e1071 as well as randomForest. Cross-validation of DEGs was conducted using lasso regression and the random forest (RF) algorithm in R software to pinpoint intersecting genes that met the criteria. Among these, one gene was selected as the target for correlation analysis to identify DEGs related to the target gene. Enrichment analysis utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases and Gene Ontology (GO) data. Gene Set Enrichment Analysis (GSEA) was performed to compare the expression levels of the target gene (PDK4) across various biological pathways and functions in groups with high and low expression. The relationship between target gene expression levels and cellular immune function was assessed using the immune function score technique. The discrepancy in immune cell distribution between the control and experimental groups, as well as their correlation with target gene expression levels, was elucidated using CIBERSORT. The relationships between mRNA, lncRNA, and miRNA were depicted in the ceRNA regulation network. The expression levels of the target gene were validated using Western blot and qRT-PCR.

RESULTS

In this study, six intersecting genes meeting the criteria were identified through cross-validation, and PDK4 was chosen as the target gene for further investigation. Functional analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that PDK4-associated DEGs are primarily enriched in the PPAR signaling pathway, thereby regulating synovial cell proliferation and migration, ultimately influencing the onset and progression of rheumatoid arthritis (RA). Immune infiltration analysis suggested that eosinophil quantity may influence the progression of RA. Experimental results from PCR and Western blot confirmed the downregulation of PDK4 in the RA group.

CONCLUSION

The significant downregulation of PDK4 expression in patients diagnosed with rheumatoid arthritis (RA) was confirmed. PDK4 may function as a novel regulatory factor in the onset and progression of RA, with potential applications as a diagnostic biomarker for the condition.

From molecular subgroups to molecular targeted therapy in rheumatoid arthritis: A bioinformatics approach

1Background

Rheumatoid Arthritis (RA) is a heterogeneous autoimmune disease with multiple unidentified pathogenic factors. The inconsistency between molecular subgroups poses challenges for early diagnosis and personalized treatment strategies. In this study, we aimed to accurately distinguish RA patients at the transcriptome level using bioinformatics methods.

2Methods

We collected a total of 362 transcriptome datasets from RA patients in three independent samples from the GEO database. Consensus clustering was performed to identify molecular subgroups, and clinical features were assessed. Differential analysis was employed to annotate the biological functions of specifically upregulated genes between subgroups.

3Results

Based on consensus clustering of RA samples, we identified three robust molecular subgroups, with Subgroup III representing the high-risk subgroup and Subgroup II exhibiting a milder phenotype, possibly associated with relatively higher levels of autophagic ability. Subgroup I showed biological functions mainly related to viral infections, cellular metabolism, protein synthesis, and inflammatory responses. Subgroup II involved autophagy of mitochondria and organelles, protein localization, and organelle disassembly pathways, suggesting heterogeneity in the autophagy process of mitochondria that may play a protective role in inflammatory diseases. Subgroup III represented a high-risk subgroup with pathological processes including abnormal amyloid precursor protein activation, promotion of inflammatory response, and cell proliferation.

4Conclusion

The classification of the RA dataset revealed pathological heterogeneity among different subgroups, providing new insights and a basis for understanding the molecular mechanisms of RA, identifying potential therapeutic targets, and developing personalized treatment approaches.

Comparative Transcriptome Analysis Unveils Regulatory Factors Influencing Fatty Liver Development in Lion-Head Geese under High-Intake Feeding Compared to Normal Feeding

The lion-head goose is the only large goose species in China, and it is one of the largest goose species in the world. Lion-head geese have a strong tolerance for massive energy intake and show a priority of fat accumulation in liver tissue through special feeding. Therefore, the aim of this study was to investigate the impact of high feed intake compared to normal feeding conditions on the transcriptome changes associated with fatty liver development in lion-head geese. In this study, 20 healthy adult lion-head geese were randomly assigned to a control group (CONTROL, n = 10) and high-intake-fed group (CASE, n = 10). After 38 d of treatment, all geese were sacrificed, and liver samples were collected. Three geese were randomly selected from the CONTROL and CASE groups, respectively, to perform whole-transcriptome analysis to analyze the key regulatory genes. We identified 716 differentially expressed mRNAs, 145 differentially expressed circRNAs, and 39 differentially expressed lncRNAs, including upregulated and downregulated genes. GO enrichment analysis showed that these genes were significantly enriched in molecular function. The node degree analysis and centrality metrics of the mRNA-lncRNA-circRNA triple regulatory network indicate the presence of crucial functional nodes in the network. We identified differentially expressed genes, including HSPB9, Pgk1, Hsp70, ME2, malic enzyme, HSP90, FADS1, transferrin, FABP, PKM2, Serpin2, and PKS, and we additionally confirmed the accuracy of sequencing at the RNA level. In this study, we studied for the first time the important differential genes that regulate fatty liver in high-intake feeding of the lion-head goose. In summary, these differentially expressed genes may play important roles in fatty liver development in the lion-head goose, and the functions and mechanisms should be investigated in future studies.

[An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes]

OBJECTIVE

To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.

METHODS

The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes, and the differential genes were identified by Random forest, LASSO regression and SVM algorithms. Based on these differential genes, an artificial neural network model was constructed, and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807. The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma. The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.

RESULTS

A total of 24 differential genes were obtained, including 11 up-regulated and 13 down-regulated genes. Seven most relevant mitochondria-related genes (POLB, GSR, KRAS, NT5DC2, NOX4, IGF1, and TGM2) were screened using 3 machine learning algorithms, and the artificial neural network diagnostic model was constructed. The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis (0.740 for the verification dataset and 0.980 for cross-over validation). RT-qPCR detected significant up-regulation of POLB, GSR, KRAS, NOX4, IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma. Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells, immature B cells, resting dendritic cells, memory activated CD4+T cells, M0 macrophages, monocytes, resting memory CD4+T cells and mast cell activation.

