Combining Circadian Rhythm-Related Gene Expression and Immune Infiltration to Identify Diagnostic Biomarkers in Acute Myocardial Infarction

Combining Circadian Rhythm-Related Gene Expression and Immune Infiltration to Identify Diagnostic Biomarkers in Acute Myocardial Infarction | 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 Combining Circadian Rhythm-Related Gene Expression and Immune Infiltration to Identify Diagnostic Biomarkers in Acute Myocardial Infarction Xiao Yu, Xiaopeng Zhang, Hazrat Bilal, Chang Shi, Lei Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4822907/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Current diagnostic biomarkers for acute myocardial infarction (AMI), such as troponins, often lack specificity, leading to false positives under non-cardiac conditions. Recent studies have implicated circadian rhythm and immune infiltration in the pathogenesis of AMI. This study hypothesizes that analyzing the interplay between circadian rhythm-related gene expression and immune infiltration can pinpoint more accurate diagnostic biomarkers for AMI. Our results demonstrated differential expression of 15 circadian rhythm-related genes (CRGs) between AMI patients and healthy individuals, with five key genes—JUN, NAMPT, S100A8, SERPINA1, and VCAN—emerging as central to this process. Functional enrichment analyses suggest these genes significantly influence cytokine and chemokine production in immune responses. Immune infiltration assessments using ssGSEA indicated elevated levels of neutrophils, macrophages, and eosinophils in AMI patients. Additionally, we identified potential therapeutic implications with 13 pivotal miRNAs and 10 candidate drugs targeting these genes. RT-qPCR analysis further confirmed the upregulation of these five genes under hypoxic conditions, compared to controls. Collectively, our findings highlight the critical role of CRGs in AMI, offering new insights into its diagnosis and potential therapeutic targets. Health sciences/Cardiology Health sciences/Cardiology/Cardiovascular biology Circadian rhythm-related genes Acute myocardial infarction Biomarker Immune infiltration Bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Myocardial infarction (MI) remains a leading global health challenge, significantly contributing to mortality and long-term disability worldwide 1 . Acute myocardial infarction (AMI), often triggered by sudden coronary events, can lead to severe outcomes, including myocardial rupture and sudden cardiac death, particularly when medical intervention is delayed 2 . The initiation of precise diagnosis and prompt therapeutic measures are of paramount importance for AMI patient prognosis. Thus, the prompt detection of AMI at the initial signs of chest pain is vital for averting detrimental consequences and enhancing patient survival rates. Extensive research has highlighted the utility of various biomarkers in diagnosing AMI, including myoglobin, cardiac troponin I (cTnI), and creatine kinase-MB (CK-MB) 3 . Among these, myoglobin serves as the earliest indicator, detectable within three hours of the onset of chest pain. However, it is predominantly indicative of skeletal muscle injury rather than myocardial damage 4 , 5 . Conversely, the cTnI and CK-MB, while comprehensive, tend to manifest later and therefore do not support the most timely diagnosis of AMI 6 , 7 . Moreover, in addition to AMI, elevations in cTnI could occur in a number of conditions, diminishing its specificity for diagnosis 8 . This often leads to missed opportunities for optimal treatment. Consequently, there is a critical need for novel biomarkers that can more precisely and reliably diagnose AMI. Circadian rhythms, endogenous biological cycles inherent to most living organisms, orchestrate a vast array of physical, mental, and behavioral changes. This regulation is achieved through the tightly coordinated modulation of gene expression and biochemical functions. Key genes involved in circadian rhythms include CLOCK, BMAL1 (ARNTL), PER1/2/3 (Period), CRY1/2 (Cryptochrome), and others such as TIM (Timeless), NR1D1 (REV-ERBα), NR1D2 (REV-ERBβ), CSNK1D (Casein Kinase 1 Delta), and CSNK1E (Casein Kinase 1 Epsilon) 9 , 10 . Under physiological conditions, circadian rhythms-related genes (CRGs) were involving in regulating heart rate, cardiac electrophysiology, blood pressure, blood coagulability, and vascular tone 11 , 12 . Emerging evidence suggests that, in addition to regulating cardiovascular physiologic processes, circadian rhythms also influence cardiovascular diseases, including atherosclerosis and thrombosis and myocardial injury subsequent to MI 13 – 15 . These findings illuminate the potential of CRGs to enhance the early diagnosis of AMI, offering promising avenues for therapeutic intervention and improved patient outcomes. Recent studies have increasingly underscored the critical impact of immune cell infiltration on the development and progression of AMI. This process is involved in various stages of coronary artery atherosclerosis, such as lipid core enlargement, fibrous cap degradation, and plaque angiogenesis, which collectively elevate the risk of plaque rupture and thrombosis 2 , 16 . Furthermore, immune infiltration plays a vital role in the course of AMI by mediating injury and repair mechanisms 17 , 18 . Despite the acknowledged importance of immune cells in these processes, significant gaps remain in our understanding of their specific roles post-MI, particularly concerning inflammatory cell functions. Such studies are essential not only for elucidating the mechanisms at play but also for exploring the potential of immune cell profiles as early diagnostic markers for AMI. Extant literature suggests that circadian rhythm-related genes significantly influence inflammatory responses in heart failure 19 , 20 . This insight underpins our hypothesis that a combined analysis of CRGs and immune infiltration could enhance the precision in identifying diagnostic biomarkers for AMI. In our study, we performed a systematic evaluation of the expression of CRGs and their correlation with immune infiltration in AMI patients. This analysis led to the identification of five distinctive genes, which were used to construct a diagnostic model. Materials and Methods Collection of Datasets Gene expression profiles GSE48060 and GSE66360 were retrieved from the Gene Expression Omnibus (GEO). Both datasets were produced using the GPL570-Affymetrix Human Genome U133 Plus 2.0 Array [HG-U133_Plus_2]. Specifically, GSE48060 includes peripheral blood samples from 31 patients with acute myocardial infarction (AMI) and 21 control individuals with normal cardiac function. The GSE66360 dataset comprises peripheral blood samples from 49 AMI patients and 50 healthy controls. Data Preparation and Identification of CRDEGs Raw data from the datasets were converted into expression matrices using the "limma" package in R (Version 4.2.1) 21 . The batch effects were removed using the “sva” package after merging the two datasets (GSE48060, GSE66360) 22 . Differential expression genes (DEGs) were determined using the "limma" package, with a significance threshold set at |log2FC| >1 and P < 0.05. Results were displayed using volcano plots and heatmaps created with the "ggplot2" and "pheatmap" packages, respectively. Circadian rhythm-related genes (CRGs) were derived from prior studies and intersected with DEGs to identify circadian rhythm-dependent DEGs (CRDEGs), which were visualized using "ggplot2" 23 , 24 . CRGs were obtained via intersecting the DEGs from each array and the circadian rhythm-related genes using a Venn Diagram, and they were visualized as a Heatmap with R package "ggplot2". The overlapped CRDEGs among the two arrays were eventually obtained. Functional enrichment analysis Subsequently, functional enrichment analysis of differential CRDEGs, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed using the “clusterProfiler” R package 25 . The pathways with p < 0.05 were identified as significant. PPI network construction and hub CRDEGs identification The PPI network of the differential CRDEGs was constructed via the STRING database ( https://www.string-db.org/ ) with the cutoff interaction score set at 0.7. Then the top 10 hub CRDEGs with the highest maximal clique centrality (MCC, Bottlenecks, Degree, DMNC and EPC) values were selected via the "Cytohubba" plugin of Cytoscape and visualized using Cytoscape software (version 3.10.1). Infiltration analysis of immune cells and functions The infiltrating score of 24 immune cells in healthy and AMI groups were calculated with single-sample gene set enrichment analysis (ssGSEA) via the “gsva” R package 26 and visualized by heatmap using the “pheatmap” package 27 . The box plots were used to compare and visualize the ssGSEA scores of infiltrated immune cells between the healthy and AMI samples by “ggpubr” R package 28 . The correlation heatmap, which revealed the correlation of 24 types of immune cells and related functions, was performed using the “corrplot” R package. Construction and verification of the diagnostic model To screen the CRDEGs with diagnostic potential, we analyzed the relationship among 5 CRDEGs (JUN, NAMPT, S100A8, SERPINA1 and VCAN)with immune cells using Spearman’s correlation analysis via the “ggcorrplot” R package for predicting AMI 29 , 30 . Then the 5 CRDEGs were subjected to Logistic regression analysis to construct a nomogram model via the “rms” R package. To evaluate the diagnostic performance of feature genes, the ROC was plotted using the “pROC” R