Identification of potential blood biomarkers of coronary artery disease using a cuproptosis gene set

doi:10.21203/rs.3.rs-3109393/v1

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Identification of potential blood biomarkers of coronary artery disease using a cuproptosis gene set

https://doi.org/10.21203/rs.3.rs-3109393/v1

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Background

Coronary artery disease (CAD) is a multifactorial cardiovascular disease that causes high mortality worldwide. Cuproptosis is a newly discovered method of programmed cell death, but it is unclear whether it is involved in the development of CAD.

Methods

GSE180081 was downloaded from the GEO database and genes that were differentially expressed in controls and patients with CAD were identified. These were clustered according to the cuproptosis gene set, to identify differentially expressed cuproptosis related genes. The intersection of the two sets of differentially expressed genes was used to identify genes relevant to the diagnosis of CAD using LASSO regression. A diagnostic model was created using the selected genes and logistic regression. Enriched immune genes were identified, the associated ceRNA network was characterized, and drugs that may target the identified genes were searched for.

Results

We identified 818 differentially expressed genes that were common to the CAD and cuproptosis gene sets, which principally represented the cell-substrate junction and the positive regulation of leukemia. Furthermore, HIST1H4E, IL6ST, RN7SKP45, LST1, and SNORD50B were found be potentially useful for the diagnosis of CAD using the diagnostic model. These genes were found to be closely associated with immune modification.

Conclusion

We have constructed a diagnostic prediction model based on a cuproptosis gene set using whole-blood transcriptome data. Using this, we have identified HIST1H4E, IL6ST, and LST1 as potential biomarkers of the risk of CAD. These findings provide a novel approach to the prediction, prevention, and individualized treatment of CAD.

Coronary artery disease

Cuproptosis

Machine learning

Biomarker

Prediction model

The challenge of managing coronary artery disease

Coronary artery disease (CAD) is a cardiovascular disease that has the highest mortality worldwide and is also the leading cause of death in developing countries ¹. Symptoms or fatal or disabling consequences of CAD often occur after the age of 40, although these occur earlier in men than in women ². CAD is typically caused by coronary atherosclerosis or occlusion ³, which may be associated with lipid infiltration ^{4, 5}, oxidative stress ⁶, and smooth muscle cell proliferation causing damage to the intima of the artery and initiating inflammation, fibrous hyperplasia, and the formation of atherosclerotic plaque ⁷. This leads to secondary vascular stenosis and even obstruction of blood vessels, and ultimately distal myocardial ischemia and necrosis ⁸.

Aspirin, statins, and β receptor blockers are often used to prevent the development of CAD ⁹, and severe CAD is often treated by the placement of coronary stents or coronary artery bypass surgery ¹⁰. Predictive, preventive, and personalized medicine is a popular concept in modern medicine¹¹, and genome sequencing-based screening for biomarkers and therapeutic targets is also used in many fields as part of a personalized medicine approach to the prevention and diagnosis of disease ¹². Given the availability of large disease transcriptome datasets, searches for potential biomarkers to facilitate the construction of models for the diagnosis and prediction of disease are becoming increasingly popular ^{13, 14}. The use of machine learning and statistical algorithms to help cluster and group genes associated with diseases has a number of implications for research ^{15, 16}. However, there is currently a lack of an excellent model for the prediction of the risk of CAD that is based on human blood biomarkers.

Cuproptosis is a newly described form of cell death

Copper is a cofactor for a number of essential enzymes ¹⁷. The concentration of copper in normal cells is very low, and copper homeostasis is principally maintained by means of a dynamic equilibrium between concentration gradients ^{18, 19}. Cuproptosis is a newly discovered means of programmed cell death ²⁰. American scholars discovered that disulfiram and elesclomol can carry copper ions through the cell membrane in 2019, and it has since been confirmed that these two Cu²⁺ carriers can kill specific drug-resistant cancer cells ²¹. The principal mechanism of cuproptosis involves the direct binding of copper ions (both Cu²⁺ and Cu⁺) to the tricarboxylic acid cycle during mitochondrial respiration, leading to the aggregation of fatty acylated proteins, followed by the downregulation of iron sulfur cluster protein expression, which leads to protein toxicity stress and ultimately cell death ²⁰. In addition, other studies have shown that copper homeostasis plays an important role in the immune response ^{22, 23}.

