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

Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation

Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, a regulated non-apoptotic programmed cell death, is associated with various diseases. This study investigates ferroptosis-related molecular subtypes in OA to comprehend underlying mechanisms. The Gene Expression Omnibus datasets GSE206848, GSE55457, GSE55235, GSE77298 and GSE82107 were used utilized. Unsupervised clustering identified the ferroptosis-related gene (FRG) subtypes, and their immune characteristics were assessed. FRG signatures were derived using LASSO and SVM-RFE algorithms, forming models to evaluate OA's ferroptosis-related immune features. Three FRG clusters were found to be immunologically heterogeneous, with cluster 1 displaying robust immune response. Models identified nine key signature genes via algorithms, demonstrating strong diagnostic and prognostic performance. Finally, qRT-PCR and Western blot validated these genes, offering consistent results. In addition, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.

Echinacoside alleviates glucocorticoid induce osteonecrosis of femoral head in rats through PI3K/AKT/FOXO1 pathway

Steroid-induced osteonecrosis of the femoral head (SONFH), caused by glucocorticoid (GC) administration, is known to exhibit a high incidence worldwide. Although osteoblast apoptosis has been reported as an important cytological basis of SONFH, the precise mechanism remains elusive. Echinacoside (Ech), a natural phenylethanoid glycoside, exerts multiple beneficial effects, such as facilitation of cell proliferation and anti-inflammatory and anticancer activities. Herein, we aimed to explore the regulatory mechanism underlying glucocorticoid-induced osteoblast apoptosis and determine the protective efficacy of Ech against SONFH. We comprehensively surveyed multiple public databases to identify SONFH-related genes. Using bioinformatics analysis, we identified that the PI3K/AKT/FOXO1 signaling pathway was most strongly associated with SONFH. We examined the protective effect of Ech against SONFH using in vivo and in vitro experiments. Specifically, dexamethasone (Dex) decreased p-PI3K and p-AKT levels, which were reversed following Ech addition. Validation of the PI3K inhibitor (LY294002) and molecular docking of Ech and PI3K/AKT further indicated that Ech could directly enhance PI3K/AKT activity to alleviate Dex-induced inhibition. Interestingly, Dex upregulated the expression of FOXO1, Bax, cleaved-caspase-9, and cleaved-caspase-3 and enhanced MC3T3-E1 apoptosis; application of Ech and siRNA-FOXO1 reversed these effects. In vitro, Ech decreased the number of empty osteocytic lacunae, reduced TUNEL and FOXO1 positive cells, and improved bone microarchitecture. Our results provide robust evidence that PI3K/AKT/FOXO1 plays a crucial role in the development of SONFH. Moreover, Ech may be a promising candidate drug for the treatment of SONFH.

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

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

Prediction and analysis of osteoarthritis hub genes with bioinformatics

Background

Osteoarthritis (OA) is the most common type of arthritis. OA can cause joint pain, stiffness, and loss of function. The pathogenesis of OA is not completely clear. Moreover, there is no effective treatment, and clinical management is limited to symptomatic relief or joint surgery. This study utilized bioinformatics to analyze normal and OA articular cartilage samples to find biomarkers and therapeutic targets for OA.

Methods

The GSE169077 gene chip dataset was downloaded from the public gene chip data platform of the National Biotechnology Information Center. The dataset included 6 samples of OA tissues and 5 samples of healthy cartilage tissues. Differentially expressed genes (DEGs) were screened using the R language "limma" function package under the threshold of log2[fold change (FC)] ≥2 and a P value <0.05. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways of the target genes were enriched and analyzed using the database for annotation, visualization, and integrated discovery (DAVID), and a protein-protein interaction (PPI) network was further constructed using the search tool for the retrieval of interacting genes/proteins (STRING) database. The coexpression relationship of the genes in the module was visualized and screened with Cytoscape.

Results

A total of 27 DEGs were identified, including 9 downregulated genes and 18 upregulated genes. GO signal pathway enrichment analysis showed involvement in hypoxic response, fibrous collagen trimer, and extracellular matrix structural components. KEGG analysis demonstrated associations with protein digestion and absorption, extracellular matrix receptor interaction, and the peroxisome proliferator-activated receptor signal pathway, among several other pathways. A PPI network was obtained through STRING analysis, and the results were imported into Cytoscape software. The 27 DEGs were sequenced by the cytoHubba plug-in by various calculation methods, and 5 hub genes (COL1A1, COL1A2, POSTN, BMP1, and MMP13) were finally selected. These genes were analyzed by PPI again and annotated with GO and KEGG in different colors.

