Unveiling the Role of Melatonin in Coronary Heart Disease: Identification and Experimental Validation of Novel Biomarkers

Unveiling the Role of Melatonin in Coronary Heart Disease: Identification and Experimental Validation of Novel Biomarkers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unveiling the Role of Melatonin in Coronary Heart Disease: Identification and Experimental Validation of Novel Biomarkers Yongchao Peng, Xuemin Zhou, Yaowu Xie, Li Huang, Xuanlan Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8816168/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Melatonin (ME) affects multiple systems in coronary heart disease (CHD), including lipid/glucose metabolism, blood pressure, and sleep-wake regulation, while also promoting coronary thrombosis through unknown mechanisms. This study uses bioinformatics to identify ME-related biomarkers for CHD diagnosis and treatment. The GSE179789 and GSE113079 datasets were obtained from a public database. Biomarkers were selected via differential analysis and expression validation. Gene set enrichment analysis (GSEA) was performed, and regulatory as well as drug‑biomarker networks were constructed. Biomarker expression was further validated in clinical samples using RT‑qPCR. MAP2K2 and PGD were identified as reliable CHD biomarkers, showing significant up‑regulation in CHD samples across both datasets. GSEA indicated their involvement in multiple pathways, including ribosome, prion diseases, and Parkinson's disease. Complex regulatory interactions were observed among lncRNAs, miRNAs, and biomarkers; for instance, four lncRNAs (NEAT1, AP000766.1, LINC02381, XIST) regulated PGD via hsa‑let‑7e‑5p. Additionally, 29 transcription factors (e.g., STAT1, BRD3, HDAC1, CBFB) co‑regulated both biomarkers. Finally, 41 drugs (e.g., cobimetinib fumarate, selumetinib sulfate) were predicted to target MAP2K2, while three (penicillamine, pralmorelin, phenobarbital) targeted PGD. In summary, MAP2K2 and PGD serve as CHD biomarkers, offering new insights into disease pathogenesis. Health sciences/Biomarkers Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases coronary heart disease biomarkers bioinformatics analysis therapeutic targets Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coronary heart disease (CHD) remains a globally widespread chronic disorder, associated with high rates of illness and death [ 1 ] . The disease entity is characterized by insufficient blood supply or obstruction of the coronary arteries, leading to myocardial ischemia, hypoxia, angina pectoris, palpitations, shortness of breath and fatigue; it may also present with other symptoms [ 2 ] . In severe cases, it can cause myocardial infarction or arrhythmia, and even be life-threatening [ 2 ] . A Atherosclerosis Risk in Communities (ARIC) study found approximately 60500 new myocardial infarctions and 200,000 recurrent events every year between 2005 and 2014 [ 3 ] . The pathogenesis of CHD is complex and among the main causes of CHD, atherosclerosis has been widely regarded as the main factor [ 4 ] . Inflammation, endothelial dysfunction, hypertension, dyslipidemia, insulin resistance, environmental exposures, and pathogens all contribute to endothelial injury, which in turn contributes to the formation and progression of atherosclerotic plaques [ 5 ] . The development of a CHD diagnosis depends on clinical symptoms, physical examination and imaging characteristics (such as electrocardiogram, echocardiogram, coronary angiography, etc) [ 6 , 7 ] . In current therapies, pharmacological agents, which include antiplatelets, statins, β-blockers, etc, are used along with interventional treatments, such as primary coronary intervention (PCI) and coronary artery bypass surgery (CABG) [ 8 , 9 ] . However, there are some issues with these treatments. For example, drugs may be ineffective or cause resistance in certain patient populations, and vascular stent technology still suffers from many complications, including in-stent restenosis, late thrombosis, artery injury, and high re-occlusion rates [ 10 ] . In recent years, some scientists believe that to develop novel therapeutic targets and therapies for CHD, an in-depth understanding of the disease's pathogenesis is vital. In particular, research on biomarkers can help early diagnosis of CHD, assess the severity of the disease, predict prognosis, and guide individualized therapeutic strategies. Secreted predominantly by the pineal gland nocturnally, ME, an endogenous hormone, modulates circadian rhythms and the sleep - wake cycle [ 11 ] . Additionally, ME controls various physiological processes, including anti-inflammation, antioxidant, immune regulation, and so on [ 12 ] . In the past few years, there has been much research into how CHD may be related to ME. Some research suggests that ME may be associated with the development and risk of CHD [ 13 , 14 ] . For example, ME can improve ischemia in patients with ST-segment elevation myocardial infarction, and calculation of left ventricular mass by Cardiac Magnetic Resonance (CMR) showed better outcomes in the melatonin group than in the placebo group [ 15 ] . Moreover a study found that patients with CHD have reduced nocturnal ME secretion compared with healthy patients, although there are large differences between individuals in the rate of ME secretion pattern [ 16 ] . Results from some studies suggest that low ME production is most closely related to CHD, infarction, and congestive heart failure [ 17 , 18 ] . Nevertheless, the specific mechanism by which ME promotes CHD remains unclear, it is vital to study the role of ME in the progression of CHD, which could present new perspectives on the prevention, diagnosis and treatment of CHD. This study is based on transcriptome-related data on CHD in the GEO database, melatonin-related biomarkers in CHD were identified through differential analysis, machine learning, etc. Then, we conducted correlation analysis, functional enrichment analysis, regulatory network construction and drug prediction for the above genes. These findings offer novel perspectives for the clinical management of CHD. Results Acquisition of 102 candidate genes The 881 CHD-DEGs were identified, with 677 CHD-DEGs showing up-regulation and 204 CHD-DEGs showing down-regulation in CHD samples compared to control samples ( Figure 1 a - b ). MERGs scores were significantly different between the two groups, indicating that ME was associated with the occurrence of CHD (p < 0.05) ( Figure 1 c ). The cluster analysis conducted revealed no outliers in the GSE179789 dataset ( Figure 1 d ). The β was set to 27 when the ordinate scale-free R2 approaches the threshold of 0.85. The mean connectivity also converges towards zero, indicating that the network tends towards a scale-free distribution ( Figure 1 e ). Then the nine modules were acquired ( Figure 1 f ). The correlation coefficient of module MEblack and MERGs was -0.55 (p < 0.05), with the strongest significant correlation ( Figure 1 g ). Therewith, the 185 ME-related module genes were obtained from this module. Whereafter, the intersection of 881 CHD-DEGs and 185 ME-related module genes resulted in the identification of 102 candidate genes.( Figure 2 a ). Among the findings of the GO analysis, enrichment of candidate genes was observed in processes such as Golgi vesicle transport, vesicle organization, secretory granule membrane, and cadherin binding, etc. ( Figure 2 b ). Among the KEGG pathways, candidate genes were significantly enriched in Long-term potentiation, Insulin signaling pathway, Salmonella infection, and Phospholipase D signaling pathway ( Figure 2 c ). Screening of candidate biomarkers After removing the discrete proteins, a PPI network of 58 proteins was built, including 58 nodes and 53 edges ( Figure 3 a ). Then, ARAF, RRAS, ACTR1A, ARF3, RAPGEF1, MAP2K2, RALGDS, ACO2, GGA3, PGD, FLNA were the top 11 genes in the Degree algorithm (the scores of genes ACTR1A, RAPGEF1, MAP2K2, RALGDS, GGA3, PGD and FLNA were consistent, so 11 genes were selected) ( Figure 3 b ). These 11 genes were input into the LASSO regression model, and finally four candidate biomarkers with regression coefficients not penalized as 0 were obtained, namely RRAS, MAP2K2, RALGDS and PGD ( Figure 3 c - d ). MAP2K2 and PGD were dependable biomarkers for CHD In the GSE179789 and GSE113079 datasets, boxplots depicted the biomarker expression levels in CHD and control samples, respectively ( Figure 4 a - b ). Notably, two candidate biomarkers, MAP2K2 and PGD, exhibited similar expression patterns, showed significant differential expression in both datasets and were subsequently employed for diagnostic validation. The GSE179789 dataset exhibited AUC values of MAP2K2 (AUC = 0.969) and PGD (AUC = 0.969), respectively ( Figure 4 c ). In the GSE113079 dataset, the AUC values for MAP2K2 and PGD were observed to be 0.737 and 0.805 ( Figure 4 d ). The AUC values of MAP2K2 and PGD exceeded 0.7, suggesting that they exhibit strong diagnostic efficacy as biomarkers. The hidden layer was set to 3, and the ANN model was constructed based on two biomarkers in GSE179789 and GSE113079 ( Figure 4 e - f ). The AUC values of the models exceeded 0.7, indicating a significant level of accuracy in the model constructed using the biomarkers MAP2K2 and PGD, which further proved the predictive accuracy of biomarkers ( Figure 4 g - h ). MAP2K2 and PGD were enriched in ribosome, prion diseases, Parkinson's disease, etc. The expression of MAP2K2 and PGD correlated significantly and positively (R = 0.70, p < 0.05) ( Figure 5 a ). And they interacted with 20 genes such as MAPK1, KSR1, PGLS, RPE and so on in glucose 6-phosphate metabolic process, organelle inheritance, NADPH regeneration, pentose-phosphate shunt, and Golgi inheritance functions ( Figure 5 b ). MAP2K2 was enriched in 53 pathways, such as ribosome, protein export, and prion diseases ( Figure 5 c , Supplementary Table 2 ). GSEA enrichment analysis revealed significant correlations between PGD with B-cell receptor signaling pathway, Leishmania infection, acute myeloid leukemia, and other pathways ( Figure 5 d , Supplementary Table 3 ). They were all enriched in the ribosome, prion diseases, Parkinson's disease, etc. There were intricate regulatory relationships between lncRNA, miRNA, and mRNA Totally, 45 