Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease

Disparity of gene expression in coronary artery disease: insights from MEIS1, HIRA, and Myocardin

INTRODUCTION

Coronary artery disease (CAD) in young adults can have devastating consequences. The cardiac developmental gene MEIS1 plays important roles in vascular networks and heart development. This gene effects on the regeneration capacity of the heart. Considering role of MEIS1 in cardiac tissue development and the progression of myocardial infarction this study investigated the expression levels of the MEIS1, HIRA, and Myocardin genes in premature CAD patients compared to healthy subjects and evaluated the relationships between these genes and possible inflammatory factors.

METHODS AND RESULTS

The study conducted a case-control design involving 35 CAD patients and 35 healthy individuals. Peripheral blood mononuclear cells (PBMCs) were collected, and gene expression analysis was performed using real-time PCR. Compared with control group, the number of PBMCs in the CAD group exhibited greater MEIS1 and HIRA gene expression, with fold changes of 2.45 and 3.6. The expression of MEIS1 exhibited a negative correlation with IL-10 (r= -0.312) expression and positive correlation with Interleukin (IL)-6 (r = 0.415) and tumor necrosis factor (TNF)-α (r = 0.534) gene expression. Moreover, there was an inverse correlation between the gene expression of HIRA and that of IL-10 (r= -0.326), and a positive correlation was revealed between the expression of this gene and that of the IL-6 (r = 0.453) and TNF-α (r = 0.572) genes.

CONCLUSION

This research demonstrated a disparity in expression levels of MEIS1, HIRA, and Myocardin, between CAD and healthy subjects. The results showed that, MEIS1 and HIRA play significant roles in regulating the synthesis of proinflammatory cytokines, namely, TNF-α and IL-6.

Online application for the diagnosis of atherosclerosis by six genes

BACKGROUND

Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS.

METHODS

We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction.

RESULTS

Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples.

CONCLUSIONS

A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

CDKN2B-AS1 may act as miR-92a-3p sponge in coronary artery disease

BACKGROUND

LncRNAs, miRNAs, and the sponge effect between them exert diverse biological influences on the pathogenesis and progression of coronary artery disease (CAD), thus necessitating an exploration of the lncRNA-miRNA-gene regulatory network in CAD.

METHODS

Expression profile GSE98583 was obtained from NCBI, containing the data of 12 CAD patients and 6 controls. Limma package was utilized to determine the differentially expressed genes (DEGs). Functional enrichment analysis was performed by DAVID. The CAD-related miRNA-DEG associations were retrieved via HMDD and miRTarBase, and the CAD-related lncRNA-miRNA associations were retrieved via LncRNADisease and starBase. The CAD-related lncRNA-miRNA-DEG regulatory network was constructed by combining these associations. The dual luciferase test was carried out to validate the connections among lncRNA, miRNA, and gene.

RESULTS

Overall, 534 DEGs were identified between CAD samples and controls, including 243 up-regulated and 291 down-regulated, and were enriched in various gene ontology biological processes and KEGG pathways. The CAD-related miRNAs targeting DEGs included hsa-miR-206, has-miR-320b, has-miR-4513, has-miR-765, and has-miR-92a-3p, and hsa-miR-92a-3p regulated the most DEGs. In the lncRNA-miRNA associations, only CDKN2B-AS1 regulated the CAD-related miRNA, hsa-miR-92a-3p, which was validated using the dual luciferase test.

CONCLUSIONS

CDKN2B-AS1 may act as an hsa-miR-92a-3p sponge to regulate the downstream DEGs in CAD. CDKN2B-AS1/ hsa-miR-92a-3p/GATA2 might be a novel mechanism for CAD.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Use of Machine Learning for the Identification and Validation of Immunogenic Cell Death Biomarkers and Immunophenotypes in Coronary Artery Disease

Objective

Immunogenic cell death (ICD) is part of the immune system's response to coronary artery disease (CAD). In this study, we bioinformatically evaluated the diagnostic and therapeutic utility of immunogenic cell death-related genes (IRGs) and their relationship with immune infiltration features in CAD.

Methods

We acquired the CAD-related datasets GSE12288, GSE71226, and GSE120521 from the Gene Expression Omnibus (GEO) database and the IRGs from the GeneCards database. After identifying the immune cell death-related differentially expressed genes (IRDEGs), we developed a risk model and detected immune subtypes in CAD. IRDEGs were identified using least absolute shrinkage and selection operator (LASSO) analysis. Using a nomogram, we confirmed that both the LASSO model and ICD signature genes had good diagnostic performance.

Results

There was a high degree of coincidence and immune representativeness between two CAD groups based on characteristic genes and hub genes. Hub genes were associated with the interaction of neuroactive ligands with receptors and cell adhesion receptors. The two groups differed in terms of adipogenesis, allograft rejection, and apoptosis, as well as the ICD signature and hub gene expression levels. The two CAD-ICD subtypes differed in terms of immune infiltration.

Conclusion

Quantitative real-time PCR (qRT-PCR) correlated CAD with the expression of OAS3, ITGAV, and PIBF1. The ICD signature genes are candidate biomarkers and reference standards for immune grouping in CAD and can be beneficial in precise immune-targeted therapy.

Altered Peripheral Blood Gene Expression in Childhood Cancer Survivors With Anthracycline-Induced Cardiomyopathy - A COG-ALTE03N1 Report

Background Anthracycline-induced cardiomyopathy is a leading cause of premature death in childhood cancer survivors, presenting a need to understand the underlying pathogenesis. We sought to examine differential blood-based mRNA expression profiles in anthracycline-exposed childhood cancer survivors with and without cardiomyopathy. Methods and Results We designed a matched case-control study (Children's Oncology Group-ALTE03N1) with mRNA sequencing on total RNA from peripheral blood in 40 anthracycline-exposed survivors with cardiomyopathy (cases) and 64 matched survivors without (controls). DESeq2 identified differentially expressed genes. Ingenuity Pathway Analyses (IPA) and Gene Set Enrichment Analyses determined the potential roles of altered genes in biological pathways. Functional validation was performed by gene knockout in human-induced pluripotent stem cell-derived cardiomyocytes using CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) technology. Median age at primary cancer diagnosis for cases and controls was 8.2 and 9.7 years, respectively. Thirty-six differentially expressed genes with fold change ≥±2 were identified; 35 were upregulated. IPA identified "hepatic fibrosis" and "iron homeostasis" pathways to be significantly modulated by differentially expressed genes, including toxicology functions of myocardial infarction, cardiac damage, and cardiac dilation. Leading edge analysis from Gene Set Enrichment Analyses identified lactate dehydrogenase A (LDHA) and cluster of differentiation 36 (CD36) genes to be significantly upregulated in cases. Interleukin 1 receptor type 1, 2 (IL1R1, IL1R2), and matrix metalloproteinase 8, 9 (MMP8, MMP9) appeared in multiple canonical pathways. LDHA-knockout human-induced pluripotent stem cell-derived cardiomyocytes showed increased sensitivity to doxorubicin. Conclusions We identified differential mRNA expression profiles in peripheral blood of anthracycline-exposed childhood cancer survivors with and without cardiomyopathy. Upregulation of LDHA and CD36 genes suggests metabolic perturbations in a failing heart. Dysregulation of proinflammatory cytokine receptors IL1R1 and IL1R2 and matrix metalloproteinases, MMP8 and MMP9 indicates structural remodeling that accompanies the clinical manifestation of symptomatic cardiotoxicity.

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

CCND1 Overexpression in Idiopathic Dilated Cardiomyopathy: A Promising Biomarker?

