Gene Expression Profiles Analyzed Using Integrating RNA Sequencing, and Microarray Reveals Increased Inflammatory Response, Proliferation, and Osteoclastogenesis in Pigmented Villonodular Synovitis

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis

Background

The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.

Methods

The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results.

Results

Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA.

Conclusion

The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.

Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage

Aims

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

Methods

Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.

Results

A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms.

Conclusion

The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.

Identification of potential diagnostic biomarkers for tenosynovial giant cell tumour by integrating microarray and single-cell RNA sequencing data

PURPOSE

Tenosynovial giant cell tumour (TGCT) is a benign hyperplastic and inflammatory disease of the joint synovium or tendon sheaths, which may be misdiagnosed due to its atypical symptoms and imaging features. We aimed to identify biomarkers with high sensitivity and specificity to aid in diagnosing TGCT.

METHODS

Two scRNA-seq datasets (GSE210750 and GSE152805) and two microarray datasets (GSE3698 and GSE175626) were downloaded from the Gene Expression Omnibus (GEO) database. By integrating the scRNA-seq datasets, we discovered that the osteoclasts are abundant in TGCT in contrast to the control. The single-sample gene set enrichment analysis (ssGSEA) further validated this discovery. Differentially expressed genes (DEGs) of the GSE3698 dataset were screened and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were conducted. Osteoclast-specific up-regulated genes (OCSURGs) were identified by intersecting the osteoclast marker genes in the scRNA-seq and the up-regulated DEGs in the microarray and by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The expression levels of OCSURGs were validated by an external dataset GSE175626. Then, single gene GSEA, protein-protein interaction (PPI) network, and gene-drug network of OCSURGs were performed.

RESULT

22 seurat clusters were acquired and annotated into 10 cell types based on the scRNA-seq data. TGCT had a larger population of osteoclasts compared to the control. A total of 159 osteoclast marker genes and 104 DEGs (including 61 up-regulated genes and 43 down-regulated genes) were screened from the scRNA-seq analysis and the microarray analysis. Three OCSURGs (MMP9, SPP1, and TYROBP) were finally identified. The AUC of the ROC curve in the training and testing datasets suggested a favourable diagnostic capability. The PPI network results illustrated the protein-protein interaction of each OCSURG. Drugs that potentially target the OCSURGs were predicted by the DGIdb database.

CONCLUSION

MMP9, SPP1, and TYROBP were identified as osteoclast-specific up-regulated genes of the tenosynovial giant cell tumour via bioinformatic analysis, which had a reasonable diagnostic efficiency and served as potential drug targets.

Bioinformatics identification and integrative analysis of ferroptosis-related key lncRNAs in patients with osteoarthritis

BACKGROUND

Ferroptosis and dysregulation of long non-coding RNA (lncRNA) have been described to be strictly relevant to the pathogenesis of osteoarthritis (OA). However, the connection between ferroptosis and lncRNA in OA is poorly appreciated. Herein, we investigated the functional contribution of lncRNA markers correlated with the progression of human OA by comprehensive bioinformatics analysis of a panoramic network of competing endogenous RNA (ceRNA) based on ferroptosis-related genes (FRGs).

METHODS

FRGs-related competing endogenous RNA (ceRNA) networks were generated using differentially expressed genes based on OA-related whole transcriptome data from the Gene Expression Omnibus (GEO) database via starBase, miRTarBase, and miRWalk databases. The pivotal lncRNAs were ascertained by topological features (degree, betweenness, and closeness) and subceRNA networks were re-visualized. The expression difference of pivotal lncRNAs was verified by quantitative real-time polymerase chain reaction (qRT-PCR). The latent molecular mechanisms of the global ceRNA and subceRNA networks were uncovered by the R package clusterProfiler-based enrichment analysis.

RESULTS

A total of 98 dysregulated lncRNA-miRNA-mRNA regulatory relationships were attained in the FRGs-related panoramic ceRNA network of OA, covering 26 mRNAs, 20 miRNAs, and 20 lncRNAs. Three lncRNAs (AC011511.5, AL358072.1, and C9orf139) were ascertained as the central lncRNAs in the panoramic ceRNA network. Functional ensemble analysis illustrated that both the panoramic ceRNA network and the subceRNA network were integrally affiliated with the immune-inflammatory response, oxygen homeostasis, and cell death (apoptosis, autophagy, and ferroptosis).

CONCLUSION

Comprehensive bioinformatics analysis of the FRGs-related ceRNA network determined three molecular biomarkers of lncRNAs that might be affiliated with OA progression.

