Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

Identification of Key lncRNAs, circRNAs, and mRNAs in Osteoarthritis via Bioinformatics Analysis

Osteoarthritis (OA) is a common degenerative joint disorder that adversely affects the quality of life of patients. Identification of novel diagnostic biomarkers is pivotal for the early detection and prevention of OA. Dataset GSE185059 was selected from Gene Expression Omnibus database to obtain differentially expressed lncRNAs (DE-lncRNAs), mRNAs (DE-mRNAs), and circRNAs (DE-circRNAs) between OA and normal samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as protein-protein interaction (PPI) network construction of DE-mRNAs were conducted. Hub genes were identified from PPI networks and validated by RT-qPCR. starBase database was utilized for predicting miRNAs binding with hub genes, selected DE-lncRNAs and DE-circRNAs, respectively. The competing endogenous RNA (ceRNA) networks were constructed. A total of 818 DE-mRNAs, 191 DE-lncRNAs, and 2053 DE-circRNAs were identified. The DE-mRNAs were significantly enriched in several inflammation-related GO terms and KEGG pathways such as positive regulation of cell-cell adhesion, TNF-alpha signaling pathway and NF-kappa B signaling pathway. Thirteen hub genes were identified, which were CFTR, GART, SMAD2, NCK1, TJP1, UBE2D1, EFTUD2, PRKACB, IL10, SNRPG, CHD4, RPS24, and SRSF6. OA-related DE-lncRNA/circRNA-miRNA-hub gene networks were constructed. We identified 13 hub genes and constructed the ceRNA networks related to OA, providing a theoretical basis for further research.

Comprehensive Analysis of Sphingolipid Metabolism-Related Genes in Osteoarthritic Diagnosis and Synovial Immune Dysregulation

BACKGROUND Osteoarthritis (OA) is a chronic degenerative disease characterized by synovitis and has been implicated in sphingolipid metabolism disorder. However, the role of sphingolipid metabolism pathway (SMP)-related genes in the occurrence of OA and synovial immune dysregulation remains unclear. MATERIAL AND METHODS In this study, we obtained synovium-related databases from GEO (n=40 for both healthy controls and OA) and analyzed the expression levels of SMP-related genes. Using 2 algorithms, we identified hub genes and developed a diagnostic model incorporating these hub genes to predict the occurrence of OA. Subsequently, the hub genes were further validated in peripheral blood samples from OA patients. Additionally, CIBERSORT and MCP-counter analyses were employed to explore the correlation between hub genes and immune dysregulation in OA synovium. WGCNA was used to determine enriched modules in different clusters. RESULTS Overall, the expression levels of SMP genes were upregulated in OA synovium. We identified 6 hub genes of SMP and constructed an excellent diagnostic model (AUC=0.976). The expression of re-confirmed hub genes showed associations with immune-related cell infiltration and levels of inflammatory cytokines. Furthermore, we observed heterogeneity in the expression patterns of hub genes across different clusters of OA. Notably, older patients displayed increased susceptibility to elevated levels of pain-related inflammatory cytokines and infiltration of immune cells. CONCLUSIONS The SMP-related hub genes have the potential to serve as diagnostic markers for OA patients. Moreover, the 4 hub genes of SMP demonstrate wide participation in immune dysregulation in OA synovium. The activation of different pathways is observed among different populations of patients with OA.

Exploration of effective biomarkers and infiltrating Immune cells in Osteoarthritis based on bioinformatics analysis

Osteoarthritis (OA) is a multi-factorial chronic joint disease mainly identified by synovial inflammation, cartilage damage, and degeneration. Our study applied bioinformatics analysis to uncover the immunity in OA and tried to explore the underlying immune-related molecular mechanism. First, OA-related gene-expression profiling data were retrieved from GEO database. Then, we analysed a series of datadata with using the xCell algorithm, GEO2R, enrichment analysis of SangerBox website, CytoHubba, ROC logistic regression and correlation analysis. Finally, Nine infiltrating immune cells with differential abundance between OA and normal samples were obtained. There were 42 IODEGs in OA, and their functions were associated with immune cells and corresponding biological processes. Moreover, 5 hub genes, including GREM1, NRP1, VEGFA, FYN and IL6R, were identified. Correlation analysis demonstrated that NRP1 was negatively associated with NKT cells, NRP1 and GREM1 were positively associated with aDC, VEGFA was positively associated with CD8+ naïve T cells, while VEGFA, FYN and IL6R were negatively associated with Macrophages M1. The 5 hub genes could be employed as effective diagnostic biomarkers for OA. In addition, they may participate in OA pathogenesis via interactions with infiltrating immune cells.