Study design

In summary, cuproptosis plays an important role in cell homeostasis, but its specific effects require further characterization. In the present study, we downloaded whole-blood transcriptome data relating to CAD from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes in patients with CAD and controls. We then clustered and grouped the CAD data according to the cuproptosis gene set, and searched for genes that were differentially expressed between the groups. The intersection of the differentially expressed genes and clinical events was screened and regression analysis was performed, which yielded five genes. We used these to construct a diagnostic risk prediction model, and identified three genes as potential biomarkers which might have value in the diagnosis and prevention of CAD.

Data collection

The gene expression profiles GSE180081 and GSE180082 were downloaded from the GEO database. GSE180081 comprises 96 clinical samples, 48 with CAD and 48 controls, and contains 44,265 genes after the removal of duplicates; and GSE180082 comprises 80 clinical samples, 43 with CAD and 37 controls, and contains 44,265 genes after processing in the same way. GSE180081 was used as a training set and GSE180082 was used as a validation set. In addition, we used the Drug Gene Interaction Database (DGIdb) to identify potential gene-targeting drugs ²⁴.

Human blood collection

Arterial blood samples were collected from patients with CAD who were undergoing coronary artery bypass grafting and venous blood samples were collected from healthy people at Health Management Center to provide control samples. Informed consent was provided by all the participants, and the study was approved by the Ethics committee of Xiangya hospital, Central South University. The exclusion criteria included diabetes mellitus, previous cardiac surgery, previous percutaneous coronary intervention, rheumatic heart disease, systemic lupus erythematosus, and other connective tissue and immune-mediated diseases. The following data were collected at baseline: age; sex; and the history of smoking, hypertension, stroke, and hyperlipidemia.

Data clustering

After obtaining the cuproptosis gene dataset ²⁰, we used the ConsumusClusterPlus package to cluster the training set based on its relationship with the cuproptosis gene dataset (from k = 2 to k = 6) ²⁵. The optimal result of the cluster analysis was used to divide the cohort into two groups for subsequent analyses.

Differential expression analysis

The downloaded gene expression matrix (RPKM) was converted to TPM data and the GSE180081 dataset was divided into CAD and non-CAD groups. All the CAD samples were grouped according to the clustering results, and differential expression analysis was conducted using the limma package in R ²⁶. The screening conditions were as follows: LogFC > 1 and P < 0.05. ggplot2 package ²⁷ was used to construct volcano and heat maps to display the differentially expressed genes.

Functional enrichment analyses

To identify the potential functional pathways that include the differentially expressed genes, we use the clusterprofiler and fgsea packages to search the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases and conduct functional and GSEA enrichment analyses in R ^28–30.

Identification and validation of diagnostic cuproptosis-related genes for CAD

We analyzed the intersection between the genes that were differentially expressed genes in the CAD and non-CAD groups and those in the cuproptosis clusters in the training set. The Least absolute shrinkage and selection operator (LASSO) in the glmnet package was used for all the differentially expressed genes to reduce the dimensions of the data and identify genes related to a diagnosis of CAD ³¹. The genes obtained from the LASSO regression screening were used to construct a diagnostic model by means of logistic regression ^{32, 33}. We used the glm package to conduct predictions with the training set ³⁴ and evaluate the diagnostic ability of the model generated using receiver operating characteristic (ROC) analysis. We then attempted to validate its diagnostic performance using the GSE180082 dataset. Models are potentially useful predictors when the area under the curve (AUC) is > 0.60 and good predictors when the AUC is > 0.75) ^{35, 36}.