package. Identification of pivotal miRNAs and candidate drugs The candidate drugs were determined using the DSigDB database. The access of the Targetscan database and DSigDB database are acquired through Enrichr ( http://amp.pharm.mssm.edu/Enrichr/ ) platform. Cell culture and processing of cardiomyocyte cell line Human AC16 cardiomyocytes (Wuhan sunncell Biotech Co.,Ltd) were cultured in Dulbecco's Modified Eagle's Medium (DMEM, Gibco) supplemented with 10% FBS (Yeasen) and 1% penicillin–streptomycin (Cytiva) at 37℃ in an incubator containing 5% CO 2 . A hypoxia incubator chamber (STEMCELL Technologies, Canada) connected to a Proox Model 21 controller (BioSpherix, Redfield, NY) was used to establish a hypoxic environment. Thereafter, the hypoxic conditions were built through 24 hours exposure of cells to the hypoxic environment (94% N 2 , 1% O 2 and 5% CO 2 ). Quantitative real-time PCR Total RNA isolation was carried out with using TRizol reagent according to the manufacturer’s instructions (Sevenbio, SM139). All-in-one First Strand cDNA Synthesis Kit Ⅲ (Sevenbio, SM135) was used to reverse transcribe cDNA. The reaction mixtures containing SYBR Green (Sevenbio, SM143) were composed following the manufacturer’s protocol and then CT values were obtained using a qPCR platform (Bioer, LineGene9600 FQD-96a v1.0.13 RC 20200911, Hangzhou, China). The genes expression levels of JUN, NAMPT, S100A8, SERPINA1, and VCAN in AC16 were measured using RT-qPCR. The ACTB gene served as the reference gene for the data. Relative quantitation was performed using the 2 −△△CT method. Primer details are shown in Table 1 . Table 1 Sequences of the primers used for RT-qPCR Name (homo) Primers for RT-qPCR (5′-3′) ACTB Forward: GGGAAATCGTGCGTGACATT Reverse: GGAACCGCTCATTGCCAAT S100A8 Forward: ATGCCGTCTACAGGGATGAC Reverse: ACTGAGGACACTCGGTCTCTA SERPINA1 Forward: GGAGGCTCAGATCCATGAAGG Reverse: GGTGTCCCCGAAGTTGACAG VCAN Forward: GTAACCCATGCGCTACATAAAGT Reverse: GGCAAAGTAGGCATCGTTGAAA NAMPT Forward: ATCCTGTTCCAGGCTATTCTGT Reverse: CCCCATATTTTCTCACACGCAT JUN Forward: TCCAAGTGCCGAAAAAGGAAG Reverse: CGAGTTCTGAGCTTTCAAGGT Statistical analysis Statistical analyses were performed using R software (version 4.2.1), SPSS Statistics (version 26.0) and GraphPad Prism (version 10.1.2). Continuous variables were expressed as mean ± SD or median (quartile range). The Student’s t-test was employed to analyze continuous variables with normal distribution. Categorical variables were presented as numbers (percentages) and analyzed using the chi-square test. Statistical significance was set at (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns: not significant). Results Differential CRGs between AMI patients and healthy controls We obtained 71 healthy controls and 80 AMI patient samples data from two datasets (GSE48060 and GSE66360) in GEO. The expression data were normalized and visualized by box plots (Fig. 1 A and 1 B). The batch effect was corrected through the PCA algorithm (Fig. 1 C and 1 D). Then, through the Pearson correlation analysis, circadian rhythm genes were obtained using the “limma[3.52.2]” package in the R software (Version 4.2.1) 21 . Based on the criterion |log2FC | >1 and p < 0.05, differential CRGs were identified and visualized by the volcano plot and heatmap (Fig. 1 E and 1 F). Functional analysis of the differential CRGs Functional enrichment analysis indicated that the differential CRGs were mainly involved in inflammatory and immune response biological process (BP) terms, including “negative regulation of NF-kappaB transcription factor activity”, “positive regulation of smooth muscle cell proliferation”, “positive regulation of interleukin-6 production”, “chemokine production” and “regulation of cytokine production involved in immune response” (Fig. 2 A). The cellular component (CC) analysis enriched in “tertiary granule membrane”, “specific granule membrane”, “phagocytic vesicle membrane”, “endopeptidase complex” and “azurophil granule lumen” (Fig. 2 A). The molecular function (MF) analysis enriched in “signaling receptor activator activity”, “calcium-dependent protein binding”, “chemokine receptor binding”, “monocarboxylic acid binding” and “scaffold protein binding” (Fig. 2 A). Our analysis revealed significant enrichment of DEGs in several key biological pathways, indicating potential mechanisms underlying the disease process. Notably, enriched pathways included the complement and coagulation cascades, cytokine-cytokine receptor interaction, transcriptional misregulation in cancer, chemokine signaling pathway, neutrophil extracellular trap formation, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, osteoclast differentiation, TNF signaling pathway and toll-like receptor signaling pathway (Fig. 2 B). A total of 78 differentially expressed genes (DEGs, blue) and 1475 CRGs (red) were identified, with 15 genes overlapping between the two groups (Fig. 2 C). After removing the isolated nodes, the 15 hub CRGs, including JUN, PTX3, LYZ, VCAN, S100A8, VNN1, PLAUR, SERPINA1, S100P, DDIT3, NFIL3, PPP1R15A, AQP9, ACSL1 and NAMPT were identified by “Cytohubba” via the Cytoscape software (Fig. 2 D). Identification of hub CRGs The Venn diagram illustrates the overlap in gene identification among five different methods: Degree, BottleNeck, DMNC, EPC and MCC (Fig. 3 A). The central overlap indicates that 5 genes are identified by all five methods, namely S100A8, SERPINA1, VCAN, JUN and NAMPT , suggesting their potential significance. The top 10 hub CRGs, including JUN, SERPINA1, NAMPT, S100A8, AQP9, PLAUR, VCAN, LYZ, PPP1R15A and NFIL3 , were identified by the Degree method (Fig. 3 B). The BottleNeck method identified the top 10 hub CRGs as S100A8, SERPINA1, LYZ, VCAN, JUN, S100P, PTX3, DDIT3, NAMPT and PPP1R15A (Fig. 3 C). Using the MCC algorithm, the top 10 hub CRGs were determined to be JUN, S100A8, SERPINA1, PLAUR, AQP9, VCAN, LYZ, NAMPT, PTX3 and ACSL1 (Fig. 3 D). The EPC method identified JUN, SERPINA1, S100A8, PLAUR, NAMPT, AQP9, VCAN, LYZ, ACSL1 and NFIL3 as the top 10 hub CRGs (Fig. 2 E). According to the DMNC method, the top 10 hub CRGs were PTX3, VCAN, PLAUR, AQP9, JUN, ACSL1, SERPINA1, NAMPT, S100A8 and PPP1R15A (Fig. 3 F). Identification of 5 feature genes for diagnosing AMI To identify diagnostic biomarkers for AMI, we utilized the 5 hub CRGs ( S100A8, SERPINA1, VCAN, JUN and NAMPT ) as diagnostic feature genes to predict AMI and construct a nomogram (Fig. 4 A). The diagnostic performance of the 5-gene signature model was evaluated using the ROC curve, with the training dataset yielding an AUC value of 0.881, indicating a promising predictive value for AMI (Fig. 4 B). Nonetheless, the accuracy and reliability of this diagnostic model require further validation in future clinical trials. Identification of pivotal miRNAs and candidate drugs To identify the pivotal miRNAs and candidate drugs targeting the 5 feature genes, the data was collected from the Targetscan database and DSigDB database. Ultimately, a total of 13 miRNAs were screened with a set p < 0.05 (Fig. 5 A). Notably, SERPINA1 could be regulated by all 9 miRNAs (hsa-miR-744, mmu-miR-379, mmu-miR-1193-5p, mmu-miR-3079-5p, hsa-miR-4706, hsa-miR-4749-5p, mmu-miR-2183, hsa-miR-4506 and mmu-miR-3094). JUN interacts with 7 miRNAs: hsa-miR-744, mmu-miR-1893, hsa-miR-4706, hsa-miR-4749-5p, hsa-miR-4506, mmu-miR-3094 and mmu-miR-3471). The VCAN interacted with 5 miRNAs: mmu-miR-379, mmu-miR-1193-5p, mmu-miR-3079-5p, mmu-miR-6690-5p and mmu-miR-878-5p. NAMPT was demonstrated to interact with 6 miRNAs: mmu-miR-2183, hsa-miR-4506, mmu-miR-3094, mmu-miR-6690-5p, mmu-miR-878-5p and mmu-miR-3471). We screened the top 10 drug molecules based on an adjusted p < 0.05 using the DSigDB database (Fig. 5 B– 5 F). Among these, VALPROIC ACID CTD 00006977 and estradiol CTD 00005920 were associated with all five feature genes. Dexamethasone CTD 00005779 was linked to three feature genes, including S100A8, SERPINA1 and NAMPT . The remaining drug molecules also demonstrated interactions with the feature genes. These drug candidates offer promising avenues for further research and development in the treatment of AMI. Correlation matrix and infiltration analysis of immune cells and gene expression We further investigated the immune cell infiltration between AMI patients and healthy controls, the enrichment scores of distinct immune cell subpopulations and functions were assessed using ssGSEA. The results were visualized via the heatmap (Fig. 6 A). AMI patients showed elevated levels of eosinophils, iDC, macrophages, mast cells, neutrophils, NK CD56 bright cells and Th1 cells, but decreased levels of CD8 T cells, cytotoxic cells, T cells, T helper cells, Tcm, Tem, Tgd, Th17 cells and Th2 cells (Fig. 6 E). The correlation of 24 immune cells indicated that neutrophils were positively correlated with macrophages, eosinophils and mast cells, while neutrophils were negatively correlated with T cells and T helper cells. In addition, cytotoxic cells were positively connected with Tgd and CD8 T cells (Fig. 6 D). These findings indicate that the immune cell infiltration patterns differ significantly between AMI patients and healthy individuals, potentially playing a crucial role in the pathophysiological processes of the disease. As shown in (Fig. 6 F), the heatmap illustrates the correlation between the expression of 5 genes ( S100A8, SERPINA1, VCAN, JUN and NAMPT ) and the abundance of different immune cell types. Notably, genes such as NAMPT and SERPINA1 show strong positive correlations with neutrophils, suggesting a potential