Cardiomyopathy, a disorder of electrical or heart muscle function, represents a type of cardiac muscle failure and culminates in severe heart conditions. The prevalence of dilated cardiomyopathy (DCM) is higher than that of other types (hypertrophic cardiomyopathy and restrictive cardiomyopathy) and causes many deaths. Idiopathic dilated cardiomyopathy (IDCM) is a type of DCM with an unknown underlying cause. This study aims to analyze the gene network of IDCM patients to identify disease biomarkers. Data were first extracted from the Gene Expression Omnibus (GEO) dataset and normalized based on the RMA algorithm (Bioconductor package), and differentially expressed genes were identified. The gene network was mapped on the STRING website, and the data were transferred to Cytoscape software to determine the top 100 genes. In the following, several genes, including VEGFA, IGF1, APP, STAT1, CCND1, MYH10, and MYH11, were selected for clinical studies. Peripheral blood samples were taken from 14 identified IDCM patients and 14 controls. The RT-PCR results revealed no significant differences in the expression of the genes APP, MYH10, and MYH11 between the two groups. By contrast, the STAT1, IGF1, CCND1, and VEGFA genes were overexpressed in patients more than in controls. The highest expression was found for VEGFA, followed by CCND1 (p < 0.001). Overexpression of these genes may contribute to disease progression in patients with IDCM. However, more patients and genes need to be analyzed in order to achieve more robust results.

RNAseq profiling of blood from patients with coronary artery disease: Signature of a T cell imbalance

Background

Cardiovascular disease had a global prevalence of 523 million cases and 18.6 million deaths in 2019. The current standard for diagnosing coronary artery disease (CAD) is coronary angiography either by invasive catheterization (ICA) or computed tomography (CTA). Prior studies employed single-molecule, amplification-independent RNA sequencing of whole blood to identify an RNA signature in patients with angiographically confirmed CAD. The present studies employed Illumina RNAseq and network co-expression analysis to identify systematic changes underlying CAD.

Methods

Whole blood RNA was depleted of ribosomal RNA (rRNA) and analyzed by Illumina total RNA sequencing (RNAseq) to identify transcripts associated with CAD in 177 patients presenting for elective invasive coronary catheterization. The resulting transcript counts were compared between groups to identify differentially expressed genes (DEGs) and to identify patterns of changes through whole genome co-expression network analysis (WGCNA).

Results

The correlation between Illumina amplified RNAseq and the prior SeqLL unamplified RNAseq was quite strong (r = 0.87), but there was only 9 % overlap in the DEGs identified. Consistent with the prior RNAseq, the majority (93 %) of DEGs were down-regulated ~1.7-fold in patients with moderate to severe CAD (>20 % stenosis). DEGs were predominantly related to T cells, consistent with known reductions in Tregs in CAD. Network analysis did not identify pre-existing modules with a strong association with CAD, but patterns of T cell dysregulation were evident. DEGs were enriched for transcripts associated with ciliary and synaptic transcripts, consistent with changes in the immune synapse of developing T cells.

Conclusions

These studies confirm and extend a novel mRNA signature of a Treg-like defect in CAD. The pattern of changes is consistent with stress-related changes in the maturation of T and Treg cells, possibly due to changes in the immune synapse.

Retinol binding protein 4 promotes the phenotypic transformation of vascular smooth muscle cells under high glucose condition via modulating RhoA/ROCK1 pathway

BACKGROUND

Phenotypic switch of vascular smooth muscle cells (VSMCs) contributes to the pathogenesis of atherosclerosis (AS). High level of retinol binding protein 4 (RBP4) is regarded as a risk factor in cardiac-cerebral vascular disease. This study is performed to clarify the biological function of RBP4 in modulating the phenotypic switch of VSMCs induced via RhoA/ROCK1 signaling pathway.

METHODS AND MATERIALS

In vivo experiment, all the rats were dividedinto control group (NC), diabetic group (DM) and diabetic atherosclerosis group(DAS). The expressions of biochemical indicators, RhoA and Rho associated coiled-coil containing protein kinase 1 (ROCK1) were detected. In vitro experiment, VSMCs were cultured under high glucose condition, and ectogenic RBP4, HA-1100, rapamycin or 3-Methyladenine (3-MA) were supplemented to treat the VSMCs, respectively. The proliferation and migration of VSMCs were evaluated. The regulatory relationship between RBP4 and ROCK1was predicted by bioinformatics analysis, and validated by qRT-PCR and Western blot. The regulatory effects of RBP4 on contractile phenotypic markers such as calponin, MYH11, α-SMA and autophagy markers including LC3II, LC3I and Beclin-1 as well as mTOR were also detected. Moreover, VSMCs were cultured exposed to ROCK1 overexpressed plasmid or short hairpin RNA (shRNA), the proliferation and migration of VSMCs were evluated and the regulatory effects of RhoA/ROCK1 signaling pathway on contractile phenotypic markers and autophagy markers were also detected.

RESULTS

In vivo, RhoA, ROCK1 and mTOR were highly expressed in the rats intraperitoneally injected with RBP4. In vitro, the expressions of calponin, MYH11, α-SMA, LC3II, LC3I and Beclin-1 were decreased in VSMCs treated with ROCK1-OA under high glucose condition, conversely, the expressions were increased in VSMCs exposed to ROCK1-shRNA. Ectogenic RBP4 facilitated high glucose-induced proliferation and migration of VSMCs, and it repressed the expression of calponin, MYH11, α-SMA, LC3II/Iand Beclin-1 in VSMCs. As expected, ROCK1 inhibit or counteracted the biological effects of RBP4 on VSMCs. In addition, the expressions of contractile phenotypic markers, LC3II/I and Beclin-1 were promoted and mTOR were decreased after the VSMCs treated with autophagy agonist, whereas no significant difference was observed in the expressions of ROCK1, RhoA.

CONCLUSION

RBP4 is an injurious factor in the pathogenesis of diabetic AS, and it promotes the phenotypic switch of VSMCs via activating RhoA/ROCK1 pathway and inhibiting autophagy.

ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction

Lipid metabolism plays an essential role in the genesis and progress of acute myocardial infarction (AMI). Herein, we identified and verified latent lipid-related genes involved in AMI by bioinformatic analysis. Lipid-related differentially expressed genes (DEGs) involved in AMI were identified using the GSE66360 dataset from the Gene Expression Omnibus (GEO) database and R software packages. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to analyze lipid-related DEGs. Lipid-related genes were identified by two machine learning techniques: least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE). The receiver operating characteristic (ROC) curves were used to descript diagnostic accuracy. Furthermore, blood samples were collected from AMI patients and healthy individuals, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the RNA levels of four lipid-related DEGs. Fifty lipid-related DEGs were identified, 28 upregulated and 22 downregulated. Several enrichment terms related to lipid metabolism were found by GO and KEGG enrichment analyses. After LASSO and SVM-RFE screening, four genes (ACSL1, CH25H, GPCPD1, and PLA2G12A) were identified as potential diagnostic biomarkers for AMI. Moreover, the RT-qPCR analysis indicated that the expression levels of four DEGs in AMI patients and healthy individuals were consistent with bioinformatics analysis results. The validation of clinical samples suggested that 4 lipid-related DEGs are expected to be diagnostic markers for AMI and provide new targets for lipid therapy of AMI.

Identification of molecular subtypes of coronary artery disease based on ferroptosis- and necroptosis-related genes

Aim: Coronary artery disease (CAD) is a heterogeneous disorder with high morbidity, mortality, and healthcare costs, representing a major burden on public health. Here, we aimed to improve our understanding of the genetic drivers of ferroptosis and necroptosis and the clustering of gene expression in CAD in order to develop novel personalized therapies to slow disease progression.