miRNAs were predicted in TarBase, and 47 were predicted in miRTarBase. Then, 10 key miRNAs were shared by the two databases. Among them, the nine key miRNAs corresponded to eight lncRNAs. LncRNA, miRNA, and mRNA were engaged in elaborate post - transcriptional regulatory crosstalk ( Figure 6 a ). For example, four lncRNAs regulated (NEAT1, AP000766.1, LINC02381, XIST) PGD by hsa-let-7e-5p. MAP2K2 predicted 40 TFs and PGD predicted 36 TFs to construct the TF-mRNA network, of which 29 TFs were predicted by both biomarkers, such as STAT1, BRD3, HDAC1, and CBFB ( Figure 6 b ). MAP2K2 searched 41 target drugs, such as COBIMETINIB FUMARATE and SELUMETINIB SULFATE. And PGD predicted three drugs: PENICILLAMINE, PRALMORELIN, and PHENOBARBITAL ( Figure 6 c ). The expression of biomarkers in clinical samples was consistent with the dataset The results of RT-qPCR revealed that MAP2K2 and PGD were markedly up-regulated in clinical CHD samples, which was consistent with the findings of the datasets ( Figure 7 a - b ) . Discussion CHD, also known as coronary atherosclerotic heart disease, ranks among the most enormous burdens for public health and the economy of the low-income and lower-middle-income nations [ 19 ] . The main cause of CHD is atherosclerosis, which is characterized by the deposition of plaques of fatty material within the arteries. As a result of plaque development in coronary arteries and a blockage or narrowing of the vessels, CHD occurs [ 20 , 21 ] . Some studies have shown that circulating ME levels are altered in patients with CHD. For example, there is an independent relationship between nocturnal oxidized low-density lipoprotein and melatonin levels in patients with myocardial infarction [ 22 ] . Inadequate sleep or poor sleep quality may increase the risk of CHD, and melatonin secretion is closely related to sleep [ 23 , 24 ] . Low serum ME levels have been reported in individuals, which exacerbates the possibility of ischemia-reperfusion injury leading to further cardiac damage, as melatonin has been described as a direct free radical scavenger that is highly effective in preventing reactive oxygen and nitrogen damage [ 13 ] . Several studies have shown the beneficial effects of ME supplementation on biomarkers of oxidative stress and inflammation, blood pressure, glycemic control, and lipids in animal models and patients with T2DM and MetS [ 25 , 26 ] . To further explore the relationship between ME and CHD, by utilizing bioinformatic techniques, two MRGs (MAP2K2, PGD) in patients with CHD were identified in the present study, which may offer a new perspective for treating CHD. MAP2K2 (Mitogen-Activated Protein Kinase Kinase 2) is a key component of the MAPK signaling pathway. This gene encodes a dual-specificity kinase that activates downstream MAP kinases in response to extracellular stimuli [ 27 ] . Continuous activation of MAPK signaling due to Ras pathway dysfunction can lead to endothelial cell dysfunction and speed up atherosclerosis progression. Several studies have suggested that mutations or deletions in the MAP2K2 gene may be implicated in the etiology of RASopathies, a collection of developmental disorders resulting from mutations in genes encoding components of the RAS-MAPK pathway [ 28 ] . Proliferation, differentiation, growth, aging and deaths of cells depend on the MAPK signaling pathway, meanwhile it plays an essential role in atherosclerosis, which is the underlying pathogenesis of CHD [ 29 ] . During the onset and development of atherosclerosis, MAP2K2, which is an important member of the MAPK pathway, expression level increased result in inflammation via activation downstream MAPK pathway factors such as ERK phosphorylation [ 30 ] . In the meantime, inflammatory responses are amplified through activation of the MAP2K/ERK pathway and the expression of various cytokines, which may damage the vascular system [ 31 ] . MAP2K2 indirectly phosphorylates ERK by upregulating lnc_000048 and ultimately activates the inflammatory response through MAPK pathways, reducing plaque stability [ 32 ] . According to some studies, MAP2K2 expression is significantly suppressed in patients with metabolic syndrome, which is associated with an increased risk of cardiovascular disease, type 2 diabetes (DM), and cardiovascular-specific mortality [ 33 ] . As MAP2K2 is metabolized through the MAPK signaling pathway, it may affect cardiovascular disease (CVD), including CHD. According to the qPCR results of this study, the upregulation of map2k2 expression in the disease group was in agreement with the bioinformatics analysis findings. Given that MAP2K2 is metabolized through the MAPK signaling pathway, it is speculated that MAP2K2 may affect cardiovascular diseases, including CHD. PGD (Phosphogluconate Dehydrogenase) also referred to as 6PGD, is the third enzyme of the pentose phosphate pathway (PPP) that catalyzes 6-phosphogluconate (6-PG) degradation into ribulose-5-phosphate (Ru-5-P) while simultaneously reducing NADP + to NADPH [ 34 ] . A study has found that Ru-5-P derived from PGD inhibits 5'-adenosine monophosphate-activated protein kinase (AMPK) by destroying the upstream LKB1 complex, thereby increasing fatty acid synthesis, especially saturated fatty acids (SFA) [ 35 ] . The effects of fatty acids on CVD are thought to lie in multiple mechanisms, such as cardio-metabolism, lipo-toxicity, electromechanical properties of cardiomyocytes, and inflammatory processes [ 36 ] . This suggests that PGD may influence the onset and progression of cardiovascular diseases, including CHD, by participating in the synthesis of fatty acids. This finding could offer valuable insights for diagnosis and treatment of CHD. This research found that GSEA enrichment analysis indicated MAP2K2 and PGD were involved in signaling pathways like ribosomes, prion diseases, and Parkinson's disease. These pathways are frequently enriched in the context of these biomarkers. Ribosome, composed of RNA and proteins, are increasingly recognized for their important roles in cell function, responses to stimuli, and disease development. Proteins involved in ribosome biogenesis play a crucial role in regulating cell growth and proliferation [ 36 ] ; disruption of this process can result in aberrant cell proliferation and the development of pathological conditions, including cancer and metabolic disorders [ 37 ] . Some studies have shown that ribosomal proteins (RPs) have been linked to the development of cardiovascular disease [ 38 , 39 ] . Smolock EM56, et al. discovered that L17, functioning as a ribosomal protein, serves as a potent inhibitor of vascular smooth muscle cell growth, effectively impeding the thickening of the intimal layer in the mouse carotid artery. The expression of L17 hinders the proliferation of vascular smooth muscle cells by inducing cell cycle arrest in the G0/G1 phase. S6K, a ribosomal protein, is a prominent downstream effector of mTOR (mammalian target of rapamycin) and plays a crucial role in various cardiovascular and metabolic processes [ 40 ] . Research indicates that following myocardial infarction, S6K is rapidly and robustly activated, contributing to maladaptive cardiac remodeling via heightened Akt signaling in the infarcted myocardium [ 41 ] . The study revealed that MAP2K2 is enriched in the ribosomal signaling pathway, indicating it might influence the protein synthesis process linked to cardiovascular diseases, leading to abnormal expression of proteins essential for the cardiovascular system's normal function, such as influencing proteins involved in vasodilation and contraction, or altering the expression of proteins associated with the structure and function of heart muscle cells, thereby disturbing the cardiovascular system's equilibrium, as a result, it causes or exacerbates CHD. There has also been an increased interest in the potential link between cardiovascular disease and Parkinson's disease in recent years. It has also been reported that many of the potential risk factors for Parkinson's disease are also typically cardiovascular risk factors (such as advanced age, male sex, diabetes, HTN, and obesity), and that there are common mechanistic hypotheses for both diseases (such as chronic inflammation and oxidative stress) [ 42 , 43 ] . Despite this, there is still no clear conclusion regarding how cardiovascular disease and Parkinson's disease are related. There is an increasing agreement among researchers that the risk of cardiovascular disease in patients with Parkinson's disease significantly rises as the disease advances. Using data from two different populations, Li et al found that stroke and coronary artery disease (CAD) may contribute to Parkinson's disease (PD) pathogenesis [ 44 ] . Liang et al. found that the risk of myocardial infarction in PD patients was significantly elevated [ 45 ] . Another study found that among newly diagnosed PD patients, 62.5% had moderate to high cardiovascular risk, which was higher than the general population [ 46 ] . These studies further support the hypothesis that cardiovascular disease is associated with Parkinson's disease. However, several opinions have suggested that PD does not increase the overall risk of myocardial infarction and cardiovascular death, and that there is no difference in the risk of myocardial infarction and cardiovascular death between PD and non-PD individuals [ 47 , 48 ] . Even meta-analysis suggests that PD is associated with a reduced risk of MI [ 49 ] . Therefore, more research is needed to prove the link between PD and CVD. Our study provides a reference for the study of the mechanism of action of MRGs in CHD. According to the study, 29 transcription factors were co-regulated by two biomarkers, namely STAT1, BRD3, HDAC1, and CBFB. In the immune response, STAT1, activated by the IFN-γ/JAK2 pathway [ 50 ] , contributes by regulating the activation of immune cells, secretion of inflammatory factors, and expression of inflammatory genes [ 51 , 52 ] . It was found in the study that the STAT1 promoter's methylation level was significantly reduced in coronary heart disease patients compared to the control group [ 53 ] . The binding site (GAS/ISRE) was notably enriched in the promoter region of genes related to plaque (CCL2/5/19, VEGFC, etc.), indicating its role in plaque formation through the regulation of