Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes

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.

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.

Bioinformatics analysis and validation of the critical genes associated with adamantinomatous craniopharyngioma

Adamantinomatous craniopharyngioma (ACP) is an epithelial tumor that arises when Rathke’s pouch remains during embryonic development. The pathogenesis of ACP remains unclear, and treatment options are limited. Here, we reveal the critical genes expressed in ACP and provide a basis for further research and treatment. The raw dataset GSE94349 was downloaded from the GEO database. We selected 24 ACP and 27 matched samples from individuals with no documented tumor complications (control group). Then, we screened for differentially expressed genes (DEGs) to identify key signaling pathways and associated DEGs. A total of 470 DEGs were identified (251 upregulated and 219 downregulated). Hierarchical clustering showed that the DEGs could precisely distinguish the ACP group from the control group (CG). Gene Ontology (GO) enrichment analysis indicated that the upregulated DEGs were mainly involved in cell adhesion, inflammatory responses, and extracellular matrix management. The downregulated DEGs were primarily involved in cell junction and nervous system development. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that the critical pathway was pathways in cancer. In the PPI network, CDH1, SHH, and WNT5A had the highest degrees of interaction and were associated with the formation of ACP. CDH1 was verified as a critical gene by quantitative reverse transcription–polymerase chain reaction (qRT-PCR) in ACP and CG samples. We found that CDH1 may play an important role in the pathways in cancer signaling pathway that regulates ACP development. The CDH1 gene may be a target for future research and treatment of ACP.

Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

Background

Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the main cause of this disease. The purpose of this research was to discover genes involved in PD development.

Methods

In this study, the microarray dataset (GSE22491) provided by GEO was used for further analysis. The Limma package under R software was used to examine and assess gene expression and identify DEGs. The DAVID online tool was used to accomplish GO enrichment analysis and KEGG pathway for DEGs. Furthermore, the PPI network of these DEGs was depicted using the STRING database and analyzed through the Cytoscape to identify hub genes. Support vector machine (SVM) classifier was subsequently employed to predict the accuracy of genes.

Result

PPI network consisted of 264 nodes as well as 502 edges was generated using the DEGs recognized from the Limma package under the R software. Moreover, three genes were identified as hubs: GNB5, GNG11, and ELANE. By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved.

Conclusion

According to the findings of the study, the 3 hub genes GNB5, GNG11, and ELANE may be used as PD detection biomarkers. Moreover, the results obtained from SVM with high accuracy can be considered as PD biomarkers in further investigations.

Single-Cell Profiles of Age-Related Osteoarthritis Uncover Underlying Heterogeneity Associated With Disease Progression

Objective: Osteoarthritis (OA) is the most common chronic degenerative joint disease, which represents the leading cause of age-related disability. Here, this study aimed to depict the intercellular heterogeneity of OA synovial tissues.

Methods: Single-cell RNA sequencing (scRNA-seq) data were preprocessed and quality controlled by the Seurat package. Cell cluster was presented and cell types were annotated based on the mRNA expression of corresponding marker genes by the SingleR package. Cell-cell communication was assessed among different cell types. After integrating the GSE55235 and GSE55457 datasets, differentially expressed genes were identified between OA and normal synovial tissues. Then, differentially expressed marker genes were overlapped and their biological functions were analyzed.

Results: Totally, five immune cell subpopulations were annotated in OA synovial tissues including macrophages, dendritic cells, T cells, monocytes and B cells. Pseudo-time analysis revealed the underlying evolution process in the inflammatory microenvironment of OA synovial tissue. There was close crosstalk between five cell types according to the ligand-receptor network. The genetic heterogeneity was investigated between OA and normal synovial tissues. Furthermore, functional annotation analysis showed the intercellular heterogeneity across immune cells in OA synovial tissues.

Conclusion: This study offered insights into the heterogeneity of OA, which provided in-depth understanding of the transcriptomic diversities within synovial tissue. This transcriptional heterogeneity may improve our understanding on OA pathogenesis and provide potential molecular therapeutic targets for OA.

Comprehensive Analysis of mRNA Expression Profiling and Identification of Potential Diagnostic Biomarkers in Coronary Artery Disease

The aim of this study is to investigate mRNA expression profiling by RNA sequencing (RNA-seq) in patients with coronary artery disease (CAD) and validate differentially expressed genes (DEGs) as novel biomarkers for CAD. Transcriptome-wide mRNA expression analysis of peripheral blood mononuclear cells was performed in five CAD patients and five controls. Functional enrichment analyses, protein-protein interaction network construction, and hub gene selection were further conducted. Relative expression levels of hub genes were validated by quantitative reverse transcription PCR in larger cohorts. Spearman correlation test and multiple linear regression analysis were applied to examine the relationship between confounding factors with severity of coronary artery atherosclerosis. Receiver operating characteristic (ROC) curve analysis was adopted to identify potentially diagnostic biomarkers for CAD. A total of 527 upregulated and 653 downregulated mRNAs were identified as DEGs in CAD patients. The relative expression levels of beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC), F-box and leucine-rich repeat protein 4 (FBXL4), ubiquitin conjugating enzyme E2 D2 (UBE2D2), and ankyrin repeat and SOCS box containing 1 (ASB1) were significantly different between two groups (all p ≤ 0.05). The severity of coronary artery atherosclerosis was negatively associated with the BTRC gene relative expression level (r = -0.323, p < 0.001) and positively with UBE2D2 (r = 0.285, p < 0.001). ROC analysis of BTRC and UBE2D2 genes showed that the areas under the curve were 0.782 (95% CI: 0.720-0.845, p < 0.001) and 0.753 (95% CI: 0.681-0.824, p < 0.001), respectively. We described the characteristics of mRNA expression in the peripheral blood of CAD patients and controls by RNA-seq. Combined with Spearman correlation analysis and ROC analyses, BTRC and UBE2D2 genes had significantly diagnostic values, which may have potential to act as novel diagnostic biomarkers and therapeutic targets for CAD.

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

INTRODUCTION/OBJECTIVES

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation.

METHOD

GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction.

RESULTS

A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue.

CONCLUSION

Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

LINC01278 Sponges miR-500b-5p to Regulate the Expression of ACTG2 to Control Phenotypic Switching in Human Vascular Smooth Muscle Cells During Aortic Dissection

Background Phenotypic switching in vascular smooth muscle cells (VSMCs) is involved in the pathogenesis of aortic dissection (AD). This study aims to explore the potential mechanisms of linc01278 during VSMC phenotypic switching. Methods and Results Twelve samples (6 AD and 6 control) were used for lncRNA, microRNA, and mRNA microarray analysis. We integrated the mRNA microarray data set with GSE52093 to determine the differentially expressed genes. Bioinformatic analysis, including Gene Expression Omnibus 2R, Venn diagram analysis, gene ontology, pathway enrichment, and protein-protein interaction networks were used to identify the target lncRNA, microRNA, and mRNA involved in AD. Subsequently, we validated the bioinformatics data using techniques in molecular biology in human tissues and VSMCs. Linc01278, microRNA-500b-5p, and ACTG2 played an important role in the vascular smooth muscle contraction pathway. Linc01278 and ACTG2 were downregulated and miR-500b-5p was upregulated in AD tissues. Molecular markers of VSMC phenotypic switching, including SM22α, SMA, calponin, and MYH11, were downregulated in AD tissues. Plasmid-based overexpression and RNA interference-mediated downregulation of linc01278 weakened and enhanced VSMC proliferation and phenotypic switching, respectively. Dual-luciferase reporter assays confirmed that linc01278 regulated miR-500b-5p that directly targeted ACTG2 in HEK293T cells. Conclusions These data demonstrate that linc01278 regulates ACTG2 to control the phenotypic switch in VSMCs by sponging miR-500b-5p. This linc01278-miR-500b-5p-ACTG2 axis has a potential role in developing diagnostic markers and therapeutic targets for AD.

Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses

OBJECTIVES

Rheumatoid arthritis (RA) is the most common inflammatory arthritis in the world, but its underlying mechanism is still unclear. The present study aims to screen and verify the potential biomarkers of RA.

METHODS

We searched the Gene Expression Omnibus (GEO) database for synovial expression profiling from different RA microarray studies to perform a systematic analysis. Functional annotation of differentially expressed genes (DEGs) was conducted, including GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The expression levels of the hub genes in normal membranes and RA synovium were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot system.

RESULTS

A total of 444 differential expression genes were identified, including 172 up-regulated and 272 down-regulated genes in RA synovium compared with normal controls. The top ten hub genes; protein tyrosine phosphatase receptor type C (PTPRC), LCK proto-oncogene (LCK), cell division cycle 20 (CDC20), Jun proto-oncogene (JUN), cyclin-dependent kinase 1 (CDK1), kinesin family member 11 (KIF11), epidermal growth factor receptor (epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGFA), mitotic arrest deficient 2 like 1 (MAD2L1), and signal transducer and activator of transcription 1 (STAT1) were identified from the PPI network, and the expression level of VEGFA and EGFR was significantly increased in RA membranes (P<0.05).

CONCLUSION

Our results indicate that the hub genes VEGFA and EGFR may have essential effects during the development of RA and can be used as potential biomarkers of RA.

Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold-change | >1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

Integration of Gene Expression Profile Data of Human Epicardial Adipose Tissue from Coronary Artery Disease to Verification of Hub Genes and Pathways

Background

This study aim to identify the core pathogenic genes and explore the potential molecular mechanisms of human coronary artery disease (CAD).

Methodology

Two gene profiles of epicardial adipose tissue from CAD patients including GSE 18612 and GSE 64554 were downloaded and integrated by R software packages. All the coexpression of deferentially expressed genes (DEGs) were picked out and analyzed by DAVID online bioinformatic tools. In addition, the DEGs were totally typed into protein-protein interaction (PPI) networks to get the interaction data among all coexpression genes. Pictures were drawn by cytoscape software with the PPI networks data. CytoHubba were used to predict the hub genes by degree analysis. Finally all the top 10 hub genes and prediction genes in Molecular complex detection were analyzed by Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis. qRT-PCR were used to identified all the 10 hub genes.

Results

The top 10 hub genes calculated by the degree method were AKT1, MYC, EGFR, ACTB, CDC42, IGF1, FGF2, CXCR4, MMP2 and LYN, which relevant with the focal adhesion pathway. Module analysis revealed that the focal adhesion was also acted an important role in CAD, which was consistence with cytoHubba. All the top 10 hub genes were verified by qRT-PCR which presented that AKT1, EGFR, CDC42, FGF2, and MMP2 were significantly decreased in epicardial adipose tissue of CAD samples (p < 0.05) and MYC, ACTB, IGF1, CXCR4, and LYN were significantly increased (p < 0.05).

Conclusions

These candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of CAD.

Identification of Hub Genes and Pathways in a Rat Model of Renal Ischemia-Reperfusion Injury Using Bioinformatics Analysis of the Gene Expression Omnibus (GEO) Dataset and Integration of Gene Expression Profiles

Background

This study aimed to identify hub genes and pathways in a rat model of renal ischemia-reperfusion injury (IRI) using bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset and integration of gene expression profiles.

Material/Methods

GEO software and the GEO2R calculation method were used to analyze two mRNA profiles, including GSE 39548 and GSE 108195. The co-expression of differentially expressed genes (DEGs) were identified and searched in the DAVID and STRING databases for pathway and protein-protein interaction (PPI) analysis. Cytoscape was used to draw the PPI network. DEGs were also analyzed using the Molecular Complex Detection (MCODE) algorithm. Cytoscape and cytoHubba were used to analyze the hub genes and visualize the molecular interaction networks. Rats (n=20) included the IRI model group (n=10) and a control group (n=10). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to measure and compare the expression of the identified genes in rat renal tissue in the IRI model and the control group.

Results

Ten hub genes were identified, STAT3, CD44, ITGAM, CCL2, TIMP1, MYC, THBS1, IGF1, SOCS3, and CD14. Apart from IGF1, qRT-PCR showed that expression of these genes was significantly increased in renal tissue in the rat model of IRI. The HIF-1α signaling pathway was involved in IRI in the rat model, which was supported by MCODE analysis.

Conclusions

In a rat model of renal IRI, bioinformatics analysis of the GEO dataset and integration of gene expression profiles identified involvement of HIF-1α signaling and the STAT3 hub gene.