Immune infiltration analysis

To further explore the differences between the subgroups in this diagnostic model, we divided the training set into a high-risk group and a low-risk group on the basis of the median risk score of this model. Next, we used GSVA single-sample gene set enrichment analysis (ssGSEA) ³⁷ to identify the differences in immune cells and immune function between groups. We used the Cibersort package to analyze the representation of 22 types of immune cell in the high-risk group ^{38, 39}. Finally, the estimate algorithm ⁴⁰ was used to predict the immune status of, and to analyze the differences in the levels of immune cell infiltration between, the high-risk and low-risk groups.

Prediction of the effects of immunomodulation

We combined the data regarding immunosuppressant, immune activator, and MHC-related genes in the TISIDB database ⁴¹ with the expression matrix of the participants with CAD, then conducted Pearson correlation analysis of the relationships between these biomarkers using the ggcor package. The correlation analysis was performed for the relationships between these two sets of genes, and one-way linear regression analysis was performed for immune loci with close correlations.

Single gene set enrichment analysis (ssGSEA)

GSEA was used to identify the set of genes with concordant differences from the expression matrix of all the identified genes. This permits genes with small differences in expression to be taken into account ⁴². To further identify the function of the five genes used to construct the diagnostic model, single-gene GSEA analysis was performed on the GSE180081 dataset using the clusterProfiler package.

ceRNA network construction

The MicroCosm database was searched using the multiMiR package ⁴³ to identify micro (mi)RNAs that are predicted to target the five genes of interest. In addition, a search for long non-coding (lnc)RNAs associated with the identified miRNAs was conducted using the starBase database ⁴⁴, with all the node information being collated in Cytoscape (v3.9.0) for visualization.

mRNA expression of the genes of interest

Peripheral blood was collected from 12 participants in the CAD or non-CAD groups, then the peripheral blood mononuclear cells were isolated and RNA was extracted using an RNA Extraction Kit (Accurate Biology, AG21023, Changsha, China). We then reverse-transcribed the RNA to cDNA using a Reverse Transcription Kit (Accurate Biology, AG11728, Changsha, China), and analyzed target gene expression using real-time quantitative fluorescence polymerase chain reaction (RT-PCR, Genecopoeia, QP001, Changsha, China). All the procedures were conducted according to the manufacturers’ instructions. The primer sequences for LST1, IL6ST, and HIST1H4E are listed in Table 1.

Table 1

Primer sequences used in the study
Gene name	Forward	Reverse
LST1	GCCCCTGATCATTTCGCCTA	CTGGGACCAGGACAGAAGGT
IL6ST	TGTTTTAACTATCCCTGCCTGT	CATTTGCTCTCTGCTAAGTTCC
HIST1H4E	TAAGGTCCTGCGAGATAACATC	GTAAGTCACAGCATCACGAATC
ACTB	CATGTACGTTGCTATCCAGGC	CTCCTTAATGTCACGCACGAT

Statistical analysis

All the statistical analyses were performed using R software version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria). The limma package was used to identify differentially expressed genes. ROC curve analysis was performed using the pROC package. Pearson correlation analysis was used to evaluate the relationships between variables. Differences were regarded as statistically significant when P < 0.05.

Identification of DEGs in the GSE180081 cohort

The study procedures are outlined in a flowchart in Fig. 1. The GSE180081 dataset obtained from the GEO database comprised 96 samples (48 from patients with CAD and 48 from those without). Of the 44,265 genes represented in the GSE180081 dataset, 943 were differentially expressed (CAD-DEG), of which 645 were upregulated and 298 were downregulated (LogFC > 1; P < 0.05) (Fig. 2a, b).

