Lipid metabolism patterns and relevant clinical and molecular features of coronary artery disease patients: an integrated bioinformatic analysis

Comprehensive Analysis Based on Genes Associated With Cuproptosis, Ferroptosis, and Pyroptosis for the Prediction of Diagnosis and Therapies in Coronary Artery Disease

Coronary artery disease (CAD) is a complex condition influenced by genetic factors, lifestyle, and other risk factors that contribute to increased mortality. This study is aimed at evaluating the diagnostic potential of genes associated with cuproptosis, ferroptosis, and pyroptosis (CFP) using network modularization and machine learning methods. CAD-related datasets GSE42148, GSE20680, and GSE20681 were sourced from the GEO database, and genes related to CFP genes were gathered from MsigDB and FerrDb datasets and literature. To identify diagnostic genes linked to these pathways, weighted gene coexpression network analysis (WGCNA) was used to isolate CAD-related modules. The diagnostic accuracy of key genes in these modules was then assessed using LASSO, SVM, and random forest models. Immunity and drug sensitivity correlation analyses were subsequently performed to investigate possible underlying mechanisms. The function of a potential gene, STK17B, was analyzed through western blot and transwell assays. Two CAD-related modules with strong correlations were identified and validated. The SVM model outperformed LASSO and random forest models, demonstrating superior discriminative power (AUC = 0.997 in the blue module and AUC = 1.000 in the turquoise module), with nine key genes identified: CTDSP2, DHRS7, NLRP1, MARCKS, PELI1, RILPL2, JUNB, STK17B, and SLC40A1. Knockdown of STK17B inhibited cell migration and invasion in human umbilical vein endothelial cells. In summary, our findings suggest that CFP genes hold potential as diagnostic biomarkers and therapeutic targets, with STK17B playing a role in CAD progression.

Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KCTD10, a protein implicated in a variety of biological processes, has garnered significant attention for its role in cardiovascular diseases and metabolic regulation. As a member of the KCTD protein family, KCTD10 is characterized by the presence of a T1 domain that interacts with voltage-gated potassium channels, a critical interaction for modulating channel activity and intracellular signal transduction. In our study, KCTD10 was identified as a focal point through an integrative analysis of differentially expressed genes (DEGs) across multiple datasets (GSE43292 and GSE9820) from the GEO database, aligned with immune-related gene sets from the ImmPort database. Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection. We further applied Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to these gene sets, revealing significant enrichment in immune-related pathways. The relationship between KCTD10 expression and immune processes was examined using CIBERSORT and ESTIMATE algorithms to assess tumor microenvironment characteristics, suggesting increased immune cell infiltration associated with higher KCTD10 expression. Validation of these findings was conducted using data from the GSE9820 dataset. Among 10 DEGs linked with KCTD10, 13 were identified as hub genes through LASSO and SVM-RFE analyses. Functional assays highlighted KCTD10's role in enhancing viral defense mechanisms, cytokine production, and immune cascades. Notably, KCTD10 expression correlated positively with several immune cells, including naive CD4 + T cells, eosinophils, resting NK cells, neutrophils, M0 macrophages, and particularly M1 macrophages, indicating a significant association. This research elucidates the complex relationship between KCTD10 and AS, underscoring its potential as a novel biomarker for diagnosing and monitoring the disease. Our findings provide a solid foundation for further investigations, suggesting that targeting KCTD10-related pathways could markedly advance our understanding and management of AS, offering new avenues for therapeutic intervention.

Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy

Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins. The predictive performance of three ML models, including Extreme Gradient Boosting (XGBoost), Random Forest, and Logistic Regression, was evaluated using electronic medical records from the Asan Medical Center in Seoul across six performance metrics. Additionally, all three models achieved an average AUROC of 0.695 despite reducing features by over 43%. SHAP analysis was conducted to identify key features influencing model predictions, aiming insights into patient characteristics associated with achieving LDL-C targets. This study suggests that ML-based approaches may help identify patients likely to benefit from moderate-dose statins, potentially supporting personalized treatment strategies and clinical decision-making for LDL-C management.

Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene

BACKGROUND

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in AS, remain largely unexplored and are a subject of ongoing research.

METHODS

This study employed advanced bioinformatics techniques to identify and validate FAMGs associated with AS. We conducted differential expression analysis on a select list of 49 candidate FAMGs. GSEA and GSVA were utilized to elucidate the potential biological roles and pathways of these FAMGs. Subsequently, Lasso regression and SVM-RFE were applied to identify key hub genes and assess the diagnostic efficacy of seven FAMGs in distinguishing AS. The study also explored the correlation between these hub FAMGs and clinical features of AS. Validation of the expression levels of the seven FAMGs was performed using datasets GSE43292 and GSE9820.

RESULTS

The study pinpointed seven FAMGs with a close association to AS: ACSBG2, ELOVL4, ACSL3, CPT2, ALDH2, HSD17B10, and CPT1B. Analysis of their biological functions underscored their significant involvement in critical processes such as fatty acid metabolism, small molecule catabolism, and nucleoside bisphosphate metabolism. The diagnostic potential of these seven FAMGs in AS differentiation showed promising results.

CONCLUSIONS

This research has successfully identified seven key FAMGs implicated in AS, offering novel insights into the pathophysiology of the disease. These findings not only contribute to our understanding of AS but also present potential biomarkers for the disease, opening avenues for more effective monitoring and progression tracking of AS.

Genetically predicted HLA-DR+ natural killer cells as potential mediators in the lipid-coronary artery disease/ calcification (CAD/CAC) causal pathway

Background

Coronary artery disease (CAD) imposes a significant global health burden, necessitating a deeper comprehension of its genetic foundations to uncover innovative therapeutic targets. Employing a comprehensive Mendelian randomization (MR) approach, we aimed to explore the genetic associations between lipid profiles, immune cell phenotypes, and CAD risk.