role in the inflammatory response (Fig. 6 B and 6 C). Conversely, these genes tend to have negative correlations with T cells and Th2 cells, indicating a complex interaction between gene expression and immune regulation in the context of AMI. RT-qPCR validation of 5 CRGs in the AC16 hypoxia culture model RT-qPCR was performed to quantify the expression of the five CRGs in AC16 cells exposed to 24 hours of hypoxia, aiming to validate the bioinformatics analysis results. As predicted, the results showed that the five key genes ( S100A8, SERPINA1, VCAN, JUN and NAMPT ) were highly expressed in AC16 under hypoxic conditions compared to normoxic conditions (Fig. 7 A- 7 E). These findings indicate the potential of these genes as promising diagnostic targets. Discussion Circadian rhythm-related genes (CRGs) play a crucial role in coordinating the regulation of circadian rhythms, the body’s endogenous timing system. Emerging evidence has well established the impact of circadian rhythms on cardiovascular function and myocardial ischemia injury, strongly supporting CRGs as candidate diagnostic biomarkers 31 , 32 . Simultaneously, a study reveals a potentially complex relationship between CRGs and immune infiltration in heart failure 19 . In the present study, we used bioinformatics tools to investigate the role of CRGs in AMI as well as their interaction with immune infiltration, attempting to identify more precise AMI diagnostic markers and treatment targets. We systematically screened 15 differential CRGs in the peripheral blood of 71 healthy controls and 80 AMI patients. Functional enrichment analysis revealed that the differential CRGs were enriched in the regulation of cytokine production involved in immune response and the chemokine production. Cytokines and chemokines are essential mediators that orchestrate the inflammatory response in atherosclerosis procession, such as monocyte/lymphocyte recruitment, regulating plaque stability, rupture and thrombus formation 33 – 35 . During acute myocardial infarction, the balance of pro-inflammatory and anti-inflammatory cytokines can influence the recruitment immune cells to the site of injury, facilitate repair processes and subsequent cardiac remodeling 36 , 37 . These supported that CRGs might play an important role in the initiation and subsequent AMI via inflammatory responses. Then, we constructed the PPI network and screened 15 hub CRGs, namely JUN, PTX3, LYZ, VCAN, S100A8, VNN1, PLAUR, SERPINA1, S100P, DDIT3, NFIL3, PPP1R15A, AQP9, ACSL1 and NAMPT , all of which are potential candidate biomarkers closely related to AMI. AMI patients showed elevated levels of neutrophils, positively correlating with macrophages, implying the clearance of necrotic cardiomyocytes and repair processes 38 . An in vivo study showed that neutrophils induce macrophages towards a reparative phenotype via neutrophil gelatinase-associated lipocalin in the MI 39 . The resolution of inflammation is marked by the efflux of macrophages via the lymphatic system 38 . Our results showed that mast cells and eosinophils were increased in AMI patients, positively relating to neutrophils. To date, the impact of mast cells on infarcted myocardial tissue remains a subject of ongoing debate. Nonetheless, mast cells are implicated in protective mechanisms after MI, including promoting angiogenesis, regulating cardiomyocyte contractility, enhancing hypoxia resistance and facilitating the conversion of fibroblasts to myofibroblasts 40 . In addition, eosinophils have shown potential as biomarkers for AMI and play a role in tissue repair processes 41 – 43 . In the present study, we demonstrated that the immune function of neutrophil was elevated in the AMI group. In the follow-up analysis, we identified the 5 hub CFRGs ( S100A8, SERPINA1, VCAN, JUN and NAMPT ) most associated with immune infiltration as diagnostic feature genes. The expression levels of all 5 feature genes were strongly positively linked with the neutrophils. These suggests that the 5 feature genes participate in the immune and inflammatory responses of AMI, through neutrophil signaling pathways. In AMI patients, the S100A8 levels positively correlated with neutrophil counts 44 . Furthermore, S100A8 is associated with cardiac rupture, serving as a robust predictor and potentially causal mediator 45 , 46 . Consistent with our result, a weighted gene co-expression network analysis research showed that the SERPINA1 was identified and validated for the predictive value in identifying future heart failure after AMI 47 . In our study, the SERPINA1 was highly expressed under the 24 hours hypoxia culture, with validation achieved through RT-qPCR. VCAN, as one of the ten strongly interlinked hub genes, was identified in the remodeling of non-infarcted myocardium following acute myocardial infarction 48 . Similarly, our study also confirmed the VCAN high expression in response to hypoxia. Studies showed that JUN involved in MI process 49 , 50 . It has been documented that inhibition of NAMPT may attenuate tissue damage mediated by neutrophilic inflammation and oxidative stress during the initial stages of re-perfusion following myocardial infarction 51 , 52 . These findings indicate that these 5 central CRGs may represent promising diagnostic and therapeutic targets for AMI. We predicted the miRNAs and candidate drugs that regulate the five CRGs. Hsa-miR-4506 was identified as a regulator of three key genes ( SERPINA1, JUN and NAMPT ) and has been recognized as a highly predictive miRNA for colon and rectal cancer 53 . Hsa-miR-744, which regulates SERPINA1 and JUN, was found to be elevated in various cancers, with significant implications for prognosis 54 – 56 . Studies have shown that up-regulation of mmu-miR-379 occurs in both the plasma of Crohn’s disease patients and the development of obesity, the latter being associated with an increased risk of cardiovascular disease 57 , 58 . Hsa-miR-4706 was found to be elevated in people blood samples with head and neck cancer (HNC), suggesting its potential as a predictive biomarker for HNC 59 . Additionally, our study is the first to report the involvement of mmu-miR-3471, mmu-miR-1893, mmu-miR-1193-5p, mmu-miR-3079-5p, hsa-miR-4749-5p, mmu-miR-2183 and mmu-miR-3094 in relation to CRGs in AMI. These miRNAs may serve as independent predictors of AMI, however, their specific mechanisms of action in AMI require further investigation. Among all candidate drugs, VALPROIC ACID CTD 00006977 and estradiol CTD 00005920 ranked in the top two with targeting all 5 feature CRGs. Dexamethasone CTD 00005779 was linked to three feature genes, including S100A8, SERPINA1 and NAMPT . The VALPROIC ACID CTD 00006977, the drug identified in our screening, serves as a reverse agonist of the retinoic acid-related orphan receptor α (RORα) 60 . RORα is involved in the regulation of inflammatory macrophages under pathological conditions, such as myocardial infarction 61 . These suggested that VALPROIC ACID CTD 00006977 might affect the process of AMI by impacting genes involved in CRGs. The potential effectiveness of other proposed drugs is also being considered, and these drugs may warrant further validation through chemical experiments. In summary, this study used bioinformatics method to analyze the transcriptional expression characteristics of AMI and screened five biomarkers ( S100A8, SERPINA1, VCAN, JUN and NAMPT ) related to AMI. Drug database enrichment found that these five CRGs may be the drug targets of AMI, and VALPROIC ACID CTD 00006977 and estradiol CTD 00005920 may be a potential targeted therapeutic drug. Hsa-miR-4506 plays an important role in regulating CRGs in AMI. Further experimentation is necessary to demonstrate the effects and underlying mechanisms of other miRNAs. While our discoveries are notable, the study is constrained by limitations such as only in vitro validation and the absence of clinical specimens. By addressing these limitations, future research can provide novel insights into the diagnosis and management of AMI. Conclusions In this study, we identified differential expression of 1475 CRGs and identified 15 genes as potential biomarkers for early acute myocardial infarction (AMI) diagnosis by comparing AMI patients to healthy individuals. These findings suggest a key role for circadian rhythm in the development of AMI. Through bioinformatics analysis, we identified five immune-related CRGs as potential biomarkers for the early diagnosis of AMI. The significant predictive value of these genes was demonstrated by ROC curve analysis. Furthermore, validation in the AC16 hypoxia culture model confirmed five feature genes, supporting their potential as biomarkers for AMI diagnosis. Declarations Competing interests The authors declare no competing interests Reprints and permissions information is available at www.nature.com/reprints . Funding This work was supported by grants from the Natural Science Foundation of China (82170276) and Liaoning Provincial Program for Top Discipline of Basic Medical Sciences. Author Contribution XY, CS, and XZ designed and drafted the original manuscript. XZ performed data collection and wrote the figure legends. XY and XZ performed the RT-qPCR experiment. HB reviewed and edited. CS conducted data analysis. XY and CS plotted the figures. LS supervised, edited, and conceptualized the manuscript. All authors have read and approved the final manuscript. Acknowledgement We thank colleagues in Professor Hiroshi Egawa (Fukuoka Clinical Pathology Institute, Japan) for their technical help and intellectual inspiration in the course of this study. Data Availability Data analysed was obtained from Gene Expression Omnibus database under accession number GSE48060 and GSE66360, are available at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48060, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66360; The PPI network of the differential genes were constructed via string database, are available at the following URL: https://www.string-db.org/; The candidate drugs were obtained from DSigDB database, are available at the following URL: https://dsigdb.tanlab.org/DSigDBv1.0/geneSearch.html. Pivotal miRNAs were obtained from miRDB database, are available at the following URL: http://mirdb.org/miRDB/ Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Oliveira, G. B. F., Avezum, A. & Roever, L. 