Analysis of pathways and networks influencing the differential expression of genes in coronary artery disease

Coronary artery disease (CAD) is the leading cause of death worldwide. Microarray analysis is a practical approach to study gene transcription changes that may reflect signatures that underlie the pathogenesis of CAD. Using gene expression profile data from the Gene Expression Omnibus database, we identified differentially expressed genes that can contribute to the pathology of CAD. Further pathway and network analyses were also implemented to identify pathways and hub genes related to the disease. We observed 466 downregulated and 560 upregulated genes. The ribosome pathway was the most significantly over-represented pathway with differentially expressed genes. Over 35% of the genes in this pathway were downregulated. Hub genes in the network, such as IL7R, FYN, CALM1 ESR1 and PLCG1, may play crucial roles in the pathogenesis of CAD. Our results facilitate the identification of molecular mechanisms that underlie CAD.

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Analysis of Differentially Expressed Genes in Coronary Artery Disease by Integrated Microarray Analysis

Biomolecules ◽

10.3390/biom10010035 ◽

2019 ◽

Vol 10 (1) ◽

pp. 35 ◽

Cited By ~ 1

Author(s):

Meenashi Vanathi Balashanmugam ◽

Thippeswamy Boreddy Shivanandappa ◽

Sivagurunathan Nagarethinam ◽

Basavaraj Vastrad ◽

Chanabasayya Vastrad

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Molecular Mechanism ◽

Differentially Expressed Genes ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Interaction Network ◽

Differentially Expressed ◽

Artery Disease

Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the superpathway of steroid hormone biosynthesis, ABC transporters, oxidative ethanol degradation III and Complement and coagulation cascades. Similarly, geneontology enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the forebrain neuron differentiation, filopodium membrane, platelet degranulation and blood microparticle. In the PPI network and modules (up and down regulated), MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74 and VHL were hub genes. In the target genes-miRNA regulatory network and target genes—TF regulatory network (up and down regulated), TAOK1, KHSRP, HSD17B11 and PAH were target genes. In conclusion, the pathway and GO ontology enriched by DEGs may reveal the molecular mechanism of CAD. Its hub and target genes, MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74, VHL, TAOK1, KHSRP, HSD17B11 and PAH were expected to be new targets for CAD. Our finding provided clues for exploring molecular mechanism and developing new prognostics, diagnostic and therapeutic strategies for CAD.

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

BioMed Research International ◽

10.1155/2019/8340573 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Zhaoyan Li ◽

Lei Zhong ◽

Zhenwu Du ◽

Gaoyang Chen ◽

Jing Shang ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Receptor Interaction ◽

Hub Genes ◽

Expression Levels ◽

Network Analyses ◽

Synovial Membranes

Background. Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis. Methods. The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system. Results. A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).

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Gene expression profiling in whole blood of patients with coronary artery disease

Clinical Science ◽

10.1042/cs20100043 ◽

2010 ◽

Vol 119 (8) ◽

pp. 335-343 ◽

Cited By ~ 86

Author(s):

Chiara Taurino ◽

William H. Miller ◽

Martin W. McBride ◽

John D. McClure ◽

Raya Khanin ◽

...

Keyword(s):

Gene Expression ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Gene Expression Profiling ◽

Whole Blood ◽

Expression Profiling ◽

Gene Expression Analysis ◽

Differentially Expressed ◽

Differentially Expressed Mirnas ◽

Artery Disease

Owing to the dynamic nature of the transcriptome, gene expression profiling is a promising tool for discovery of disease-related genes and biological pathways. In the present study, we examined gene expression in whole blood of 12 patients with CAD (coronary artery disease) and 12 healthy control subjects. Furthermore, ten patients with CAD underwent whole-blood gene expression analysis before and after the completion of a cardiac rehabilitation programme following surgical coronary revascularization. mRNA and miRNA (microRNA) were isolated for expression profiling. Gene expression analysis identified 365 differentially expressed genes in patients with CAD compared with healthy controls (175 up- and 190 down-regulated in CAD), and 645 in CAD rehabilitation patients (196 up- and 449 down-regulated post-rehabilitation). Biological pathway analysis identified a number of canonical pathways, including oxidative phosphorylation and mitochondrial function, as being significantly and consistently modulated across the groups. Analysis of miRNA expression revealed a number of differentially expressed miRNAs, including hsa-miR-140-3p (control compared with CAD, P=0.017), hsa-miR-182 (control compared with CAD, P=0.093), hsa-miR-92a and hsa-miR-92b (post- compared with pre-exercise, P<0.01). Global analysis of predicted miRNA targets found significantly reduced expression of genes with target regions compared with those without: hsa-miR-140-3p (P=0.002), hsa-miR-182 (P=0.001), hsa-miR-92a and hsa-miR-92b (P=2.2×10−16). In conclusion, using whole blood as a ‘surrogate tissue’ in patients with CAD, we have identified differentially expressed miRNAs, differentially regulated genes and modulated pathways which warrant further investigation in the setting of cardiovascular function. This approach may represent a novel non-invasive strategy to unravel potentially modifiable pathways and possible therapeutic targets in cardiovascular disease.

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

BioMed Research International ◽

10.1155/2019/8567306 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Weitie Wang ◽

Qing Liu ◽

Yong Wang ◽

Hulin Piao ◽

Bo Li ◽

...

Keyword(s):

Coronary Artery Disease ◽

Adipose Tissue ◽

Coronary Artery ◽

Focal Adhesion ◽

Molecular Mechanisms ◽

Epicardial Adipose Tissue ◽

Hub Genes ◽

Qrt Pcr ◽

Ppi Networks ◽

Artery Disease

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.

