Weighted gene co-expression network analysis reveals specific modules and hub genes related to immune infiltration of osteoarthritis

Identifying Key Genes and Functionally Enriched Pathways in Osteoporotic Patients by Weighted Gene Co-Expression Network Analysis

Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

A Potential Role of NFIL3 in Atherosclerosis

Nuclear factor interleukin-3 (NFIL3), a proline- and acidic-residue-rich (PAR) bZIP transcription factor, is called the E4 binding protein 4 (E4BP4) as well, which is relevant to regulate the circadian rhythms and the viability of cells. More and more evidence has shown that NFIL3 is associated with different cardiovascular diseases. In recent years, it has been found that NFIL3 has significant functions in the progression of atherosclerosis (AS) via the regulation of inflammatory response, macrophage polarization, some immune cells and lipid metabolism. In this overview, we sum up the function of NFIL3 during the development of AS and offer meaningful views how to treat cardiovascular disease related to AS.

Identification of Diagnostic Markers in Synovial Tissue of Osteoarthritis by Weighted Gene Coexpression Network

Osteoarthritis (OA) is a serious threat to human health. However, the etiology and pathogenesis of the disease are not fully understood. Most researchers believe that the degeneration and imbalance of articular cartilage, extracellular matrix, and subchondral bone are the fundamental causes of osteoarthritis. However, recent studies have shown that synovial lesions may precede cartilage, which may be an important precipitating factor in the early stage of OA and the whole course of the disease. This study aimed to conduct an analysis based on sequence data from the Gene Expression Omnibus (GEO) database to investigate the presence of effective biomarkers in the synovial tissue of osteoarthritis for the diagnosis and control of OA progression. In this study, the differentially expressed OA-related genes (DE-OARGs) in osteoarthritis synovial tissues were extracted in the GSE55235 and GSE55457 datasets using the Weighted Gene Co-expression Network Analysis (WGCNA) and limma. Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select the diagnostic genes based on the DE-OARGs by glmnet package. 7 genes were selected as diagnostic genes including SAT1, RLF, MAFF, SIK1, RORA, ZNF529, and EBF2. Subsequently, the diagnostic model was constructed and the results of the Area Under the Curve (AUC) demonstrated that the diagnostic model had high diagnostic performance for OA. Additionally, among the 22 immune cells of the Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and the 24 immune cells of the single sample Gene Set Enrichment Analysis (ssGSEA), 3 immune cells and 5 immune cells were different between the OA and normal samples, respectively. The expression trends of the 7 diagnostic genes were consistent in the GEO datasets and the results of the real-time reverse transcription PCR (qRT-PCR). The results of this study demonstrate that these diagnostic markers have important significance in the diagnosis and treatment of OA, and will provide further evidence for the clinical and functional studies of OA.

Integrated bioinformatics analysis of differentially expressed genes in the temporomandibular joint internal derangement

OBJECTIVES

This study aimed to identify significant mechanisms and potential treatments for temporomandibular joint internal derangement (TMJD) through integrated bioinformatics analysis.

MATERIALS AND METHODS

Gene expression data sets (GSE66864) from the Gene Expression Omnibus (GEO) database were downloaded. Differentially expressed genes (DEGs) were identified both in the treatment groups and in controls by R packages. Network analysis of protein-protein interaction (PPI) and Human Protein Atlas was used to explore DEGs' potential function. DGIdb database was utilized to gain potential drug targets.

RESULTS

In conclusion, 126 DEGs were selected for TMJD through bioinformatics analysis. Both GO and Kyoto Encyclopedia of Genes and Genomes analyses combined showed the pathways involved in TMJD. A PPI network was constructed to select the top 10 hub genes, of which five hub genes were chosen for further investigation. Moreover, the microenvironment of immune cells related to hub genes was evaluated by R packages. Macrophages play an important role in inflammation and oral-related tumors. The Human Protein Atlas analysis indicated that the five hub genes are highly related to head and neck cancer. Finally, eight potential drugs were selected for two genes using the DGIdb database.