CONCLUSION

The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Delineation and authentication of ferroptosis genes in ventilator-induced lung injury

BACKGROUND

Mechanical ventilation, a critical support strategy for individuals enduring severe respiratory failure and general anesthesia, paradoxically engenders ventilator-induced lung injury (VILI). Ferrostatin-1 mitigates lung injury via ferroptosis inhibition, yet the specific ferroptosis genes contributing significantly to VILI remain obscure.

METHODS

Leveraging the Gene Expression Omnibus database, we acquired VILI-associated datasets and identified differentially expressed genes (DEGs). To identify the hub genes, we constructed a protein-protein interaction network and used three parameters from CytoHubba. Consequently, we identified hub genes and ferroptosis genes as ferroptosis hub genes for VILI (VFHGs). We conducted enrichment analysis and established receiver operating characteristic (ROC) curves for VFHGs. Subsequently, to confirm the correctness of the VFHGs, control group mice and VILI mouse models, as well as external dataset validation, were established. For further research, a gene-miRNA network was established. Finally, the CIBERSORT algorithm was used to fill the gap in the immune infiltration changes in the lung during VILI.

RESULTS

We identified 64 DEGs and 4 VFHGs (Il6,Ptgs2,Hmox1 and Atf3) closely related to ferroptosis. ROC curves demonstrated the excellent diagnostic performance of VFHGs in VILI. PCR and external dataset validation of the VILI model demonstrated the accuracy of VFHGs. Subsequently, the gene-miRNA network was successfully established. Ultimately, an Immune cell infiltration analysis associated with VILI was generated.

CONCLUSIONS

The results emphasize the importance of 4 VFHGs and their involvement in ferroptosis in VILI, confirming their potential as diagnostic biomarkers for VILI.

LCK, FOXC1 and hsa-miR-146a-5p as potential immune effector molecules associated with rheumatoid arthritis

Rheumatoid arthritis (RA) is a type of systemic immune disease characterized by chronic inflammatory disease of the joints. However, the aetiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify potential immune effector molecules involved in RA. The three microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen 115 overlapping differentially expressed genes (DEGs). Subsequently, we constructed a protein-protein interaction (PPI) network encoded by these DEGs and identified 10 genes closely associated with RA - LCK, GZMA, GZMB, CD2, LAG3, IL-15, TNFRSF4, CD247, CCR5 and CCR7. Furthermore, in the miRNA-hub gene networks, we screened out hsa-miR-146a-5p, which is the miRNA controlling the largest number of hub genes. Finally, we found some transcription factors that closely interact with hub genes, such as FOXC1, GATA2, YY1, RUNX2, SREBF1, CEBPB and NFIC. This study successfully predicted that LCK, FOXC1 and hsa-miR-146a-5p can be used as potential immune effector molecules of RA. Our study may have potential implications for future prediction of disease progression in patients with symptomatic RA, and has important significance for the pathogenesis and targeted therapy of RA.

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.

Effects of different physical training protocols on inflammatory markers in Zymosan-induced rheumatoid arthritis in Wistar rats

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and involvement of the synovial membrane, causing joint damage and deformities. No effective drug treatment is available, and physical exercise has been utilized to alleviate the inflammatory processes. This study aimed to investigate the effects of different exercise training protocols on Zymosan-induced RA inflammatory markers in the right knee of Wistar rats. The rodents were subjected to aerobic, resisted, and combined physical training protocols with variations in the total training volume (50% or 100% of resistance and aerobic training volume) for 8 weeks. All physical training protocols reduced cachexia and systemic inflammatory processes. The histological results showed an increase in the inflammatory influx to the synovial tissue of the right knee in all physical training protocols. The rats that underwent combined physical training with reduced volume had a lower inflammatory influx compared to the other experimental groups. A reduction in the mRNA expression of inflammatory genes and an increase in anti-inflammatory gene expression were also observed. The physical training protocol associated with volume reduction attenuated systemic and synovial inflammation of the right knee, reducing the impact of Zymosan-induced RA in rats.

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

Background

Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA's diagnostic signatures and the effect of immune cell infiltration in this pathology.

Methods

Gene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA's diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells.

Results

On the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA's diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells.

Conclusions

In conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence.

Anti-CD20 Therapy Alters the Protein Signature in Experimental Murine AIH, but Not Exclusively towards Regeneration

Background: Autoimmune hepatitis (AIH) is a chronic autoimmune inflammatory disease that usually requires lifelong immunosuppression. Frequent recurrences after the discontinuation of therapy indicate that intrahepatic immune regulation is not restored by current treatments. Studies of other autoimmune diseases suggest that temporary depletion of B cells can improve disease progression in the long term. Methods: We tested a single administration of anti-CD20 antibodies to reduce B cells and the amount of IgG to induce intrahepatic immune tolerance. We used our experimental murine AIH (emAIH) model and treated the mice with anti-CD20 during the late stage of the disease. Results: After treatment, the mice showed the expected reductions in B cells and serum IgGs, but no improvements in pathology. However, all treated animals showed a highly altered serum protein expression pattern, which was a balance between inflammation and regeneration. Conclusions: In conclusion, anti-CD20 therapy did not produce clinically measurable results because it triggered inflammation, as well as regeneration, at the proteomic level. This finding suggests that anti-CD20 is ineffective as a sole treatment for AIH or emAIH.