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Inflammatory Biomarkers in Coronary Artery Disease: Insights From Mendelian Randomization and Transcriptomics
Zhilin Xiao1,2, Xunjie Cheng1, Yongping Bai1,2
1Department of Geriatric Disease, Center of Coronary Circulation, Xiangya Hospital, Central South University, Changsha, People's Republic of China.
Insights
Researchers identified two key inflammatory genes, HIF1A and TNFAIP3, causally linked to coronary artery disease (CAD). These findings offer potential new targets for managing and treating CAD.
Area of Science:
- Genetics and Molecular Biology
- Cardiovascular Disease Research
- Immunology
Background:
- Understanding the genetic basis of coronary artery disease (CAD) is crucial for identifying new therapeutic strategies.
- Inflammatory processes play a significant role in the pathogenesis of CAD.
- Identifying specific inflammatory genes involved in CAD can lead to targeted treatments.
Purpose of the Study:
- To identify novel inflammatory genes associated with coronary artery disease (CAD).
- To explore the causal relationship between identified genes and CAD.
- To develop predictive models for CAD incidence based on these genes.
Main Methods:
- Downloaded inflammation-related genes (IRGs) from Msigdb.
- Identified differentially expressed genes (DEGs) in CAD datasets (GSE113079, GSE12288).
- Utilized weighted gene co-expression network analysis (WGCNA) to find key module genes.
- Integrated DEGs, module genes, and IRGs to identify differentially expressed IRGs (DE-IRGs).
- Employed machine learning algorithms to derive feature genes.
- Conducted Mendelian randomization (MR) analysis to assess causality.
- Developed logistic regression and alignment diagram models for CAD prediction.
Main Results:
- Identified 92 differentially expressed inflammation-related genes (DE-IRGs).
- Discovered twelve feature genes using four machine learning algorithms.
- Confirmed a causal relationship between HIF1A and TNFAIP3 with CAD (OR=1.031, P=0.024 for HIF1A; OR=1.104, P=0.007 for TNFAIP3).
- Demonstrated the efficacy of logistic regression and alignment diagram models in predicting CAD.
- Found significant associations of TNFAIP3 and HIF1A with T-cell receptor and NOD-like receptor signaling pathways.
Conclusions:
- TNFAIP3 and HIF1A are identified as causal inflammatory biomarkers for coronary artery disease (CAD).
- These genes present significant clinical potential as novel targets for CAD management and treatment.
- The findings provide valuable insights into the molecular mechanisms underlying CAD.
Background:
The identification of inflammatory genes linked to coronary artery disease (CAD) helps to enhance our understanding of the disease's pathogenesis and facilitate the identification of novel therapeutic targets.
Methods:
Inflammation-related genes (IRGs) were downloaded from the Msigdb database. Differentially expressed genes (DEGs) were determined by comparing CAD group with the control group in the GSE113079 and GSE12288 datasets. Key module genes associated with CAD were identified through weighted gene co-expression network analysis (WGCNA). Differentially expressed IRGs (DE-IRGs) were established by intersecting the DEGs, key module genes, and IRGs. Feature genes were derived using machine learning techniques. Mendelian randomization (MR) analysis was conducted to explore the causal relationship between CAD and the identified feature genes. Subsequently, a logistic regression model and an alignment diagram model were developed to predict the incidence of CAD.
Results:
In the given datasets, a total of 92 DE-IRGs were identified. Furthermore, twelve feature genes were discerned utilizing four distinct machine learning algorithms. Notably, two pivotal genes, HIF1A (odds ratio (OR) = 1.031, P = 0.024) and TNFAIP3 (OR = 1.104, P = 0.007), exhibited a causal relationship with coronary artery disease (CAD). Additionally, logistic regression and alignment diagram models demonstrated their efficacy in predicting the incidence of CAD. Ultimately, TNFAIP3 and HIF1A were significantly associated with T-cell receptor and NOD-like receptor signaling pathways.
Conclusion:
The identification of TNFAIP3 and HIF1A as causal inflammatory biomarkers of CAD offers novel insights with significant clinical potential, which may provide valuable targets for the management and treatment of CAD.
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