Potential Regulatory Role of lncRNA-miRNA-mRNA in Coronary Artery Disease (CAD)

Long Non-Coding RNA PCAT19 Suppresses Cell Proliferation and Angiogenesis in Coronary Artery Disease through Interaction with GCNT2

LncRNAs involvement in heart disease, however, the effect of lncRNA prostate cancer-associated transcript 19 (PCAT19) in coronary artery disease (CAD) remains unclear. In the current study, we aimed to verify the role of PCAT19 in CAD. We first investigated the differentially expressed lncRNAs in different Genes Expression Omnibus (GEO) database. We then detected lncRNAs expression in healthy volunteers and acute myocardial infarction (AMI) patients by qRT‑PCR. The correlation of PCAT19 and Glucosaminyl (N-Acetyl) Transferase 2 (GCNT2) was analyzed. Human coronary artery endothelial cells (HCAECs) was used to conduct cell hypoxia-reoxygenation (H/R) injury model to imitate AMI injury. CCK8, BrdU, tube formation assay were used to detect cell viability, proliferation, and angiogenesis. Immunofluorescence, western blotting were used to detect ki67, VEGFA, PCNA, CD31, and GCNT2 expression, respectively. We obtained six different lncRNAs from GEO database and identified PCAT19 high expression in AMI patients. PCAT19 was positive correlation to GCNT2. Further experiments presented that PCAT19 knockdown promoted cell viability, proliferation and angiogenesis, GCNT2 knockdown also promoted cell viability, proliferation, and angiogenesis. These results confirmed by the inhibition of Ki67 and VEGFA. Importantly, PCAT19 overexpression suppressed cell proliferation and angiogenesis, these results also confirmed by the inhibition of PCNA and CD31. However, the inhibitory effect of PCAT19 overexpression was reversed by GCNT2 knockdown. Our study indicated that PCAT19 plays an important role in the CAD disease, its effects was related to GCNT2. Our research provides a novel sight for the effect of PCAT19 on CAD.

Potential roles of microRNAs and long noncoding RNAs as diagnostic, prognostic and therapeutic biomarkers in coronary artery disease

Coronary artery disease (CAD), which is mainly caused by atherosclerotic processes in coronary arteries, became a significant health issue. MicroRNAs (miRNAs), and long noncoding RNAs (lncRNAs), have been shown to be stable in plasma and could thereby be adopted as biomarkers for CAD diagnosis and treatment. MiRNAs can regulate CAD development through different pathways and mechanisms, including modulation of vascular smooth muscle cell (VSMC) activity, inflammatory responses, myocardial injury, angiogenesis, and leukocyte adhesion. Similarly, previously studies have indicated that the causal effects of lncRNAs in CAD pathogenesis and their utility in CAD diagnosis and treatment, has been found to lead to cell cycle transition, proliferation dysregulation, and migration in favour of CAD development. Differential expression of miRNAs and lncRNAs in CAD patients has been identified and served as diagnostic, prognostic and therapeutic biomarkers for the assessment of CAD patients. Thus, in the current review, we summarize the functions of miRNAs and lncRNAs, which aimed to identify novel targets for the CAD diagnosis, prognosis, and treatment.

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods

Background

Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis.

Methods

All statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to identify differentially expressed genes (DEGs) between CAD samples and healthy controls. The WGCNA package was conducted to recognize CAD-related gene modules and hub genes, followed by recursive feature elimination analysis to select the optimal features genes (OFGs). The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. Further validation and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the classification performance.

Results

In total, 374 DEGs, eight gene modules, 33 hub genes and 12 OFGs (HTR4, KISS1, CA12, CAMK2B, KLK2, DDC, CNGB1, DERL1, BCL6, LILRA2, HCK, MTF2) were identified. ROC curve analysis showed that the accuracy of SVM, RF and LR were 75.58%, 63.57% and 63.95% in validation; with area under the curve of 0.813 (95% confidence interval, 95% CI 0.761–0.866, P < 0.0001), 0.727 (95% CI 0.665–0.788, P < 0.0001) and 0.783 (95% CI 0.725–0.841, P < 0.0001), respectively.

Conclusions

In conclusion, this study found 12 gene signatures involved in the pathogenic mechanism of CAD. Among the CAD classifiers constructed by three machine learning methods, the SVM model has the best performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02481-4.