scholarly journals Bioinformatics analysis identifies potential diagnostic signatures for coronary artery disease

2020 ◽  
Vol 48 (12) ◽  
pp. 030006052097985
Author(s):  
Dong Zhang ◽  
Liying Guan ◽  
Xiaoming Li
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Differential Expression ◽  
Protein Interactions ◽  
Diagnostic Model ◽  
Ppi Network ◽  
Kegg Pathways ◽  
Clinical Diagnoses ◽  
Key Genes ◽  
Artery Disease

Background Coronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD. Method We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein–protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed. Results A total of 2127 differentially expressed genes (DEGs) were identified in GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls. Conclusion We identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals Differential expression of vitamin D associated genes in the aorta of coronary artery disease patients with and without rheumatoid arthritis

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202346 ◽  
Author(s):  
Ingvild Oma ◽  
Ole Kristoffer Olstad ◽  
Jacqueline Kirsti Andersen ◽  
Torstein Lyberg ◽  
Øyvind Molberg ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Coronary Artery Disease ◽  
Vitamin D ◽  
Coronary Artery ◽  
Differential Expression ◽  
Artery Disease

scholarly journals Identification of key genes and pathways affected in epicardial adipose tissue from patients with coronary artery disease by integrated bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8763
Author(s):  
Liao Tan ◽  
Qian Xu ◽  
Qianchen Wang ◽  
Ruizheng Shi ◽  
Guogang Zhang
Keyword(s):  
Coronary Artery Disease ◽  
Adipose Tissue ◽  
Coronary Artery ◽  
Epicardial Adipose Tissue ◽  
Enrichment Analysis ◽  
Pathological Process ◽  
Gene Set Enrichment Analysis ◽  
Rt Pcr ◽  
Key Genes ◽  
Artery Disease

Background Coronary artery disease (CAD) is a common disease with high cost and mortality. Here, we studied the differentially expressed genes (DEGs) between epicardial adipose tissue (EAT) and subcutaneous adipose tissue (SAT) from patients with CAD to explore the possible pathways and mechanisms through which EAT participates in the CAD pathological process. Methods Microarray data for EAT and SAT were obtained from the Gene Expression Omnibus database, including three separate expression datasets: GSE24425, GSE64554 and GSE120774. The DEGs between EAT samples and SAT control samples were screened out using the limma package in the R language. Next, we conducted bioinformatic analysis of gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to discover the enriched gene sets and pathways associated with DEGs. Simultaneously, gene set enrichment analysis was carried out to discover enriched gene functions and pathways from all expression data rather than DEGs. The PPI network was constructed to reveal the possible protein interactions consistent with CAD. Mcode and Cytohubba in Cytoscape revealed the possible key CAD genes. In the next step, the corresponding predicted microRNAs (miRNAs) were analysed using miRNA Data Integration Portal. RT-PCR was used to validate the bioinformatic results. Results The three datasets had a total of 89 DEGs (FC log2 > 1 and P value < 0.05). By comparing EAT and SAT, ten common key genes (HOXA5, HOXB5, HOXC6, HOXC8, HOXB7, COL1A1, CCND1, CCL2, HP and TWIST1) were identified. In enrichment analysis, pro-inflammatory and immunological genes and pathways were up-regulated. This could help elucidate the molecular expression mechanism underlying the involvement of EAT in CAD development. Several miRNAs were predicted to regulate these DEGs. In particular, hsa-miR-196a-5p and hsa-miR-196b-5p may be more reliably associated with CAD. Finally, RT-PCR validated the significant difference of OXA5, HOXC6, HOXC8, HOXB7, COL1A1, CCL2 between EAT and SAT (P value < 0.05). Conclusions Between EAT and SAT in CAD patients, a total of 89 DEGs, and 10 key genes, including HOXA5, HOXB5, HOXC6, HOXC8, HOXB7, COL1A1, CCND1, CCL2, HP and TWIST1, and miRNAs hsa-miR-196a-5p and hsa-miR-196b-5p were predicted to play essential roles in CAD pathogenesis. Pro-inflammatory and immunological pathways could act as key EAT regulators by participating in the CAD pathological process.

