Bioinformatics analysis and population sample validation of ferroptosis related genes in coronary heart disease

doi:10.21203/rs.3.rs-4696566/v1

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Bioinformatics analysis and population sample validation of ferroptosis related genes in coronary heart disease

https://doi.org/10.21203/rs.3.rs-4696566/v1

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The aim of this study was to investigate the association between coronary heart disease (CHD) and ferroptosis, furthermore, to identify the relevant genes and potential diagnostic biomarkers by bioinformatics analysis. CHD was the main cause of cardiovascular diseases, its pathogenesis was complex. As an iron-dependent form of cell death, ferroptosis was believed to play a significant role in various diseases. Studies have shown that genes associated with ferroptosis include PRKAA2, NOX4, GLS2, and G6PD. Further data analysis and meta-analysis revealed that NOX4 gene expression was significantly upregulated in patients with CHD (P<0.05), which may be related to coronary atherosclerosis and myocardial injury. Additionally, the upregulation of NOX4 expression was validated through RT-PCR. In summary, this study revealed the association between CHD and ferroptosis, providing insights into the pathogenesis of CHD. Genes such as NOX4 may serve as diagnostic biomarkers for CHD, offering new perspectives and approaches for its early diagnosis and treatment.

Coronary heart disease

Bioinformatics analysis

PRKAA2

NOX4

GLS2

G6PD

Coronary heart disease (CHD) refers to the heart disease caused by coronary atherosclerosis under the action of a variety of pathogenic factors, which further leads to stenosis or complete occlusion of the coronary artery lumen, and ultimately leads to myocardial ischemia, damage or even necrosis. Coronary heart disease is currently the major cause of the increasing incidence and mortality rate of cardiovascular diseases worldwide, and it is also the disease with the highest mortality rate globally.^[1] According to the data in the According to Cardiovascular Health and Disease Report of China in 2018, the annual incidence of cardiovascular diseases is 290 million people, with up to 10 million patients suffering from coronary heart disease.^[2] As people's lifestyle changes, the overall patients with cardiovascular disease, diabetes, hypertension, hyperlipidemia tend to be younger. The patients suffering from coronary heart disease nowadays are becoming younger and no longer limited to the elderly. The number of acute myocardial infarction patients under the age of 35 is increasing with years. ^[3] As a result, early diagnosis and treatment of coronary heart disease has become an urgent priority.

Cell death plays an important role in the normal development of cells and in maintaining homeostasis in the body, and it can also prevent excessive proliferation of various diseases. In recent years, researchers have discovered that autophagy, paraptosis, pyroptosis, oncosis, and other forms of cell death are also involved in different pathophysiological conditions. In 2012, Dixon et al. ^[4] Proposed a novel form of cell death known as ferroptosis, which is iron-dependent. Ferroptosis is driven by the loss of activity of glutathione peroxidase 4 (GPX4) as a lipid repair enzyme and the accumulation of iron dependent lipid reactive oxygen species, leading to cell death ^[5]. This mode of cell death is involved in various pathological and physiological processes in the human body, including tumor development, neurodegeneration, and ischemia/reperfusion injury ^[6][7].

The occurrence and development of coronary heart disease are mostly related to vascular endothelial dysfunction and atherosclerosis. The accumulation of oxidative metabolites in vascular endothelial cells combined with oxidative stress leads to endothelial dysfunction. At the same time, the accumulation of lipid peroxide in cells is also an important mechanism for the occurrence of ferroptosis. Therefore, exploring the relationship between coronary heart disease and ferroptosis is of great significance for the pathogenesis research and prevention of coronary heart disease. The occurrence and progression of coronary heart disease are closely related to vascular endothelial dysfunction and atherosclerosis. The accumulation of oxidative metabolites in vascular endothelial cells, coupled with oxidative stress, leads to endothelial dysfunction. Additionally, the accumulation of lipid peroxides within cells is also an important mechanism for ferroptosis. Therefore, exploring the relationship between coronary heart disease and ferroptosis holds significant implications for understanding the pathogenesis of coronary heart disease and for exploring the corresponding prevention and treatment strategies.