Methods: CAD datasets were obtained from the Gene Expression Omnibus. The identification of ferroptosis- and necroptosis-related differentially expressed genes (DEGs) and the consensus clustering method including the classification algorithm used km and distance used spearman were performed to differentiate individuals with CAD into two clusters (cluster A and cluster B) based expression matrix of DEGs. Next, we identified four subgroup-specific genes of significant difference between cluster A and B and again divided individuals with CAD into gene cluster A and gene cluster B with same methods. Additionally, we compared differences in clinical information between the subtypes separately. Finally, principal component analysis algorithms were constructed to calculate the cluster-specific gene score for each sample for quantification of the two clusters.

Results: In total, 25 ferroptosis- and necroptosis-related DEGs were screened. The genes in cluster A were mostly related to the neutrophil pathway, whereas those in cluster B were mostly related to the B-cell receptor signaling pathway. Moreover, the subgroup-specific gene scores and CAD indices were higher in cluster A and gene cluster A than in cluster B and gene cluster B. We also identified and validated two genes showing upregulation between clusters A and B in a validation dataset.

Conclusion: High expression of CBS and TLR4 was related to more severe disease in patients with CAD, whereas LONP1 and HSPB1 expression was associated with delayed CAD progression. The identification of genetic subgroups of patients with CAD may improve clinician knowledge of disease pathogenesis and facilitate the development of methods for disease diagnosis, classification, and prognosis.

Lipid metabolism patterns and relevant clinical and molecular features of coronary artery disease patients: an integrated bioinformatic analysis

Background

Hyperlipidaemia is an important factor that induces coronary artery disease (CAD). This study aimed to explore the lipid metabolism patterns and relevant clinical and molecular features of coronary artery disease patients.

Methods

In the current study, datasets were fetched from the Gene Expression Omnibus (GEO) database and nonnegative matrix factorization clustering was used to establish a new CAD classification based on the gene expression profile of lipid metabolism genes. In addition, this study carried out bioinformatics analysis to explore intrinsic biological and clinical characteristics of the subgroups.

Results

Data for a total of 615 samples were extracted from the Gene Expression Omnibus database and were associated with clinical information. Then, this study used nonnegative matrix factorization clustering for RNA sequencing data of 581 lipid metabolism relevant genes, and the 296 patients with CAD were classified into three subgroups (NMF1, NMF2, and NMF3). Subjects in subgroup NMF2 tended to have an increased severity of CAD. The CAD index and age of group NMF1 were similar to those of group NMF3, but their intrinsic biological characteristics exhibited significant differences. In addition, weighted gene coexpression network analysis (WGCNA) was used to determine the most important modules and screen lipid metabolism related genes, followed by further analysis of the DEGs in which the significant genes were identified based on clinical information. The progression of coronary atherosclerosis may be influenced by genes such as PTGDS and DGKE.

Conclusion

Different CAD subgroups have their own intrinsic biological characteristics, indicating that more personalized treatment should be provided to patients in each subgroup, and some lipid metabolism related genes (PDGTS, DGKE and so on) were related significantly with clinical characteristics.

Over-expression of IL1R2 in PBMCs of Patients with Coronary Artery Disease and Its Clinical Significance

BACKGROUND

IL-1 has been widely explored and played a role in regulating inflammatory and immune responses to various disorders. Nevertheless, the role of interleukin-1 receptor type II, a protein-coding gene of interleukin-1 in coronary artery disease patients with peripheral blood mononuclear cells, persists to be undetermined.

METHODS

Our study discovered the IL-1 receptor type II expression through gene expression omnibus (GEO) public repository based on bioinformatics tools and further validation was carried out between coronary artery disease patients and healthy participants using peripheral blood mononuclear cells samples in Second Hospital of Tianjin Medical University. A total of 180 participants, comprising 90 cases of coronary artery disease and 90 samples of healthy control were retrospectively evaluated and the correlation of IL-1 receptor type II was observed between serum levels of oxidized low-density lipoprotein and SYNTAX score. Furthermore, the clinical significance of IL-1 receptor type II was evaluated in peripheral blood mononuclear cells of coronary artery disease patients by the receiver operating curve using the area under the curve.

RESULTS

IL-1 receptor type II was markedly overexpressed in peripheral blood mononuclear cells and severe patients with coronary artery disease compared to the healthy control participants. Meanwhile, a positive correlation of IL-1 receptor type II expression was significantly observed between SYNTAX score and oxidized low-density lipoprotein of coronary artery disease patients. Further, the receiver operating curve achieved a significantly higher area under the curve of 0.813 in coronary artery disease patients with peripheral blood mononuclear cells. Thus, IL-1 receptor type II expressions were not only directly correlated with peripheral blood mononuclear cells but also showed potential significance in coronary artery disease patients.

CONCLUSION

IL-1 receptor type II might be involved in the immune/inflammatory responses of coronary artery disease accompanied by other cytokine receptor genes.

Identification of Biomarkers Associated With CD8+ T Cells in Coronary Artery Disease and Their Pan-Cancer Analysis

Purpose

To identify biomarkers associated with CD8+ T cells in coronary artery disease (CAD) and initially explore their potential role in the tumor immune microenvironment.

Materials and Methods

CAD-related datasets GSE12288, GSE34198, and GSE66360, were downloaded from the GEO database. First, GSVA was performed based on the GSE12288 dataset. Then WGCNA analysis was performed to identify the most relevant module and candidate hub gene for CD8+ T cells, followed by GO and KEGG analysis of this module. Secondly, the relationship between candidate hub genes and CD8+ T cells was verified using GSE34198 and GSE66360, which led to the identification of hub genes. The relationship of hub genes with CD8+ T cells in cancer was analyzed using the TIMER database. Methylation analysis of hub genes was performed using the DiseaseMeth database. CAD, pan-cancer, pan-cell lines, and pan-normal tissues, correlations between hub genes. In addition, potential drugs and TFs associated with hub genes were predicted, and the ceRNA network was constructed. Finally, GSEA was performed separately for hub genes.

Results

CAD was shown to be associated with immune response by GSVA analysis. WGCNA identified the blue module as most related to CD8+ T cells and identified nine candidate hub genes. The relevance of CAD to immunity was further confirmed by GO and KEGG analysis of the module. Two additional datasets validated and identified three hub genes (FBXO7, RAD23A, and MKRN1) that significantly correlated with CD8+ T cells. In addition, we found that hub genes were positively associated with CD8+ T cells in TGCT, THCA, and KICH cancers by our analysis. Moreover, the hub gene was differentially methylated. We also analyzed the correlation between hub genes in CAD, different cancers, different cell lines, and different normal tissues. The results of all the analyses showed a positive correlation between them. Finally, we successfully constructed hub gene-associated TF-gene and ceRNA networks and predicted 11 drugs associated with hub genes. GSEA suggests that hub genes are related to multiple immune response processes.

Conclusion

FBXO7, RAD23A, and MKRN1 are significantly associated with CD8+ T cells in CAD and multiple cancers and may act through immune responses in CAD and cancer.

Whole-Blood Transcriptional Profiles Enable Early Prediction of the Presence of Coronary Atherosclerosis and High-Risk Plaque Features at Coronary CT Angiography

Existing tools to estimate cardiovascular (CV) risk have sub-optimal predictive capacities. In this setting, non-invasive imaging techniques and omics biomarkers could improve risk-prediction models for CV events. This study aimed to identify gene expression patterns in whole blood that could differentiate patients with severe coronary atherosclerosis from subjects with a complete absence of detectable coronary artery disease and to assess associations of gene expression patterns with plaque features in coronary CT angiography (CCTA). Patients undergoing CCTA for suspected coronary artery disease (CAD) were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed with RNA sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (≥70%) in the CCTA and subjects with an absence of coronary plaque. Notably, regression analysis revealed expression signatures associated with the Leaman score, the segment involved score, the segment stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole-blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole-blood transcriptional profiles may predict plaque characteristics.

