Tumor Protein p53 (TP53) Gene and Left Main Coronary Artery Disease

Pyroptosis-Related Gene Signature and Expression Patterns in the Deterioration of Atherosclerosis

Background

Pyroptosis has been shown to be involved in the overall process of atherosclerosis. This study was aimed at investigating pyroptosis-related gene expression patterns in atherosclerosis and their diagnostic significance.

Methods and Results

In GSE100927, fifty-four pyroptosis-related genes were identified. Between atherosclerotic plaques and normal samples, the expression patterns of pyroptosis-related genes were significantly different. In order to construct a pyroptosis-related risk score signature (PRSS), the least absolute shrinkage and selection operator (LASSO) was combined with multivariate logistic regression to screen twelve genes. The diagnostic efficiency of the PRSS performed well in GSE43292, as shown by the results of receiver-operating characteristics (ROCs). Consensus clustering identified two expression patterns of pyroptosis-related genes in different statuses of atherosclerotic plaque in GSE163154. The biological behavior of the different clusters was examined by the gene set variation analysis (GSVA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses revealed that the differentially expressed genes (DEGs) in the two clusters were enriched in the immune response. The Cytoscape software was used to construct protein-protein interaction (PPI) networks for hub gene screening. Following that, the Drug Gene Interaction Database (DGIdb) was utilized to find 47 possible medicines and chemical compounds that interact with hub genes in atherosclerotic plaques.

Conclusion

The results of this study showed that pyroptosis-related genes contribute to the progression of atherosclerosis and may serve as biomarkers in clinical diagnosis as well as novel therapeutic targets for the treatment of AS.

Exploring the Pharmacological Mechanisms of Tripterygium wilfordii Hook F against Cardiovascular Disease Using Network Pharmacology and Molecular Docking

Background

Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicine (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF on CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach.

Materials and Methods

The active compounds were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained from the SwissTargetPrediction database. The CVD-related therapeutic targets were collected from the DrugBank, the GeneCards database, and the OMIM database. Protein–protein interaction (PPI) network was generated by the STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug-targets-diseases-pathways was constructed by the Cytoscape software.

Results

The 41 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. Furthermore, AKT1, amyloid precursor protein (APP), mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified as the core targets involved in the mechanism of TwHF on CVD. Top ten GO (biological processes, cellular components, and molecular functions) and KEGG pathways were screened with a P value ≤0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG.

Conclusions

These findings demonstrate that the main active compound of TwHF, the core targets, and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD.

Hp1-1 as a Genetic Marker Regulating Inflammation and the Possibility of Developing Diabetic Complications in Patients with Type 2 Diabetes-Cohort Studies

Background: This study assessed the influence of the haptoglobin phenotype on markers regulating inflammation in patients with type 2 diabetes. Methods: The haptoglobin phenotypes, soluble form of CD163 receptor (sCD163), p53 concentrations and high mobility group box protein 1 (HMGB1), interleukin 10 (IL-10) secretion in serum were assayed via ELISA tests. In the first part of the project, patients were divided into three groups which differed by the haptoglobin phenotype, and afterwards into two groups according to the criterion of the presence or absence of cardiovascular disease. Results: Diabetic patients with haptoglobin phenotype 1-1 (Hp1-1) had a significantly higher concentration of IL-10 and sCD163 compared to haptoglobin phenotype 2-1 (Hp2-1) and haptoglobin phenotype 2-2 (Hp2-2). Moreover, diabetic patients with Hp1-1 had a significantly lower concentration of p53 and HMGB1 compared to diabetic patients with Hp2-1 and Hp2-2. The results have shown that diabetics with Hp2-1 had a significantly lower postprandial glucose level compared to diabetics with Hp2-2. Apart from that, there were no differences in the occurrence of haptoglobin variants between patients with or without cardiovascular disease. Conclusions: Our study provides new data for a relationship between the type of haptoglobin in patients with type 2 diabetes and the concentration of factors that regulate the body’s inflammation. We have shown that the Hp1-1 can serve as a genetic marker of inflammatory processes.

Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset

Background

This study aimed to investigate the angiotensin converting enzyme (ACE) co-expression genes and their pathways involved in ST-segment elevation myocardial infarction (STEMI) at different time points.

Methods

The array data set of GSE59867 was examined for the ACE co-expression genes in peripheral blood samples from 111 patients with STEMI at four time points (admission, discharge, and 1 and 6 months after MI). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene Ontology (GO) annotation and protein-protein interaction (PPI) of the co-expression genes were determined using online analytical tools. The Cytoscape software was used to create modules and hub genes.

Results

The number of biological processes (BP), cellular components (CC) and molecular functions (MF) was 43, 22 and 24 at admission; 18, 19 and 11 at discharge; 30, 37 and 21 at 1 month after MI; and 12, 19 and 14 at 6 months after MI; respectively. There were 6 BP, 8 CC and 4 MF enriched at every time point. The co-expression genes were substantially enriched in 12, 5, 6 and 14 KEGG pathways at the four time points, respectively, but no KEGG pathway was found to be common in all time points. We identified 132 intersectional co-expression genes (90 positive and 42 negative) from the four time points and 17 BP, 13 CC, 11 MF and 7 KEGG pathways were enriched. In addition, the PPI network contained 129 nodes and 570 edges, and only 1 module was identified to be significantly enriched in just 1 BP (chromatin-mediated maintenance of transcription).

Conclusions

The results of the present study showed that the ACE co-expression genes and their pathways involved in STEMI were significantly different at four different time points. These findings may be helpful for further understanding the functions and roles of ACE in different stages of STEMI, and providing reference for the treatment of STEMI.