scholarly journals Virtual screening and network pharmacology-based synergistic mechanism identification of multiple components contained in Guanxin V against coronary artery disease

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bo Liang ◽  
Xiao-Xiao Zhang ◽  
Ning Gu
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Virtual Screening ◽  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Active Components ◽  
Kegg Pathways ◽  
Synergistic Mechanism ◽  
Artery Disease

Abstract Background Guanxin V (GXV), a traditional Chinese medicine (TCM), has been widely used to treat coronary artery disease (CAD) in clinical practice in China. However, research on the active components and underlying mechanisms of GXV in CAD is still scarce. Methods A virtual screening and network pharmacological approach was utilized for predicting the pharmacological mechanisms of GXV in CAD. The active compounds of GXV based on various TCM-related databases were selected and then the potential targets of these compounds were identified. Then, after the CAD targets were built through nine databases, a PPI network was constructed based on the matching GXV and CAD potential targets, and the hub targets were screened by MCODE. Moreover, Metascape was applied to GO and KEGG functional enrichment. Finally, HPLC fingerprints of GXV were established. Results A total of 119 active components and 121 potential targets shared between CAD and GXV were obtained. The results of functional enrichment indicated that several GO biological processes and KEGG pathways of GXV mostly participated in the therapeutic mechanisms. Furthermore, 7 hub MCODEs of GXV were collected as potential targets, implying the complex effects of GXV-mediated protection against CAD. Six specific chemicals were identified. Conclusion GXV could be employed for CAD through molecular mechanisms, involving complex interactions between multiple compounds and targets, as predicted by virtual screening and network pharmacology. Our study provides a new TCM for the treatment of CAD and deepens the understanding of the molecular mechanisms of GXV against CAD.

Differential expression of cytokines, chemokines and chemokine receptors in patients with coronary artery disease

2009 ◽  
Vol 136 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Rômulo Tadeu Dias de Oliveira ◽  
Ronei Luciano Mamoni ◽  
José Roberto Matos Souza ◽  
Juliano Lara Fernandes ◽  
Francisco José O. Rios ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Differential Expression ◽  
Chemokine Receptors ◽  
Artery Disease

scholarly journals KLRD1, FOSL2 and LILRB3 as potential biomarkers for plaques progression in acute myocardial infarction and stable coronary artery disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qiang Zhang ◽  
Yue Zheng ◽  
Meng Ning ◽  
Tong Li
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Roc Curve ◽  
Stable Coronary Artery Disease ◽  
Linear Regression Analysis ◽  
Recurrent Event ◽  
System Response ◽  
Ppi Network ◽  
Artery Disease

Abstract Background Myocardial infarction (MI) contributes to high mortality and morbidity and can also accelerate atherosclerosis, thus inducing recurrent event due to status changing of coronary artery walls or plaques. The research aimed to investigate the differentially expressed genes (DEGs), which may be potential therapeutic targets for plaques progression in stable coronary artery disease (CAD) and ST-elevated MI (STEMI). Methods Two human datasets (GSE56885 and GSE59867) were analyzed by GEO2R and enrichment analysis was applied through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. To explore the seed genes, the protein–protein interaction (PPI) network was constructed and seed genes, as well as top30 ranking neighbours were screened out. To validate these findings, one human dataset GSE120521 was analyzed. Linear regression analysis and ROC curve were also performed to determine which seed genes above mentioned could be independent factors for plaques progression. Mice MI model and ELISA of seed genes were applied and ROC curve was also performed for in vivo validation. Results 169 DEGs and 573 DEGs were screened out in GSE56885 and GSE59867, respectively. Utilizing GO and KEGG analysis, these DEGs mainly enriched in immune system response and cytokines interaction. PPI network analysis was carried out and 19 seed genes were screened out. To validate these findings, GSE120521 was analyzed and three genes were demonstrated to be targets for plaques progression and stable CAD progression, including KLRD1, FOSL2 and LILRB3. KLRD1 and LILRB3 were demonstrated to be high-expressed at 1d after MI compared to SHAM group and FOSL2 expression was low-expressed at 1d and 1w. To investigate the diagnostic abilities of seed genes, ROC analysis was applied and the AUCs of KLRD1, FOSL2 and LILRB3, were 0.771, 0.938 and 0.972, respectively. Conclusion This study provided the screened seed genes, KLRD1, FOSL2 and LILRB3, as credible molecular biomarkers for plaques status changing in CAD progression and MI recurrence. Other seed genes, such as FOS, SOCS3 and MCL1, may also be potential targets for treatment due to their special clinical value in cardiovascular diseases.

