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Updated: Jun 28, 2025
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Published on: February 20, 2019
Online application for the diagnosis of atherosclerosis by six genes
Zunlan Zhao1, Shouhang Chen2, Hongzhao Wei3
1Department of General Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Insights
This study developed an accurate atherosclerosis (AS) diagnostic model using blood gene expression data. The accessible Shiny app aids early detection and prognosis of AS, a major cardiovascular disease risk.
Area of Science:
- Biomedical Informatics
- Cardiovascular Research
- Genomics
Background:
- Atherosclerosis (AS) is a leading cause of cardiovascular disease mortality globally.
- Early detection and accurate diagnostic models for AS are crucial for reducing fatalities.
- Blood sample analysis offers a promising, yet currently lacking, accurate tool for AS diagnosis and prognosis.
Purpose of the Study:
- To develop a convenient, simple, and accurate model for the early detection of atherosclerosis.
- To identify key genes associated with AS through differential gene expression analysis.
- To create a publicly accessible tool for AS diagnosis.
Main Methods:
- Downloaded and preprocessed blood sample expression data from GEO databases, including batch effect correction.
- Performed differential expression analysis (DEA) to identify atherosclerosis-related genes.
- Utilized gradient boosting and random forest models for gene importance evaluation and prediction model construction.
Main Results:
- Integrated seven datasets comprising 403 AS and 325 healthy samples.
- Identified 331 atherosclerosis-related genes, with the top 6 highlighted by gradient boosting.
- The random forest model achieved an accuracy exceeding 0.8 in predicting AS, outperforming existing methods.
Conclusions:
- Developed a prognostic Shiny application based on six atherosclerosis-associated genes.
- The application provides accurate diagnosis for atherosclerosis.
- The tool facilitates distinguishing AS samples from normal samples for improved patient outcomes.
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.