Risk factors, transcriptomics, and outcomes of myocardial injury following lower extremity revascularization

Myocardial injury after non-cardiac surgery (MINS) is common. We investigated the incidence and outcomes of MINS, and mechanistic underpinnings using pre-operative whole blood gene expression profiling in a prospective cohort study of individuals undergoing lower extremity revascularization (LER) for peripheral artery disease (PAD). Major adverse cardiovascular and limb events (MACLE) were defined as a composite of death, myocardial infarction, stroke, major lower extremity amputation or reoperation. Among 226 participants undergoing LER, MINS occurred in 53 (23.5%). Patients with MINS had a greater incidence of major adverse cardiovascular events (49.1% vs. 22.0%, adjusted HR 1.87, 95% CI 1.07–3.26) and MACLE (67.9% vs. 44.5%; adjusted HR 1.66, 95% CI 1.08–2.55) at median 20-month follow-up. Pre-operative whole blood transcriptome profiling of a nested matched MINS case–control cohort (n = 41) identified upregulation of pathways related to platelet alpha granules and coagulation in patients who subsequently developed MINS. Thrombospondin 1 (THBS1) mRNA expression was 60% higher at baseline in patients who later developed MINS, and was independently associated with long-term cardiovascular events in the Duke Catheterization Genetics biorepository cohort. In conclusion, pre-operative THBS1 mRNA expression is higher in patients who subsequently develop MINS and is associated with incident cardiovascular events. Pathways related to platelet activity and coagulation associated with MINS provide novel insights into mechanisms of myocardial injury.

Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues

Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.

Construction of the gene expression subgroups of patients with coronary artery disease through bioinformatics approach

Coronary artery disease (CAD) is a heterogeneous disease that has placed a heavy burden on public health due to its considerable morbidity, mortality and high costs. Better understanding of the genetic drivers and gene expression clustering behind CAD will be helpful for the development of genetic diagnosis of CAD patients. The transcriptome of 352 CAD patients and 263 normal controls were obtained from the Gene Expression Omnibus (GEO) database. We performed a modified unsupervised machine learning algorithm to group CAD patients. The relationship between gene modules obtained through weighted gene co-expression network analysis (WGCNA) and clinical features was identified by the Pearson correlation analysis. The annotation of gene modules and subgroups was done by the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Three gene expression subgroups with the clustering score of greater than 0.75 were constructed. Subgroup I may experience coronary artery disease of an in-creased severity, while subgroup III is milder. Subgroup I was found to be closely related to the upregulation of the mitochondrial autophagy pathway, whereas the genes of subgroup II were shown to be related to the upregulation of the ribosome pathway. The high expression of APOE, NOS1 and NOS3 in the subgroup I suggested that the patients had more severe coronary artery disease. The construction of genetic subgroups of CAD patients has enabled clinicians to improve their understanding of CAD pathogenesis and provides potential tools for disease diagnosis, classification and assessment of prognosis.

Biomarkers of Blood from Patients with Atherosclerosis Based on Bioinformatics Analysis

Atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that attach to arteries and cause cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, computerized tomography, and nuclear scans, assess cardiovascular disease risk and treatment targets. However, there is currently no simple blood biochemical index or biological target for the diagnosis of atherosclerosis. Therefore, it is of interest to find a biochemical blood marker for atherosclerosis. Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using the Robustrankaggreg algorithm. The genes considered more critical by the Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification. Twenty-one possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY, and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cells. In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the analysis of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

Identification of key miRNAs and mRNAs related to coronary artery disease by meta-analysis

Background

To illustrate the mechanism of miRNA and mRNA in coronary artery diseasen (CAD), differentially expressed microRNAs (DEmiRNAs) and genes (DEGs) were analyzed.

Methods

The mRNA transcription profiles of GSE20680 (including 87 blood samples of CAD and 52 blood samples of control), GSE20681 (including 99 blood samples of CAD and 99 blood samples of control) and GSE12288 (including 110 blood samples of CAD and 112 blood samples of control) and the miRNA transcription profiles of GSE59421 (including 33 blood samples of CAD and 37 blood samples of control), GSE49823 (including 12 blood samples of CAD and 12 blood samples of control) and GSE28858 (including 13 blood samples of CAD and 13 blood samples of control) were downloaded from Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/). Then, the homogenous expressed mRNAs and miRNAs across the three mRNA transcription profiles and three miRNA transcription profiles were screened using the Fishers exact test in MetaDE. ES package. The weighted gene co-expression network analysis (WGCNA) was used to analyze gene modules. Additionally, the integrated miRNAs–targets regulatory network using the DEmiRNA and their targets was constructed using Cytoscape.

Results

A total of 1201 homogenously statistically significant DEGs were identified including 879 up-regulated and 322 down-regulated DEGs, while a total of 47 homogenously statistically significant DEmiRNAs including 37 up-regulated and 10 down-regulated DEmiRNAs in CAD compared with the controls across the three mRNA transcription profiles and the three miRNA transcription profiles. A total of 5067 genes were clustered into 9 modules in the training dataset, among which, 8 modules were validated. In the miRNAs-targets network, there existed 267 interaction relationships among 5 miRNAs (hsa-miR-361-5p, hsa-miR-139-5p, hsa-miR-146b-5p, hsa-miR-502-5p and hsa-miR-501-5p) and 213 targets. CAV1 could be the target of hsa-miR-361-5 while HSF2 was the target of both hsa-miR-361-5p and hsa-miR-146b-5p. CAV1 was significantly enriched in the GO term of regulation of cell proliferation.

Conclusion

hsa-miR-361-5p, has-miR-146b-5p, CAV1 and HSF2 could play an important role in CAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02211-2.

RNA sequencing of blood in coronary artery disease: involvement of regulatory T cell imbalance

BACKGROUND

Cardiovascular disease had a global prevalence of 523 million cases and 18.6 million deaths in 2019. The current standard for diagnosing coronary artery disease (CAD) is coronary angiography. Surprisingly, despite well-established clinical indications, up to 40% of the one million invasive cardiac catheterizations return a result of 'no blockage'. The present studies employed RNA sequencing of whole blood to identify an RNA signature in patients with angiographically confirmed CAD.

METHODS

Whole blood RNA was depleted of ribosomal RNA (rRNA) and analyzed by single-molecule sequencing of RNA (RNAseq) to identify transcripts associated with CAD (TRACs) in a discovery group of 96 patients presenting for elective coronary catheterization. The resulting transcript counts were compared between groups to identify differentially expressed genes (DEGs).

RESULTS

Surprisingly, 98% of DEGs/TRACs were down-regulated ~ 1.7-fold in patients with mild to severe CAD (> 20% stenosis). The TRACs were independent of comorbid risk factors for CAD, such as sex, hypertension, and smoking. Bioinformatic analysis identified an enrichment in transcripts such as FoxP1, ICOSLG, IKZF4/Eos, SMYD3, TRIM28, and TCF3/E2A that are likely markers of regulatory T cells (Treg), consistent with known reductions in Tregs in CAD. A validation cohort of 80 patients confirmed the overall pattern (92% down-regulation) and supported many of the Treg-related changes. TRACs were enriched for transcripts associated with stress granules, which sequester RNAs, and ciliary and synaptic transcripts, possibly consistent with changes in the immune synapse of developing T cells.