cell adhesion, migration, and inflammation [ 54 ] . This investigation proposes that the progression of coronary heart disease could be impacted by map2k2 and PGD via the modulation of the STAT1 signaling axis. Identified as potential biomarkers and therapeutic targets for CHD, these two genes could revolutionize the approach to diagnosing and treating CHD patients, paving the way for precision medicine in this field. However, based on bioinformatics analysis of public databases, this study needs further testing to investigate the mechanism of action of biomarkers and more clinical studies to verify its results. Materials and methods Data collection The Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database offered the retrieve of the GSE179789 (GPL570) and GSE113079 (GPL20115) datasets related to CHD. The GSE179789 dataset, which included 8 CHD patients and 8 controls with peripheral blood monocyte (PBMC) samples, served as the training set. In contrast, the validation set, GSE113079, contained PBMC samples of 93 CHD patients and 48 controls. We obtained 12 ME - related genes (MERGs) from the literature [55] , that is, MTNR1A, MTNR1B, GPR50, AANAT, ASMT, CYP1A2, CYP2C19, RORA, PER2, PER3, ARNTL, and CLOCK. Differential Analysis For the identification of CHD-differentially expressed genes (DEGs) between two groups within the training set, differential analysis was conducted using the limma package (V 3.52.4) [56] . |log2FoldChange(FC)| > 0.5, p < 0.05 was used as the screening criterion. The generation of volcano plots was facilitated by the ggplot2 package (V 3.4.1) [57] . Additionally, heat maps were constructed to visualize the expression patterns of CHD-DEGs in control and CHD samples by utilizing the ComplexHeatmap package (V 2.14.0) [58] . Weighted gene co-expression network analysis (WGCNA) The scores of the 12 MERGs were first calculated in different samples of the GSE179789 dataset by the GSVA package (V 1.42.0) [59] . The score of MERGs was used as a trait, and the R package WGCNA (V 1.71) [60] was used for analysis to screen the modules and genes related to the trait. Firstly, prior to clustering, gene expression values were screened, and only genes with expression values above 1 were selected for sample cluster analysis. The sample cluster analysis was adopted to identify any outlier samples that may need to be excluded to ensure the accuracy of subsequent analyses. Secondly, to maximize the scale - free distribution of gene interactions, the soft threshold (β) was determined. With the hybrid dynamic tree - cutting algorithm as the clustering criterion, module partitioning was carried out. The procedure was parameterized by β and constrained each module to have a minimum of 50 genes. Thereafter, by computing the correlation coefficients between each module and the MRGs score, we aimed to determine the module that had the strongest association with MRGs. Finally, we obtained ME-related module genes in the most relevant module. Identification and functional enrichment analysis of candidate genes The CHD-DEGs and ME-related module genes were intersected using ggvenn (V 0.1.9) [61] to acquired candidate genes. The cluster Profiler package (V 4.7.1.3) [62] was applied to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the candidate genes, aiming to identify common functions and related pathways among them [63-65] . Establishment of protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) analysis A PPI network of the candidate genes was constructed by the STRING website (http://string.embl.de/) to investigate the interaction among the candidate genes (confidence score = 0.5). Hereafter, the Degree algorithm in cytoHubba, a built-in plugin of Cytoscape (V 3.8.2) [66] , was leveraged to determine the connectivity score of each gene. Subsequently, the top 10 genes were selected for LASSO method by glmnet package (V 4.1.4) [67] . Genes whose regression coefficients were not penalized to zero when the optimal lambda was reached in the LASSO regression model were regarded as candidate biomthearkers. Expression level verification and Receiver Operating Characteristic (ROC) analysis The GSE179789 and GSE113079 datasets were adopted to compare the expression of candidate biomarkers in CHD patients and controls. Meanwhile, candidate biomarkers that exhibited consistent expression trends and significant distinctions between the groups in both datasets were screened for ROC analysis (p < 0.05). In detail, the area under the curve (AUC) values were determined to quantitatively measure the diagnostic accuracy of the genes, and genes with an AUC exceeding 0.7 were considered reliable biomarkers. Artificial Neural Network (ANN) and correlation analysis Aiming further to validate the overall disease diagnosis results of biomarkers, the neuralnet package (V 1.44.2) [68] was utilized to construct ANN models based on biomarkers in GSE179789 and GSE113079. Subsequently, the diagnostic ability of the model was assessed by plotting an ROC curve. Furthermore, in the GSE179789 dataset, Spearman correlation analyzed biomarker expression correlations. Gene set enrichment analysis (GSEA) and gene-gene interaction (GGI) network To understand the biological characteristics of these biomarkers, we utilized GeneMANIA (http://www.genemania.org/) to construct and analyze a GGI network between the biomarkers and their co-expressed genes. We performed GSEA pathway enrichment analysis according to biomarkers to delve deeper into the associated functions. Concretely, according to their biomarker levels, CHD samples from GSE179789 were partitioned into two distinct subsets: those with high biomarker expression and those with low biomarker expression. The limma package (V 3.52.4) [56] was used for differential analysis based on high and low expression groups, and log2FC of obtained genes was calculated. The log2FC was sorted from largest to smallest, after which GSEA was carried out by the clusterProfiler package (V 4.7.1.3) (|NES| > 1, p < 0.05) [62] . The “c2. cp.kegg.v2023.1. Hs.symbols.gmt” as the reference gene set in the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb). Construction of regulatory network and drug prediction Transcription factors (TFs) against biomarkers were forecasted employing the hTFtarget database (http://bioinfo.life.hust.edu.cn/hTFtarget), and datasets greater than 1 were screened. Similarly, TarBase (http://www.diana.pcbi.upenn.edu/tarbase) and miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) databases were utilized for the prediction of miRNA biomarkers, with the intersection of these two databases results as key miRNAs. Subsequently, the Starbase database (http://starbase.sysu.edu.cn/) was implemented to predict the lncRNAs targeted by key miRNAs. In an effort to discover novel medications for CHD treatment, the Drug-Gene Interaction Database (DGIdb, http://www.dgidb.org) was queried to retrieve drugs that specifically target the key biomarkers. Ultimately, the Cytoscape (V 3.8.2) [66] was utilized to present the lncRNA-miRNA-mRNA, TF-mRNA, and mRNA-drug networks. Validation of biomarker expression in clinical samples To assess biomarker expression in clinical samples, we procured five pairs of blood samples from the Jiangxi Provincial People’s Hospital for reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis, comprising five control and five CHD samples. The patients included were those admitted to Jiangxi Provincial People’s Hospital from January 1, 2024, to January 31, 2024. All human research procedures were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations approved by Medical Ethics Committee of Jiangxi Provincial People's Hospital (Approval Number: Kekuai 2023(15)- Project). Informed consent was obtained from all participants and/or their legal guardians prior to the study initiation. Total RNA was extracted from the samples with TRIzol reagent (Ambion, Austin, USA) following the manufacturer's instructions. Subsequently, cDNA synthesis was performed using the SureScript First-strand cDNA synthesis kit (Servicebio, Wuhan, China), following the instructions. For RT-qPCR analysis, the 2x Universal Blue SYBR Green qPCR Master Mix (Servicebio) was utilized. Supplenemntary Table 1 provides the primer sequences employed for PCR amplification. Biomarker expression levels were quantified using the 2 -ΔΔCt method [69] . Statistical analysis Data analysis was performed using R (version 4.2.2), with statistical significance defined as p < 0.05. Abbreviations Abbreviation Full Term ME Melatonin CHD Coronary Heart Disease DEGs Differentially Expressed Genes GSEA Gene Set Enrichment Analysis RT-qPCR Reverse Transcription-quantitative Polymerase Chain Reaction AUC Area Under the Curve ROC Receiver Operating Characteristic ANN Artificial Neural Network PPI Protein-Protein Interaction LASSO Least Absolute Shrinkage and Selection Operator WGCNA Weighted Gene Co-expression Network Analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes GGI Gene-Gene Interaction TF Transcription Factor miRNA MicroRNA lncRNA Long Non-coding RNA MAP2K2 Mitogen-Activated Protein Kinase Kinase 2 PGD Phosphogluconate Dehydrogenase PPP Pentose Phosphate Pathway NADPH Nicotinamide Adenine Dinucleotide Phosphate AMPK 5'-Adenosine Monophosphate-activated Protein Kinase mTOR Mammalian Target of Rapamycin STAT1 Signal Transducer and Activator of Transcription 1 ERK Extracellular Signal-regulated Kinase MAPK Mitogen-Activated Protein Kinase CVD Cardiovascular Disease PBMC Peripheral Blood Mononuclear Cell GEO Gene Expression Omnibus GSVA Gene Set Variation Analysis MSigDB Molecular Signatures Database DGIdb Drug-Gene Interaction Database Declarations Competing interests "The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.” Funding This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Yongchao Peng: Data curation, Validation, Visualization, Writing–review & editing. Xuemin Zhou: Validation, Writing–review & editing. Li Huang: Data curation, Investigation, Resources, Visualization. Yaowu Xie: Conceptualization, Project administration, Supervision, Writing–review & editing. Xuanlan Chen: Conceptualization, Supervision, Writing–review & editing. Zhifeng Zhang: Conceptualization, Data curation, Validation, Visualization, Writing–original draft, Writing–review & editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors. Acknowledgments We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Yongchao Peng, Xuemin Zhou, Xuanlan Chen, Yaowu Xie, Zhifeng Zhang. 