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Journal of Atherosclerosis and Thrombosis 24, 827–840,(2017). Niccoli, G. et al. Pre-intervention eosinophil cationic protein serum levels predict clinical outcomes following implantation of drug-eluting stents. European Heart Journal 30, 1340–1347,(2009). Heredia, J. E. et al. Type 2 Innate Signals Stimulate Fibro/Adipogenic Progenitors to Facilitate Muscle Regeneration. Cell 153, 376–388,(2013). Sreejit, G. et al. Neutrophil-Derived S100A8/A9 Amplify Granulopoiesis After Myocardial Infarction. Circulation 141, 1080–1094,(2020). Shi, S. & Yi, J. L. S100A8/A9 promotes MMP-9 expression in the fibroblasts from cardiac rupture after myocardial infarction by inducing macrophages secreting TNFα. European Review for Medical and Pharmacological Sciences 22, 3925–3935,(2018). Ma, J. et al. S100A8/A9 as a prognostic biomarker with causal effects for post-acute myocardial infarction heart failure. Nature Communications 15,(2024). Niu, X. et al. Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction. Frontiers in Genetics 10,(2019). Wang, L., Zhang, Y., Yu, M. & Yuan, W. Identification of Hub Genes in the Remodeling of Non-Infarcted Myocardium Following Acute Myocardial Infarction. Journal of Cardiovascular Development and Disease 9,(2022). Wu, Y. et al. CAV1 Protein Encapsulated in Mouse BMSC-Derived Extracellular Vesicles Alleviates Myocardial Fibrosis Following Myocardial Infarction by Blocking the TGF-β1/SMAD2/c-JUN Axis. Journal of Cardiovascular Translational Research,(2023). Reiss, K. et al. ANG II receptors, c-myc, and c-jun in myocytes after myocardial infarction and ventricular failure. The American journal of physiology 264, H760-769,(1993). Montecucco, F. et al. Inhibition of Nicotinamide Phosphoribosyltransferase Reduces Neutrophil-Mediated Injury in Myocardial Infarction. Antioxidants & Redox Signaling 18, 630–641,(2013). Wang, S. & Cao, N. Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis. Molecular Medicine Reports 22, 4383–4395,(2020). Pellatt, D. F. et al. Expression Profiles of miRNA Subsets Distinguish Human Colorectal Carcinoma and Normal Colonic Mucosa. Clinical and Translational Gastroenterology 7,(2016). Ab Mutalib, N.-S. et al. Differential microRNA expression and identification of putative miRNA targets and pathways in head and neck cancers. International Journal of Molecular Medicine 28, 327–336,(2011). Phuah, N. H. et al. Alterations of MicroRNA Expression Patterns in Human Cervical Carcinoma Cells (Ca Ski) toward 1′ S-1′-Acetoxychavicol Acetate and Cisplatin. Reproductive Sciences 20, 567–578,(2013). Qiao, Z. et al. Hsa-miR-557 Inhibits Osteosarcoma Growth Through Targeting KRAS. Frontiers in Genetics 12,(2022). Jensen, M. D. et al. Circulating microRNAs as biomarkers of adult Crohn's disease. European Journal of Gastroenterology & Hepatology 27, 1038–1044,(2015). Chartoumpekis, D. V. et al. Differential Expression of MicroRNAs in Adipose Tissue after Long-Term High-Fat Diet-Induced Obesity in Mice. Plos One 7,(2012). Torso, N. D. G. et al. miR-6805-5p as a biomarker of cisplatin-induced nephrotoxicity in patients with head and neck cancer. Frontiers in Pharmacology 14,(2023). Liu, G., He, G., Zhang, J., Zhang, Z. & Wang, L. Identification of SCRG1 as a Potential Therapeutic Target for Human Synovial Inflammation. Frontiers in Immunology 13,(2022). Cho, D. I. et al. Antiinflammatory activity of ANGPTL4 facilitates macrophage polarization to induce cardiac repair. Jci Insight 4,(2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Dec, 2024 Reviewers agreed at journal 05 Nov, 2024 Reviews received at journal 30 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers invited by journal 18 Sep, 2024 Editor assigned by journal 18 Sep, 2024 Editor invited by journal 11 Aug, 2024 Submission checks completed at journal 11 Aug, 2024 First submitted to journal 29 Jul, 2024 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-4822907","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":348325434,"identity":"19b21aa5-06fd-4bfe-958a-06283bf53a60","order_by":0,"name":"Xiao Yu","email":"","orcid":"","institution":"Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Yu","suffix":""},{"id":348325435,"identity":"b4612150-8df5-4f7d-8f64-9f19a12e3a2d","order_by":1,"name":"Xiaopeng Zhang","email":"","orcid":"","institution":"Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaopeng","middleName":"","lastName":"Zhang","suffix":""},{"id":348325436,"identity":"364f240c-3467-47f8-bb0a-ad06e721c928","order_by":2,"name":"Hazrat Bilal","email":"","orcid":"","institution":"Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hazrat","middleName":"","lastName":"Bilal","suffix":""},{"id":348325437,"identity":"39de2d6f-e55f-4639-a6a3-6cc881ce51fe","order_by":3,"name":"Chang Shi","email":"","orcid":"","institution":"First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Shi","suffix":""},{"id":348325438,"identity":"007932ac-a769-4f37-83fa-a6ba816adb33","order_by":4,"name":"Lei Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNv7mAwc+VLDJ2R/vf/ggoaKGsBY+iWOJD2ec4TNmOHOG2eDBmWOEtcgx5Cgb87bJJTbc8GGTfNjCTITDGM6wSfOcMWNsnMF7rCKxgY2Bv707Ab8W5t5jknMq0piZpfvSbiTukGGQOHN2AwFbzqVJvDlzjI1N5oDZjcQzbAwGErmEtOSYSfC2/efhkUgwK0hsYyZKi7EhbxubhIREjhkDcVoggcxmYMBzLFki4cwxHoJ+ke+HRGX9Bvbmgx9/VNTI8bf34teCAXhIUz4KRsEoGAWjACsAAEqITKiF7ySzAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-07-29 14:51:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4822907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4822907/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88568-2","type":"published","date":"2025-02-01T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66123085,"identity":"9c26d284-2f06-407f-be02-462d9b27ad3f","added_by":"auto","created_at":"2024-10-08 02:23:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":499533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification the DEGs between healthy control and AMI patient samples. (A-B)\u003c/strong\u003e Box plot shows pre- and post-normalization of two expression datasets (\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48060\"\u003eGSE48060\u003c/a\u003e and \u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66360\"\u003eGSE66360\u003c/a\u003e).\u003cstrong\u003e (C, D)\u003c/strong\u003e Principal Component Analysis (PCA) plots showing the chosen GEO datasets pre- and post- batch effect removal. \u003cstrong\u003e(E)\u003c/strong\u003e Volcano plot of the CRGs from combined database.\u003cstrong\u003e(F)\u003c/strong\u003e Heatmap of the CRGs from the combined database.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/7c140cf488300803806242f0.jpg"},{"id":66123866,"identity":"4b19c4e8-9cc1-4d0f-b979-ce0fc44b1dfa","added_by":"auto","created_at":"2024-10-08 02:31:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of differential CRGs. (A)\u003c/strong\u003e GO analysis of differential CRGs. \u003cstrong\u003e(B)\u003c/strong\u003e Combined GO and KEGG analysis with logFC of differential genes. \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram showing the overlap between DEGs and CRGs, highlighting 15 genes common in two specimens. \u003cstrong\u003e(D)\u003c/strong\u003ePPI network of the 15 hub CRGs.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/de55c4d44f5f03b124e503f4.jpg"},{"id":66123087,"identity":"9eb7a915-6d7c-4012-8fd9-af2ad6d869d5","added_by":"auto","created_at":"2024-10-08 02:23:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":317673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetermination of hub CRGs using multiple methods.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Venn diagram showing the overlap 5 methods in network including Degree, BottleNeck, MCC, EPC, DMNC, highlighting 5 genes common in five methods. \u003cstrong\u003e(B)\u003c/strong\u003eIdentification of top 10 hub CRGs with Degree method. \u003cstrong\u003e(C)\u003c/strong\u003e Identification of top 10 hub CRGs with BottleNeck method. \u003cstrong\u003e(D)\u003c/strong\u003e Identification of top 10 hub CRGs with MCC method. \u003cstrong\u003e(E)\u003c/strong\u003e Identification of top 10 hub CRGs with EPC method. \u003cstrong\u003e(F)\u003c/strong\u003e Identification of top 10 hub CRGs with DMNC method.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/df85007deec8796f3e1f059d.jpg"},{"id":66123086,"identity":"97ac8c4f-4b4f-43b0-8d2b-2db4cdba0c0f","added_by":"auto","created_at":"2024-10-08 02:23:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic model construction. (A)\u003c/strong\u003e Construction of a nomogram model with 5 feature genes. \u003cstrong\u003e(B)\u003c/strong\u003e ROC curve for evaluating 5 genes signature model's diagnostic performance.