CONCLUSIONS

These studies identify a novel mRNA signature of a Treg-like defect in CAD patients and provides a blueprint for a diagnostic test for CAD. The pattern of changes is consistent with stress-related changes in the maturation of T and Treg cells, possibly due to changes in the immune synapse.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease

This investigation seeks to dissect coronary artery disease molecular target candidates along with its underlying molecular mechanisms. Data on patients with CAD across three separate array data sets, GSE66360, GSE19339 and GSE97320 were extracted. The gene expression profiles were obtained by normalizing and removing the differences between the three data sets, and important modules linked to coronary heart disease were identified using weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were applied in order to identify statistically significant genetic modules with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov ). The online STRING tool was used to construct a protein-protein interaction (PPI) network, followed by the use of Molecular Complex Detection (MCODE) plug-ins in Cytoscape software to identify hub genes. Two significant modules (green-yellow and magenta) were identified in the CAD samples. Genes in the magenta module were noted to be involved in inflammatory and immune-related pathways, based on GO and KEGG enrichment analyses. After the MCODE analysis, two different MCODE complexes were identified in the magenta module, and four hub genes (ITGAM, degree = 39; CAMP, degree = 37; TYROBP, degree = 28; ICAM1, degree = 18) were uncovered to be critical players in mediating CAD. Independent verification data as well as our RT-qPCR results were highly consistent with the above finding. ITGAM, CAMP, TYROBP and ICAM1 are potential targets in CAD. The underlying mechanism may be related to the transendothelial migration of leukocytes and the immune response.

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

Objectives

To analyze the gene expression profile of peripheral blood monocytes in different stages of coronary artery disease (CAD) by transcriptome sequencing, and to explore potential genes and pathway involved in CAD pathogenesis.

Methods

According to the screening of coronary angiography and quality control of blood samples, eight intermediate coronary lesion patients were selected, then eight patients with acute myocardial infarction, and eight patients with normal coronary angiography were matched by age and gender. Transcriptomics sequencing was conducted for the peripheral blood monocytes of these 24 samples by using the Illumina HiSeq high-throughput platform. Then, differentially expressed genes (DEGs) were analyzed. Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, and protein-protein interaction (PPI) network were applied to annotate the potential functions of DEGs.

Results

Compared with the normal coronary angiography group, we identified a total of 169 DEGs in the intermediate coronary lesion group, which were significantly enriched in 59 GO terms and 17 KEGG pathways. Compared with the normal coronary angiography group, we found a total of 2,028 DEGs, which were significantly enriched in 311 GO terms and 20 KEGG pathways in the acute myocardial infarction group. The cross-comparison between normal versus intermediate coronary lesion group, and normal versus acute myocardial infarction group included 98 differential genes with 65 up regulated and 33 down regulated genes, which were significantly enriched in 46 GO terms and 10 KEGG pathways. During the progression of CAD, there was a significant up-regulated expression of CSF3, IL-1A, CCR7, and IL-18, and down-regulated expression of MAPK14. Besides GO items such as inflammatory response was significantly enriched, KEGG analysis showed the most remarkable enrichments in IL-17 signaling pathway and cytokine-cytokine receptor interactions.

Conclusions

Transcriptomics profiles vary in patients with different severity of CAD. CSF3, IL-1A, CCR7, IL-18, and MAPK14, as well as IL-17 signaling pathway and cytokine and cytokine receptor interaction signaling pathway related with inflammatory response might be the potential biomarker and targets for the treatment of coronary artery disease.

Deciphering key genes in cardio-renal syndrome using network analysis

Cardio-renal syndrome (CRS) is a rapidly recognized clinical entity which refers to the inextricably connection between heart and renal impairment, whereby abnormality to one organ directly promotes deterioration of the other one. Biological markers help to gain insight into the pathological processes for early diagnosis with higher accuracy of CRS using known clinical findings. Therefore, it is of interest to identify target genes in associated pathways implicated linked to CRS. Hence, 119 CRS genes were extracted from the literature to construct the PPIN network. We used the MCODE tool to generate modules from network so as to select the top 10 modules from 23 available modules. The modules were further analyzed to identify 12 essential genes in the network. These biomarkers are potential emerging tools for understanding the pathophysiologic mechanisms for the early diagnosis of CRS. Ontological analysis shows that they are rich in MF protease binding and endo-peptidase inhibitor activity. Thus, this data help increase our knowledge on CRS to improve clinical management of the disease.

Identification of Potential Biomarkers for CAD Using Integrated Expression and Methylation Data

DNA methylation plays an essential role in the pathogenesis of coronary artery disease (CAD) through regulating mRNA expressions. This study aimed to identify hub genes regulated by DNA methylation as biomarkers of CAD. Gene expression and methylation datasets of peripheral blood leukocytes (PBLs) of CAD were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, multiple computational approaches were performed to analyze the regulatory networks and to recognize hub genes. Finally, top hub genes were verified in a case-control study, based on their differential expressions and methylation levels between CAD cases and controls. In total, 535 differentially expressed-methylated genes (DEMGs) were identified and partitioned into 4 subgroups. TSS200 and 5′UTR were confirmed as high enrichment areas of differentially methylated CpGs sites (DMCs). The function of DEMGs is enriched in processes of histone H3-K27 methylation, regulation of post-transcription and DNA-directed RNA polymerase activity. Pathway enrichment showed DEMGs participated in the VEGF signaling pathway, adipocytokine signaling pathway, and PI3K-Akt signaling pathway. Besides, expressions of hub genes fibronectin 1 (FN1), phosphatase (PTEN), and tensin homolog and RNA polymerase III subunit A (POLR3A) were discordantly expressed between CAD patients and controls and related with DNA methylation levels. In conclusion, our study identified the potential biomarkers of PBLs for CAD, in which FN1, PTEN, and POLR3A were confirmed.

In silico identification of common and specific signatures in coronary heart diseases

Coronary heart disease (CHD) is on the increase in developing countries, where lifestyle choices such as smoking, bad diet, and no exercise contribute and increase the incidence of high blood pressure and high cholesterol levels to cause CHD. Through utilization of a biomarker-based approach for developing interventions, the aim of the study was to identify differentially expressed genes (DEGs) and their association and impact on various bio-targets. The microarray datasets of both healthy and CHD patients were analyzed to identify the DEGs and their interactions using Gene Ontology, PANTHER, Reactome, and STRING (for the possible associated genes with multiple targets). Our data mining approach suggests that the DEGs were associated with molecular functions, including protein binding (75%) and catalytic activity (56%); biological processes such as cellular process (83%), biological regulation (57%), and metabolic process (44%); and cellular components such as cell (65%) and organelle (58%); as well as other associations including apoptosis, inflammatory, cell development and metabolic pathways. The molecular functions were further analyzed, and protein binding in particular was analyzed using network analysis to determine whether there was a clear association with CHD and disease. The ingenuity pathway analysis revealed pathways related to cell cholesterol biosynthesis, the immune system including cytokinin signaling, in which, the understanding of DEGs is crucial to predict the advancement of preventive strategies. Results of the present study showed that, there is a need to validate the top DEGs to rule out their molecular mechanism in heart failure caused by CHD.

Identification of Four Potential Biomarkers Associated With Coronary Artery Disease in Non-diabetic Patients by Gene Co-expression Network Analysis

Background

Coronary artery disease (CAD) is a type of cardiovascular disease that greatly hurts the health of human beings. Diabetic status is one of the largest clinical factors affecting CAD-associated gene expression changes. Most of the studies focus on diabetic patients, whereas few have been done for non-diabetic patients. Since the pathophysiological processes may vary among these patients, we cannot simply follow the standard based on the data from diabetic patients. Therefore, the prognostic and predictive diagnostic biomarkers for CAD in non-diabetic patient need to be fully recognized.

