scholarly journals Prediction of Mechanosensitive Genes in Vascular Endothelial Cells Under High Wall Shear Stress

2022 ◽  
Vol 12 ◽  
Author(s):  
Lei Shen ◽  
Kaige Zhou ◽  
Hong Liu ◽  
Jie Yang ◽  
Shuqi Huang ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Shear Stress ◽  
Microarray Data ◽  
Vascular Endothelial Cells ◽  
Enrichment Analysis ◽  
Atherosclerotic Plaques ◽  
Data Sets ◽  
Vascular Endothelial ◽  
Ppi Network ◽  
Hub Genes

Objective: The vulnerability of atherosclerotic plaques is among the leading cause of ischemic stroke. High wall shear stress (WSS) promotes the instability of atherosclerotic plaques by directly imparting mechanical stimuli, but the specific mechanisms remain unclear. We speculate that modulation of mechanosensitive genes may play a vital role in accelerating the development of plaques. The purpose of this study was to find mechanosensitive genes in vascular endothelial cells (ECs) through combining microarray data with bioinformatics technology and further explore the underlying dynamics–related mechanisms that cause the progression and destabilization of atherosclerotic plaques.Methods: Microarray data sets for human vascular ECs under high and normal WSS were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified through the R language. The performance of enrichment analysis and protein–protein interaction (PPI) network presented the biological function and signaling pathways of the DEGs. Hub genes were identified based on the PPI network and validated by GEO data sets. Predicted transcription factor (TF) genes and miRNAs interaction with potential mechanosensitive genes were identified by NetworkAnalyst.Results: A total of 260 DEGs, 121 upregulated and 139 downregulated genes, were screened between high and normal WSS from GSE23289. A total of 10 hub genes and four cluster modules were filtered out based on the PPI network. The enrichment analysis showed that the biological functions of the hub genes were mainly involved in responses to unfolded protein and topologically incorrect protein, and t to endoplasmic reticulum stress. The significant pathways associated with the hub genes were those of protein processing in the endoplasmic reticulum, antigen processing, and presentation. Three out of the 10 hub genes, namely, activated transcription factor 3 (ATF3), heat shock protein family A (Hsp70) member 6 (HSPA6), and dual specificity phosphatase 1 (DUSP1, also known as CL100, HVH1, MKP-1, PTPN10), were verified in GSE13712. The expression of DUSP1 was higher in the senescent cell under high WSS than that of the young cell. The TF–miRNA–mechanosensitive gene coregulatory network was constructed.Conclusion: In this work, we identified three hub genes, ATF3, HSPA6, and DUSP1, as the potential mechanosensitive genes in the human blood vessels. DUSP1 was confirmed to be associated with the senescence of vascular ECs. Therefore, these three mechanosensitive genes may have emerged as potential novel targets for the prediction and prevention of ischemic stroke. Furthermore, the TF–miRNA–mechanosensitive genes coregulatory network reveals an underlying regulatory mechanism and the pathways to control disease progression.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

scholarly journals Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

scholarly journals Shear stress augments mitochondrial ATP generation that triggers ATP release and Ca2+ signaling in vascular endothelial cells

2018 ◽  
Vol 315 (5) ◽  
pp. H1477-H1485 ◽  
Author(s):  
Kimiko Yamamoto ◽  
Hiromi Imamura ◽  
Joji Ando
Keyword(s):  
Endothelial Cells ◽  
Shear Stress ◽  
Vascular Endothelial Cells ◽  
Critical Role ◽  
Atp Release ◽  
Vascular Endothelial ◽  
The Novel ◽  
Caveolin 1 ◽  
Atp Generation