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/2a840ed923baf3649b98088b.jpg"},{"id":66123090,"identity":"a4bebaa0-c286-4bb0-acb4-e88fae36edc0","added_by":"auto","created_at":"2024-10-08 02:23:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12280111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMiRNA-mRNA and drug-gene network construction.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e 13 Pivotal miRNAs targeting 5 feature genes.\u003cstrong\u003e (B-F) \u003c/strong\u003eTen candidate drug molecules targeting the 5 feature genes JUN, NAMPT, S100A8, SERPINA1 and VACN.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/154191b5f74bdc4722afdc60.jpg"},{"id":66123089,"identity":"0fa9b1ec-ff18-47e9-99d3-25fd575437d3","added_by":"auto","created_at":"2024-10-08 02:23:59","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":138890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation among hub CRDEGs with differentially infiltrated immune cells and functions in AMI patients and healthy controls. (A)\u003c/strong\u003e Heatmap of differential immune cells and functions. \u003cstrong\u003e(B, C)\u003c/strong\u003e Scatter diagram of the correlation between neutrophils and SERPINA1 and NAMPT. \u003cstrong\u003e(D)\u003c/strong\u003e Correlation matrix of 24 immune cells.\u003cstrong\u003e(E)\u003c/strong\u003e The ssGSEA scores of 24 immune cells.\u003cstrong\u003e (F)\u003c/strong\u003e Heatmap of correlation among 5 hub CRDEGs with immune cells and functions (***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/5124c3f285a9b7a5ae178411.jpg"},{"id":66123091,"identity":"7c76ac87-2566-40b2-9746-1ea5ee1ea5d0","added_by":"auto","created_at":"2024-10-08 02:23:59","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2570432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenes expression in control and 24 hours hypoxia in AC16 cells.\u003c/strong\u003e Control and 24 hours hypoxia mRNA levels of JUN\u003cstrong\u003e (A)\u003c/strong\u003e, NAMPT \u003cstrong\u003e(B)\u003c/strong\u003e, S100A8\u003cstrong\u003e (C)\u003c/strong\u003e, SERPINA1 \u003cstrong\u003e(D)\u003c/strong\u003e and VCAN\u003cstrong\u003e (E)\u003c/strong\u003e in AC16 cells (****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/77782424396dcb9d96af0ab3.jpg"},{"id":75351235,"identity":"ae3732ac-aa94-4b87-8e48-d63217c132b4","added_by":"auto","created_at":"2025-02-03 16:08:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17341797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4822907/v1/b1d9b19d-4e28-4596-856f-80b5595cd20e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining Circadian Rhythm-Related Gene Expression and Immune Infiltration to Identify Diagnostic Biomarkers in Acute Myocardial Infarction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyocardial infarction (MI) remains a leading global health challenge, significantly contributing to mortality and long-term disability worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Acute myocardial infarction (AMI), often triggered by sudden coronary events, can lead to severe outcomes, including myocardial rupture and sudden cardiac death, particularly when medical intervention is delayed \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The initiation of precise diagnosis and prompt therapeutic measures are of paramount importance for AMI patient prognosis. Thus, the prompt detection of AMI at the initial signs of chest pain is vital for averting detrimental consequences and enhancing patient survival rates. Extensive research has highlighted the utility of various biomarkers in diagnosing AMI, including myoglobin, cardiac troponin I (cTnI), and creatine kinase-MB (CK-MB) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Among these, myoglobin serves as the earliest indicator, detectable within three hours of the onset of chest pain. However, it is predominantly indicative of skeletal muscle injury rather than myocardial damage \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Conversely, the cTnI and CK-MB, while comprehensive, tend to manifest later and therefore do not support the most timely diagnosis of AMI \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Moreover, in addition to AMI, elevations in cTnI could occur in a number of conditions, diminishing its specificity for diagnosis \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This often leads to missed opportunities for optimal treatment. Consequently, there is a critical need for novel biomarkers that can more precisely and reliably diagnose AMI.\u003c/p\u003e \u003cp\u003eCircadian rhythms, endogenous biological cycles inherent to most living organisms, orchestrate a vast array of physical, mental, and behavioral changes. This regulation is achieved through the tightly coordinated modulation of gene expression and biochemical functions. Key genes involved in circadian rhythms include CLOCK, BMAL1 (ARNTL), PER1/2/3 (Period), CRY1/2 (Cryptochrome), and others such as TIM (Timeless), NR1D1 (REV-ERBα), NR1D2 (REV-ERBβ), CSNK1D (Casein Kinase 1 Delta), and CSNK1E (Casein Kinase 1 Epsilon)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Under physiological conditions, circadian rhythms-related genes (CRGs) were involving in regulating heart rate, cardiac electrophysiology, blood pressure, blood coagulability, and vascular tone \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Emerging evidence suggests that, in addition to regulating cardiovascular physiologic processes, circadian rhythms also influence cardiovascular diseases, including atherosclerosis and thrombosis and myocardial injury subsequent to MI \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These findings illuminate the potential of CRGs to enhance the early diagnosis of AMI, offering promising avenues for therapeutic intervention and improved patient outcomes.\u003c/p\u003e \u003cp\u003eRecent studies have increasingly underscored the critical impact of immune cell infiltration on the development and progression of AMI. This process is involved in various stages of coronary artery atherosclerosis, such as lipid core enlargement, fibrous cap degradation, and plaque angiogenesis, which collectively elevate the risk of plaque rupture and thrombosis \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, immune infiltration plays a vital role in the course of AMI by mediating injury and repair mechanisms \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Despite the acknowledged importance of immune cells in these processes, significant gaps remain in our understanding of their specific roles post-MI, particularly concerning inflammatory cell functions. Such studies are essential not only for elucidating the mechanisms at play but also for exploring the potential of immune cell profiles as early diagnostic markers for AMI.\u003c/p\u003e \u003cp\u003eExtant literature suggests that circadian rhythm-related genes significantly influence inflammatory responses in heart failure \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This insight underpins our hypothesis that a combined analysis of CRGs and immune infiltration could enhance the precision in identifying diagnostic biomarkers for AMI. In our study, we performed a systematic evaluation of the expression of CRGs and their correlation with immune infiltration in AMI patients. This analysis led to the identification of five distinctive genes, which were used to construct a diagnostic model.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of Datasets\u003c/h2\u003e \u003cp\u003eGene expression profiles GSE48060 and GSE66360 were retrieved from the Gene Expression Omnibus (GEO). Both datasets were produced using the GPL570-Affymetrix Human Genome U133 Plus 2.0 Array [HG-U133_Plus_2]. Specifically, GSE48060 includes peripheral blood samples from 31 patients with acute myocardial infarction (AMI) and 21 control individuals with normal cardiac function. The GSE66360 dataset comprises peripheral blood samples from 49 AMI patients and 50 healthy controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Preparation and Identification of CRDEGs\u003c/h2\u003e \u003cp\u003eRaw data from the datasets were converted into expression matrices using the \"limma\" package in R (Version 4.2.1) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The batch effects were removed using the \u0026ldquo;sva\u0026rdquo; package after merging the two datasets (GSE48060, GSE66360) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Differential expression genes (DEGs) were determined using the \"limma\" package, with a significance threshold set at |log2FC| \u0026gt;1 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Results were displayed using volcano plots and heatmaps created with the \"ggplot2\" and \"pheatmap\" packages, respectively. Circadian rhythm-related genes (CRGs) were derived from prior studies and intersected with DEGs to identify circadian rhythm-dependent DEGs (CRDEGs), which were visualized using \"ggplot2\" \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. CRGs were obtained via intersecting the DEGs from each array and the circadian rhythm-related genes using a Venn Diagram, and they were visualized as a Heatmap with R package \"ggplot2\". The overlapped CRDEGs among the two arrays were eventually obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eSubsequently, functional enrichment analysis of differential CRDEGs, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed using the \u0026ldquo;clusterProfiler\u0026rdquo; R package \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The pathways with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePPI network construction and hub CRDEGs identification\u003c/h2\u003e \u003cp\u003eThe PPI network of the differential CRDEGs was constructed via the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.string-db.org/\u003c/span\u003e\u003cspan address=\"https://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the cutoff interaction score set at 0.7. Then the top 10 hub CRDEGs with the highest maximal clique centrality (MCC, Bottlenecks, Degree, DMNC and EPC) values were selected via the \"Cytohubba\" plugin of Cytoscape and visualized using Cytoscape software (version 3.10.