For the CAD group, we clustered the training set based on its relationship with the cuproptosis gene dataset (from k = 2 to k = 6). Based on the results of the cluster analysis, we divided the samples into two groups (Cu_group1, Cu_group2), which comprised 21 and 27 samples, respectively. (Fig. 2c). Of the total of 44,265 genes represented in the two groups, 1,108 were differentially expressed (Cu-DEG), of which 793 were upregulated and 369 were downregulated (LogFC > 1; P < 0.05) (Fig. 2d, e). The gene set at the intersection of CAD-DEG with Cu-DEG, referred to as Co-DEG, comprised 818 differentially expressed genes, of which 231 were upregulated and 587 were downregulated (Fig. 2f).

Functional analyses of the Co-DEGs

To elucidate the biological functions and potential pathways of the Co-DEGs, GO enrichment and KEGG pathway analyses were performed. As shown in the scatter graph of the GO analysis (Fig. 3a), the differential genes were principally derived from the positive regulation of cell activation, positive regulation of leukocyte activation, actin filament organization, leukocyte cell-cell adhesion, and positive regulation of lymphocyte activation pathways, which implies that leukocyte activation and interactions are important in this interaction. The KEGG analysis showed that the Co-DEGs were principally associated with infectious diseases, including COVID-19 (Fig. 3b). Thus, when the results of the two analyses are combined, the differentially expressed genes appear to be principally associated with immune function. GSEA analysis showed that these genes were principally derived from the Apical junction, Complement, and Hypoxia pathways (Fig. 3c). To better visualize the key differentially expressed genes in the Co-DEG set, we used a chordal diagram to show the genes that are involved in the pathways identified in the GO analysis (Fig. 3d).

Co-DEGs identified to be diagnostic for CAD

LASSO regression screening was used to identify five genes that were associated with the diagnostic features of CAD: HIST1H4E, IL6ST, RN7SKP45, LST1, and SNORD50B (Fig. 4a, b). In addition, these five genes could be used to distinguish the CAD and control groups in the LASSO regression model (Fig. 4c).

We next used these five genes and the R package glm to construct a logistic regression model and analyzed this using ROC analysis. This showed that the model was capable of satisfactorily identifying CAD in the test set (AUC 0.83, Fig. 4d). To further verify the accuracy of this model, we used ROC curve analysis of the GSE180082 dataset, which yielded an AUC of 0.62 (Fig. 4d).

Immune landscape analysis

We combined the risk groups identified using the proposed model with the expression matrix to compare the immune cell infiltration of the groups using three methods: ssGSEA, estimate, and Cibersort. The ssGSEA analysis showed differences in the numbers of 14 of the 28 types of immune cell, and all of these except the CD4⁺ T cells were present in larger numbers in the high-risk group (Fig. 5a, b). Each of the five identified genes is closely associated with the function of multiple immune cells, and in particular, the LST1 gene is associated with 20 types of immune cell, and therefore may be a key candidate for future basic research (Fig. 5c).

The immunization scores obtained using the estimate package showed differences between the high-risk and low-risk groups, with a higher overall immunization score in the high-risk group, consistent with the ssGSEA findings (Fig. 5d). Cibersort analysis also showed differences in immunity between the two groups, with the high-risk group showing a lower number of memory CD4⁺ T cells (Fig. 5e). Thus, although the exact immune statuses of the two groups remain to be verified in laboratory experiments, we have shown that there are differences between the subgroups of the diagnostic model constructed using the cuproptosis gene set.

The identified genes are closely associated with CAD-related pathways

To ensure that multicollinearity during the logistic regression analysis did not interfere with the findings, we performed correlation analysis on the five genes used to construct the model (Fig. 6a, b), which suggested that there were no close correlations between them, and implies that the model was robust. To further explore the function of these five genes (Table 2), we performed GSEA analysis of each of them (Fig. 6c, d, e, f, g), and found that they are associated with pathways involved in cardiovascular diseases and the regulation of inflammation, including Leukocyte transendothelial migration, Type Ⅰ diabetes mellitus, Viral myocarditis, Arrhythmogenic right ventricular cardiomyopathy, and Glycine, serine, and threonine metabolism. Therefore, the roles of these five genes in CAD should be further explored.