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Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 19 Feb, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 18 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8816168","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625505962,"identity":"798be062-574d-4eeb-a3a7-21cab0643af6","order_by":0,"name":"Yongchao Peng","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yongchao","middleName":"","lastName":"Peng","suffix":""},{"id":625505963,"identity":"413d511b-dd42-4c4a-a38f-59e828b50944","order_by":1,"name":"Xuemin Zhou","email":"","orcid":"","institution":"Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College)","correspondingAuthor":false,"prefix":"","firstName":"Xuemin","middleName":"","lastName":"Zhou","suffix":""},{"id":625505964,"identity":"7b941532-a4b7-4b0d-bc72-1a55a4436993","order_by":2,"name":"Yaowu Xie","email":"","orcid":"","institution":"Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College)","correspondingAuthor":false,"prefix":"","firstName":"Yaowu","middleName":"","lastName":"Xie","suffix":""},{"id":625505965,"identity":"61c31def-b26f-4d76-83cc-342fd984bf17","order_by":3,"name":"Li Huang","email":"","orcid":"","institution":"Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College)","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Huang","suffix":""},{"id":625505966,"identity":"39708c35-8e5f-45cf-94d6-22f9e231876a","order_by":4,"name":"Xuanlan Chen","email":"","orcid":"","institution":"Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College)","correspondingAuthor":false,"prefix":"","firstName":"Xuanlan","middleName":"","lastName":"Chen","suffix":""},{"id":625505967,"identity":"c2f14861-9ba5-4d12-a4fe-530b05f2f299","order_by":5,"name":"Zhifeng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3QsUrDQBzH8QuBZvlJ1v+RQF7hIJC2UMyrHAjtktn5ILN7RJ+j878EnKQ+QB10KQgOCYhkyGCMdey1o+B9h7sb/p+DOyFcrr8YjSsjMONhgTA0ZxLweFjGsuLziDiQeqGMtovkrty/fvTPMYKb+q3rn6AEe01bHCfe/cM0jbEHsF3OCDtMfePL2/Vx4pPOIqIaORWZUrTD3PDEv7CQCa0+I1I1kLxnSqstFGs7wXC5bPRACOkLaz5NiIrrSPBAUGSe4SvIalNa35JUq7Xs+jpH8Ji2XX+Zh2G5aVoLGb8Av+/62T1jn/8e6Q60OTnqcrlc/7IvKNBOVnlDrZIAAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College)","correspondingAuthor":true,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-07 14:24:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8816168/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8816168/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107490458,"identity":"c7ea5a23-0bdc-4a2f-aef1-7b211acf0b27","added_by":"auto","created_at":"2026-04-22 02:52:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1598223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of coronary heart disease (CHD)-related differentially expressed genes (DEGs) and melatonin-associated modules.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Volcano plot displaying DEGs between CHD and control samples. Red dots represent upregulated genes and blue dots represent downregulated genes; the top 10 genes with the most significant up/downregulation differences are shown. (\u003cstrong\u003eb\u003c/strong\u003e) Heatmap of DEG expression patterns in CHD and control groups. The top10 upregulated and downregulated gene expression density heat maps of the samples are shown in the upper part, showing the lines of the five percentiles and the average value; the bottom part is the expression heat map of the top10 upregulated and downregulated genes of the samples. (\u003cstrong\u003ec\u003c/strong\u003e) Comparison of melatonin-related gene (MERG) scores between CHD and healthy samples. (\u003cstrong\u003ed\u003c/strong\u003e) Sample clustering dendrogram showing no outliers. (\u003cstrong\u003ee\u003c/strong\u003e) Scale-free topology analysis for WGCNA soft-threshold selection. The branches represent the samples, and the vertical coordinate represents the height of hierarchical clustering. The horizontal axis represents the value of the weight parameter power, and the vertical axis of the left figure shows the scale-free fit index, namely signed R2. The higher the square of the correlation coefficient, the closer the network is to the scale-free distribution. The vertical axis of the right figure represents the mean value of all gene adjacency functions in the corresponding gene module. (\u003cstrong\u003ef\u003c/strong\u003e) Module clustering dendrogram. The upper part is the hierarchical clustering tree of genes, and the lower part is the gene module, with different colors representing different modules. (\u003cstrong\u003eg\u003c/strong\u003e) Module-trait correlation heatmap, highlighting the MEblack module as most associated with MERGs. The darker the color, the higher the correlation. Red is positive correlation and blue is negative correlation.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/bd1bfed73a53991704b69f29.png"},{"id":107450923,"identity":"8fb1ef33-7bea-4c5a-8294-b135531d3678","added_by":"auto","created_at":"2026-04-21 15:15:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2135557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of candidate genes.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Venn diagram showing the overlap between CHD-DEGs and ME-related module genes. (\u003cstrong\u003eb\u003c/strong\u003e) Gene Ontology (GO) enrichment terms for biological processes, cellular components, and molecular functions. The left inner circle shows a bar chart. The height of the bar represents the significance of the Term, with higher values indicating greater significance. The color of the bar indicates the z-score, where darker colors correspond to larger z-scores. Red and blue bars respectively represent upregulated and downregulated genes. The right side displays descriptions of GO enrichment entries. (\u003cstrong\u003ec\u003c/strong\u003e) Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) pathway enrichment analysis of candidate genes. On the left is the gene, on the right is the pathway, and the name of the pathway is shown below.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/836ee86979db27ef78bef975.png"},{"id":107487770,"identity":"22522128-fbaf-40a7-9568-c1f73b9ae5e8","added_by":"auto","created_at":"2026-04-22 02:42:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2002902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of candidate biomarkers via protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) PPI network of candidate genes. (\u003cstrong\u003eb\u003c/strong\u003e) Top hub genes ranked by connectivity scores. (\u003cstrong\u003ec\u003c/strong\u003e) LASSO coefficient profiles of candidate genes. The horizontal axis is the logarithm of lambdas, and the vertical axis is the coefficient of the variable. Each line represents a gene. (\u003cstrong\u003ed\u003c/strong\u003e) Cross-validation curve for optimal lambda selection in LASSO regression. The horizontal axis is the logarithm of lambdas and the vertical axis is the model error.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/992220bf56e1b0131f2b12a2.png"},{"id":107490162,"identity":"af637088-3616-4853-93cb-7267ca7b8cf3","added_by":"auto","created_at":"2026-04-22 02:50:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":876705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of biomarker diagnostic performance.\u003c/strong\u003e (\u003cstrong\u003ea-b\u003c/strong\u003e) Boxplots comparing MAP2K2 and Phosphogluconate Dehydrogenase (PGD) expression levels in Coronary Heart Disease(CHD) vs. control samples across training (GSE179789) and validation (GSE113079) datasets. The horizontal axis is gene; the vertical axis is expression level; red represents disease samples, blue represents control samples; asterisk indicates significance: *, p \u0026lt;0.05; **, p \u0026lt;0.01; ***, p \u0026lt;0.001. (C-D) ROC curves evaluating the diagnostic accuracy of MAP2K2 and PGD in both datasets. (\u003cstrong\u003ec\u003c/strong\u003e) Training set GSE179789, (D) validation set GSE113079. (\u003cstrong\u003ee-f\u003c/strong\u003e)Architecture of the artificial neural network (ANN) model. The nodes marked as \"1\" in this diagram are called bias units. The leftmost layer (Layer 1) serves as the input layer, the middle layer (Layer 2) is the hidden layer, and the rightmost layer (Layer 3) functions as the output layer. In this configuration, there are 2 input units (including the bias unit), 2 output units, and 3 hidden units (excluding the bias unit). The coefficients in the connections represent weights, which indicate how much each variable contributes to the next node's output. (\u003cstrong\u003eg-h\u003c/strong\u003e) Artificial neural network ROC curve. The x-axis is specificity and the y-axis is sensitivity. The area enclosed by the curve and the x-axis is called AUC.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/933097de814e4b243a166199.png"},{"id":107704460,"identity":"cc034a0a-29ea-4544-ba56-cfb406205a0f","added_by":"auto","created_at":"2026-04-24 08:45:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2115921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional and pathway associations of MAP2K2 and phosphogluconate dehydrogenase (PGD).\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Correlation scatter plot of MAP2K2 and PGD expression levels. The horizontal and vertical coordinates are the expression levels of biomarkers, R represents the correlation, and p represents whether the correlation is significant. (\u003cstrong\u003eb\u003c/strong\u003e) Gene-gene interaction (GGI) network of biomarkers and co-expressed genes. (\u003cstrong\u003ec-d\u003c/strong\u003e) MAP2K2 and PGD GSEA enrichment analysis. The top section displays an enrichment fraction line graph, where each curve represents a pathway. The peak of each curve indicates the pathway's enrichment fraction, with genes preceding the peak being core genes in that pathway. Peaks in the upper-left quadrant represent core genes primarily identified through differential expression analysis based on biological markers' expression levels, while those in the lower-right quadrant indicate core genes mainly identified through differential expression analysis based on high/low marker expression levels. The second section uses dotted lines to mark genes within the gene set. The third section presents the rank value distribution chart for all genes.