Data Acquisition for Coronary Heart Disease and Ferroptosis-related Genes

The coronary heart disease dataset (GSE23561) was downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo). The dataset contains 35 samples, of which 9 are normal samples and 6 are coronary heart disease samples. A total of 259 ferroptosis-related genes were obtained from the FerrDb database (http://www.zhounan.org/ferrdb/).

Identification of Differentially Expressed Genes

Differential analysis on the GSE23561 dataset has been performed by means of the "limma" package in R language. Genes with adjusted P < 0.05 and |log2FC| ≥ 0.5 were selected for further analysis. The resulting differentially expressed genes were intersected with the ferroptosis-related genes from the FerrDb database to obtain the differentially expressed genes related to ferroptosis.

Functional Enrichment Analysis

GO analysis, KEGG pathway enrichment analysis, and DO disease enrichment analysis on the ferroptosis-related differentially expressed genes selected through the "Cluster Profiler" package in R language has been performed. Enrichment with an adjusted P < 0.05 was considered significant. Additionally, gene set enrichment analysis (GSEA) was conducted on the gene expression matrix, with |NES| > 1 and FDR < 0.25 indicating significant enrichment.

Selection of Diagnostic Biomarkers

LASSO logistic regression was employed for feature selection to identify diagnostic biomarkers for coronary artery disease (CAD). LASSO regression was used to further screen the differentially expressed genes related to ferroptosis in order to obtain the optimal diagnostic gene biomarkers.

Meta-analysis to Validate the Optimal Diagnostic Gene Biomarkers

The mRNA expression chips from the GEO database related to coronary heart disease has been screened by the following search strategies.: (Coronary Artery Disease) OR (Coronary heart disease) OR (CAD) OR (CHD). After the preliminary retrieval through the above-mentioned search strategy, further screening of chips that meet the requirements has been carried out based on the following inclusion and exclusion criteria. The inclusion criteria are as follows: (1) Research subjects are human being; (2) The dataset includes peripheral blood samples from both coronary artery disease patients and normal controls; (3) The dataset contains mRNA expression data. Chips were excluded if they met any of the following criteria: (1) Research subjects are animals or plants; (2) The samples in the dataset are not peripheral blood; (3) The control group is absent in the dataset; (4) The dataset does not contain mRNA expression data.

Validation of Optimal Diagnostic Gene Biomarkers in Human

Samples of coronary artery disease patient (n = 32) and healthy control group(n = 26) were collected from The First Affiliated Hospital of Guangxi Medical University. RT-qPCR analysis has been performed on peripheral blood leukocytes samples from both of the coronary heart disease patients and the healthy control group to verify the expression of optimal diagnostic gene biomarkers in the patients and healthy people. The primer sequence of NOX4 is 5'- CCCATCTGGTGAATGCCCTC − 3' (forward) and 5'- GAGGAATAGCACCACCACCA − 3' (reverse).

1.Identification of Ferroptosis-Related Differentially Expressed Genes After screening with | log2 (FC) |>0.5 and corrected p < 0.05 as threshold value, 16317 differentially expressed genes were identified in the GSE23561 dataset (Fig. 1A). And 122 related genes were obtained (Fig. 1B) when intersected with the 259 ferroptosis related genes provided by FerrDb website, including 112 up-regulated genes and 10 down-regulated genes.

Figure 1. shows the intersection between differentially expressed genes identified in the GSE23561 dataset (A) and ferroptosis-related genes provided by the FerrDb website (B) to obtain ferroptosis-related differentially expressed genes.

2. Analysis of Ferroptosis-Related Differentially Expressed Genes

GO analysis of the selected 122 genes indicates that Biological Processes (BP) was enriched in response to oxidative stress, cellular response to oxidative stress, reactive oxygen species metabolic process;

Cellular Components (CC) was enriched in: Caveola, membrane raft, membrane microdomain, membrane region;

Molecular Function (MF) was enriched in: Cofactor binding, oxidoreductase activity acting on NAD(P)H, antioxidant activity.