Materials and Methods

To screen out candidate genes associated with CAD in non-diabetic patients, weighted gene co-expression network analysis (WGCNA) was constructed to conduct an analysis of microarray expression profiling in patients with CAD. First, the microarray data GSE20680 and GSE20681 were downloaded from NCBI. We constructed co-expression modules via WGCNA after excluding the diabetic patients. As a result, 18 co-expression modules were screened out, including 1,225 differentially expressed genes (DEGs) that were obtained from 152 patients (luminal stenosis ≥50% in at least one major vessel) and 170 patients (stenosis of <50%). Subsequently, a Pearson's correlation analysis was conducted between the modules and clinical traits. Then, a functional enrichment analysis was conducted, and we used gene network analysis to reveal hub genes. Last, we validated the hub genes with peripheral blood samples in an independent patient cohort using RT-qPCR.

Results

The results showed that the midnight blue module and the yellow module played vital roles in the pathogenesis of CAD in non-diabetic patients. Additionally, CD40, F11R, TNRC18, and calcium/calmodulin-dependent protein kinase type II gamma (CAMK2G) were screened out and validated using enzyme-linked immunosorbent assay (ELISA) in an independent patient cohort and immunohistochemical (IHC) staining in an atherosclerosis mouse model.

Conclusion

Our findings demonstrate that hub genes, CD40, F11R, TNRC18, and CAMK2G, are surrogate diagnostic biomarkers and/or therapeutic targets for CAD in non-diabetic patients and require deeper validation.

Whole blood transcriptome profile at hospital admission discriminates between patients with ST-segment elevation and non-ST-segment elevation acute myocardial infarction

Whether ST-segment (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) should be regarded as distinct pathophysiological entities is a matter of debate. We tested the hypothesis that peripheral blood gene-expression profiles at presentation distinguish STEMI from NSTEMI. We performed a case-control study collecting whole-blood from 60 STEMI and 58 NSTEMI (defined according to the third universal definition of MI) consecutive patients on hospital admission. We used RNA-sequencing for the discovery phase, comparing 15 STEMI vs. 15 NSTEMI patients, matched for age, sex, and cardiovascular risk factors, and quantitative PCR in the remaining unmatched patients for validating top-significant genes. Gene-level differential expression analysis identified significant differences in the expression of 323 genes: 153 genes withstood correction for admission cardiac troponin I (cTnI), differentiating the two conditions independently of myocardial necrosis extent. Functional annotation analysis uncovered divergent modulation in leukocyte and platelet activation, cell migration, and mitochondrial respiratory processes. Linear regression analysis revealed gene expression patterns on admission predicting infarct size, as indexed by cTnI peak (R² = 0.58–0.75). Our results unveil distinctive pathological traits for these two MI subtypes and provide insights into the early assessment of injury extent. This could translate into RNA-based disease-specific biomarkers for precision diagnosis and risk stratification.

RNA-sequencing analysis of differential gene expression associated with arterial stiffness

OBJECTIVES

Arterial stiffness is recognized as an important predictor of cardiovascular disease morbidity and mortality, independent of traditional cardiovascular disease risk factors. Given that arterial tissue is not easily accessible, most gene expression studies on arterial stiffness have been conducted on animals or on patients who have undergone by-pass surgeries. In order to obtain a deeper understanding of early changes of arterial stiffness, this study compared transcriptome profiles between healthy adults with higher and lower arterial stiffness.

METHODS

The sample included 20 healthy female adults without cardiovascular disease. Arterial stiffness was measured by carotid-femoral pulse wave velocity, the "gold-standard" measure of central arterial stiffness. Peripheral blood samples collected to PAXgene™ RNA tubes were used for RNA sequencing (RNA-seq). The potential confounding effects of age, body mass index, and mean arterial pressure were controlled for in RNA-seq analysis. To validate RNA-seq results, quantitative real-time PCR (qRT-PCR) was performed for six selected genes.

RESULTS

The findings demonstrated that genes including CAPN9, IL32, ERAP2, RAB6B, MYBPH, and miRNA626 were down-regulated, and that MOCS1 gene was up-regulated among the people with higher arterial stiffness. Real-time PCR showed that the changes of CAPN9, IL32, ERAP2, and RAB6B were in concordance with RNA-seq data, and confirmed the validity of the gene expression profiles obtained by RNA-seq analysis.

CONCLUSIONS

Previous studies have suggested the potential roles of CAPN9, IL32, and ERAP2 in structural changes of the arterial wall through up-regulation of metalloproteinases. However, the current study showed that CAPN9, IL32, and ERAP2 were down-regulated in the individuals with higher arterial stiffness, compared with those with lower arterial stiffness. The unexpected directions of expression of these genes may indicate an effort to maintain vascular homeostasis during increased arterial stiffness among healthy individuals. Further studies are guaranteed to investigate the roles of CAPN9, IL32, and ERAP2 in regulating arterial stiffness in people with and without cardiovascular disease.

Genes associated with inflammation may serve as biomarkers for the diagnosis of coronary artery disease and ischaemic stroke

Background

The current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms.

Methods

Two array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients.

Results

A total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3.

Conclusions

The CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.

Model-based clustering of multi-tissue gene expression data

MOTIVATION

Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues.

RESULTS

We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals.

AVAILABILITY AND IMPLEMENTATION

Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

New candidate genes for ST-elevation myocardial infarction

BACKGROUND

Despite extensive research in atherosclerosis, the mechanisms of coronary atherothrombosis in ST-elevation myocardial infarction (STEMI) patients are undetermined.

OBJECTIVES

Our aim was to find candidate genes involved in STEMI by analysing leucocyte gene expression in STEMI patients, without the influence of secondary inflammation from innate immunity, which was assumed to be a consequence rather than the cause of coronary atherothrombosis.

METHODS

Fifty-one patients were included at coronary angiography because of STEMI. Arterial blood was sampled in the acute phase (P1), at 24-48 h (P2) and at 3 months (P3). Leucocyte RNA was isolated and gene expression analysis was performed by Affymetrix Human Transcriptome Array 2.0. By omission of up- or downregulated genes at P2, secondary changes from innate immunity were excluded. Genes differentially expressed in P1 when compared to the convalescent sample in P3 were determined as genes involved in STEMI.

RESULTS

Three genes were upregulated at P1 compared to P3; ABCG1 (P = 5.81 × 10^-5 ), RAB20 (P = 3.69 × 10^-5 ) and TMEM2 (P = 7.75 × 10^-6 ) whilst four were downregulated; ACVR1 (P = 9.01 × 10^-5 ), NFATC2IP (P = 8.86 × 10^-5 ), SUN1 (P = 3.87 × 10^-5 ) and TTC9C (P = 7.18 × 10^-6 ). These genes were also highly expressed in carotid atherosclerotic plaques.

CONCLUSIONS

We found seven genes involved in STEMI. The study is unique regarding the blood sampling in the acute phase and omission of secondary expressed genes from innate immunity. However, the results need to be replicated by future studies.

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease

BACKGROUND

The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.

OBJECTIVE

Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.

METHODS

Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.

RESULTS

The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).

CONCLUSIONS

This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.