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/1adf14c7d43a801be18c0854.png"},{"id":107487774,"identity":"e3191c26-cbc7-4ff7-ad95-02d09cddfbe7","added_by":"auto","created_at":"2026-04-22 02:42:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1760474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory networks and drug prediction for biomarkers. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) lncRNA-miRNA-mRNA regulatory network for MAP2K2 and PGD. Red for mRNA; green for miRNA; yellow for lncRNA. (\u003cstrong\u003eb\u003c/strong\u003e) Transcription factor (TF)-mRNA interaction network. Red: biomarkers; yellow: transcription factor TF. (\u003cstrong\u003ec\u003c/strong\u003e) Drug-target network for predicted therapeutic compounds.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/b5641cea9d5303f0706cb5c3.png"},{"id":107704448,"identity":"633f99e1-f9e3-4767-b768-8db931533fc0","added_by":"auto","created_at":"2026-04-24 08:45:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":87776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental validation of biomarker expression in clinical samples.\u003c/strong\u003e(\u003cstrong\u003ea-b\u003c/strong\u003e) everse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) results confirming upregulation of MAP2K2 and PGD in CHD tissues compared to controls.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/cd0f164a473ac77c62acc113.png"},{"id":107708436,"identity":"e57082a0-f341-479e-a7fc-5714c4caf2f1","added_by":"auto","created_at":"2026-04-24 09:27:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11019095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/b282100c-20ae-4021-9b55-9131ada44cf6.pdf"},{"id":107488829,"identity":"f0612d65-8604-4d00-9abf-438d16814691","added_by":"auto","created_at":"2026-04-22 02:45:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":459090,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8816168/v1/480f47756b014c8e5d46df4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Role of Melatonin in Coronary Heart Disease: Identification and Experimental Validation of Novel Biomarkers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary heart disease (CHD) remains a globally widespread chronic disorder, associated with high rates of illness and death \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The disease entity is characterized by insufficient blood supply or obstruction of the coronary arteries, leading to myocardial ischemia, hypoxia, angina pectoris, palpitations, shortness of breath and fatigue; it may also present with other symptoms \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In severe cases, it can cause myocardial infarction or arrhythmia, and even be life-threatening \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. A Atherosclerosis Risk in Communities (ARIC) study found approximately 60500 new myocardial infarctions and 200,000 recurrent events every year between 2005 and 2014 \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The pathogenesis of CHD is complex and among the main causes of CHD, atherosclerosis has been widely regarded as the main factor \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Inflammation, endothelial dysfunction, hypertension, dyslipidemia, insulin resistance, environmental exposures, and pathogens all contribute to endothelial injury, which in turn contributes to the formation and progression of atherosclerotic plaques \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The development of a CHD diagnosis depends on clinical symptoms, physical examination and imaging characteristics (such as electrocardiogram, echocardiogram, coronary angiography, etc) \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In current therapies, pharmacological agents, which include antiplatelets, statins, β-blockers, etc, are used along with interventional treatments, such as primary coronary intervention (PCI) and coronary artery bypass surgery (CABG) \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, there are some issues with these treatments. For example, drugs may be ineffective or cause resistance in certain patient populations, and vascular stent technology still suffers from many complications, including in-stent restenosis, late thrombosis, artery injury, and high re-occlusion rates \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In recent years, some scientists believe that to develop novel therapeutic targets and therapies for CHD, an in-depth understanding of the disease's pathogenesis is vital. In particular, research on biomarkers can help early diagnosis of CHD, assess the severity of the disease, predict prognosis, and guide individualized therapeutic strategies.\u003c/p\u003e \u003cp\u003eSecreted predominantly by the pineal gland nocturnally, ME, an endogenous hormone, modulates circadian rhythms and the sleep - wake cycle \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Additionally, ME controls various physiological processes, including anti-inflammation, antioxidant, immune regulation, and so on \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In the past few years, there has been much research into how CHD may be related to ME. Some research suggests that ME may be associated with the development and risk of CHD \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. For example, ME can improve ischemia in patients with ST-segment elevation myocardial infarction, and calculation of left ventricular mass by Cardiac Magnetic Resonance (CMR) showed better outcomes in the melatonin group than in the placebo group \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Moreover a study found that patients with CHD have reduced nocturnal ME secretion compared with healthy patients, although there are large differences between individuals in the rate of ME secretion pattern \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Results from some studies suggest that low ME production is most closely related to CHD, infarction, and congestive heart failure \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the specific mechanism by which ME promotes CHD remains unclear, it is vital to study the role of ME in the progression of CHD, which could present new perspectives on the prevention, diagnosis and treatment of CHD.\u003c/p\u003e \u003cp\u003eThis study is based on transcriptome-related data on CHD in the GEO database, melatonin-related biomarkers in CHD were identified through differential analysis, machine learning, etc. Then, we conducted correlation analysis, functional enrichment analysis, regulatory network construction and drug prediction for the above genes. These findings offer novel perspectives for the clinical management of CHD.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eAcquisition of 102 candidate genes\u003c/h2\u003e\n\u003cp\u003eThe 881 CHD-DEGs were identified, with 677 CHD-DEGs showing up-regulation and 204 CHD-DEGs showing down-regulation in CHD samples compared to control samples (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). MERGs scores were significantly different between the two groups, indicating that ME was associated with the occurrence of CHD (p \u0026lt; 0.05) (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e). The cluster analysis conducted revealed no outliers in the GSE179789 dataset (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e). The \u0026beta; was set to 27 when the ordinate scale-free R2 approaches the threshold of 0.85. The mean connectivity also converges towards zero, indicating that the network tends towards a scale-free distribution (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e). Then the nine modules were acquired (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e). The correlation coefficient of module MEblack and MERGs was -0.55 (p \u0026lt; 0.05), with the strongest significant correlation (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003eg\u003c/strong\u003e). Therewith, the 185 ME-related module genes were obtained from this module. Whereafter, the intersection of 881 CHD-DEGs and 185 ME-related module genes resulted in the identification of 102 candidate genes.(\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). Among the findings of the GO analysis, enrichment of candidate genes was observed in processes such as Golgi vesicle transport, vesicle organization, secretory granule membrane, and cadherin binding, etc. (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). Among the KEGG pathways, candidate genes were significantly enriched in Long-term potentiation, Insulin signaling pathway, Salmonella infection, and Phospholipase D signaling pathway (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eScreening of candidate biomarkers\u003c/h2\u003e\n\u003cp\u003eAfter removing the discrete proteins, a PPI network of 58 proteins was built, including 58 nodes and 53 edges (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). Then, ARAF, RRAS, ACTR1A, ARF3, RAPGEF1, MAP2K2, RALGDS, ACO2, GGA3, PGD, FLNA were the top 11 genes in the Degree algorithm (the scores of genes ACTR1A, RAPGEF1, MAP2K2, RALGDS, GGA3, PGD and FLNA were consistent, so 11 genes were selected) (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). These 11 genes were input into the LASSO regression model, and finally four candidate biomarkers with regression coefficients not penalized as 0 were obtained, namely RRAS, MAP2K2, RALGDS and PGD (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eMAP2K2 and PGD were dependable biomarkers for CHD\u003c/h2\u003e\n\u003cp\u003eIn the GSE179789 and GSE113079 datasets, boxplots depicted the biomarker expression levels in CHD and control samples, respectively (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). Notably, two candidate biomarkers, MAP2K2 and PGD, exhibited similar expression patterns, showed significant differential expression in both datasets and were subsequently employed for diagnostic validation. The GSE179789 dataset exhibited AUC values of MAP2K2 (AUC = 0.969) and PGD (AUC = 0.969), respectively (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e4\u003cstrong\u003ec\u003c/strong\u003e). In the GSE113079 dataset, the AUC values for MAP2K2 and PGD were observed to be 0.737 and 0.805 (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e). The AUC values of MAP2K2 and PGD exceeded 0.7, suggesting that they exhibit strong diagnostic efficacy as biomarkers. The hidden layer was set to 3, and the ANN model was constructed based on two biomarkers in GSE179789 and GSE113079 (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e). The AUC values of the models exceeded 0.7, indicating a significant level of accuracy in the model constructed using the biomarkers MAP2K2 and PGD, which further proved the predictive accuracy of biomarkers (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eh\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eMAP2K2 and PGD were enriched in ribosome, prion diseases, Parkinson\u0026apos;s disease, etc.