Novel CSF1R-positive tenosynovial giant cell tumor cell lines and their pexidartinib (PLX3397) and sotuletinib (BLZ945)-induced apoptosis

Tenosynovial giant cell tumor (TGCT) is a mesenchymal tumor derived from the synovium of the tendon sheath and joints, most frequently in the large joints. The standard of care for TGCTs is surgical resection. A new targeting approach for treating TGCTs has emerged from studies on the role of the CSF1/CSF1 receptor (CSF1R) in controlling cell survival and proliferation during the pathogenesis of TGCTs. We established four novel cell lines isolated from the primary tumor tissues of patients with TGCTs. The cell lines were designated Si-TGCT-1, Si-TGCT-2, Si-TGCT-3, and Si-TGCT-4, and the TGCT cells were characterized by CSF1R and CD68. These TGCT cells were then checked for cell proliferation using an MTT assay and three-dimensional spheroid. The responses to pexidartinib (PLX3397) and sotuletinib (BLZ945) were evaluated by two-dimensional MTT assays. All cells were positive for α‑smooth muscle actin (α‑SMA), fibroblast activation protein (FAP), CSF1R, and CD68. Except for Si-TGCT-4, all TGCT cells had high CSF1R expressions. The cells exhibited continuous growth as three-dimensional spheroids formed. Treatment with pexidartinib and sotuletinib inhibited TGCT cell growth and induced cell apoptosis correlated with the CSF1R level. Only Si-TGCT-4 cells demonstrated resistance to the drugs. In addition, the BAX/BCL-2 ratio increased in cells treated with pexidartinib and sotuletinib. With the four novel TGCT cell lines, we have an excellent model for further in vitro and in vivo studies.

Unbiased transcriptome mapping and modeling identify candidate genes and compounds of osteoarthritis

Osteoarthritis (OA) is a chronic degenerative joint disease characterized by progressive cartilage loss, subchondral bone remodeling, and synovial inflammation. Given that the current therapies for advanced OA patients are limited, the understanding of mechanisms and novel therapies are urgently needed. In this study, we employed the weighted gene co-expression network (WGCNA) method and the connectivity map (CMap) database to identify the candidate target genes and potential compounds. Four groups of co-expressing genes were identified as the OA-related modules. The biological annotations of these modules indicated some critical hallmarks of OA and aging, such as mitochondrial dysfunctions and abnormal energy metabolism, and the signaling pathways, such as MAPK, TNF, and PI3K/Akt signaling pathways. Some genes, such as RELA and GADD45B, were predicted to extensively involve these critical pathways, indicating their potential functions in OA mechanisms. Moreover, we constructed the co-expressing networks of modules and identified the hub genes based on network topology. GADD45B, MAFF, and MYC were identified and validated as the hub genes. Finally, anisomycin and MG-262 were predicted to target these OA-related modules, which may be the potential drugs for OA therapy. In conclusion, this study identified the significant modules, signaling pathways, and hub genes relevant to OA and highlighted the potential clinical value of anisomycin and MG-262 as novel therapies in OA management.

Immune Cell Infiltration Characteristics of Pigmented Villous Nodular Synovitis and Prediction of Potential Diagnostic Markers Based on Bioinformatics

Background

Pigmented villous nodular synovitis (PVNS) is a tumor-like proliferative disease characterized by impairment of daily activities, decreased quality of life, and a high recurrence rate. However, the specific pathological mechanisms are still ill-defined and controversial. The purpose of this study was to define potential diagnostic markers and evaluate immune cell infiltration in the pathogenesis of PVNS.

Method

The expression profile of GSE3698 was reanalyzed in the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) were identified using the R package “limma” and analyzed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Next, the DEGs were imported into the STRING database and Cytoscape to construct a protein–protein interaction (PPI) network. Then, cytoHubba and ROC curve analyses were used to determine potential diagnostic biomarkers of PVNS. Finally, we used CIBERSORT to estimate the proportions of 22 immune cell subtypes in PVNS and analyzed the correlation between diagnostic markers and infiltrating immune cells.

Result

We found 139 DEGs (including 93 upregulated genes and 46 downregulated genes). TYROBP, FCER1G, LAPTM5, and HLA-DPB1 were identified as potential diagnostic biomarkers of PVNS. Immune cell infiltration analysis indicated that neutrophils and M2 macrophages might be associated with the genesis and progression of PVNS. Furthermore, our correlation analysis of diagnostic markers and infiltrating immune cells found that TYROBP, FCER1G, LAPTM5, and HLA-DPB1 were positively correlated with M2 macrophage infiltration and that neutrophils, TYROBP, FCER1G, and LAPTM5 were negatively correlated with plasma cell infiltration.