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInfiltration analysis of immune cells and functions\u003c/h2\u003e \u003cp\u003eThe infiltrating score of 24 immune cells in healthy and AMI groups were calculated with single-sample gene set enrichment analysis (ssGSEA) via the \u0026ldquo;gsva\u0026rdquo; R package \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and visualized by heatmap using the \u0026ldquo;pheatmap\u0026rdquo; package \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The box plots were used to compare and visualize the ssGSEA scores of infiltrated immune cells between the healthy and AMI samples by \u0026ldquo;ggpubr\u0026rdquo; R package \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The correlation heatmap, which revealed the correlation of 24 types of immune cells and related functions, was performed using the \u0026ldquo;corrplot\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and verification of the diagnostic model\u003c/h2\u003e \u003cp\u003eTo screen the CRDEGs with diagnostic potential, we analyzed the relationship among 5 CRDEGs (JUN, NAMPT, S100A8, SERPINA1 and VCAN)with immune cells using Spearman\u0026rsquo;s correlation analysis via the \u0026ldquo;ggcorrplot\u0026rdquo; R package for predicting AMI \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Then the 5 CRDEGs were subjected to Logistic regression analysis to construct a nomogram model via the \u0026ldquo;rms\u0026rdquo; R package. To evaluate the diagnostic performance of feature genes, the ROC was plotted using the \u0026ldquo;pROC\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of pivotal miRNAs and candidate drugs\u003c/h2\u003e \u003cp\u003eThe candidate drugs were determined using the DSigDB database. The access of the Targetscan database and DSigDB database are acquired through Enrichr (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://amp.pharm.mssm.edu/Enrichr/\u003c/span\u003e\u003cspan address=\"http://amp.pharm.mssm.edu/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and processing of cardiomyocyte cell line\u003c/h2\u003e \u003cp\u003eHuman AC16 cardiomyocytes (Wuhan sunncell Biotech Co.,Ltd) were cultured in Dulbecco's Modified Eagle's Medium (DMEM, Gibco) supplemented with 10% FBS (Yeasen) and 1% penicillin\u0026ndash;streptomycin (Cytiva) at 37℃ in an incubator containing 5% CO\u003csub\u003e2\u003c/sub\u003e. A hypoxia incubator chamber (STEMCELL Technologies, Canada) connected to a Proox Model 21 controller (BioSpherix, Redfield, NY) was used to establish a hypoxic environment. Thereafter, the hypoxic conditions were built through 24 hours exposure of cells to the hypoxic environment (94% N\u003csub\u003e2\u003c/sub\u003e, 1% O\u003csub\u003e2\u003c/sub\u003e and 5% CO\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR\u003c/h2\u003e \u003cp\u003eTotal RNA isolation was carried out with using TRizol reagent according to the manufacturer\u0026rsquo;s instructions (Sevenbio, SM139). All-in-one First Strand cDNA Synthesis Kit Ⅲ (Sevenbio, SM135) was used to reverse transcribe cDNA. The reaction mixtures containing SYBR Green (Sevenbio, SM143) were composed following the manufacturer\u0026rsquo;s protocol and then CT values were obtained using a qPCR platform (Bioer, LineGene9600 FQD-96a v1.0.13 RC 20200911, Hangzhou, China). The genes expression levels of JUN, NAMPT, S100A8, SERPINA1, and VCAN in AC16 were measured using RT-qPCR. The ACTB gene served as the reference gene for the data. Relative quantitation was performed using the 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e method. Primer details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of the primers used for RT-qPCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName (homo)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimers for RT-qPCR (5\u0026prime;-3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GGGAAATCGTGCGTGACATT\u003c/p\u003e \u003cp\u003eReverse: GGAACCGCTCATTGCCAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS100A8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ATGCCGTCTACAGGGATGAC\u003c/p\u003e \u003cp\u003eReverse: ACTGAGGACACTCGGTCTCTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSERPINA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GGAGGCTCAGATCCATGAAGG\u003c/p\u003e \u003cp\u003eReverse: GGTGTCCCCGAAGTTGACAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GTAACCCATGCGCTACATAAAGT\u003c/p\u003e \u003cp\u003eReverse: GGCAAAGTAGGCATCGTTGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAMPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ATCCTGTTCCAGGCTATTCTGT\u003c/p\u003e \u003cp\u003eReverse: CCCCATATTTTCTCACACGCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TCCAAGTGCCGAAAAAGGAAG\u003c/p\u003e \u003cp\u003eReverse: CGAGTTCTGAGCTTTCAAGGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software (version 4.2.1), SPSS Statistics (version 26.0) and GraphPad Prism (version 10.1.2). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (quartile range). The Student\u0026rsquo;s t-test was employed to analyze continuous variables with normal distribution. Categorical variables were presented as numbers (percentages) and analyzed using the chi-square test. Statistical significance was set at (*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ns: not significant).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDifferential CRGs between AMI patients and healthy controls\u003c/h2\u003e \u003cp\u003eWe obtained 71 healthy controls and 80 AMI patient samples data from two datasets (GSE48060 and GSE66360) in GEO. The expression data were normalized and visualized by box plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The batch effect was corrected through the PCA algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Then, through the Pearson correlation analysis, circadian rhythm genes were obtained using the \u0026ldquo;limma[3.52.2]\u0026rdquo; package in the R software (Version 4.2.1) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Based on the criterion |log2FC | \u0026gt;1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, differential CRGs were identified and visualized by the volcano plot and heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of the differential CRGs\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis indicated that the differential CRGs were mainly involved in inflammatory and immune response biological process (BP) terms, including \u0026ldquo;negative regulation of NF-kappaB transcription factor activity\u0026rdquo;, \u0026ldquo;positive regulation of smooth muscle cell proliferation\u0026rdquo;, \u0026ldquo;positive regulation of interleukin-6 production\u0026rdquo;, \u0026ldquo;chemokine production\u0026rdquo; and \u0026ldquo;regulation of cytokine production involved in immune response\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The cellular component (CC) analysis enriched in \u0026ldquo;tertiary granule membrane\u0026rdquo;, \u0026ldquo;specific granule membrane\u0026rdquo;, \u0026ldquo;phagocytic vesicle membrane\u0026rdquo;, \u0026ldquo;endopeptidase complex\u0026rdquo; and \u0026ldquo;azurophil granule lumen\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The molecular function (MF) analysis enriched in \u0026ldquo;signaling receptor activator activity\u0026rdquo;, \u0026ldquo;calcium-dependent protein binding\u0026rdquo;, \u0026ldquo;chemokine receptor binding\u0026rdquo;, \u0026ldquo;monocarboxylic acid binding\u0026rdquo; and \u0026ldquo;scaffold protein binding\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Our analysis revealed significant enrichment of DEGs in several key biological pathways, indicating potential mechanisms underlying the disease process. Notably, enriched pathways included the complement and coagulation cascades, cytokine-cytokine receptor interaction, transcriptional misregulation in cancer, chemokine signaling pathway, neutrophil extracellular trap formation, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, osteoclast differentiation, TNF signaling pathway and toll-like receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A total of 78 differentially expressed genes (DEGs, blue) and 1475 CRGs (red) were identified, with 15 genes overlapping between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). After removing the isolated nodes, the 15 hub CRGs, including \u003cem\u003eJUN, PTX3, LYZ, VCAN, S100A8, VNN1, PLAUR, SERPINA1, S100P, DDIT3, NFIL3, PPP1R15A, AQP9, ACSL1\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e were identified by \u0026ldquo;Cytohubba\u0026rdquo; via the Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of hub CRGs\u003c/h2\u003e \u003cp\u003eThe Venn diagram illustrates the overlap in gene identification among five different methods: Degree, BottleNeck, DMNC, EPC and MCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The central overlap indicates that 5 genes are identified by all five