\u003c/h2\u003e\n\u003cp\u003eThe expression of MAP2K2 and PGD correlated significantly and positively (R = 0.70, p \u0026lt; 0.05) (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). And they interacted with 20 genes such as MAPK1, KSR1, PGLS, RPE and so on in glucose 6-phosphate metabolic process, organelle inheritance, NADPH regeneration, pentose-phosphate shunt, and Golgi inheritance functions (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). MAP2K2 was enriched in 53 pathways, such as ribosome, protein export, and prion diseases (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2\u003c/strong\u003e). GSEA enrichment analysis revealed significant correlations between PGD with B-cell receptor signaling pathway, Leishmania infection, acute myeloid leukemia, and other pathways (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;3\u003c/strong\u003e). They were all enriched in the ribosome, prion diseases, Parkinson\u0026apos;s disease, etc.\u003c/p\u003e\n\u003ch2\u003eThere were intricate regulatory relationships between lncRNA, miRNA, and mRNA\u003c/h2\u003e\n\u003cp\u003eTotally, 45 miRNAs were predicted in TarBase, and 47 were predicted in miRTarBase. Then, 10 key miRNAs were shared by the two databases. Among them, the nine key miRNAs corresponded to eight lncRNAs. LncRNA, miRNA, and mRNA were engaged in elaborate post - transcriptional regulatory crosstalk (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). For example, four lncRNAs regulated (NEAT1, AP000766.1, LINC02381, XIST) PGD by hsa-let-7e-5p. MAP2K2 predicted 40 TFs and PGD predicted 36 TFs to construct the TF-mRNA network, of which 29 TFs were predicted by both biomarkers, such as STAT1, BRD3, HDAC1, and CBFB (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). MAP2K2 searched 41 target drugs, such as COBIMETINIB FUMARATE and SELUMETINIB SULFATE. And PGD predicted three drugs: PENICILLAMINE, PRALMORELIN, and PHENOBARBITAL (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eThe expression of biomarkers in clinical samples was consistent with the dataset\u003c/h2\u003e\n\u003cp\u003eThe results of RT-qPCR revealed that MAP2K2 and PGD were markedly up-regulated in clinical CHD samples, which was consistent with the findings of the datasets (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCHD, also known as coronary atherosclerotic heart disease, ranks among the most enormous burdens for public health and the economy of the low-income and lower-middle-income nations \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The main cause of CHD is atherosclerosis, which is characterized by the deposition of plaques of fatty material within the arteries. As a result of plaque development in coronary arteries and a blockage or narrowing of the vessels, CHD occurs \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Some studies have shown that circulating ME levels are altered in patients with CHD. For example, there is an independent relationship between nocturnal oxidized low-density lipoprotein and melatonin levels in patients with myocardial infarction \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Inadequate sleep or poor sleep quality may increase the risk of CHD, and melatonin secretion is closely related to sleep \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Low serum ME levels have been reported in individuals, which exacerbates the possibility of ischemia-reperfusion injury leading to further cardiac damage, as melatonin has been described as a direct free radical scavenger that is highly effective in preventing reactive oxygen and nitrogen damage \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Several studies have shown the beneficial effects of ME supplementation on biomarkers of oxidative stress and inflammation, blood pressure, glycemic control, and lipids in animal models and patients with T2DM and MetS \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. To further explore the relationship between ME and CHD, by utilizing bioinformatic techniques, two MRGs (MAP2K2, PGD) in patients with CHD were identified in the present study, which may offer a new perspective for treating CHD.\u003c/p\u003e \u003cp\u003eMAP2K2 (Mitogen-Activated Protein Kinase Kinase 2) is a key component of the MAPK signaling pathway. This gene encodes a dual-specificity kinase that activates downstream MAP kinases in response to extracellular stimuli \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Continuous activation of MAPK signaling due to Ras pathway dysfunction can lead to endothelial cell dysfunction and speed up atherosclerosis progression. Several studies have suggested that mutations or deletions in the MAP2K2 gene may be implicated in the etiology of RASopathies, a collection of developmental disorders resulting from mutations in genes encoding components of the RAS-MAPK pathway \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Proliferation, differentiation, growth, aging and deaths of cells depend on the MAPK signaling pathway, meanwhile it plays an essential role in atherosclerosis, which is the underlying pathogenesis of CHD \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. During the onset and development of atherosclerosis, MAP2K2, which is an important member of the MAPK pathway, expression level increased result in inflammation via activation downstream MAPK pathway factors such as ERK phosphorylation \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In the meantime, inflammatory responses are amplified through activation of the MAP2K/ERK pathway and the expression of various cytokines, which may damage the vascular system \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. MAP2K2 indirectly phosphorylates ERK by upregulating lnc_000048 and ultimately activates the inflammatory response through MAPK pathways, reducing plaque stability \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. According to some studies, MAP2K2 expression is significantly suppressed in patients with metabolic syndrome, which is associated with an increased risk of cardiovascular disease, type 2 diabetes (DM), and cardiovascular-specific mortality \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. As MAP2K2 is metabolized through the MAPK signaling pathway, it may affect cardiovascular disease (CVD), including CHD. According to the qPCR results of this study, the upregulation of map2k2 expression in the disease group was in agreement with the bioinformatics analysis findings. Given that MAP2K2 is metabolized through the MAPK signaling pathway, it is speculated that MAP2K2 may affect cardiovascular diseases, including CHD.\u003c/p\u003e \u003cp\u003ePGD (Phosphogluconate Dehydrogenase) also referred to as 6PGD, is the third enzyme of the pentose phosphate pathway (PPP) that catalyzes 6-phosphogluconate (6-PG) degradation into ribulose-5-phosphate (Ru-5-P) while simultaneously reducing NADP\u0026thinsp;+\u0026thinsp;to NADPH \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. A study has found that Ru-5-P derived from PGD inhibits 5'-adenosine monophosphate-activated protein kinase (AMPK) by destroying the upstream LKB1 complex, thereby increasing fatty acid synthesis, especially saturated fatty acids (SFA) \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The effects of fatty acids on CVD are thought to lie in multiple mechanisms, such as cardio-metabolism, lipo-toxicity, electromechanical properties of cardiomyocytes, and inflammatory processes \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. This suggests that PGD may influence the onset and progression of cardiovascular diseases, including CHD, by participating in the synthesis of fatty acids. This finding could offer valuable insights for diagnosis and treatment of CHD.\u003c/p\u003e \u003cp\u003eThis research found that GSEA enrichment analysis indicated MAP2K2 and PGD were involved in signaling pathways like ribosomes, prion diseases, and Parkinson's disease. These pathways are frequently enriched in the context of these biomarkers. Ribosome, composed of RNA and proteins, are increasingly recognized for their important roles in cell function, responses to stimuli, and disease development. Proteins involved in ribosome biogenesis play a crucial role in regulating cell growth and proliferation \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e; disruption of this process can result in aberrant cell proliferation and the development of pathological conditions, including cancer and metabolic disorders \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Some studies have shown that ribosomal proteins (RPs) have been linked to the development of cardiovascular disease \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Smolock EM56, et al. discovered that L17, functioning as a ribosomal protein, serves as a potent inhibitor of vascular smooth muscle cell growth, effectively impeding the thickening of the intimal layer in the mouse carotid artery. The expression of L17 hinders the proliferation of vascular smooth muscle cells by inducing cell cycle arrest in the G0/G1 phase. S6K, a ribosomal protein, is a prominent downstream effector of mTOR (mammalian target of rapamycin) and plays a crucial role in various cardiovascular and metabolic processes \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Research indicates that following myocardial infarction, S6K is rapidly and robustly activated, contributing to maladaptive cardiac remodeling via heightened Akt signaling in the infarcted myocardium \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The study revealed that MAP2K2 is enriched in the ribosomal signaling pathway, indicating it might influence the protein synthesis process linked to cardiovascular diseases, leading to abnormal expression of proteins essential for the cardiovascular system's normal function, such as influencing proteins involved in vasodilation and contraction, or altering the expression of proteins associated with the structure and function of heart muscle cells, thereby disturbing the cardiovascular system's equilibrium, as a result, it causes or exacerbates CHD.