Biomarkers of Blood from Patients with Atherosclerosis Based on Bioinformatics Analysis

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.

MgCl2 Attenuates ox-LDL-Induced Vascular Smooth Muscle-Derived Foam Cells Pyroptosis by Downregulating the TLR4/NF-κB Signaling Pathway

Pyroptosis is a type of programmed cell death that is generally upregulated during atherosclerosis (AS). Magnesium, an important cation in the body, has exhibited an antiatherosclerotic effect. We collected AS model datasets from the Gene Expression Omnibus (GEO) and explored the correlation between pyroptosis and AS through a series of bioinformatics methods. We next investigated the impact of oxidized low-density lipoprotein (ox-LDL) on primary cultured vascular smooth muscle cells (VSMCs) foaminess and pyroptosis. Finally, foam cells were preconditioned with different concentrations of MgCl2 to explore its influence on ox-LDL-induced VSMCs pyroptosis. NLRP3-mediated pyroptosis plays a core role in regulating AS progression as shown by bioinformatic analysis. Ox-LDL (50/75/100 mg/L) increased CE/TE ratio (> 50%) in VSMCs and prompted VSMC-derived foam cell formation, and (75/100 mg/L) ox-LDL-induced pyroptosis. Compared to 1 mmol/L MgCl2, 10 mmol/L MgCl2 significantly downregulated the expression of pyroptosis related molecules in VSMCs induced by 75 mg/L ox-LDL, including NLRP3, ASC, caspase-1, and GSDMD. The secretion of IL-1β, IL-18, and LDH was also inhibited by MgCl2. According to CCK-8 and Hoechst 33,342/PI staining, the damage to VSMCs viability induced by ox-LDL was ameliorated by MgCl2. In addition, MgCl2 attenuated the upregulation of TLR4, IKKβ, and p65 and the downregulation of IκBα in VSMCs induced by ox-LDL. The present study demonstrated that pyroptosis-related genes were the core genes in AS. We also revealed the effect and underlying mechanism of MgCl2 on ox-LDL-induced VSMCs pyroptosis, suggesting that MgCl2 has promising clinical applications for AS pyroptosis prevention and treatment.

Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field

The atherosclerotic vascular disease is a cardiovascular continuum in which the main role is attributed to atherosclerosis, from its appearance to its associated complications. The increasing prevalence of cardiovascular risk factors, population ageing, and burden on both the economy and the healthcare system have led to the development of new diagnostic and therapeutic strategies in the field. The better understanding or discovery of new pathophysiological mechanisms and molecules modulating various signaling pathways involved in atherosclerosis have led to the development of potential new biomarkers, with key role in early, subclinical diagnosis. The evolution of technological processes in medicine has shifted the attention of researchers from the profiling of classical risk factors to the identification of new biomarkers such as midregional pro-adrenomedullin, midkine, stromelysin-2, pentraxin 3, inflammasomes, or endothelial cell-derived extracellular vesicles. These molecules are seen as future therapeutic targets associated with decreased morbidity and mortality through early diagnosis of atherosclerotic lesions and future research directions.