methods, namely \u003cem\u003eS100A8, SERPINA1, VCAN, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e, suggesting their potential significance. The top 10 hub CRGs, including \u003cem\u003eJUN, SERPINA1, NAMPT, S100A8, AQP9, PLAUR, VCAN, LYZ, PPP1R15A\u003c/em\u003e and \u003cem\u003eNFIL3\u003c/em\u003e, were identified by the Degree method (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The BottleNeck method identified the top 10 hub CRGs as \u003cem\u003eS100A8, SERPINA1, LYZ, VCAN, JUN, S100P, PTX3, DDIT3, NAMPT\u003c/em\u003e and \u003cem\u003ePPP1R15A\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Using the MCC algorithm, the top 10 hub CRGs were determined to be JUN, S100A8, \u003cem\u003eSERPINA1, PLAUR, AQP9, VCAN, LYZ, NAMPT, PTX3\u003c/em\u003e and \u003cem\u003eACSL1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The EPC method identified \u003cem\u003eJUN, SERPINA1, S100A8, PLAUR, NAMPT, AQP9, VCAN, LYZ, ACSL1\u003c/em\u003e and \u003cem\u003eNFIL3\u003c/em\u003e as the top 10 hub CRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). According to the DMNC method, the top 10 hub CRGs were \u003cem\u003ePTX3, VCAN, PLAUR, AQP9, JUN, ACSL1, SERPINA1, NAMPT, S100A8\u003c/em\u003e and \u003cem\u003ePPP1R15A\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of 5 feature genes for diagnosing AMI\u003c/h2\u003e \u003cp\u003eTo identify diagnostic biomarkers for AMI, we utilized the 5 hub CRGs (\u003cem\u003eS100A8, SERPINA1, VCAN, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) as diagnostic feature genes to predict AMI and construct a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The diagnostic performance of the 5-gene signature model was evaluated using the ROC curve, with the training dataset yielding an AUC value of 0.881, indicating a promising predictive value for AMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Nonetheless, the accuracy and reliability of this diagnostic model require further validation in future clinical trials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of pivotal miRNAs and candidate drugs\u003c/h2\u003e \u003cp\u003eTo identify the pivotal miRNAs and candidate drugs targeting the 5 feature genes, the data was collected from the Targetscan database and DSigDB database. Ultimately, a total of 13 miRNAs were screened with a set \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, SERPINA1 could be regulated by all 9 miRNAs (hsa-miR-744, mmu-miR-379, mmu-miR-1193-5p, mmu-miR-3079-5p, hsa-miR-4706, hsa-miR-4749-5p, mmu-miR-2183, hsa-miR-4506 and mmu-miR-3094). JUN interacts with 7 miRNAs: hsa-miR-744, mmu-miR-1893, hsa-miR-4706, hsa-miR-4749-5p, hsa-miR-4506, mmu-miR-3094 and mmu-miR-3471). The VCAN interacted with 5 miRNAs: mmu-miR-379, mmu-miR-1193-5p, mmu-miR-3079-5p, mmu-miR-6690-5p and mmu-miR-878-5p. NAMPT was demonstrated to interact with 6 miRNAs: mmu-miR-2183, hsa-miR-4506, mmu-miR-3094, mmu-miR-6690-5p, mmu-miR-878-5p and mmu-miR-3471). We screened the top 10 drug molecules based on an adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using the DSigDB database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Among these, VALPROIC ACID CTD 00006977 and estradiol CTD 00005920 were associated with all five feature genes. Dexamethasone CTD 00005779 was linked to three feature genes, including \u003cem\u003eS100A8, SERPINA1\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e. The remaining drug molecules also demonstrated interactions with the feature genes. These drug candidates offer promising avenues for further research and development in the treatment of AMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation matrix and infiltration analysis of immune cells and gene expression\u003c/h2\u003e \u003cp\u003eWe further investigated the immune cell infiltration between AMI patients and healthy controls, the enrichment scores of distinct immune cell subpopulations and functions were assessed using ssGSEA. The results were visualized via the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). AMI patients showed elevated levels of eosinophils, iDC, macrophages, mast cells, neutrophils, NK CD56 bright cells and Th1 cells, but decreased levels of CD8 T cells, cytotoxic cells, T cells, T helper cells, Tcm, Tem, Tgd, Th17 cells and Th2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). The correlation of 24 immune cells indicated that neutrophils were positively correlated with macrophages, eosinophils and mast cells, while neutrophils were negatively correlated with T cells and T helper cells. In addition, cytotoxic cells were positively connected with Tgd and CD8 T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These findings indicate that the immune cell infiltration patterns differ significantly between AMI patients and healthy individuals, potentially playing a crucial role in the pathophysiological processes of the disease. As shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), the heatmap illustrates the correlation between the expression of 5 genes (\u003cem\u003eS100A8, SERPINA1, VCAN, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) and the abundance of different immune cell types. Notably, genes such as NAMPT and SERPINA1 show strong positive correlations with neutrophils, suggesting a potential role in the inflammatory response (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Conversely, these genes tend to have negative correlations with T cells and Th2 cells, indicating a complex interaction between gene expression and immune regulation in the context of AMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRT-qPCR validation of 5 CRGs in the AC16 hypoxia culture model\u003c/h2\u003e \u003cp\u003eRT-qPCR was performed to quantify the expression of the five CRGs in AC16 cells exposed to 24 hours of hypoxia, aiming to validate the bioinformatics analysis results. As predicted, the results showed that the five key genes (\u003cem\u003eS100A8, SERPINA1, VCAN, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) were highly expressed in AC16 under hypoxic conditions compared to normoxic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). These findings indicate the potential of these genes as promising diagnostic targets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCircadian rhythm-related genes (CRGs) play a crucial role in coordinating the regulation of circadian rhythms, the body\u0026rsquo;s endogenous timing system. Emerging evidence has well established the impact of circadian rhythms on cardiovascular function and myocardial ischemia injury, strongly supporting CRGs as candidate diagnostic biomarkers \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Simultaneously, a study reveals a potentially complex relationship between CRGs and immune infiltration in heart failure \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In the present study, we used bioinformatics tools to investigate the role of CRGs in AMI as well as their interaction with immune infiltration, attempting to identify more precise AMI diagnostic markers and treatment targets.\u003c/p\u003e \u003cp\u003eWe systematically screened 15 differential CRGs in the peripheral blood of 71 healthy controls and 80 AMI patients. Functional enrichment analysis revealed that the differential CRGs were enriched in the regulation of cytokine production involved in immune response and the chemokine production. Cytokines and chemokines are essential mediators that orchestrate the inflammatory response in atherosclerosis procession, such as monocyte/lymphocyte recruitment, regulating plaque stability, rupture and thrombus formation \u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. During acute myocardial infarction, the balance of pro-inflammatory and anti-inflammatory cytokines can influence the recruitment immune cells to the site of injury, facilitate repair processes and subsequent cardiac remodeling \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These supported that CRGs might play an important role in the initiation and subsequent AMI via inflammatory responses. Then, we constructed the PPI network and screened 15 hub CRGs, namely \u003cem\u003eJUN, PTX3, LYZ, VCAN, S100A8, VNN1, PLAUR, SERPINA1, S100P, DDIT3, NFIL3, PPP1R15A, AQP9, ACSL1\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e, all of which are potential candidate biomarkers closely related to AMI.