The enrichment results reveal that the differentially expressed genes enrichment is mostly associated with oxidative stress and cellular membrane functions (Fig. 2).

Additionally, KEGG pathway analysis showed that genes were significantly enriched in pathways such as Ferroptosis, Autophagy–animal, and Fluid shear stress and atherosclerosis, indicating their relevance to ferroptosis, autophagy, and atherosclerosis, etc (Fig. 3A).

In terms of disease enrichment analysis, the differentially expressed genes were mainly associated with cardiovascular-related diseases such as heart failure and atherosclerosis (Fig. 3B).

The final GSEA analysis revealed that the differentially expressed genes were enriched in the EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION pathway, which is related to abnormal proliferation and motility of gastric epithelial cells (Fig. 3C).

3. LASSO Regression Identified 4 Diagnostic Gene Biomarkers

Using LASSO regression, the 122 differentially expressed genes related to ferroptosis were further screened to identify hub genes with diagnostic value. Finally, four hub genes, PRKAA2, NOX4, GLS2, and G6PD, were obtained (Fig. 4A, B), all of which were highly expressed genes in the GSE23561 dataset (Fig. 4C).

Figure 4. Presents the LASSO regression analysis of the screened ferroptosis-related differentially expressed genes, resulting in the identification of four hub genes with diagnostic significance: PRKAA2, NOX4, GLS2, and G6PD (A,B). These four genes were found to be highly expressed in the GSE23561 dataset (C).

4. Meta-analysis to Validate the Hub Genes

A total of 9 GEO gene expression microarray datasets related to coronary artery disease, which meet the inclusion criteria, are presented in Table 1. The datasets consist of 334 peripheral blood samples from coronary artery disease patients and 322 peripheral blood samples from healthy controls.Meta-analysis was conducted on four hub genes from the 9 datasets (Note: GSE113079 had no GLS2 expression). The results showed that compared to the healthy control group, the expression of NOX4 mRNA in peripheral blood of coronary artery disease patients was upregulated (SMD = 0.23, 95% CI [0.07–0.39], P = 0.006) (Fig. 5). However, the analysis results of PRKAA2, GLS2, and G6PD did not show any significant differences.

Table 1

PRKAA2 Each data set expresses quantitative information
	CAD			normal
GSE	n	mean	SD	n	mean	SD
GSE19339	4	2.1657	0.12495	4	2.1299	0.02739
GSE7638	50	2.992	0.15843	110	2.9456	0.16122
GSE10195	27	0.1668	0.80828	14	0.2493	1.22126
GSE12288	110	5.1926	1.28815	112	5.0445	1.18077
GSE71226	3	5.3008	0.31987	3	5.3346	0.96107
GSE98583	12	3.1022	1.42037	6	3.1527	2.04328
GSE42148	13	2.1379	1.02522	11	2.0445	1.0968
GSE113079	93	-3.9523	0.62795	48	-4.1724	0.72976
GSE141512	6	2.4688	0.03681	6	2.48	0.05751
GSE116780	16	0.0417	0.05904	8	0.1409	0.127

Table 2

NOX4 Each data set expresses quantitative information
	CAD			normal
GSE	n	mean	SD	n	mean	SD
GSE19339	4	3.5491	0.89247	4	2.6531	0.05778
GSE7638	50	3.0191	0.1733	110	2.9914	0.15894
GSE10195	27	0.2037	0.84346	14	0.0345	1.18206
GSE12288	110	4.828	1.33173	112	4.4027	1.36514
GSE71226	3	3.4164	1.91929	3	2.7965	1.10774
GSE98583	12	3.9576	1.81005	6	3.4197	1.25568
GSE42148	13	2.8125	0.54666	11	2.6874	0.3446
GSE113079	93	-5.1054	0.5736	48	-5.21	0.78833
GSE141512	6	2.6522	0.02254	6	2.6295	0.03189
GSE116780	16	0.0264	0.04971	8	0.2002	0.42499