\u003c/p\u003e \u003cp\u003eThere has also been an increased interest in the potential link between cardiovascular disease and Parkinson's disease in recent years. It has also been reported that many of the potential risk factors for Parkinson's disease are also typically cardiovascular risk factors (such as advanced age, male sex, diabetes, HTN, and obesity), and that there are common mechanistic hypotheses for both diseases (such as chronic inflammation and oxidative stress) \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Despite this, there is still no clear conclusion regarding how cardiovascular disease and Parkinson's disease are related. There is an increasing agreement among researchers that the risk of cardiovascular disease in patients with Parkinson's disease significantly rises as the disease advances. Using data from two different populations, Li et al found that stroke and coronary artery disease (CAD) may contribute to Parkinson's disease (PD) pathogenesis \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Liang et al. found that the risk of myocardial infarction in PD patients was significantly elevated \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Another study found that among newly diagnosed PD patients, 62.5% had moderate to high cardiovascular risk, which was higher than the general population \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. These studies further support the hypothesis that cardiovascular disease is associated with Parkinson's disease. However, several opinions have suggested that PD does not increase the overall risk of myocardial infarction and cardiovascular death, and that there is no difference in the risk of myocardial infarction and cardiovascular death between PD and non-PD individuals \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Even meta-analysis suggests that PD is associated with a reduced risk of MI \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Therefore, more research is needed to prove the link between PD and CVD. Our study provides a reference for the study of the mechanism of action of MRGs in CHD.\u003c/p\u003e \u003cp\u003eAccording to the study, 29 transcription factors were co-regulated by two biomarkers, namely STAT1, BRD3, HDAC1, and CBFB. In the immune response, STAT1, activated by the IFN-γ/JAK2 pathway \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, contributes by regulating the activation of immune cells, secretion of inflammatory factors, and expression of inflammatory genes \u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. It was found in the study that the STAT1 promoter's methylation level was significantly reduced in coronary heart disease patients compared to the control group \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. The binding site (GAS/ISRE) was notably enriched in the promoter region of genes related to plaque (CCL2/5/19, VEGFC, etc.), indicating its role in plaque formation through the regulation of cell adhesion, migration, and inflammation \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. This investigation proposes that the progression of coronary heart disease could be impacted by map2k2 and PGD via the modulation of the STAT1 signaling axis.\u003c/p\u003e \u003cp\u003eIdentified as potential biomarkers and therapeutic targets for CHD, these two genes could revolutionize the approach to diagnosing and treating CHD patients, paving the way for precision medicine in this field. However, based on bioinformatics analysis of public databases, this study needs further testing to investigate the mechanism of action of biomarkers and more clinical studies to verify its results.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eThe Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database offered the retrieve of the GSE179789 (GPL570) and GSE113079 (GPL20115) datasets related to CHD. The GSE179789 dataset, which included 8 CHD patients and 8 controls with peripheral blood monocyte (PBMC) samples, served as the training set. In contrast, the validation set, GSE113079, contained PBMC samples of 93 CHD patients and 48 controls. We obtained 12 ME - related genes (MERGs) from the literature \u003csup\u003e[55]\u003c/sup\u003e , that is, MTNR1A, MTNR1B, GPR50, AANAT, ASMT, CYP1A2, CYP2C19, RORA, PER2, PER3, ARNTL, and CLOCK.\u003c/p\u003e\n\u003ch2\u003eDifferential Analysis\u003c/h2\u003e\n\u003cp\u003eFor the identification of CHD-differentially expressed genes (DEGs) between two groups within the training set, differential analysis was conducted using the limma package (V 3.52.4) \u003csup\u003e[56]\u003c/sup\u003e. |log2FoldChange(FC)| \u0026gt; 0.5, p \u0026lt; 0.05 was used as the screening criterion. The generation of volcano plots was facilitated by the ggplot2 package (V 3.4.1) \u003csup\u003e[57]\u003c/sup\u003e. Additionally, heat maps were constructed to visualize the expression patterns of CHD-DEGs in control and CHD samples by utilizing the ComplexHeatmap package (V 2.14.0) \u003csup\u003e[58]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eWeighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e\n\u003cp\u003eThe scores of the 12 MERGs were first calculated in different samples of the GSE179789 dataset by the GSVA package (V 1.42.0) \u003csup\u003e[59]\u003c/sup\u003e . The score of MERGs was used as a trait, and the R package WGCNA (V 1.71) \u003csup\u003e[60]\u003c/sup\u003e was used for analysis to screen the modules and genes related to the trait. Firstly, prior to clustering, gene expression values were screened, and only genes with expression values above 1 were selected for sample cluster analysis. The sample cluster analysis was adopted to identify any outlier samples that may need to be excluded to ensure the accuracy of subsequent analyses. Secondly, to maximize the scale - free distribution of gene interactions, the soft threshold (\u0026beta;) was determined. With the hybrid dynamic tree - cutting algorithm as the clustering criterion, module partitioning was carried out. The procedure was parameterized by \u0026beta; and constrained each module to have a minimum of 50 genes. Thereafter, by computing the correlation coefficients between each module and the MRGs score, we aimed to determine the module that had the strongest association with MRGs. Finally, we obtained ME-related module genes in the most relevant module.\u003c/p\u003e\n\u003ch2\u003eIdentification and functional enrichment analysis of candidate genes\u003c/h2\u003e\n\u003cp\u003eThe CHD-DEGs and ME-related module genes were intersected using ggvenn (V 0.1.9)\u003csup\u003e[61]\u003c/sup\u003e to acquired candidate genes. The cluster Profiler package (V 4.7.1.3) \u003csup\u003e[62]\u003c/sup\u003e was applied to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the candidate genes, aiming to identify common functions and related pathways among them \u003csup\u003e[63-65]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eEstablishment of protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) analysis\u003c/h2\u003e\n\u003cp\u003eA PPI network of the candidate genes was constructed by the STRING website (http://string.embl.de/) to investigate the interaction among the candidate genes (confidence score = 0.5). Hereafter, the Degree algorithm in cytoHubba, a built-in plugin of Cytoscape (V 3.8.2) \u003csup\u003e[66]\u003c/sup\u003e, was leveraged to determine the connectivity score of each gene. Subsequently, the top 10 genes were selected for LASSO method by glmnet package (V 4.1.4) \u003csup\u003e[67]\u003c/sup\u003e. Genes whose regression coefficients were not penalized to zero when the optimal lambda was reached in the LASSO regression model were regarded as candidate biomthearkers.\u003c/p\u003e\n\u003ch2\u003eExpression level verification and Receiver Operating Characteristic (ROC) analysis\u003c/h2\u003e\n\u003cp\u003eThe GSE179789 and GSE113079 datasets were adopted to compare the expression of candidate biomarkers in CHD patients and controls. Meanwhile, candidate biomarkers that exhibited consistent expression trends and significant distinctions between the groups in both datasets were screened for ROC analysis (p \u0026lt; 0.05). In detail, the area under the curve (AUC) values were determined to quantitatively measure the diagnostic accuracy of the genes, and genes with an AUC exceeding 0.7 were considered reliable biomarkers.\u003c/p\u003e\n\u003ch2\u003eArtificial Neural Network (ANN) and correlation analysis\u003c/h2\u003e\n\u003cp\u003eAiming further to validate the overall disease diagnosis results of biomarkers, the neuralnet package (V 1.44.2) \u003csup\u003e[68]\u003c/sup\u003e was utilized to construct ANN models based on biomarkers in GSE179789 and GSE113079. Subsequently, the diagnostic ability of the model was assessed by plotting an ROC curve. Furthermore, in the GSE179789 dataset, Spearman correlation analyzed biomarker expression correlations.\u003c/p\u003e\n\u003ch2\u003eGene set enrichment analysis (GSEA) and gene-gene interaction (GGI) network\u003c/h2\u003e\n\u003cp\u003eTo understand the biological characteristics of these biomarkers, we utilized GeneMANIA (http://www.genemania.org/) to construct and analyze a GGI network between the biomarkers and their co-expressed genes.