Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

Ren, Conglin; Li, Mingshuang; Du, Weibin; Lü, Jianlan; Zheng, Yang; Xu, Haipeng; Quan, Renfu

doi:10.1155/2020/6943103

Cited by 27 publications

(20 citation statements)

References 54 publications

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“…Therefore, identification and investigation of the underlying biomarkers for earlystage screening and diagnosis of cardiac hypertrophy are urgently required. Recently, data mining strategies on public access databases and integrative bioinformatics analysis have been demonstrated to be valid methods to identify potential biomarkers or even new therapeutic targets in complex diseases [13][14][15]. To the best of our knowledge, the present study was the first to identify candidate diagnostic biomarkers for hypertensive patients with LVH by using the publicly available GEO dataset and comprehensive bioinformatics approaches, which could provide novel insight into the molecular mechanism associated with the pathogenesis of cardiac hypertrophy.…”

Section: Discussionmentioning

confidence: 98%

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Tie-gang

Wang

Gan

et al. 2022

Aging

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Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machinerecursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

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Section: Discussionmentioning

confidence: 98%

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Tie-gang

Wang

Gan

et al. 2022

Aging

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“…In previous studies, the data sets in the GEO database were used for bioinformatics analysis of RA synovial tissue, such as GSE55235, GSE12021, etc. These studies are based on previous chip information, with different data sets and different genes identi ed [24,25]. To further investigate the biomarkers of synovial tissue in RA synovial tissue.…”

Section: Discussionmentioning

confidence: 99%

RNA-Seq and Network Analysis Reveal Unique Chemokine Activity Gene Expression Signatures in Synovial Tissue From Rheumatoid Arthritis

Zhang¹,

Jin²,

Chang³

et al. 2021

Preprint

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Objective: This study identified to provide a comprehensive understanding of genome-wide expression patterns of synovial tissue from rheumatoid arthritis (RA) patients to investigate the potential mechanism RA occurrence and development. Methods: The transcription profiles of 9 RA and 15 control (osteoarthritis OA) synovial tissue were generated by RNA-Seq. Gene set enrichment analysis (GSEA) was used to analyze all detected genes and differentially expressed genes (DEGs) were identified by DESeq. To further analyze the DEGs, Gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. The protein-protein interaction (PPI) network of DEGs were constructed by STRING and the hub genes were identified by topology clustering with MCODE-Cytoscape. The most important hub genes were validated by quantitative real time polymerase chain reaction (qRT-PCR). Results: A total of 17736 genes were detected and 651 DEGs were identified. For the GSEA, significantly enriched gene sets positively correlated with the RA group were CD40 signaling over-activation, Th1 cytotoxic module in C2, over-activation of immune response, adaptive immune response in C5, In C7, the up-regulation of the gene set of effective versus memory CD8 T cell is related to RA group. The down-regulation of gene set of naïve versus effective CD8 T cell is related to RA group.in C7. Biology process enrichment analysis showed that the DEGs were significantly enriched for signal transduction (P=1.52×10-08), immune response (P=1.94×10-22) and inflammatory response (P=1.11×10-11). Molecule function enrichment analysis revealed over-represented calcium ion binding (P=8.61×10-03), receptor binding (P=7.03×10-05) and chemokine activity (P=4.15×10-15). The DEGs were significantly enriched for plasma membrane (P=2.26×10-20), integral component of membrane (P=7.79×10-07), extracellular region (P=3.43×10-16) in cellular component. The KEGG pathway analysis showed that the DEGs were enriched in the cytokine-cytokine receptor interaction (P=6.86×10-21), chemokine signaling pathway (P=2.03×10-11), systemic lupus erythematosus(P=9.23×10-07), T cell receptor signaling pathway (P=6.59×10-06) and rheumatoid arthritis (P=3.24×10-05). We confirmed RA over-expressed PPI network hub genes included CXCL13, CXCL6, CCR5, CXCR5, CCR2, CXCL3, CXCL10 and RA down-regulated hub genes included SSTR1. Conclusions: The study identified and verified the DEGs between RA and OA synovial tissue which highlighted the activity of a subset of chemokine genes, thereby providing novel insights into the molecular mechanisms of RA pathogenesis and identified potential diagnostic and therapeutic targets for RA.

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“…The deconvolution approach CIBERSORT has been widely used to study various diseases, including ulcerative colitis, 7 lupus, 8 and rheumatoid arthritis, due to its validity and reliability. 9 The white blood cell gene bio-signature matrix in CIBERSORT comprises 547 genes, known as LM22, which is used to differentiate the 22 immune cell types consisting of regulatory T cells (Tregs), naive CD4 T cells, macrophages M2, activated memory CD4 T cells, plasma cells, follicular helper T cells, gamma delta T cells, CD8 T cells, macrophages M1, memory B cells, activated NK cells, macrophages M0, resting NK cells, monocytes, resting mast cells, resting memory CD4 T cells, activated mast cells, resting dendritic cells, neutrophils, naive B cells, activated dendritic cells, and eosinophils. 10 Herein, CIBERSORT was used to determine the proportions of the 22 immune cells in the IBM and normal samples of GSE3112.…”

Section: Immune Infiltration Analysismentioning

confidence: 99%

Identification of Hub Genes and Biological Pathways in Inclusion Body Myositis Using Bioinformatics Analysis

Wu¹,

Zhao²,

Zhang³

et al. 2022

IJGM

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Background: Inclusion body myositis (IBM) is a unique idiopathic inflammatory myopathy with unclear pathogenesis and poor prognosis. Although previous publications have identified some molecular biomarkers, the value of these biomarkers is unknown. Objective: To identify hub genes and signaling pathways related to IBM for understanding the IBM-related mechanisms and providing guidance for therapy development. Methods: Two microarray datasets (GSE3112 and GSE128470) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to detect differentially expressed genes (DEGs) between IBM and normal muscle tissues. The hub genes were determined using protein-protein interaction (PPI) network in Cytoscape. The specific signaling pathways and biological functions of IBM were identified using GO, KEGG, and GSEA enrichment analyses. Moreover, CIBERSORT was applied to estimate the expression level of 22 immune cell types in IBM and normal muscle tissue. The relationship between the immune cell types and hub genes was then explored. Results: A total of 219 DEGs and 10 hub genes were identified. Enrichment analyses revealed that the chemokine signaling pathway, cellular response to interferon-gamma, and P53 pathway have crucial roles in IBM. Immune infiltration analyses showed that IBM was associated with high level of CD8 T cells, Tregs, and macrophages. Finally, five potential drugs were predicted for IBM patients through CMap (connectivity map) database. Conclusion:In this study, the underlying molecular mechanisms and immunological landscape of IBM were investigated, and thus may provide new directions for future research on IBM pathogenesis.

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Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

Cited by 27 publications

References 54 publications

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

RNA-Seq and Network Analysis Reveal Unique Chemokine Activity Gene Expression Signatures in Synovial Tissue From Rheumatoid Arthritis

Identification of Hub Genes and Biological Pathways in Inclusion Body Myositis Using Bioinformatics Analysis

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