RNA-sequencing reveals that STRN, ZNF484 and WNK1 add to the value of mitochondrial MT-COI and COX10 as markers of unstable coronary artery disease

Markers in monocytes, precursors of macrophages, which are related to CAD, are largely unknown. Therefore, we aimed to identify genes in monocytes predictive of a new ischemic event in patients with CAD and/or discriminate between stable CAD and acute coronary syndrome. We included 66 patients with stable CAD, of which 24 developed a new ischemic event, and 19 patients with ACS. Circulating CD14+ monocytes were isolated with magnetic beads. RNA sequencing analysis in monocytes of patients with (n = 13) versus without (n = 11) ischemic event at follow-up and in patients with ACS (n = 12) was validated with qPCR (n = 85). MT-COI, STRN and COX10 predicted new ischemic events in CAD patients (power for separation at 1% error rate of 0.97, 0.90 and 0.77 respectively). Low MT-COI and high STRN were also related to shorter time between blood sampling and event. COX10 and ZNF484 together with MT-COI, STRN and WNK1 separated ACS completely from stable CAD patients. RNA expressions in monocytes of MT-COI, COX10, STRN, WNK1 and ZNF484 were independent of cholesterol lowering and antiplatelet treatment. They were independent of troponin T, a marker of myocardial injury. But, COX10 and ZNF484 in human plaques correlated to plaque markers of M1 macrophage polarization, reflecting vascular injury. Expression of MT-COI, COX10, STRN and WNK1, but not that of ZNF484, PBMCs paired with that in monocytes. The prospective study of relation of MT-COI, COX10, STRN, WNK1 and ZNF484 with unstable CAD is warranted.

Increased grp78 transcription is correlated to reduced tlr4 transcription in patients surviving sepsis

Regulated transcriptional readthrough during stress maintains genome structure and ensures access to genes that are necessary for cellular recovery. A broad number of genes, including of the bacterial sensor Toll-like receptor 4 (TLR-4), are markedly transcribed on initiating the systemic inflammatory response. Here we study the transcriptional patterns of tlr4 and of its modulator grp78 during human sepsis, and establish their correlations with the outcome of patients. We measured the daily tlr4 and grp78 RNA expression levels in peripheral blood of septic patients, immediately after admission to intensive care, and modeled these RNA values with a sine damping function. We obtained negative correlations between the transcription of tlr4 and grp78 RNA in the survivor group. In contrast, such relation is lost in the deceased patients. Loss of transcriptional homeostasis predicted by our model within the initial 4 days of hospitalization was confirmed by death of those patients up to 28 days later.

Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation

BACKGROUND AND AIMS

We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction, Support Vector Machine (SVM) algorithm, and multi-cohort validation.

METHODS

In the discovery phase, a public blood-based microarray dataset of 110 patients with known CAD was analyzed by weighted gene coexpression network analysis and protein-protein interaction network analysis to identify candidate hub genes. In the training set with 151 CAD patients, bioinformatically identified hub genes were experimentally verified by real-time polymerase chain reaction, and statistically filtered with the SVM algorithm to develop a GES. Internal and external validation of GES was performed in patients with suspected CAD from two validation cohorts (n = 209 and 206).

RESULTS

The discovery phase screened 15 network-centric hub genes significantly correlated with the Duke CAD Severity Index. In the training cohort, 12 of 15 hub genes were filtered to construct a blood-based GES12, which showed good discrimination for higher modified Gensini scores (AUC: 0.798 and 0.812), higher Sullivan Extent scores (AUC: 0.776 and 0.778), and the presence of obstructive CAD (AUC: 0.834 and 0.792) in two validation cohorts. A nomogram comprising GES12, smoking status, hypertension status, low density lipoprotein cholesterol level, and body mass index further improved performance, with respect to discrimination, risk classification, and clinical utility, for prediction of coronary stenosis severity.

CONCLUSIONS

GES12 is useful in predicting the severity of coronary artery stenosis in patients with known or suspected CAD.

Gene dysregulation in peripheral blood of moyamoya disease and comparison with other vascular disorders

Objective

Moyamoya disease (MMD) is a chronic occlusive cerebrovascular disease with unknown etiology, sharing many similar clinical symptoms with other vascular disorders. This study aimed to investigate gene dysregulation in peripheral blood of MMD and compare it with other vascular disorders.

Methods

Transcriptomic profiles of 12 MMD patients and 8 healthy controls were obtained using RNA sequencing. Differentially expressed genes (DEGs) were identified and several were validated by quantitative real-time PCR in independent samples. Biological pathway enrichment analysis of DEGs and deconvolution of leukocyte subsets in peripheral blood were performed. Expression profiles for other vascular diseases were downloaded from public database and consistent DEGs were calculated. Gene set enrichment analysis (GSEA) was conducted to compare gene dysregulation pattern between MMD and other vascular diseases.

Results

A total of 533 DEGs were identified for MMD. Up-regulated genes were mainly involved in extracellular matrix (ECM) organization, whereas down-regulated genes were primarily associated with inflammatory and immune responses. As for cell populations, significantly increased naïve B cells and naïve CD4 cells as well as obviously decreased resting natural killer cells were observed in peripheral blood of MMD patients. GSEA analysis indicated that only up-regulated genes of ischemic stroke and down-regulated genes of coronary artery disease and myocardial infarction were enriched in up-regulated and down-regulated genes of MMD, respectively.

Conclusion

Dysregulated genes in peripheral blood of MMD mainly played key roles in ECM organization, inflammatory and immune responses. This gene dysregulation pattern was specific compared with other vascular diseases. Besides, naïve B cells, naïve CD4 cells and resting natural killer cells were aberrantly disrupted in peripheral blood of MMD patients. These results will help elucidate the complicated pathogenic mechanism of MMD.

Identification of the molecular subgroups in coronary artery disease by gene expression profiles

Coronary artery disease (CAD) is the most common type of cardiovascular disease and becomes a leading cause of death worldwide. Aiming to uncover the underlying molecular features for different types of CAD, we classified 352 CAD cases into three subgroups based on gene expression profiles, which were retrieved from the Gene Expression Omnibus database. Also, these subgroups present different expression patterns and clinical characteristics. To uncover the transcriptomic differences between the subgroups, weighted gene co-expression analysis (WGCNA) was used and identified six subgroup-specific WGCNA modules. Characterization of the WCGNA modules revealed that lipid metabolism pathways, specifically upregulated in subgroup I, might be an indicator of increased severity. Moreover, subgroup II was considered as an early-stage of CAD because of normal-like gene expression patterns. In contrast, the mammalian target of rapamycin signaling pathway was significantly upregulated in subgroup III. Although subgroups II and III did not have a significant prognostic difference, their intrinsic biological characteristics were highly different, suggesting that the transcriptome classification may represent risk factors of both age and the intrinsic biological characteristics. In conclusion, the transcriptome classification of CAD cases revealed that cases from different subgroups may have their unique gene expression patterns, indicating that patients in each subgroup should receive more personalized treatment.

Integrated analysis of gene expression changes associated with coronary artery disease

BACKGROUND

This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms.

METHODS

Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules.

RESULTS

We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals.

CONCLUSIONS

The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05-0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05-0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction.

Evidence of association of circulating epigenetic-sensitive biomarkers with suspected coronary heart disease evaluated by Cardiac Computed Tomography

Circulating biomarkers available in clinical practice do not allow to stratify patients with coronary heart disease (CHD) prior the onset of a clinically relevant event. We evaluated the methylation status of specific genomic segments and gene expression in peripheral blood of patients undergoing Cardiac Computed Tomography (CCT) for CHD (n = 95).

We choose to investigate cholesterol metabolism. Methylation and gene expression of low density lipoprotein receptor (LDLR), sterol regulatory element-binding factor 2 (SREBF2) and ATP-binding cassette transporter 1 (ABCA1) were evaluated by qRT-PCR. Calcium score (CACS), stenosis degree, total plaque volume (TPV), calcified plaque volume (CPV), non-calcified plaque volume (NCPV) and plaque burden (PB) were assessed in all CHD patients (n = 65). The percentage of methylation at the specific analyzed segment of LDLR promoter was higher in CHD patients vs healthy subjects (HS) (n = 30) (p = 0.001). LDLR, SREBF2 and ABCA1 mRNAs were up-regulated in CHD patients vs HS (p = 0.02; p = 0.019; p = 0.008). SREBF2 was overexpressed in patients with coronary stenosis ≥50% vs subjects with stenosis <50% (p = 0.036). After adjustment for risk factors and clinical features, ABCA1 (p = 0.005) and SREBF2 (p = 0.010) gene expression were identified as independent predictors of CHD and severity. ROC curve analysis revealed a good performance of ABCA1 on predicting CHD (AUC = 0.768; p<0.001) and of SREBF2 for the prediction of disease severity (AUC = 0.815; p<0.001). Moreover, adjusted multivariate analysis demonstrated SREBF2 as independent predictor of CPV, NCPV and TPV (p = 0.022; p = 0.002 and p = 0.006) and ABCA1 as independent predictor of NCPV and TPV (p = 0.002 and p = 0.013).