Table 2

Results of the *limma* analysis, demonstrating low expression of five genes
Gene	logFC	AveExpr		P-value	adj. P-value	B	Change
HIST1H4E	−1.28	6.82	−8.44	3.65E − 13	1.66E − 11	19.16	DOWN
IL6ST	−1.26	6.70	−7.78	8.85E − 12	1.74E − 10	16.23	DOWN
RN7SKP45	−1.39	6.89	−7.7	1.28E − 11	2.38E − 10	15.75	DOWN
LST1	−1.42	7.37	−7.66	1.62E − 11	2.90E − 10	15.41	DOWN
SNORD50B	−1.21	6.70	−6.93	5.17E − 10	6.40E − 09	12.48	DOWN

Associations of the identified genes with immunosuppressant, immune activator, and MHC-related genes

We next analyzed the relationships of well-established immunosuppressant genes (Fig. 7a), immune activator genes (Fig. 7b), and MHC genes (Fig. 7c) with the five identified genes. We found that the identified genes closely correlated with BTLA and VTCN1 among the immunosuppressant genes, CD276 and TMEM173 among the immune activator genes, and HLA-DQA1 and TABPBP among the MHC-related genes. Subsequently, each group of correlations was examined, HIST1H4E was found to be closely associated with BTLA and TMEM173, and LST1 was found to be closely associated with TMEM173 and TAPBP (Fig. 7d). Therefore, the expression of HIST1H4E and LST1 may be regulated by these genes, providing a basis for further studies.

Creation of a ceRNA network based on the identified genes and the prediction of drugs that would target these genes

We constructed a ceRNA network based on the three genes using information from the MicroCosm and starBase databases and the multiMiR package. The network contained 59 nodes (3 genes, 47 miRNAs, 9 lncRNAs) and 59 edges (Fig. 8b). We identified many miRNA regulatory networks targeting LST1, with 27 of the miRNAs being involved in regulation, and the principal associated lncRNAs including DANCR, NEAT1, and SNHG22. The regulatory network of IL6ST was also found to be diverse, with 15 miRNAs being found to be involved in regulation and five associated lncRNAs, including let-7b-5p and miR-628-5p. Six miRNAs were found to be involved in the regulation of HIST1H4E, among which miR-148a-3p was found to have two related lncRNAs: NUTM2A-AS1 and AL049840.4.

We also attempted to identify drugs that may target the identified genes using the DGIdb database, and the results obtained using the Cytoscape software are shown in Fig. 8a. We identified three drugs that target IL6ST and one that targets LST1. These findings provided a basis for further study of these genes in the future.

RT-qPCR analysis of the expression of the identified genes in mononuclear cells obtained from the peripheral blood of patients with CAD and healthy individuals

We collected fresh blood from 12 patients with CAD and 12 healthy people who were undergoing a physical examination. RNA was extracted from peripheral monocytes and RT-qPCR was performed to measure the expression of LST1, IL6ST, and HIST1H4E. We found that the expression of LST1 and HIST1H4E was significantly lower in the CAD group (Fig. 9), which is consistent with the results of the bioinformatic analysis. In contrast, IL6ST was expressed at a significantly higher level in the CAD group (Fig. 9), suggesting that further research regarding this gene is merited. Thus, we have confirmed that LST1, HIST1H4E, and IL6ST might represent biomarkers of CAD and provide a basis for further study of these genes in the future.

Selection and establishment of methods of predicting CAD

CAD represents one of the largest public health burdens worldwide, and therefore the early identification of individuals at risk of CAD is essential to reduce morbidity and mortality. A diagnosis of CAD is principally made on the basis of imaging examinations, which commonly include coronary computed tomography and coronary angiography. However, the identification of better non-invasive methods of predicting CAD will be important for the prevention and individualized treatment of CAD ⁴⁵. Therefore, it would be of great significance to identify peripheral blood predictors of CAD.