Vascular endothelial cells (ECs) sense and transduce hemodynamic shear stress into intracellular biochemical signals, and Ca2+ signaling plays a critical role in this mechanotransduction, i.e., ECs release ATP in the caveolae in response to shear stress and, in turn, the released ATP activates P2 purinoceptors, which results in an influx into the cells of extracellular Ca2+. However, the mechanism by which the shear stress evokes ATP release remains unclear. Here, we demonstrated that cellular mitochondria play a critical role in this process. Cultured human pulmonary artery ECs were exposed to controlled levels of shear stress in a flow-loading device, and changes in the mitochondrial ATP levels were examined by real-time imaging using a fluorescence resonance energy transfer-based ATP biosensor. Immediately upon exposure of the cells to flow, mitochondrial ATP levels increased, which was both reversible and dependent on the intensity of shear stress. Inhibitors of the mitochondrial electron transport chain and ATP synthase as well as knockdown of caveolin-1, a major structural protein of the caveolae, abolished the shear stress-induced mitochondrial ATP generation, resulting in the loss of ATP release and influx of Ca2+ into the cells. These results suggest the novel role of mitochondria in transducing shear stress into ATP generation: ATP generation leads to ATP release in the caveolae, triggering purinergic Ca2+ signaling. Thus, exposure of ECs to shear stress seems to activate mitochondrial ATP generation through caveola- or caveolin-1-mediated mechanisms. NEW & NOTEWORTHY The mechanism of how vascular endothelial cells sense shear stress generated by blood flow and transduce it into functional responses remains unclear. Real-time imaging of mitochondrial ATP demonstrated the novel role of endothelial mitochondria as mechanosignaling organelles that are able to transduce shear stress into ATP generation, triggering ATP release and purinoceptor-mediated Ca2+ signaling within the cells.

Icariin protects vascular endothelial cells from oxidative stress through inhibiting endoplasmic reticulum stress

2019 ◽  
Vol 17 (3) ◽  
pp. 205-212 ◽  
Author(s):  
Fang-yuan Wang ◽  
Jian Jia ◽  
Huan-huan Song ◽  
Cheng-ming Jia ◽  
Chang-bo Chen ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Endothelial Cells ◽  
Endoplasmic Reticulum ◽  
Endoplasmic Reticulum Stress ◽  
Vascular Endothelial Cells ◽  
Vascular Endothelial

scholarly journals Low Shear Stress Upregulates CX3CR1 Expression by Inducing VCAM-1 via the NF-κB Pathway in Vascular Endothelial Cells

2020 ◽  
Vol 78 (3) ◽  
pp. 383-389 ◽  
Author(s):  
Yiwei Zhao ◽  
Peile Ren ◽  
Qiufang Li ◽  
Shafiu Adam Umar ◽  
Tan Yang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Shear Stress ◽  
Protein Expression ◽  
Vascular Endothelial Cells ◽  
Critical Role ◽  
Mrna Levels ◽  
Vascular Endothelial ◽  
Low Shear Stress ◽  
Low Shear ◽  
Cx3cr1 Expression

Abstract Atherosclerosis is a significant cause of mortality and morbidity. Studies suggest that the chemokine receptor CX3CR1 plays a critical role in atherogenesis. Shear stress is an important mechanical force that affects blood vessel function. In this study, we investigated the effect of shear stress on CX3CR1 expression in vascular endothelial cells (VECs). First, cells were exposed to different shear stress and then CX3CR1 mRNA and protein were measured by quantitative RT-PCR and western blot analysis, respectively. CX3CR1 gene silencing was used to analyze the molecular mechanisms underlying shear stress-mediated effects on CX3CR1 expression. CX3CR1 mRNA and protein expression were significantly increased with 4.14 dyne/cm2 of shear stress compared with other tested levels of shear stress. We observed a significant increase in CX3CR1 mRNA levels at 2 h and CX3CR1 protein expression at 4 h. CX3CR1-induced VCAM-1 expression in response to low shear stress by activating NF-κB signaling pathway in VECs. Our findings demonstrate that low shear stress increases CX3CR1 expression, which increases VCAM-1 expression due to elevated NF-κB activation. The current study provides evidence of the correlation between shear stress and atherosclerosis mediated by CX3CR1.

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