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Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm
Zongjin Li1, Liqin Tian2,3, Libing Bai2
1College of Science, North China University of Science and Technology, Tangshan, China.
Insights
A new deep graph clustering algorithm identifies significant hypertension gene biomarkers. This method improves classification accuracy, offering a powerful tool for understanding and treating this global health issue.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Hypertension is a major global health concern and a leading cause of cardiovascular mortality.
- Identifying reliable hypertension biomarkers from gene expression data is challenging due to small sample sizes and high dimensionality.
- Understanding hypertension mechanisms is crucial for effective prevention and treatment strategies.
Purpose of the Study:
- To develop a novel algorithm for identifying biologically significant hypertension gene biomarkers.
- To overcome the limitations of small sample sizes and high dimensionality in gene expression data analysis.
- To enhance the accuracy and reliability of hypertension biomarker discovery.
Main Methods:
- Proposed a deep graph clustering feature selection (DeepGCFS) algorithm.
- Utilized graph networks and Graph Neural Networks (GNNs) to model gene interactions.
- Employed link prediction, self-supervised learning, hybrid clustering, and integrated feature selection.
Main Results:
- The DeepGCFS algorithm identified ten significantly differentiated hypertension biomarkers.
- Achieved a classification performance AUC of 97.50%, outperforming existing methods.
- Validated findings on a separate dataset (GSE113439) with an AUC of 95.45% and identified seven significant genes.
- Six previously reported hypertension-associated genes were among the identified biomarkers.
- Demonstrated superior gene feature vector clustering performance compared to other methods.
Conclusions:
- The DeepGCFS algorithm effectively identifies biologically significant hypertension gene biomarkers.
- The proposed method offers significant advantages for hypertension biomarker discovery and analysis.
- This approach holds promise for advancing hypertension research and clinical applications.
Abstract:
Hypertension is a critical risk factor and cause of mortality in cardiovascular diseases, and it remains a global public health issue. Therefore, understanding its mechanisms is essential for treating and preventing hypertension. Gene expression data is an important source for obtaining hypertension biomarkers. However, this data has a small sample size and high feature dimensionality, posing challenges to biomarker identification. We propose a novel deep graph clustering feature selection (DeepGCFS) algorithm to identify hypertension gene biomarkers with more biological significance. This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. The algorithm then uses hybrid clustering methods for gene module detection. Finally, it combines integrated feature selection methods to determine the gene biomarkers. The experiment revealed that all the ten identified hypertension biomarkers were significantly differentiated, and it was found that the classification performance of AUC can reach 97.50%, which is better than other literature methods. Six genes (PTGS2, TBXA2R, ZNF101, KCNJ2, MSRA, and CMTM5) have been reported to be associated with hypertension. By using GSE113439 as the validation dataset, the AUC value of classification performance was to be 95.45%, and seven of the genes (LYSMD3, TBXA2R, KLC3, GPR171, PTGS2, MSRA, and CMTM5) were to be significantly different. In addition, this algorithm's performance of gene feature vector clustering was better than other comparative methods. Therefore, the proposed algorithm has significant advantages in selecting potential hypertension biomarkers.
