Gene Expression Analysis Suggests Immunological Changes of Peripheral Blood Monocytes in the Progression of Patients With Coronary Artery Disease

Analysis of the prognostic value of mitochondria-related genes in patients with acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a leading cause of death worldwide. Mitochondrial dysfunction is a key determinant of cell death post-AMI. Preventing mitochondrial dysfunction is thus a key therapeutic strategy. This study aimed to explore key genes and target compounds related to mitochondrial dysfunction in AMI patients and their association with major adverse cardiovascular events (MACE).

METHODS

Differentially expressed genes in AMI were identified from the Gene Expression Omnibus (GEO) datasets (GSE166780 and GSE24519), and mitochondria-related genes were obtained from MitoCarta3.0 database. By intersection of the two gene groups, mitochondria-related genes in AMI were identified. Next, the identified genes related to mitochondria were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Protein-protein interaction (PPI) network was constructed, and key genes were screened. Then, targeted drug screening and molecular docking were performed. Blood samples from AMI patients and healthy volunteers were analyzed for the key genes expressions using quantitative real time polymerase chain reaction (qRT-PCR). Later, receiver operating characteristic (ROC) curves assessed the diagnostic value of key genes, and univariate and multivariate COX analyses identified risk factors and protective factors for MACE in AMI patients.

RESULTS

After screening and identification, 138 mitochondria-related genes were identified, mainly enriched in the processes and pathways of cellular respiration, redox, mitochondrial metabolism, apoptosis, amino acid and fatty acid metabolism. According to the PPI network, 5 key mitochondria-related genes in AMI were obtained: translational activator of cytochrome c oxidase I (TACO1), cytochrome c oxidase subunit Va (COX5A), PTEN-induced putative kinase 1 (PINK1), SURF1, and NDUFA11. Molecular docking showed that Cholic Acid, N-Formylmethionine interacted with COX5A, nicotinamide adenine dinucleotide + hydrogen (NADH) and NDUFA11. Subsequent basic experiments revealed that COX5A and NDUFA11 expressions were significantly lower in the blood of patients with AMI than those in the corresponding healthy volunteers; also, AMI patients with MACE had lower COX5A and NDUFA11 expressions in the blood than those without MACE (P < 0.01). ROC analysis also showed high diagnostic value for COX5A and NDUFA11 [area under the curve (AUC) > 0.85]. In terms of COX results, COX5A, NDUFA11 and left ventricular ejection fraction (LVEF) were protective factors for MACE in AMI, while C-reactive protein (CRP) was a risk factor.

CONCLUSION

COX5A and NDUFA11, key mitochondria-related genes in AMI, may be used as biomarkers to diagnose AMI and predict MACE.

A Transcriptomic Analysis of Smoking-Induced Gene Expression Alterations in Coronary Artery Disease Patients

Smoking is a well established risk factor for coronary artery disease (CAD). Despite this, there have been no previous studies investigating the effects of smoking on blood gene expression in CAD patients. This single-centre cross-sectional study was designed with clearly defined inclusion criteria to address this gap. We conducted a high-throughput approach using next generation sequencing analysis with a single-end sequencing protocol and a read length of 75-cycles. Sixty-one patients with a median age of 67 years (range: 28-88 years) were recruited, and only 44 subjects were included for further analyses. Our investigation revealed 120 differentially expressed genes (DEGs) between smokers and nonsmokers, with a fold change (FC) of ≥1.5 and a p-value < 0.05. Among these DEGs, 15 were upregulated and 105 were downregulated. Notably, when applying a more stringent adjusted FC ≥ 2.0, 31 DEGs (5 upregulated, annotated to immune response pathways, and 26 downregulated, involving oxygen and haem binding or activity, with FDR ≤ 0.03) remained statistically significant at an alpha level of <0.05. Our results illuminate the molecular mechanisms underlying CAD, fortifying existing epidemiological evidence. Of particular interest is the unexplored overexpression of RCAN3, TRAV4, and JCHAIN genes, which may hold promising implications for the involvement of these genes in CAD among smokers.