scholarly journals Identification of key genes and crucial modules associated with coronary artery disease by bioinformatics analysis

2014 ◽  
Vol 34 (3) ◽  
pp. 863-869 ◽  
Author(s):  
XUEMEI ZHANG ◽  
XIAOSHU CHENG ◽  
HUIFENG LIU ◽  
CHUNHUA ZHENG ◽  
KUNRUI RAO ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Bioinformatics Analysis ◽  
Key Genes ◽  
Artery Disease

scholarly journals Association of Pericardiac Adipose Tissue With Coronary Artery Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingxuan Li ◽  
Lin Qi ◽  
Yanglei Li ◽  
Shuyi Zhang ◽  
Lei Lin ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Adipose Tissue ◽  
Coronary Artery ◽  
Gene Network ◽  
Molecular Mechanisms ◽  
Inflammatory Cells ◽  
Ppi Network ◽  
Elective Cardiac Surgery ◽  
Pericardial Adipose Tissue ◽  
Artery Disease

Background and AimCoronary artery disease (CAD) poses a worldwide health threat. Compelling evidence shows that pericardial adipose tissue (PAT), a brown-like adipose adjacent to the external surface of the pericardium, is associated with CAD. However, the specific molecular mechanisms of PAT in CAD are elusive. This study aims to characterize human PAT and explore its association with CAD.MethodsWe acquired samples of PAT from 31 elective cardiac surgery patients (17 CAD patients and 14 controls). The transcriptome characteristics were assessed in 5 CAD patients and 4 controls via RNA-sequencing. Cluster profile R package, String database, Cytoscape were applied to analyze the potential pathways and PPI-network key to DEGS, whereas the hubgenes were predicted via Metascape, Cytohubba, and MCODE. We use Cibersort, ENCORI, and DGIDB to predict immunoinfiltration, mRNA-miRNA target gene network, and search potential drugs targeting key DEGs. The predictable hubgenes and infiltrating inflammatory cells were validated in 22 patients (12 CAD samples and 10 control samples) through RT-qPCR and immunohistochemistry.ResultsA total of 147 different genes (104 up-regulated genes and 43 down-regulated genes) were identified in CAD patients. These different genes were associated with immunity and inflammatory dysfunction. Cibersort analysis showed monocytes and macrophages were the most common subsets in immune cells, whereas immunohistochemical results revealed there were more macrophages and higher proportion of M1 subtype cells in PAT of CAD patients. The PPI network and module analysis uncovered several crucial genes, defined as candidate genes, including Jun, ATF3, CXCR4, FOSB, CCl4, which were validated through RT-qPCR. The miRNA-mRNA network implicated hsa-miR-185-5p as diagnostic targets and drug-gene network showed colchicine, fenofibrate as potential therapeutic drugs, respectively.ConclusionThis study demonstrates that PAT is mainly associated with the occurrence of CAD following the dysfunction of immune and inflammatory processes. The identified hubgenes, predicted drugs and miRNAs are promising biomarkers and therapeutic targets for CAD.

Sign in / Sign up

Export Citation Format

Share Document