Identification of Novel Diagnostic Biomarkers and Classification Patterns for Osteoarthritis by Analyzing a Specific Set of Genes Related to Inflammation

Osteoarthritis (OA) is a prevalent joint disease globally. TNFA is recognized as a crucial inflammatory cytokine that plays a significant role in the pathophysiological mechanisms that occur during the progression of OA. However, the TNFA_SIGNALING_VIA_NFKB (TSVN)-related genes (TRGs) during the progression of OA remain unclear. By conducting a combinatory analysis of OA transcriptome data from three datasets, various differentially expressed TRGs were identified. The logistic regression model was used to mine hub TRGs for OA, and a nomogram prediction model was subsequently constructed using these TRGs. To identify new molecular subgroups, we performed consensus clustering. We then conducted functional analyses, including GO, KEGG, GSVA, and GSEA, to elucidate the underlying mechanisms. To determine the immune microenvironment, we applied xCell. The logistic regression analysis identified three hub TRGs (BHLHE40, BTG2, and CCNL1) as potential biomarkers for OA. Based on these TRGs, we constructed an OA predictive model. This model has demonstrated promising results in enhancing the accuracy of OA diagnosis, as evident from the ROC analysis (AUC merged dataset = 0.937, AUC validating dataset = 0.924). We identified two molecular subtypes, C1 and C2, and found that the C1 subtype showed activation of immune- and inflammation-related pathways. The involvement of TSVN in the development and progression of OA has been established. We identified several hub genes, such as BHLHE40, BTG2, and CCNL1, that may have a significant association with the progression of OA. Furthermore, our logistic regression model based on these genes has shown promising results in accurately diagnosing OA patients.

The novel angiogenesis regulator circFAM169A promotes the metastasis of colorectal cancer through the angiopoietin-2 signaling axis

BACKGROUND

Angiogenesis plays an important role in the metastasis of cancers. However, the mechanisms whereby circular RNAs (circRNAs) regulate angiogenesis and affect cancer metastasis are still unclear.

METHODS

We used gene set variation and Spearman's correlation analyses to identify novel angiogenesis-related circRNAs, including circFAM169A. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology were used to assess the potential biological function of circFAM169A. A quantitative reverse transcription-PCR (qRT-PCR) analysis of 20 pairs of colorectal cancer (CRC) samples was performed to detect the expression level of circFAM169A. Transwell assays, tube formation assays, and nude mouse metastatic tumor models were used to study the function of circFAM169A in CRC. qRT-PCR, dual-luciferase reporter gene assay, RNA antisense purification assay, and Western blot were performed to analyze the competing endogenous RNA mechanism of circFAM169A in promoting CRC angiogenesis.

RESULTS

circFAM169A was highly correlated with the hallmark of angiogenesis in CRC patients. It was up-regulated in liver metastasized CRC patients. circFAM169A overexpression promoted the angiogenesis, migration, and invasion of CRC cells while its down-regulation had the opposite effects. In vivo mouse models further highlighted the pro-metastatic role of circFAM169A in CRC. More importantly, we discovered that circFAM169A enhances the expression of angiopoietin-2 by binding to miR-518a-5p.