Conclusions

Bioinformatics technology effectively identified differential genes in the knee cartilage tissue of healthy controls and patients with OA, providing opportunities to further explore the mechanism and treatment of OA on a transcriptional level.

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 Biomarkers and Immune Cell Infiltration in Atopic Dermatitis by Comprehensive Bioinformatics Analysis

Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis.

Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the “Batch correction” and “RobustRankAggreg” methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the “cytoHubba” plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and lesions of AD patients, to determine the extent of immune cell infiltration. The association between the identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis.

Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the 259 DEGs and the 331 AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4+ naïve T cells, between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that the CCR7 expression level was positively correlated with the number of CD4+ naïve T cells (R = 0.42, p = 0.011).

Conclusion: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4+ naïve T cells in AD. These findings need to be corroborated in future studies.

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 and Verification of Potential Hub Genes in Amphetamine-Type Stimulant (ATS) and Opioid Dependence by Bioinformatic Analysis

Objective: Amphetamine-type stimulant (ATS) and opioid dependencies are chronic inflammatory diseases with similar symptoms and common genomics. However, their coexpressive genes have not been thoroughly investigated. We aimed to identify and verify the coexpressive hub genes and pathway involved in the pathogenesis of ATS and opioid dependencies.

Methods: The microarray of ATS- and opioid-treatment mouse models was obtained from the Gene Expression Omnibus database. GEO2R and Venn diagram were performed to identify differentially expressed genes (DEGs) and coexpressive DEGs (CDEGs). Functional annotation and protein–protein interaction network detected the potential functions. The hub genes were screened using the CytoHubba and MCODE plugin with different algorithms, and further validated by receiver operating characteristic analysis in the GSE15774 database. We also validated the hub genes mRNA levels in BV2 cells using qPCR.

Result: Forty-four CDEGs were identified between ATS and opioid databases, which were prominently enriched in the PI3K/Akt signaling pathway. The top 10 hub genes were mainly enriched in apoptotic process (CD44, Dusp1, Sgk1, and Hspa1b), neuron differentiation, migration, and proliferation (Nr4a2 and Ddit4), response to external stimulation (Fos and Cdkn1a), and transcriptional regulation (Nr4a2 and Npas4). Receiver operating characteristic (ROC) analysis found that six hub genes (Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, and Nr4a2) have an area under the curve (AUC) of more than 0.70 in GSE15774. The mRNA levels of Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, PI3K, and Akt in BV2 cells and GSE15774 with METH and heroin treatments were higher than those of controls. However, the Nr4a2 mRNA levels increased in BV2 cells and decreased in the bioinformatic analysis.

Conclusions: The identification of hub genes was associated with ATS and opioid dependencies, which were involved in apoptosis, neuron differentiation, migration, and proliferation. The PI3K/Akt signaling pathway might play a critical role in the pathogenesis of substance dependence.

Dexamethasone induces ferroptosis via P53/SLC7A11/GPX4 pathway in glucocorticoid-induced osteonecrosis of the femoral head

Recently, ferroptosis as new regulatory necrosis has attracted the scientific community. However, the study focused on the effect of ferroptosis on osteocytes in steroid (glucocorticoid)-induced osteonecrosis of the femoral head (SONFH) is still scarce. In this study, we use bioinformatic analysis to screen out differentially expressed genes (DEGs) in osteoblasts that treated by dexamethasone (Dex) in GSE10311 and found these DEGs are enriched in the ferroptosis signaling pathway. The results in vitro experiments show that Dex can induce MC3T3-E1 cells ferroptosis by down-regulating SLC7A11. Specifically, Dex inhibits the expression of SLC7A11/GPX4, decreases the activity of the intracellular antioxidant system such as intracellular glutathione (GSH), while increasing Malondialdehyde (MDA), reactive oxygen species (ROS), and lipid ROS, and reduces the volume of mitochondria, the mitochondrial ridges and a series of obvious ferroptosis features. The overexpression of SLC7A11 and the use of ferroptosis inhibitor (Fer-1) can reverse the Dex-induced MC3T3 ferroptosis. Dex can induce an increase in the expression of p53 and knocking down the expression of p53 by small interfering ribonucleic acid (siRNA) can reverse the suppression of SLC7A11 and GPX4 expression in MC3T3-E1 and MOLY4 cells, thereby reducing the production of ferroptosis. Thus, this study demonstrated that Dex induces MC3T3-E1cells ferroptosis via p53/SLC7A11/GPX4 pathway. The present finding offers novel insight to understand the underlying molecular mechanisms for glucocorticoid-induced osteonecrosis. Moreover, the suppression of ferroptosis may be a novel and promising treatment option for SONFH.