A growing body of research suggests that cell death plays important roles throughout the pathogenesis of CAD ⁴⁶. Both apoptosis and autophagy are upregulated in patients with CAD ⁴⁷. Some previous studies have sought to identify biomarkers of CAD and to construct diagnostic models based on iron-related cell death ^{34, 48, 49}, but the relationship between a newly identified form of cell death, cuproptosis, and CAD has not been elucidated ²⁰.

In the present study we have performed an in-depth analysis of blood transcriptomic data derived from patients with and without CAD and the cuproptosis gene set, in the hope of identifying biomarkers that might be useful for the prevention, prediction, and/or individualized treatment of CAD. We then further analyzed the roles of the five genes identified, HIST1H4E, RN7SKP45, IL6ST, LST1, and SNORD50B. Single-gene GSEA analysis showed that all five genes were associated with both inflammation and cardiovascular disease, although RN7SKP45 and SNORD50B were not found to be associated with relevant functions, so we focused on the remaining three genes, HIST1H4E, IL6ST, and LST1.

Biological characteristics and functions of the three identified genes in CAD

The leukocyte-specific transcript 1 (LST1) gene, encoding a member of MHC class III, contains five non-coding exons and four coding exons ^{50, 51}. High LST1 expression has been reported in inflammatory bowel disease ⁵² and rheumatoid arthritis ⁵³, suggesting that it may regulate inflammation. LST1 is polymorphic, being not only a soluble protein, but also a transmembrane protein ⁵⁴. Therefore, it may be a transmembrane adapter protein (TRAP) and associated with cell surface receptors that initiate intracellular signaling cascades ⁵⁵. There is evidence that LST1 has an important role in inflammation and immunity^{52, 56}, but the underlying mechanisms have not been elucidated. Therefore, the association between LST1 and CAD deserves further exploration.

HIST1H4E, also known as H4C5, is a member of the histone H4 family. Histone modification is a key regulator of the tightness of chromatin structure. The overexpression of histone deacetylase 4 (HDAC4) in H9c2 cardiomyocytes leads to mitochondrial dysfunction and increases the probability of cell death ⁵⁷. Histone deacetylase (HDAC) inhibitors significantly reduce the area of myocardial infarction ⁵⁸. In addition, numerous studies have shown a protective effect of HDAC inhibitors in cardiomyocytes ^59–61. HIST1H4E has been shown to be associated with melanoma metastasis ⁶², and He et al. showed that HIST1H4E could be used as an independent predictor of the prognosis of melanoma ⁶³. However, whether HIST1H4E would be useful for the diagnosis and/or treatment of CAD deserves further exploration.

Interleukin-6 signal transducer (IL6ST) is a member of the cytokine receptor family. It mediates the initiation of intracellular signaling by ligands such as IL-6 and plays important roles in growth, immune homeostasis, infection, trauma, autoimmune diseases, and cancer ⁶⁴. IL6ST is a prognostic marker for breast cancer ⁶⁵ and promotes the proliferation and migration of liver cancer cells ⁶⁶. Recent studies have also shown that it alleviates endothelial injury ^{67, 68} and participates in myocardial protection ⁶⁹.

Strengths and limitations of the present study, and future studies

In the present study, we used a cuproptosis gene set to construct a diagnostic model, which has not been a frequent approach in recent studies. The model constructed had an AUC of 0.83, which is better than those for most models reported to date ^{34, 70, 71}. The genes that were incorporated in the model may represent useful biomarkers for the diagnosis of CAD. To further clarify the underlying mechanisms, we explored the differences in immune cell profile between the high-risk and low-risk groups, and found that multiple types of immune cell were present in larger numbers in the high-risk group, which is consistent with the overrepresentation of Co-DEGs in many immune-related pathways. The involvement of immune regulation throughout the pathogenesis of CAD has been widely acknowledged ^{72, 73}. As a result, studies have also been conducted to search for immunology-related blood biomarkers of CAD ⁷⁴. However, it is difficult to identify potential biomarkers and therapeutic targets for CAD from among immunity-related genes alone.