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Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Journal of Translational Medicine ◽

10.1186/s12967-021-02834-1 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqin Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. Methods Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. Results GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. Conclusion ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

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Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Experimental Biology and Medicine ◽

10.1177/15353702211008809 ◽

2021 ◽

pp. 153537022110088

Author(s):

Jinyi Tian ◽

Yizhou Bai ◽

Anyang Liu ◽

Bin Luo

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Thyroid Tissue ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

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Comprehensive analysis of the ceRNA network in coronary artery disease

Scientific Reports ◽

10.1038/s41598-021-03688-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Weikang Bian ◽

Xiao-Xin Jiang ◽

Zhicheng Wang ◽

Yan-Rong Zhu ◽

Hongsong Zhang ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Differentially Expressed Genes ◽

Nitrogen Compound ◽

Differentially Expressed ◽

Expression Data ◽

Cerna Network ◽

High Connectivity ◽

Artery Disease ◽

And Control

AbstractWith the rapid aging of the population, coronary artery disease (CAD) has become one of the most fatal chronic diseases. However, the genetic mechanism of CAD is still unclear. The purpose of this study is to construct the lncRNA-miRNA-mRNA regulatory network for CAD diseases and systematically identify differentially expressed genes in patients with coronary heart disease. In this study, two lncRNA datasets (GSE69587 and GSE113079) and a microRNA dataset (GSE105449) which contained 393 and 38 CAD samples were selected. In addition, two mRNA datasets which named GSE113079 (98 CAD samples) and GSE9820 (8 CAD samples) were selected to search the differentially expressed genes (DEGs). By comparing the expression data between CAD and control samples, a total of 1111 lncRNAs, 2595 mRNAs and 22 miRNAs were identified. Based on the DEGs, a lncRNA-miRNA-mRNA ceRNA network was constructed to explore the hub nodes in CAD. In the ceRNA network, the lncRNAs KCNQ1OT1 and H19 showed high connectivity with the nine miRNAs. GO and KEGG results showed that genes in ceRNA networks were mainly involved in nitrogen compound metabolic process, PI3K-Akt signaling pathway and retrograde endocannabinoid signaling. These findings will improve the understanding of the occurrence and development mechanism of CAD.

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Identification of Key Methylation Differentially Expressed Genes in Posterior Fossa Ependymoma Based on Epigenomic and Transcriptome Analysis

10.21203/rs.3.rs-148618/v1 ◽

2021 ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqing Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Abstract BackgroundPosterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma(EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. MethodsUsing epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. ResultsGO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R=0.69, P=1 x 10-08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. ConclusionssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

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Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

10.21203/rs.3.rs-564869/v1 ◽

2021 ◽

Author(s):

Yanzhi Ge ◽

Zuxiang Chen ◽

Yanbin Fu ◽

Li Zhou ◽

Haipeng Xu ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Large Scale ◽

Molecular Mechanisms ◽

Immune Cell ◽

Interaction Network ◽

Differentially Expressed ◽

Specific Gene ◽

Hub Genes

Abstract Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

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Dysregulated Circulating Apoptosis- and Autophagy-Related lncRNAs as Diagnostic Markers in Coronary Artery Disease

BioMed Research International ◽

10.1155/2021/5517786 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Lijiao Zhang ◽

Dayuan Lou ◽

Dan He ◽

Ying Wang ◽

Yuhang Wu ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Expression Profiles ◽

Roc Curves ◽

Gene Expression Omnibus ◽

Diagnostic Markers ◽

Differentially Expressed ◽

Cellular Processes ◽

Artery Disease ◽

Normal Controls

Objective. Increasing evidence emphasizes the implications of dysregulated apoptosis and autophagy cellular processes in coronary artery disease (CAD). Herein, we aimed to explore apoptosis- and autophagy-related long noncoding RNAs (lncRNAs) in peripheral blood of CAD patients. Methods. The mRNA and lncRNA expression profiles were retrieved from the Gene Expression Omnibus (GEO) database. With ∣ fold change ∣ > 1.5 and adjusted p value < 0.05, differentially expressed apoptosis- and autophagy-related mRNAs were screened between CAD and healthy blood samples. Also, differentially expressed lncRNAs were identified for CAD. Using the psych package, apoptosis- and autophagy-related lncRNAs were defined with Spearson’s correlation analysis. Receiver operating characteristic (ROC) curves were conducted for the assessment of the diagnosed efficacy of these apoptosis- and autophagy-related lncRNAs. Results. Our results showed that 24 apoptosis- and autophagy-related mRNAs were abnormally expressed in CAD than normal controls. 12 circulating upregulated and 1 downregulated apoptosis- and autophagy-related lncRNAs were identified for CAD. The ROCs confirmed that AC004485.3 ( AUC = 0.899 ), AC004920.3 ( AUC = 0.93 ), AJ006998.2 ( AUC = 0.776 ), H19 ( AUC = 0.943 ), RP5-902P8.10 ( AUC = 0.956 ), RP5-1114G22.2 ( AUC = 0.883 ), RP11-247A12.1 ( AUC = 0.885 ), RP11-288L9.4 ( AUC = 0.928 ), RP11-344B5.2 ( AUC = 0.858 ), RP11-452C8.1 ( AUC = 0.929 ), RP11-565A3.1 ( AUC = 0.893 ), and XXbac-B33L19.4 ( AUC = 0.932 ) exhibited good performance in differentiating CAD from healthy controls. Conclusion. Collectively, our findings proposed that circulating apoptosis- and autophagy-related lncRNAs could become underlying diagnostic markers for CAD in clinical practice.

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