Differential Expression of Subsets of Genes Related to HDL Metabolism and Atherogenesis in the Peripheral Blood in Coronary Artery Disease

Differential expression of genes (DEGs) in coronary artery disease (CAD) and the association between transcript level and high-density lipoprotein cholesterol (HDL-C) were studied with 76 male patients with CAD and 63 control patients. The transcript level of genes related to HDL metabolism (24 genes) and atherosclerosis-prone (41 genes) in RNA isolated from peripheral blood mononuclear cells was measured by real-time RT-PCR. Twenty-eight DEGs were identified. The expression of cholesterol transporters, ALB, APOA1, and LCAT was down-regulated, while the expression of AMN, APOE, LDLR, LPL, PLTP, PRKACA, and CETP was up-regulated. The systemic inflammation in CAD is evidenced by the up-regulation of IL1B, TLR8, CXCL5, and TNFRSF1A. For the controls, TLR8 and SOAT1 were negative predictors of the HDL-C level. For CAD patients, PRKACG, PRKCQ, and SREBF1 were positive predictors, while PRKACB, LCAT, and S100A8 were negative predictors. For CAD patients, the efficiency of reverse cholesterol transport is 73-79%, and intracellular free cholesterol seems to accumulate at hyperalphalipoproteinemia. Both atheroprotective (via S100A8) and proatherogenic (via SREBF1, LCAT, PRKACG, PRKACB, and PRKCQ) associations of gene expression with HDL-C determine HDL functionality in CAD patients. The selected key genes and involved pathways may represent HDL-specific targets for the diagnosis and treatment of CAD and atherosclerosis.

Construction of the ceRNA network in the progression of acute myocardial infarction

Acute myocardial infarction (AMI) is a common disease that induced by sudden occlusion of a coronary artery and myocardial necrosis, which causes a great medical burden worldwide. Noncoding RNAs, such as circRNA, lncRNA and miRNA, play crucial roles in the progression of cardiovascular diseases. However, the circRNA-miRNA-mRNA network in the occurrence and development of AMI needs further investigation. In this study, we downloaded three AMI datasets, including circRNA (GSE160717), miRNA (GSE24591), and mRNA (GSE66360) from GEO database. The differentially expressed candidates, and GO and KEGG functions were analyzed by RStudio, and subsequently import to PPI and Cytoscape to obtain the hub genes. By using the starbase target prediction database, we further screen the ceRNA network of circRNA-miRNA-mRNA based on the selected differentially expressed candidates. We found 46 differential expressed mRNAs, 65 miRNAs, and five circRNAs. GO functions and KEGG enrichment of the 46 mRNAs focused on immune response and functions, involving IL-17 signaling pathway, Toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, and NF-kappaB signaling pathway, which may aggravate the pathologies of AMI. PPI and Cytoscape analysis showed 10 hub genes, including TLR2, IL1B, CCL4, CCL3, CCR5, TREM1, CXCL2, NLRP3, CSF3, and CCL20. By using starbase and circinteractome databases, ceRNA network construction showed that circRNA_023461 and circRNA_400027 regulate several miRNA-mRNA axes in AMI. In summary, this study uncovered the circRNA-miRNA-mRNA network based on three AMI datasets. The differentially expressed genes, including CCL20, CCL4, CSF3, and IL1B, focus on immune functions and pathways. Furthermore, circRNA_023461 and circRNA_400027 regulate several miRNA-mRNA axes, exerting important roles in AMI progression. Our founding provides new insights into AMI and improve the therapeutic strategies for AMI.

Identification of diagnostic biomarkers and therapeutic targets in peripheral immune landscape from coronary artery disease

Background

Peripheral biomarkers are increasingly vital non-invasive methods for monitoring coronary artery disease (CAD) progression. Their superiority in early detection, prognosis evaluation and classified diagnosis is becoming irreplaceable. Nevertheless, they are still less explored. This study aimed to determine and validate the diagnostic and therapeutic values of differentially expressed immune-related genes (DE-IRGs) in CAD.

Methods

We downloaded clinical information and RNA sequence data from the GEO database. We used R software, GO, KEGG and Cytoscape to analyze and visualize the data. A LASSO method was conducted to identify key genes for diagnostic model construction. The ssGSEA analysis was used to investigate the differential immune cell infiltration. Besides, we constructed CAD mouse model (low-density lipoprotein receptor deficient mice with high fat diet) to discover the correlation between the screened genes and severe CAD progress. We further uncovered the role of IL13RA1 might play in atherosclerosis.

Results

A total of 762 differential genes were identified between the peripheral blood of 218 controls and 199 CAD patients, which were significantly associated with infection, immune response and neural activity. 58 DE-IRGs were obtained by overlapping the differentially expressed genes(DEGs) and immune-related genes downloaded from ImmpDb database. Through LASSO regression, CCR9, CER1, CSF2, IL13RA1, INSL5, MBL2, MMP9, MSR1, NTS, TNFRSF19, CXCL2, HTR3C, IL1A, and NR4A2 were distinguished as peripheral biomarkers of CAD with eligible diagnostic capabilities in the training set (AUC = 0.968) and test set (AUC = 0.859). The ssGSEA analysis showed that the peripheral immune cells had characteristic distribution in CAD and also close relationship with specific DE-IRGs. RT-qPCR test showed that CCR9, CSF2, IL13RA1, and NTS had a significant correlation with LDLR^−/− mice. IL13RA1 knocked down in RAW264.7 cell lines decreased SCARB1 and ox-LDL-stimulated CD36 mRNA expression, TGF-β, VEGF-C and α-SMA protein levels and increased the production of IL-6, with downregulation of JAK1/STAT3 signal pathway.