Table 3

GLS2 Each data set expresses quantitative information
	CAD			normal
GSE	n	mean	SD	n	mean	SD
GSE19339	4	3.491	0.15863	4	3.4876	0.1875
GSE7638	50	3.618	0.20385	110	3.5847	0.18285
GSE10195	27	-0.071	0.41787	14	0.074	0.56759
GSE12288	110	5.5236	1.0239	112	5.4225	0.97815
GSE71226	3	3.7228	0.12005	3	3.5688	0.24613
GSE98583	12	4.081	0.97612	6	5.0682	0.78488
GSE42148	13	6.1188	0.89089	11	5.8288	0.80692
GSE141512	6	3.9323	0.03157	6	3.8734	0.02104
GSE116780	16	0.029	0.05056	8	0.0391	0.03436

Table 4

G6PDEach data set expresses quantitative information
	CAD			normal
GSE	n	mean	SD	n	mean	SD
GSE19339	4	6.4296	0.16766	4	6.1288	0.20048
GSE7638	50	7.6654	0.34974	110	7.808	0.26003
GSE10195	27	-0.1131	0.57804	14	0.0767	0.46659
GSE12288	110	8.2038	0.60216	112	8.1661	0.51571
GSE71226	3	6.1558	0.98515	3	5.7223	0.30659
GSE98583	12	9.3034	0.63633	6	9.602	0.2673
GSE42148	13	8.9515	0.61616	11	8.5939	0.46314
GSE113079	93	1.5278	0.29244	48	1.4201	0.29383
GSE141512	6	7.9133	0.12377	6	7.6737	0.18776
GSE116780	16	30.9216	19.22429	8	37.192	7.88142

4. Validation of NOX4 Expression Levels using RT-qPCR

The expression levels of NOX4 for both of the samples of coronary artery disease patients and healthy control group were measured by RT-qPCR. An independent samples t test was performed to analyze their expression differences (Fig. 6). The results demonstrated that NOX4 expression was significantly up-regulated in coronary artery disease patients compared to the healthy control group, with a statistically significant difference (p < 0.0001).

Coronary Artery Disease (CAD) is the most prevalent type of heart disease and one of the common causes of mortality of cardiovascular diseases worldwide which affects millions of people around the world. The mortality rate of it has shown a decreasing trend in recent years, however, it still ranks among the top in terms of mortality rates ^[8].

Coronary atherosclerosis is a complex and progressive inflammatory disease, which is characterized by stenosis or occlusion of blood vessels caused by coronary artery lesions, resulting in myocardial ischemia, hypoxia and necrosis. CAD manifests across a spectrum of clinical symptoms, ranging from asymptomatic cases to stable angina, Acute Coronary Syndrome (ACS), Sudden Cardiac Death (SCD), or Heart Failure (HF)^[9][10]. The progression of CAD is influenced by dysfunction of endothelial cells, smooth muscle cells, and macrophages, leading to the accumulation of atherosclerotic plaques ^[11]. There may be individual differences among patients, some patients may experience sudden cardiac death due to acute coronary syndrome (ACS). The clinical symptoms of these patients who have developed clinical symptoms and sought medical attention timely can be alleviated through medication, percutaneous coronary intervention (PCI), or Coronary Artery Bypass Grafting (CABG) surgery. However, due to challenges in early diagnosis, some patients might remain asymptomatic for years or even decades. Once detected, their prognosis is often unfavorable. Therefore, it is of great significance to have a deeper understanding of the intricate pathogenesis behind CAD and atherosclerosis.