Gene network analyses of larvae under different egg-protecting behaviors provide novel insights into immune response mechanisms of Amphioctopus fangsiao

Amphioctopus fangsiao was a representative economic species in cephalopods, which was vulnerable to marine bacteria. Vibrio anguillarum was a highly infectious pathogen that have recently been found to infect A. fangsiao and inhibit its growth and development. There were significant differences in the immune response mechanisms between egg-protected and egg-unprotected larvae. To explore larval immunity under different egg-protecting behaviors, we infected A. fangsiao larvae with V. anguillarum for 24 h and analyzed the transcriptome data about egg-protected and egg-unprotected larvae infected with 0, 4, 12, and 24 h using weighted gene co-expression networks (WGCNA) and protein-protein interaction (PPI) networks. Network analyses revealed a series of immune response processes after infection, and identified six key modules and multiple immune-related hub genes. Meanwhile, we found that ZNF family, such as ZNF32, ZNF160, ZNF271, ZNF479, and ZNF493 might play significant roles in A. fangsiao immune response processes. We first creatively combined WGCNA and PPI network analysis to deeply explore the immune response mechanisms of A. fangsiao larvae with different egg-protecting behaviors. Our results provided further insights into the immunity of V. anguillarum infected invertebrates, and laid the foundation for exploring the immune differences among cephalopods with different egg protecting behaviors.

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages

Background

Osteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.

Objective

The study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.

Methods

To identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein-protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.

Results

A total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.

Conclusion

Overall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis.

Development and validation of cuproptosis-related genes in synovitis during osteoarthritis progress

Osteoarthritis (OA) is one of the most common refractory degenerative joint diseases worldwide. Synovitis is believed to drive joint cartilage destruction during OA pathogenesis. Cuproptosis is a novel form of copper-induced cell death. However, few studies have examined the correlations between cuproptosis-related genes (CRGs), immune infiltration, and synovitis. Therefore, we analyzed CRGs in synovitis during OA. Microarray datasets (GSE55235, GSE55457, GSE12021, GSE82107 and GSE176308) were downloaded from the Gene Expression Omnibus database. Next, we conducted differential and subtype analyses of CRGs across synovitis. Immune infiltration and correlation analyses were performed to explore the association between CRGs and immune cell abundance in synovitis. Finally, single-cell RNA-seq profiling was performed using the GSE176308 dataset to investigate the expression of CRGs in the various cell clusters. We found that the expression of five CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) was significantly increased in the OA synovium. Moreover, abundant and various types of immune cells infiltrated the synovium during OA, which was correlated with the expression of CRGs. Additionally, single-cell RNA-seq profiling revealed that the cellular composition of the synovium was complex and that their proportions varied greatly as OA progressed. The expression of CRGs differed across various cell types in the OA synovium. The current study predicted that cuproptosis may be involved in the pathogenesis of synovitis. The five screened CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) could be explored as candidate biomarkers or therapeutic targets for OA synovitis.

LncRNA TM1-3P Regulates Proliferation, Apoptosis and Inflammation of Fibroblasts in Osteoarthritis through miR-144-3p/ONECUT2 Axis

OBJECTIVE

This study explores LncRNA TM1-3P effects on the proliferation, apoptosis, and inflammatory response of fibroblasts in osteoarthritis (OA) and its underlying mechanism.

METHODS

Bioinformatics was performed to analyze OA disease-related genes, miRNA profiles, and function. The targeted regulation of LncRNA TM1-3P and miR-144-3p, ONECUT2 and miR-144-3p were analyzed by dual luciferase reporter gene assay, RNA Binding Protein Immunoprecipitation (RIP), and RNA pull down. Histopathological morphology of the knee joint was observed by hematoxylin-eosin (HE) and Annona Red O/Fast Green. The expressions of mRNAs and proteins were detected by RT-qPCR, Western blot, and immunohistochemistry. Unpaired T test was used between groups, and the one-way analysis of variance of repeated measurement data was applied for multi-group comparison, following Tukey's post-test.