Conclusions

We constructed a diagnostic model of advanced-stage CAD based on the screened 14 DE-IRGs. We verified 4 genes of them to have a strong correlation with CAD, and IL13RA1 might participate in the inflammation, fibrosis, and cholesterol efflux process of atherosclerosis by regulating JAK1/STAT3 pathway.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03614-1.

Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells

Objective

Coronary artery disease (CAD) is a serious global health concern. Current diagnostic methods for CAD involve risk to the patient and are costly, so better diagnostic tools are needed. We defined four classifiers based on gene expression profiles in peripheral blood mononuclear cells and determined their potential for CAD detection.

Methods

We downloaded a CAD-related data set (GSE113079) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in peripheral blood mononuclear cells between CAD samples and healthy controls. DEGs were analyzed for functional enrichment. To create a robust CAD classifier, DEGs were identified by feature selection using the principal component analysis. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and support vector machine (SVM) models were created. Gene set variation analysis (GSVA) score and gene set enrichment analysis (GSEA) were also conducted. The performance of the models was evaluated in terms of the area under receiver operating characteristic curves (AUC).

Results

In the training set, we found 135 up-regulated genes and 104 down-regulated genes in CAD patients compared with controls. The DEGs were involved in some pathways associated with CAD, such as pathways involving calcium and interleukin-17 signaling. Twenty genes were identified as optimal features and used to generate the logistic classifier based on LASSO. The AUC for the classifier was 1.00 in the training set and 0.997 in the test set. Using the 20 DEGs, SVM and random forest classifiers were also generated and showed high diagnostic efficacy, with respective AUCs of 0.997 and 1.00 against the training set. A GSVA score was also established using the top 20 significant DEGs, which showed an AUC of 0.971 in the training set and 0.989 in the test set. Furthermore, GSEA showed autophagy and the proteasome to be major pathways involving the DEGs.

Conclusion

We identified a set of genes specific for CAD whose expression can be measured non-invasively. Using these genes, we defined four diagnostic classifiers using multiple methods.

A novel diagnostic signature based on three circulating exosomal mircoRNAs for chronic obstructive pulmonary disease

Exosomal microRNAs (exo-miRNAs or miRs) have demonstrated diagnostic value in various diseases. However, their diagnostic value in chronic obstructive pulmonary disease (COPD) has yet to be fully established. The purpose of the present study was to screen differentially expressed exo-miRNAs in the plasma of patients with COPD and healthy individuals and to evaluate their potential diagnostic value in COPD. Differentially expressed exo-miRNAs in the plasma of patients with COPD and controls were identified using high-throughput sequencing and confirmed using reverse transcription-quantitative PCR (RT-qPCR). Bioinformatics analysis was then performed to predict the function of the selected exo-miRNAs and their target genes in COPD. After a network model was constructed, linear regression analysis was performed to determine the association between exo-miRNA expression and the clinical characteristics of subjects in a validated cohort (46 COPD cases; 34 matched healthy controls). Receiver operating characteristic curve was subsequently plotted to test the diagnostic value of the candidate biomarkers. The top 20 significantly aberrantly expressed COPD-associated exo-miRNAs were verified using RT-qPCR. Of these, nine were then selected for subsequent analysis, five of which were found to be upregulated (miR-23a, miR-1, miR-574, miR-152 and miR-221) and four of which were downregulated (miR-3158, miR-7706, miR-685 and miR-144). The results of Gene Ontology and KEGG pathway analysis revealed that these miRNAs were mainly involved in certain biological functions, such as metabolic processes, such as galactose metabolism and signaling pathways (PI3K-AKT) associated with COPD. The expression levels of three exo-miRNAs (miR-23a, miR-221 and miR-574) were found to be negatively associated with the forced expiratory volume in the 1st second/forced vital capacity. Furthermore, the area under the curve values of the three exo-miRNAs (miR-23a, miR-221 and miR-574) for COPD diagnosis were 0.776 [95% confidence interval (CI), 0.669-0.882], 0.688 (95% CI, 0.563-0.812) and 0.842 (95% CI, 0.752-0.931), respectively. In conclusion, the three circulating exosomal miRNAs (miR-23a, miR-221 and miR-574) may serve as novel circulating biomarkers for the diagnosis of COPD. These results may also enhance our understanding and provide novel potential treatment options for patients with COPD.