Conclusions

We identified TYROBP, FCER1G, LAPTM5, and HLA-DPB1 as potential diagnostic markers for PVNS and concluded that immune cell infiltration plays an important role in the genesis and progression of PVNS.

Identification of SCRG1 as a Potential Therapeutic Target for Human Synovial Inflammation

Synovial inflammation of joint tissue is the most important cause of tissue damage, joint destruction, and disability and is associated with higher morbidity or mortality. Therefore, this study aims to identify key genes in osteoarthritis synovitis tissue to increase our understanding of the underlying mechanisms of osteoarthritis and identify new therapeutic targets. Five GEO datasets with a total of 41 normal synovial membrane tissues and 45 osteoarthritis synovial membrane samples were used for analysis, and seven common differential genes were identified. The classification model constructed by LASSO analysis showed that six genes including CDKN1A, FOSB, STMN2, SLC2A3, TAC, and SCRG1 can be used as biomarkers of osteoarthritis, and the SCRG1 gene shows importance in osteoarthritis. Furthermore, drug database enrichment found that these six DEGs may be the drug targets of synovitis in osteoarthritis, and Valproic Acid CTD 00006977 may be a potential targeted therapeutic drug of SCRG1. Spearman correlation analysis was performed on the SCRG1 gene, and 27 genes with consistent expression were obtained. Functional analysis showed that 27 genes were mainly involved in metabolism, complement, antigen presentation, apoptosis, and regulation of immune pathways. The co-regulatory network of TFs-miRNA suggested that the SCRG1 gene may be regulated by hsa-miR-363-3p miRNA. In conclusion, SCRG1, as a diagnostic marker of osteoarthritis, co-regulates immune-related pathways through the interaction of related proteins, playing an important role in the occurrence and development of osteoarthritis, which may be a novel drug target.

Integrated Analysis of miRNAs and Gene Expression Profiles Reveals Potential Biomarkers for Osteoarthritis

Purpose: Currently, the early diagnosis and treatment of osteoarthritis (OA) remain a challenge. In the present study, we attempted to explore potential biomarkers for the diagnosis and treatment of OA.

Methods: The differentially expressed genes (DEGs) were identified based on three mRNA datasets of synovial tissues for OA patients and normal controls downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used for evaluating gene function related categories. Then, miRNA sequencing was performed for differentially expressed miRNAs’ identification. Finally, weighted gene co-expression network analysis (WGCNA) was performed for genes detected by the three mRNA datasets and a competing endogenous RNA (ceRNA) network with DEGs and differentially expressed microRNAs (miRNAs) was constructed for central genes identification. In addition, the relationship between central gene expression and immune infiltration was analyzed, and the candidate agents for OA were predicted based on the Connectivity Map database. Quantitative RT-PCR (qRT-PCR), Western blotting analysis, and immunofluorescent staining were performed to validate the expression levels of differentially expressed miRNAs and differentially expressed target genes in normal and OA tissues and chondrocytes. MiRNA–mRNA network was also validated in chondrocytes in vitro.

Results: A total of 259 DEGs and 26 differentially expressed miRNAs were identified, among which 94 miRNA–mRNA interactions were predicted. The brown module in WGCNA was most closely correlated with the clinical traits of OA. After overlapping the brown module genes with miRNA–mRNA pairs, 27 miRNA–mRNA pairs were obtained. A ceRNA network was constructed with 5505 lncRNA–miRNA–mRNA interactions. B-cell translocation gene 2(BTG2), Abelson-related gene (ABL2), and vascular endothelial growth factor A (VEGFA) were identified to be the central genes with good predictive performance, which were significantly correlated with immune cell infiltration in OA, reflected by declined activated dendritic cells (aDCs), and elevated contents of B cells, macrophages, neutrophils, and T helper cells. Anisomycin, MG-132, thapsigargin, and lycorine were predicted to be the potential candidate agents for OA intervention. In vitro, the expression levels of differentially expressed miRNAs and biomarkers identified in the present study were consistent with the results obtained in normal or OA knee cartilage tissues and chondrocytes. Furthermore, BTG2 was identified to be negatively regulated by miR-125a-5p.

Conclusion: BTG2, ABL2, and VEGFA can be regarded as potential predictive and treatment biomarkers for OA, which might guide the clinical therapy of OA.