Atherosclerosis is the basic pathogenesis of coronary heart disease. Aberrant apoptosis of vascular endothelial cells (VECs), macrophages, or vascular smooth muscle cells (VSMCs) is a shared characteristic of atherosclerosis, which may lead to the formation of atherosclerotic plaque or plaque instability. The repair of the damaged endothelial cells involves numerous genes, some of which also play roles in maintaining normal cardiac function and the formation of aortic aneurysms. Recent studies highlight ferroptosis as an iron-dependent form of cell death, primarily driven by iron overload-induced lipid peroxidation[4]. David Carrick et al.'s research^[12] indicates that increased myocardial iron content is associated with adverse left ventricular remodeling post myocardial infarction, and ferroptosis-induced oxidative stress is a significant contributor to CAD pathogenesis^[13]. Excessive intracellular free iron can generate abundant reactive oxygen species (ROS) through the Fenton reaction, accelerating the oxidation of low-density lipoprotein (LDL). Oxidized LDL is engulfed by macrophages, forming foam cells that deposit in vascular endothelium, expediting CAD progression^[14]. Lipid peroxidation in cell membranes forms the basis of cell ferroptosis^[15]. Bioinformatics analysis indicates NOX4's involvement in oxidative stress-related pathways, closely related to cell membrane function. This suggests that NOX4 may promote lipid peroxidation, inducing ferroptosis and ultimately leading to lipid peroxidation damage, endothelial dysfunction, and CAD.

In order to reduce error rate and make the results more reliable, a suitable gene chip and multiple microarray data sets and clinical samples were selected and studied through gene expression data analysis and bioinformatics enrichment methods. The result provides valuable clinical insights for CAD treatment and prevention.

Furthermore, our bioinformatics analysis identified four ferroptosis-associated genes, namely PRKAA2, NOX4, GLS2, and G6PD. These genes are closely related to CAD and may also be involved in CAD patients’ cardiac immune microenvironment regulation. While gene expression may not be directly equivalent to protein expression, this study holds significant implications for understanding CAD mechanisms and potential therapeutic interventions.

Through bioinformatics analysis and population sample validation, this study has provided an in-depth exploration of ferroptosis-related genes in coronary heart disease. The results revealed 122 genes associated with ferroptosis, highlighting their significance in oxidative stress, cellular membrane functions, and cardiovascular diseases. LASSO regression identified four diagnostic biomarkers, namely PRKAA2, NOX4, GLS2, and G6PD, with NOX4 gene showing significantly elevated expression in coronary heart disease patients. Meta-analysis and experimental validation further validate the role of NOX4 in coronary heart disease. These findings offer new insights into the mechanisms of coronary heart disease as well as its early diagnosis and treatment strategies. Moreover, these findings will provide a valuable guidance for future research and clinical applications.

Acknowledgments

The study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (identifier: 2024-K211-01).

Contributions

Liheng Li and Yubo Xie designed the study. Liheng Li and Yubo Xie wrote the main manuscript text. Liheng Li prepared Tables and revised the manuscript. Liheng Li, Cuihua Chen and Qinghua Dong contributed to data collection. Qinghua Dong and Cuihua Chen revised the manuscript. Yubo Xie was responsible for clinical coordination. All authors reviewed the manuscript.

Funding

Yubo Xie was supported by Guangxi Key Research and Development Program (No. AB24010066). No other external funding or competing interests declared.

Ethics approval and consent to participate The study had been performed in accordance with the Declaration of Helsinki. The study protocol was conducted with the consent of the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University in China on 9 June 2024 (identifier: 2024-K211-01). The informed consents to participate in the study had been obtained from all participants or their legal guardian(s). The study was registered at Chinese Clinical Trial Registry (ChiCTR2400086460).

Competing interests The authors declare that they have no competing interests.

Author details

¹Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China.

²Intensive Care Unit, The Guilin Municipal Hospital of Traditional Chinese Medicine, Guangxi, China.

³ Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China

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No competing interests reported.

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You are reading this latest preprint version

Bioinformatics analysis and population sample validation of ferroptosis related genes in coronary heart disease

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Data Acquisition for Coronary Heart Disease and Ferroptosis-related Genes

Identification of Differentially Expressed Genes

Functional Enrichment Analysis

Selection of Diagnostic Biomarkers

Meta-analysis to Validate the Optimal Diagnostic Gene Biomarkers

Validation of Optimal Diagnostic Gene Biomarkers in Human

Results

2. Analysis of Ferroptosis-Related Differentially Expressed Genes

4. Meta-analysis to Validate the Hub Genes

4. Validation of NOX4 Expression Levels using RT-qPCR

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1