To provide a basis for further studies of the identified genes, we analyzed HIST1H4E, IL6ST, and LST1 for potentially relevant interactions with immunosuppressant, immune activator, and MHC-related genes, and attempted to identify drugs that target these genes. In addition, we constructed ceRNA networks that provide a bioinformatic basis for future in-depth basic research. To further validate the bioinformatic findings, we collected peripheral blood from patients with CAD and healthy individuals from whom RNA was extracted from monocytes to validate the expression of HIST1H4E, IL6ST, and LST1, and thereby to provide evidence that these three genes may represent biomarkers of CAD and to provoke further in-depth mechanistic studies.

There were some limitations to the present study. Firstly, the patient data in the GEO database is sparse, which may have affected the reliability of the model constructed and led to the omission of some potential biomarkers. Secondly, we performed a retrospective analysis, which would inevitably be associated with some bias. Therefore, prospective studies should be performed in the future in an attempt to corroborate the reliability of the identified biomarkers. Nevertheless, we have conducted a preliminary screen for markers of CAD using publicly available data and made an initial validation in patients, the results of which should provoke prospective studies of HIST1H4E, IL6ST, and LST1k and further exploration of their roles in the pathogenesis of CAD.

In summary, we have identified HIST1H4E, IL6ST, and LST1 as genes that are associated with cuproptosis genes and are biomarkers of CAD through machine learning and constructed a new CAD prediction model. Furthermore, we have explored the relevance of these genes to immunity and identified targeting drugs and regulatory networks. These results may help guide the development of targeted prevention and personalized treatment strategies for CAD.

Ethics approval and consent to participate The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the ethics committee of Xiangya hospital.

Consent for publication Informed consent was obtained from all subjects involved in the study.

Data availability Statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180081, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180082.

Conflicts of Interest The authors have no financial interests.

Funding This work was supported by the National Natural Science Foundation of China (82070352), the Hunan Key R&D Programs (2021SK2022), and Hunan Natural Science Foundation (2022JJ70075).

Author contributions Jia Li analyzed the data, completed the figures, and wrote the manuscript. Bitao Xiang, Yue Yin, and Cheng Fang processed the raw data. Can-E Tang, Zhanwei Zhu, and Fanyan Luo designed the research. Yubin Chen, Can-E Tang, Kaibo Lei and Fanyan Luo reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgements We thank Rui Li from Fujian University of Traditional Chinese Medicine for advice on data process and Dr. Jianming Zeng of the University of Macau and all the members of his bioinformatics team and biotrainees for generously sharing their experience and codes.

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Identification of potential blood biomarkers of coronary artery disease using a cuproptosis gene set

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

The challenge of managing coronary artery disease

Cuproptosis is a newly described form of cell death

Study design

Methods

Data collection

Human blood collection

Data clustering

Differential expression analysis

Functional enrichment analyses

Identification and validation of diagnostic cuproptosis-related genes for CAD

Immune infiltration analysis

Prediction of the effects of immunomodulation

Single gene set enrichment analysis (ssGSEA)

ceRNA network construction

mRNA expression of the genes of interest

Statistical analysis

Results

Identification of DEGs in the GSE180081 cohort

Functional analyses of the Co-DEGs

Co-DEGs identified to be diagnostic for CAD

Immune landscape analysis

The identified genes are closely associated with CAD-related pathways

Associations of the identified genes with immunosuppressant, immune activator, and MHC-related genes

Discussion

Selection and establishment of methods of predicting CAD

Biological characteristics and functions of the three identified genes in CAD

Strengths and limitations of the present study, and future studies

Conclusion

Declarations

References

Additional Declarations

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