CHD presence and characteristics are related to selected circulating transcriptional and epigenetic-sensitive biomarkers linked to cholesterol pathway. More extensive analysis of CHD phenotypes and circulating biomarkers might improve and personalize cardiovascular risk stratification in the clinical settings.

Expression of TLR4-PTGE2 signaling genes in atherosclerotic carotid plaques and peripheral blood

Toll-like receptor 4 (TLR4)/prostaglandine synthetase 2 (PTGS2) signaling plays a relevant role in atherosclerotic plaque vulnerability. The purpose of this study was to check the gene expression of 6 genes participating to TLR4/PTGS2 signaling (TLR4, PTGS2, ACSL4, PTGER3, PTGER4, and EPRAP) in carotid plaques and blood samples from the same individual and to evaluate these genes as biomarker of plaque progression. We investigated differential gene expression by qRT-PCR in 62 atherosclerotic patients' carotid plaques and corresponding blood sample. A very weak or no correlation was observed in the overall population or analyzing asymptomatic patients. These analyzed genes are most likely not suitable for inclusion in the clinical routine as biomarkers of plaque instability.

Potentially critical roles of TNPO1, RAP1B, ZDHHC17, and PPM1B in the progression of coronary atherosclerosis through microarray data analysis

OBJECTIVE

This study aimed to identify more potentially critical genes associated with atherosclerotic coronary artery disease (CAD).

MATERIALS AND METHODS

Gene expression profile of GSE12288 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened in atherosclerotic CAD samples compared with controls, followed by weighed gene coexpression network analysis (WGCNA) by which the most significant module was identified. Gene coexpression network was constructed based on genes in the most significant module, and functional annotation was also performed. In addition, microRNAs (miRNAs) that were directly associated with CAD were screened from the literature, and the miRNA-target regulatory network was constructed based on genes in the most significant module, followed by Gene Ontology (GO) and pathway enrichment analysis. Furthermore, we used another data set of GSE42148 from the GEO database to perform data validation.

RESULTS

WGCNA analysis showed that the turquoise module may have the most important role in atherosclerotic CAD. Genes in this module were involved in translational elongation and intracellular signal transduction. Besides, we identified five confirmed CAD-related miRNAs. TNPO1, RAP1B, and ZDHHC17 could be targeted by four of these miRNAs. Genes such as PPM1B could be regulated by three miRNAs. Moreover, TNPO1 and ZDHHC17 were involved in the GO terms associated with protein localization and transport and the immune system; RAP1B and PPM1B were linked with intracellular signal transduction-related pathways. In addition, PPM1B and ZDHHC17 had accordantly significant expression changes in another data set GSE42148.

CONCLUSION

TNPO1, RAP1B, ZDHHC17, and PPM1B may play essential roles in the progression of coronary atherosclerosis.

Transcriptomic responses of peripheral blood cells to coronary artery disease

Transcriptomic response of peripheral blood cells to coronary artery diseases (CAD) is a long recognized phenomenon. Currently, accumulating evidence indicates that such response having significant clinical utility in CAD-associated events determination. In this review, we summarized the existing data of transcriptomic biomarkers at mRNA, microRNA, long non-coding RNA, and circular RNA for the diagnosis, progression and outcome prediction and treatment response of CAD. Furthermore, we also discussed the functional significance on the gene expression patterns caused by CAD, and emphasized the importance of inflammatory pathways in CAD tissues-blood cells interaction. Based on the current knowledge, we proposed a perspective on the future strategies to further improve the robustness and reproducibility of transcriptomic biomarkers in the personalized medicine of CAD patients.

Gene expression profiling of coronary artery disease and its relation with different severities

Global gene expression profiling is a powerful tool enabling the understanding of pathophysiology and subsequent management of diseases. This study aims to explore functionally annotated differentially expressed genes (DEGs); their biological processes for coronary artery disease (CAD) and its different severities of atherosclerotic lesions. This study also aims to identify the change in expression patterns of DEGs in atherosclerotic lesions of single-vessel disease (SVD) and triple-vessel disease (TVD). The weight of different severities of lesion was estimated using a modified Gensini score. The gene expression profiling was performed using the Affymetrix microarray platform. The functional annotation for CAD was performed using DAVID v6.8. The biological network gene ontology tool (BiNGO) and ClueGO were used to explore the biological processes of functionally annotated genes of CAD. The changes in gene expression from SVD to TVD were determined by evaluating the fold change. Functionally annotated genes were found in an unique set and could be distinguishing two distinct severities of CAD. The biological processes such as cellular migration, locomotion, cell adhesion, cytokine production, positive regulation of cell death etc. enriched the functionally annotated genes in SVD, whereas, wound healing, negative regulation of cell death, blood coagulation, angiogenesis and fibrinolysis were enriched significantly in TVD patients. The genes THBS1 and CAPN10 were functionally annotated for CAD in both SVD and TVD. The 61 DEGs were identified, those have changes their expression with different severities of atherosclerotic lesions, in which 13 genes had more than two-fold change in expression between SVD and TVD. The consistent findings were obtained on validation of microarray gene expression of selected 10 genes in a separate cohort using real-time PCR. This study identified putative candidate genes and their biological processes predisposing toward and affecting the severity of CAD.

Peripheral-blood gene expression profiling studies for coronary artery disease and its severity in Xinjiang population in China

Background

Alterations in gene expression in peripheral blood cells play a curtail role in the presence and extent of coronary artery disease (CAD), but its severity reflected by gene expression alterations in peripheral blood cells is still unknown in Xinjiang population in China.

Methods

Global gene expression profiling in peripheral blood was used to explore differentially expressed genes in coronary artery stenosis patients. RNA was extracted from peripheral blood of 9 controls without coronary stenosis and 21 cases with angiographically CAD. The extent of CAD severity was categorized angiographically as no CAD, mild CAD (20 to 50% luminal diameter stenosis [LDS]), moderate CAD (50 to 75% LDS) and severe CAD (≥75% LDS). Differentially expressed genes related with CAD severity from peripheral blood cells were screened by linear mixed effects analysis using the lme4 package in R. Then the differentially expressed genes that gradually up-regulated or down-regulated were enriched by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

Results

The most significantly enrichments were toll-like receptor signaling pathway, immune responses, translational processes, cellular growth, inflammation and metabolic processes. Combined with NCBI-GeneRIF and PubMed analysis, we focused on the 12 genes associated with toll-like receptor signaling pathway in the extent of coronary artery stenosis patients. Receiver operating characteristic (ROC) analysis of 12 genes associated with toll-receptor signaling pathway in the 236 CAD patients from GEO database demonstrated that 12 genes expression could predict severe CAD with an area under the curve of 0.67, sensitivity of 77.65% and specificity of 51.52%.

Conclusion

These results suggest that 12 genes associated with toll-like receptor signaling pathway in peripheral-blood cells reflect the presence and extent of CAD severity in Xinjiang population in China.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0798-1) contains supplementary material, which is available to authorized users.