Methods

Utilizing data from recent large-scale genome-wide association studies (GWAS), we scrutinized 179 lipid and 731 immune cell phenotypes to delineate their genetic contributions to CAD pathogenesis, including coronary artery calcification (CAC). Moreover, specific immune cell phenotypes were examined as potential mediators of the lipid-CAD/CAC causal pathway.

Results

Among the 162 lipid species with qualified instrumental variables (IVs) included in the analysis, we identified 36 lipids that exhibit a genetic causal relationship with CAD, with 29 being risk factors and 7 serving as protective factors. Phosphatidylethanolamine (18:0_20:4) with 8 IVs (OR, 95% CI, P-value: 1.04, 1.02-1.06, 1.50E-04) met the Bonferroni-corrected significance threshold (0.05/162 = 3.09E-04). Notably, all 18 shared lipids were determined to be risk factors for both CAD and CAC, including 16 triacylglycerol traits (15 of which had ≥ 3 IVs), with (50:1) exhibiting the highest risk [OR (95% CI) in CAC: 1.428 (1.129-1.807); OR (95% CI) in CAD: 1.119 (1.046-1.198)], and 2 diacylglycerol traits. Furthermore, we identified HLA DR+ natural killer cells (IVs = 3) as nominally significant with lipids and as potential mediators in the causal pathway between diacylglycerol (16:1_18:1) or various triacylglycerols and CAD (mediated effect: 0.007 to 0.013).

Conclusions

This study provides preliminary insights into the genetic correlations between lipid metabolism, immune cell dynamics, and CAD susceptibility, highlighting the potential involvement of natural killer cells in the lipid-CAD/CAC causal pathway and suggesting new targets for therapy. Further evidence is necessary to substantiate our findings.

Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach

BACKGROUND

This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS.

METHODS

A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets.

RESULTS

This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results.

CONCLUSION

In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.

Identification and validation of hub genes involved in foam cell formation and atherosclerosis development via bioinformatics

Background

Foam cells play crucial roles in all phases of atherosclerosis. However, until now, the specific mechanisms by which these foam cells contribute to atherosclerosis remain unclear. We aimed to identify novel foam cell biomarkers and interventional targets for atherosclerosis, characterizing their potential mechanisms in the progression of atherosclerosis.

Methods

Microarray data of atherosclerosis and foam cells were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expression genes (DEGs) were screened using the "LIMMA" package in R software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were both carried out. Hub genes were found in Cytoscape after a protein-protein interaction (PPI) enrichment analysis was carried out. Validation of important genes in the GSE41571 dataset, cellular assays, and tissue samples.

Results

A total of 407 DEGs in atherosclerosis and 219 DEGs in foam cells were identified, and the DEGs in atherosclerosis were mainly involved in cell proliferation and differentiation. CSF1R and PLAUR were identified as common hub genes and validated in GSE41571. In addition, we also found that the expression of CSF1R and PLAUR gradually increased with the accumulation of lipids and disease progression in cell and tissue experiments.

Conclusion

CSF1R and PLAUR are key hub genes of foam cells and may play an important role in the biological process of atherosclerosis. These results advance our understanding of the mechanism behind atherosclerosis and potential therapeutic targets for future development.

Risk prediction model for major adverse cardiovascular events (MACE) during hospitalization in patients with coronary heart disease based on myocardial energy metabolic substrate

Background

The early attack of coronary heart disease (CHD) is very hidden, and clinical symptoms generally do not appear until cardiovascular events occur. Therefore, an innovative method is needed to judge the risk of cardiovascular events and guide clinical decision conveniently and sensitively. The purpose of this study is to find out the risk factors related to MACE during hospitalization. In order to develop and verify the prediction model of energy metabolism substrates, and establish a nomogram to predict the incidence of MACE during hospitalization and evaluate their performance.

Methods

The data were collected from the medical record data of Guang'anmen Hospital. This review study was collected the comprehensive clinical data of 5,935 adult patients hospitalized in the cardiovascular department from 2016 to 2021. The outcome index was the MACE during hospitalization. According to the occurrence of MACE during hospitalization, these data were divided into MACE group (n = 2,603) and non-MACE group (n = 425). Logistic regression was used to screen risk factors, and establish the nomogram to predict the risk of MACE during hospitalization. Calibration curve, C index and decision curve were used to evaluate the prediction model, and drawn ROC curve to find the best boundary value of risk factors.

Results

The logistic regression model was used to establish a risk model. Univariate logistic regression model was mainly used to screen the factors significantly related to MACE during hospitalization in the training set (each variable is put into the model in turn). According to the factors with statistical significance in univariate logistic regression, five cardiac energy metabolism risk factors, including age, albumin(ALB), free fatty acid(FFA), glucose(GLU) and apolipoprotein A1(ApoA1), were finally input into the multivariate logistic regression model as the risk model, and their nomogram were drawn. The sample size of the training set was 2,120, the sample size of the validation set was 908. The C index of the training set is 0.655 [0.621,0.689], and the C index of the validation set was 0.674 [0.623,0.724]. The calibration curve and clinical decision curve show that the model performs well. The ROC curve was used to establish the best boundary value of the five risk factors, which could quantitatively present the changes of cardiac energy metabolism substrate, and finally achieved prediction of MACE during hospitalization conveniently and sensitively.

Conclusion

Age, albumin, free fatty acid, glucose and apolipoprotein A1 are independent factors of CHD in MACE during hospitalization. The nomogram based on the above factors of myocardial energy metabolism substrate provides prognosis prediction accurately.