\u003c/p\u003e\n\u003cp\u003eWe performed GSEA pathway enrichment analysis according to biomarkers to delve deeper into the associated functions. Concretely, according to their biomarker levels, CHD samples from GSE179789 were partitioned into two distinct subsets: those with high biomarker expression and those with low biomarker expression. The limma package (V 3.52.4) \u003csup\u003e[56]\u003c/sup\u003e was used for differential analysis based on high and low expression groups, and log2FC of obtained genes was calculated. The log2FC was sorted from largest to smallest, after which GSEA was carried out by the clusterProfiler package (V 4.7.1.3) (|NES| \u0026gt; 1, p \u0026lt; 0.05) \u003csup\u003e[62]\u003c/sup\u003e . The \u0026ldquo;c2. cp.kegg.v2023.1. Hs.symbols.gmt\u0026rdquo; as the reference gene set in the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb).\u003c/p\u003e\n\u003ch2\u003eConstruction of regulatory network and drug prediction\u003c/h2\u003e\n\u003cp\u003eTranscription factors (TFs) against biomarkers were forecasted employing the hTFtarget database (http://bioinfo.life.hust.edu.cn/hTFtarget), and datasets greater than 1 were screened. Similarly, TarBase (http://www.diana.pcbi.upenn.edu/tarbase) and miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) databases were utilized for the prediction of miRNA biomarkers, with the intersection of these two databases results as key miRNAs. Subsequently, the Starbase database (http://starbase.sysu.edu.cn/) was implemented to predict the lncRNAs targeted by key miRNAs. In an effort to discover novel medications for CHD treatment, the Drug-Gene Interaction Database (DGIdb, http://www.dgidb.org) was queried to retrieve drugs that specifically target the key biomarkers. Ultimately, the Cytoscape (V 3.8.2) \u003csup\u003e[66]\u003c/sup\u003e was utilized to present the lncRNA-miRNA-mRNA, TF-mRNA, and mRNA-drug networks.\u003c/p\u003e\n\u003ch2\u003eValidation of biomarker expression in clinical samples\u003c/h2\u003e\n\u003cp\u003eTo assess biomarker expression in clinical samples, we procured five pairs of blood samples from the Jiangxi Provincial People\u0026rsquo;s Hospital for reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis, comprising five control and five CHD samples. The patients included were those admitted to Jiangxi Provincial People\u0026rsquo;s Hospital from January 1, 2024, to January 31, 2024. All human research procedures were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations approved by Medical Ethics Committee of Jiangxi Provincial People\u0026apos;s Hospital (Approval Number: Kekuai 2023(15)- Project). Informed consent was obtained from all participants and/or their legal guardians prior to the study initiation. Total RNA was extracted from the samples with TRIzol reagent (Ambion, Austin, USA) following the manufacturer\u0026apos;s instructions. Subsequently, cDNA synthesis was performed using the SureScript First-strand cDNA synthesis kit (Servicebio, Wuhan, China), following the instructions. For RT-qPCR analysis, the 2x Universal Blue SYBR Green qPCR Master Mix (Servicebio) was utilized.\u0026nbsp;\u003cstrong\u003eSupplenemntary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003eprovides the primer sequences employed for PCR amplification. Biomarker expression levels were quantified using the 2\u003csup\u003e-\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method\u0026nbsp;\u003csup\u003e[69]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eData analysis was performed using R (version 4.2.2), with statistical significance defined as p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMelatonin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCoronary Heart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRT-qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReverse Transcription-quantitative Polymerase Chain Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eANN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArtificial Neural Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWGCNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWeighted Gene Co-expression Network Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene-Gene Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTranscription Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMicroRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLong Non-coding RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMAP2K2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMitogen-Activated Protein Kinase Kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePhosphogluconate Dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePentose Phosphate Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNADPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNicotinamide Adenine Dinucleotide Phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAMPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u0026apos;-Adenosine Monophosphate-activated Protein Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMammalian Target of Rapamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignal Transducer and Activator of Transcription 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eERK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExtracellular Signal-regulated Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMitogen-Activated Protein Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiovascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePBMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePeripheral Blood Mononuclear Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMolecular Signatures Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDGIdb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDrug-Gene Interaction Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003e\"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYongchao Peng: Data curation, Validation, Visualization, Writing\u0026ndash;review \u0026amp; editing. Xuemin Zhou: Validation, Writing\u0026ndash;review \u0026amp; editing. Li Huang: Data curation, Investigation, Resources, Visualization. Yaowu Xie: Conceptualization, Project administration, Supervision, Writing\u0026ndash;review \u0026amp; editing. Xuanlan Chen: Conceptualization, Supervision, Writing\u0026ndash;review \u0026amp; editing. Zhifeng Zhang: Conceptualization, Data curation, Validation, Visualization, Writing\u0026ndash;original draft, Writing\u0026ndash;review \u0026amp; editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Yongchao Peng, Xuemin Zhou, Xuanlan Chen, Yaowu Xie, Zhifeng Zhang. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets GSE179789 and GSE113079 for this study can be found in the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTownsend, N.\u003cem\u003e et al.\u003c/em\u003e Epidemiology of cardiovascular disease in Europe. \u003cem\u003eNat Rev Cardiol\u003c/em\u003e \u003cstrong\u003e19,\u003c/strong\u003e 133-143, (2022).\u003c/li\u003e\n\u003cli\u003eByrne, R., Coughlan, J. J., Rossello, X., Ibanez, B. \u0026amp; Members of the Task Force for the, E. S. C. G. f. t. m. o. a. c. s. 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J. \u0026amp; Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. \u003cem\u003eMethods\u003c/em\u003e \u003cstrong\u003e25,\u003c/strong\u003e 402-408, (2001).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"coronary heart disease, biomarkers, bioinformatics analysis, therapeutic targets","lastPublishedDoi":"10.21203/rs.3.rs-8816168/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8816168/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMelatonin (ME) affects multiple systems in coronary heart disease (CHD), including lipid/glucose metabolism, blood pressure, and sleep-wake regulation, while also promoting coronary thrombosis through unknown mechanisms. This study uses bioinformatics to identify ME-related biomarkers for CHD diagnosis and treatment. The GSE179789 and GSE113079 datasets were obtained from a public database. Biomarkers were selected via differential analysis and expression validation. Gene set enrichment analysis (GSEA) was performed, and regulatory as well as drug‑biomarker networks were constructed. Biomarker expression was further validated in clinical samples using RT‑qPCR. MAP2K2 and PGD were identified as reliable CHD biomarkers, showing significant up‑regulation in CHD samples across both datasets. GSEA indicated their involvement in multiple pathways, including ribosome, prion diseases, and Parkinson's disease. Complex regulatory interactions were observed among lncRNAs, miRNAs, and biomarkers; for instance, four lncRNAs (NEAT1, AP000766.1, LINC02381, XIST) regulated PGD via hsa‑let‑7e‑5p. Additionally, 29 transcription factors (e.g., STAT1, BRD3, HDAC1, CBFB) co‑regulated both biomarkers. Finally, 41 drugs (e.g., cobimetinib fumarate, selumetinib sulfate) were predicted to target MAP2K2, while three (penicillamine, pralmorelin, phenobarbital) targeted PGD. In summary, MAP2K2 and PGD serve as CHD biomarkers, offering new insights into disease pathogenesis.\u003c/p\u003e","manuscriptTitle":"Unveiling the Role of Melatonin in Coronary Heart Disease: Identification and Experimental Validation of Novel Biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 15:15:33","doi":"10.21203/rs.3.rs-8816168/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-21T01:16:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35241792489161630657052343083689815503","date":"2026-04-17T13:16:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T03:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106928575467489615894850200880452153386","date":"2026-04-15T03:50:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T13:48:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-19T12:18:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T06:04:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T09:17:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-18T09:11:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73be030b-844a-4a05-b5bc-ee1050a0e907","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66580339,"name":"Health sciences/Biomarkers"},{"id":66580340,"name":"Health sciences/Cardiology"},{"id":66580341,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66580342,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-04-21T15:15:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 15:15:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8816168","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8816168","identity":"rs-8816168","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}