RESULTS

ONECUT2 and Smurf2 genes were significantly elevated in the osteoarthritis group compared with the normal group (P < 0.001, P < 0.001). Expressions of ONECUT2 and LncRNA TM1-3P were increased, and expression of miR-144-3p was decreased in interleukin (IL)-1β-induced human fibroblast synovial cells (hFSCs) (mRNA: 1.06 ± 0.24 vs. 3.29 ± 0.73, proteins: 0.22 ± 0.03 vs. 0.46 ± 0.22, 1.23 ± 0.22 vs. 3.76 ± 0.73, 1.06 ± 0.25 vs. 0.37 ± 0.13, P < 0.01, P < 0.001, P < 0.01, P < 0.05). Overexpression of miR-144-3p down-regulated the ONECUT2 expression, reduced cell proliferation, promoted apoptosis in hFSCs induced by IL-1β (mRNA: 0.89 ± 0.14 vs. 0.15 ± 0.01, P < 0.05; proteins: 0.46 ± 0.01 vs. 0.23 ± 0.01, P < 0.001; CCK8: 1.88 ± 0.07 vs. 1.65 ± 0.07; P < 0.05; EDU: 55.82 ± 1.44 vs 40.57 ± 2.24, P < 0.05; apoptosis: 10.57 ± 0.79 vs 16.36 ± 0.35, P < 0.0001). Overexpression of LncRNA TM1-3P up-regulated the expression of ONECUT2, promoted cell proliferation, and inhibited apoptosis (mRNA: 0.9 ± 0.09 vs 1.94 ± 0.12, P < 0.05; proteins: 0.61 ± 0.05 vs 0.76 ± 0.03, P > 0.05; CCK8: 2.07 ± 0.05 vs 2.47 ± 0.06; P < 0.01; EDU: 52.67 ± 1.17 vs 60.06 ± 3.24, P < 0.05; apoptosis: 10.57 ± 0.79 vs 16.36 ± 0.35, P < 0.001), which were reversed by the overexpression of miR-144-3p treatment (mRNA: 1.82 ± 0.07 vs 0.31 ± 0.07, P < 0.0001; proteins: 0.74 ± 0.02 vs 0.35 ± 0.01, P < 0.01; CCK8: 2.41 ± 0.01 vs 1.67 ± 0.02; P < 0.0001; EDU: 66.85 ± 2.86 vs 44.68 ± 1.97, P < 0.0001; apoptosis: 7.19 ± 0.19 vs 13.36 ± 0.53, P < 0.0001). Silencing LncRNA TM1-3P attenuated the injury of knee joint tissue, down-regulated the expression of ONECUT2, Smurf2, IL-1β, IL-6, TNF-α, and improved the expression of Rap1 in rats (0.71 ± 0.04 vs 0.48 ± 0.02, 0.68 ± 0.06 vs 0.36 ± 0.02, 0.74 ± 0.03 vs 0.49 ± 0.04, 0.78 ± 0.01 vs 0.54 ± 0.03, 0.68 ± 0.02 vs 0.4 ± 0.04, 0.24 ± 0.01 vs 0.4 ± 0.03, P < 0.05, P < 0.05, P < 0.05, P < 0.01, P < 0.01, P < 0.05).

CONCLUSION

LncRNA TM1-3P improved inflammation and damage of knee joints in OA rats through miR-144-3p/ONECUT2 axis, providing a new theoretical basis for gene therapy of OA.

CCNB1 and CCNB2 involvement in the pathogenesis of psoriasis: a bioinformatics study

Objective

The cell cycle-related proteins cyclin B1 (CCNB1) and cyclin B2 (CCNB2) are potentially involved in the underlying mechanisms of psoriasis. The present study aimed to explore this possibility using bioinformatics approaches.

Methods

CCNB1 and CCNB2 protein levels were evaluated in 14 psoriasis patients and five healthy controls by enzyme-linked immunosorbent assays, and their mRNA levels were evaluated using data from four publicly available datasets (GSE53552, GSE41664, GSE14905, and GSE13355). Comparison of high- and low-expressing groups were performed to reveal CCNB1- and CCNB2-related differentially expressed genes, which were then assessed based on gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Correlation analyses between CCNB1 and CCNB2 levels and immune infiltration, as well as typical targets of psoriasis, were also performed.

Results

Overall, 12 CCNB1 and CCNB2 common immune-related targets potentially involved in psoriasis were identified. These could regulate the cell cycle of through multiple pathways. In addition, CCNB1 and CCNB2 were found to potentially support the release of key molecular targets of psoriasis through the regulation of mast cell activation and macrophage polarization.

Conclusions

These findings suggest that CCNB1 and CCNB2 may represent valuable molecular biomarkers of psoriasis, contributing to its onset and progression.