CONCLUSION

Our integrated bioinformatics analysis identified DEGs in TMJD and provided potential ideas for further research and treatment approaches. However, experimental validation of the hub genes and potential drug targets is still needed. The key mechanisms of the identified genes and their potential roles as biomarkers or drug targets in TMJD require further investigation.

Receptor tyrosine kinase C-kit promotes a destructive phenotype of FLS in osteoarthritis via intracellular EMT signaling

BACKGROUND

Chronic inflammation, mainly derived from fibroblast-like synoviocytes (FLSs), plays a central role in the pathomechanism of osteoarthritis (OA). Recently, epithelial-mesenchymal transition (EMT) signaling was found to be activated in OA-derived FLSs with a pro-inflammatory phenotype. However, the role of EMT signaling in regulating FLS function and OA-related inflammation remains unknown.

METHODS

The synovium of OA patients were evaluated for EMT and inflammation markers. The FLSs with activated EMT signaling were co-cultured with chondrocytes (chond). Gene expression of OA synovial samples were analyzed. The role of receptor tyrosine kinase C-kit was investigated in OA-FLSs and an OA rat model. The downstream pathways driven by C-kit were explored in OA-FLSs.

RESULTS

EMT marker N-cadherin (N-CDH) was upregulated in 40.0% of the OA samples. These N-CDH+ OA samples showed higher expression of pro-inflammatory factors. In co-culture, FLSs derived from N-CDH+ OA samples induced a typical degenerative phenotype of chonds and stimulated their production of matrix degrading enzymes. C-kit was significantly upregulated and spatially co-localized with N-CDH in N-CDH+ OA samples. In OA-FLSs, C-kit activated intracellular EMT signaling and induced destructive features of OA-FLSs. In OA rat model, C-kit largely promoted synovial inflammation and cartilage destruction, whereas knocking-down C-kit significantly restored the health of OA joints. Using GSK3β S9A mutant, we demonstrated that C-kit drives EMT signaling in OA-FLS by promoting phosphorylation of GSK3β and nuclear retention of the EMT transcription factor Snail.

CONCLUSION

C-kit drives EMT signaling in OA-FLSs and promotes a destructive FLS phenotype, leading to synovial inflammation and cartilage destruction.

Candidate Marker Genes for Diagnosis of Osteoarthritis and Prediction of Their Regulatory Mechanisms

We have screened candidate marker genes for the diagnosis of osteoarthritis and predicted their regulatory mechanisms. Six expression chips of tissue samples and one expression chip of peripheral blood mononuclear cell (PMBC) samples were obtained from the GEO database. Differential analysis, GSEA, and WGCNA were performed on the integra-ted tissue sample data with batch correction. Can-didate genes were obtained from the intersection of the genes significantly related to osteoarthritis in the WGCNA and the differentially expressed genes. ROC analysis was performed on the candidate genes in the tissue and PMBC samples. Genes with AUC values greater than 0.6 were retained as final candidates, and their upstream regulatory miRNAs were predicted. A total of 106 genes with differential expression were found in osteoarthritis tissue samples, which were mainly enriched in cell cycle and p53 signalling pathways. WGCNA selected a gene module significantly correlated with the occurrence of osteoarthritis. Fourteen candidate genes were obtained from the intersection of the genes in the module and the differentially expressed genes. ROC analysis showed that among these 14 candidate genes, only ADM, CX3CR1 and GADD45A had AUC values greater than 0.6 in both tissue and PMBC samples. The AUC values of the gene set of these three genes were greater than 0.7. Multiple miRNAs were predicted to be regulators of these three genes. ADM, CX3CR1 and GADD45A have potential as diagnostic marker genes for osteoarthritis and may be regulated by multiple miRNAs.

Identification of susceptibility modules and hub genes of osteoarthritis by WGCNA analysis

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p-value<0.05 and | log2FC | > 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein–protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of pre-symptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes.

Six macrophage-associated genes in synovium constitute a novel diagnostic signature for osteoarthritis

Background

Synovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.

Methods

The OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein–protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.

Results

OA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.

Conclusions

We developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy.