Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis

Xiong, Yuan; Mi, Bobin; Liu, Mengfei; Xue, Hang; Wu, Qinghua; Liu, Guohui

doi:10.12659/msm.915451

Cited by 34 publications

(28 citation statements)

References 35 publications

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“…23 Xiong et al used the gene expression profiles from GEO datasets to identify the molecular mechanisms and the key hub genes that may contribute to synovial inflammation in patients with rheumatoid arthritis. 24 To identify more useful prognostic biomarkers in T2DM, we used bioinformatical methods based on three gene expression profile datasets (GSE50397, GSE38642, and GSE44035). Via GEO2R and Venn software, we revealed a total of 36 commonly changed DEGs (|log2FC| > 1 and adj.…”

Section: Discussionmentioning

confidence: 99%

<p>Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis</p>

Lin¹,

Li²,

Wu³

et al. 2020

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Background: Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis. Materials and Methods: To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Results: In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine-cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway. Conclusion: Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.

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Section: Discussionmentioning

confidence: 99%

<p>Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis</p>

Lin¹,

Li²,

Wu³

et al. 2020

DMSO

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“…We downloaded four RA synovial datasets (GSE1919, GSE55235, GSE77298, and GSE55457) from the GEO database. GSE1919 [20]was based on GPL91 platforms, GSE55235 [21] was based on GPL96 platforms,The GSE77298 [22]data was based on GPL570 platforms,GSE55457 [18]was based on GPL96 platforms. As the Normal group, we used gene expression pro ling microarray of Synovial tissue from the healthy joint and expression pro ling microarray of synovial tissue from rheumatoid arthritis joint as the RA group.…”

Section: Merging and Batch Normalization Of Datamentioning

confidence: 99%

Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

Fang

et al. 2021

Preprint

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Relevance Rheumatoid arthritis (RA) is a systemic autoimmune disease with an aggressive, chronic synovial inflammation as the main pathological change. However, the specific etiology, pathogenesis, and related biomarkers in diagnosis and treatment are still not fully elucidated. This study attempts to provide new perspectives and insights into RA at the genetic, molecular, and cellular levels through the tenet of personalized medicine. Methods Gene expression profiles of four individual knee synovial tissues were downloaded from a comprehensive gene expression database, R language was used to screen for significantly differentially expressed genes (DEGs), Gene Ontology Enrichment Analysis, Kyoto Gene Encyclopedia, and Gene Set Enrichment Analysis were performed to analyze the biological functions and signaling pathways of these DEGs, STRING online database was used to establish protein-protein interaction networks, Cytoscape software to obtain ten hub genes, Goplot to get six inflammatory immune-related hub genes, and CIBERSORT algorithm to impute immune infiltration. Results Molecular pathways that play important roles in RA were obtained: Toll-like receptors, AMPK, MAPK, TNF, FoxO, TGF-beta, PI3K and NF-κB pathways, Ten hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. among which Adcy2 and Pnoc have not been reported in RA studies, suggesting that they may be worthy targets for further study. It was also found that among the synoviocytes in RA, the proportions of plasma cells, CD8 T cells, follicular helper T cells, monocytes, γ delta T cells, and M0 macrophages were higher, while the proportions of CD4 memory resting T cells, regulatory T cells (Tregs), activated NK cells, resting dendritic cells, M1 macrophages, eosinophils, activated mast cells, resting mast cells were lower in proportion, and each cell played an important role in RA. Conclusions This study may help understand the key genes, molecular pathways, the role of inflammatory immune infiltrating cells in RA’s pathogenesis and provide new targets and ideas for the diagnosis and personalized treatment of RA.

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“…The dysregulated hub genes, proteins and key pathways are representative of genomic mechanism of the process of diseases (9,10). Therefore, the mechanism of genes and proteins correlation and key pathways in the pathogenesis of ICM are necessary to improve the prevention, diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

Cao

Liu

et al. 2021

Preprint

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BackgroundIschemic cardiomyopathy (ICM) is considered to be the common cause of heart failure, which has high prevalence and mortality. This study aimed to investigate the different expressed genes (DEGs) and pathways in the pathogenesis of ICM using bioinformatics analysis.MethodsThe control and ICM datasets GSE116250，GSE46224 and GSE5406 were collected from the gene expression omnibus (GEO) database. DEGs were identified using limma package of R software and co-expressed genes were identified with Venn diagrams. Then, the gene otology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explored the biological functions and signaling pathways. Protein-protein interaction (PPI) networks were assembled with Cytoscape software to identify hub genes related to the pathogenesis of ICM.ResultsA total of 844 DEGs were screened from GSE116250, of which 447 up-regulated and 397 down-regulated genes respectively. A total of 99 DEGs were singled out from GSE46224, of which 58 up-regulated and 41 down-regulated genes respectively. 30 DEGs were screened from GSE5406, including 10 genes with up-regulated expression and 20 genes with down-regulated expression. 5 up-regulated and 3 down-regulated co-expressed DEGs were intersected in three datasets. GO and KEGG pathway analyses revealed that DEGs mainly enriched in collagen fibril organization, protein digestion and absorption, AGE-RAGE signaling pathway and other related pathways. Collagen alpha-1(III) chain (COL3A1), collagen alpha-2(I) chain (COL1A2) and lumican (LUM) are the three hub genes in all three datasets through PPI network analysis. The expression of 5 DEGs (SERPINA3, FCN3, COL3A1, HBB, MXRA5) in heart tissues by qRT-PCR results were consistent with our GEO analysis, while expression of 3 DEGs (ASPN, LUM, COL1A2) were opposite with GEO analysis.ConclusionsThese findings from this bioinformatics network analysis investigated key hub genes, which contributed to better understand the mechanism and new therapeutic targets of ICM.

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Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis

Cited by 34 publications

References 35 publications

<p>Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis</p>

<p>Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis</p>

Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

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