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Published on: September 20, 2024
Iron metabolism-related genes reveal predictive value of acute coronary syndrome
Cong Xu1, Wanyang Li2, Tangzhiming Li1
1Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China.
Insights
Iron deficiency negatively impacts acute coronary syndrome (ACS). This study identifies five key iron metabolism genes (PADI4, HLA-DQA1, LCN2, CD7, VNN1) to create a predictive model for early ACS detection.
Area of Science:
- Cardiovascular Medicine
- Nutritional Science
- Genomics
Background:
- Iron deficiency is prevalent and linked to adverse outcomes in acute coronary syndrome (ACS).
- The precise involvement of iron metabolism in ACS pathogenesis remains unclear.
- Understanding iron metabolism's role is crucial for developing new diagnostic strategies for ACS.
Purpose of the Study:
- To develop a molecular signature based on iron metabolism-related genes (IMRGs) for ACS prediction.
- To identify novel gene markers for the early diagnosis of ACS.
- To evaluate the efficacy of an Elastic Net-based prediction model for ACS.
Main Methods:
- Collected IMRGs from established databases and literature.
- Utilized two blood transcriptome datasets (GSE61144, GSE60993) for model construction and validation.
- Employed Elastic Net regression for differential gene expression analysis and model building, identifying five key genes (PADI4, HLA-DQA1, LCN2, CD7, VNN1).
Main Results:
- Identified 22 differentially expressed iron metabolism-related genes (DEIGs) in ACS patients.
- Developed an optimal prediction model ('imSig') using five specific IMRGs.
- The Elastic Net-based 'imSig' model demonstrated superior performance compared to Lasso and Logistic regression in the validation set, showing high accuracy in ROC, PRC, Sensitivity, and Specificity.
Conclusions:
- A novel molecular signature ('imSig') based on iron metabolism-related genes shows promise for early ACS diagnosis.
- The Elastic Net model effectively predicts ACS using a panel of five iron metabolism genes.
- This gene signature could serve as a valuable tool to aid in the early detection of acute coronary syndrome.
Abstract:
Iron deficiency has detrimental effects in patients with acute coronary syndrome (ACS), which is a common nutritional disorder and inflammation-related disease affects up to one-third people worldwide. However, the specific role of iron metabolism in ACS progression is opaque. In this study, we construct an iron metabolism-related genes (IMRGs) based molecular signature of ACS and to identify novel iron metabolism gene markers for early stage of ACS. The IMRGs were mainly collected from Molecular Signatures Database (mSigDB) and two relevant studies. Two blood transcriptome datasets GSE61144 and GSE60993 were used for constructing the prediction model of ACS. After differential analysis, 22 IMRGs were differentially expressed and defined as DEIGs in the training set. Then, the 22 DEIGs were trained by the Elastic Net to build the prediction model. Five genes, PADI4, HLA-DQA1, LCN2, CD7, and VNN1, were determined using multiple Elastic Net calculations and retained to obtain the optimal performance. Finally, the generated model iron metabolism-related gene signature (imSig) was assessed by the validation set GSE60993 using a series of evaluation measurements. Compared with other machine learning methods, the performance of imSig using Elastic Net was superior in the validation set. Elastic Net consistently scores the higher than Lasso and Logistic regression in the validation set in terms of ROC, PRC, Sensitivity, and Specificity. The prediction model based on iron metabolism-related genes may assist in ACS early diagnosis.
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