\u003c/p\u003e \u003cp\u003eAMI patients showed elevated levels of neutrophils, positively correlating with macrophages, implying the clearance of necrotic cardiomyocytes and repair processes \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. An \u003cem\u003ein vivo\u003c/em\u003e study showed that neutrophils induce macrophages towards a reparative phenotype via neutrophil gelatinase-associated lipocalin in the MI \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The resolution of inflammation is marked by the efflux of macrophages via the lymphatic system \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Our results showed that mast cells and eosinophils were increased in AMI patients, positively relating to neutrophils. To date, the impact of mast cells on infarcted myocardial tissue remains a subject of ongoing debate. Nonetheless, mast cells are implicated in protective mechanisms after MI, including promoting angiogenesis, regulating cardiomyocyte contractility, enhancing hypoxia resistance and facilitating the conversion of fibroblasts to myofibroblasts \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In addition, eosinophils have shown potential as biomarkers for AMI and play a role in tissue repair processes \u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In the present study, we demonstrated that the immune function of neutrophil was elevated in the AMI group. In the follow-up analysis, we identified the 5 hub CFRGs (\u003cem\u003eS100A8, SERPINA1, VCAN, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) most associated with immune infiltration as diagnostic feature genes. The expression levels of all 5 feature genes were strongly positively linked with the neutrophils. These suggests that the 5 feature genes participate in the immune and inflammatory responses of AMI, through neutrophil signaling pathways. In AMI patients, the S100A8 levels positively correlated with neutrophil counts \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Furthermore, S100A8 is associated with cardiac rupture, serving as a robust predictor and potentially causal mediator \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Consistent with our result, a weighted gene co-expression network analysis research showed that the SERPINA1 was identified and validated for the predictive value in identifying future heart failure after AMI \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. In our study, the SERPINA1 was highly expressed under the 24 hours hypoxia culture, with validation achieved through RT-qPCR. VCAN, as one of the ten strongly interlinked hub genes, was identified in the remodeling of non-infarcted myocardium following acute myocardial infarction \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Similarly, our study also confirmed the VCAN high expression in response to hypoxia. Studies showed that JUN involved in MI process \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. It has been documented that inhibition of NAMPT may attenuate tissue damage mediated by neutrophilic inflammation and oxidative stress during the initial stages of re-perfusion following myocardial infarction \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. These findings indicate that these 5 central CRGs may represent promising diagnostic and therapeutic targets for AMI.\u003c/p\u003e \u003cp\u003eWe predicted the miRNAs and candidate drugs that regulate the five CRGs. Hsa-miR-4506 was identified as a regulator of three key genes (\u003cem\u003eSERPINA1, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) and has been recognized as a highly predictive miRNA for colon and rectal cancer \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Hsa-miR-744, which regulates SERPINA1 and JUN, was found to be elevated in various cancers, with significant implications for prognosis \u003csup\u003e\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Studies have shown that up-regulation of mmu-miR-379 occurs in both the plasma of Crohn\u0026rsquo;s disease patients and the development of obesity, the latter being associated with an increased risk of cardiovascular disease \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Hsa-miR-4706 was found to be elevated in people blood samples with head and neck cancer (HNC), suggesting its potential as a predictive biomarker for HNC \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Additionally, our study is the first to report the involvement of mmu-miR-3471, mmu-miR-1893, mmu-miR-1193-5p, mmu-miR-3079-5p, hsa-miR-4749-5p, mmu-miR-2183 and mmu-miR-3094 in relation to CRGs in AMI. These miRNAs may serve as independent predictors of AMI, however, their specific mechanisms of action in AMI require further investigation.\u003c/p\u003e \u003cp\u003eAmong all candidate drugs, VALPROIC ACID CTD 00006977 and estradiol CTD 00005920 ranked in the top two with targeting all 5 feature CRGs. Dexamethasone CTD 00005779 was linked to three feature genes, including \u003cem\u003eS100A8, SERPINA1\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e. The VALPROIC ACID CTD 00006977, the drug identified in our screening, serves as a reverse agonist of the retinoic acid-related orphan receptor α (RORα) \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. RORα is involved in the regulation of inflammatory macrophages under pathological conditions, such as myocardial infarction \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. These suggested that VALPROIC ACID CTD 00006977 might affect the process of AMI by impacting genes involved in CRGs. The potential effectiveness of other proposed drugs is also being considered, and these drugs may warrant further validation through chemical experiments.\u003c/p\u003e \u003cp\u003eIn summary, this study used bioinformatics method to analyze the transcriptional expression characteristics of AMI and screened five biomarkers (\u003cem\u003eS100A8, SERPINA1, VCAN, JUN\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) related to AMI. Drug database enrichment found that these five CRGs may be the drug targets of AMI, and VALPROIC ACID CTD 00006977 and estradiol CTD 00005920 may be a potential targeted therapeutic drug. Hsa-miR-4506 plays an important role in regulating CRGs in AMI. Further experimentation is necessary to demonstrate the effects and underlying mechanisms of other miRNAs. While our discoveries are notable, the study is constrained by limitations such as only in vitro validation and the absence of clinical specimens. By addressing these limitations, future research can provide novel insights into the diagnosis and management of AMI.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we identified differential expression of 1475 CRGs and identified 15 genes as potential biomarkers for early acute myocardial infarction (AMI) diagnosis by comparing AMI patients to healthy individuals. These findings suggest a key role for circadian rhythm in the development of AMI. Through bioinformatics analysis, we identified five immune-related CRGs as potential biomarkers for the early diagnosis of AMI. The significant predictive value of these genes was demonstrated by ROC curve analysis. Furthermore, validation in the AC16 hypoxia culture model confirmed five feature genes, supporting their potential as biomarkers for AMI diagnosis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003ch2\u003eReprints and permissions information\u003c/h2\u003e \u003cp\u003e is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.nature.com/reprints\u003c/span\u003e\u003cspan address=\"http://www.nature.com/reprints\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the Natural Science Foundation of China (82170276) and Liaoning Provincial Program for Top Discipline of Basic Medical Sciences.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXY, CS, and XZ designed and drafted the original manuscript. XZ performed data collection and wrote the figure legends. XY and XZ performed the RT-qPCR experiment. HB reviewed and edited. CS conducted data analysis. XY and CS plotted the figures. LS supervised, edited, and conceptualized the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank colleagues in Professor Hiroshi Egawa (Fukuoka Clinical Pathology Institute, Japan) for their technical help and intellectual inspiration in the course of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData analysed was obtained from Gene Expression Omnibus database under accession number GSE48060 and GSE66360, are available at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48060, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66360; The PPI network of the differential genes were constructed via string database, are available at the following URL: https://www.string-db.org/; The candidate drugs were obtained from DSigDB database, are available at the following URL: https://dsigdb.tanlab.org/DSigDBv1.0/geneSearch.html. Pivotal miRNAs were obtained from miRDB database, are available at the following URL: http://mirdb.org/miRDB/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOliveira, G. B. F., Avezum, A. \u0026amp; Roever, L. Cardiovascular Disease Burden: Evolving Knowledge of Risk Factors in Myocardial Infarction and Stroke through Population-Based Research and Perspectives in Global Prevention. Frontiers in Cardiovascular Medicine 2,(2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakic, M. \u003cem\u003eet al.\u003c/em\u003e Possible role of circulating endothelial cells in patients after acute myocardial infarction. 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Jci Insight 4,(2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Circadian rhythm-related genes, Acute myocardial infarction, Biomarker, Immune infiltration, Bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-4822907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4822907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent diagnostic biomarkers for acute myocardial infarction (AMI), such as troponins, often lack specificity, leading to false positives under non-cardiac conditions. Recent studies have implicated circadian rhythm and immune infiltration in the pathogenesis of AMI. This study hypothesizes that analyzing the interplay between circadian rhythm-related gene expression and immune infiltration can pinpoint more accurate diagnostic biomarkers for AMI. Our results demonstrated differential expression of 15 circadian rhythm-related genes (CRGs) between AMI patients and healthy individuals, with five key genes\u0026mdash;JUN, NAMPT, S100A8, SERPINA1, and VCAN\u0026mdash;emerging as central to this process. Functional enrichment analyses suggest these genes significantly influence cytokine and chemokine production in immune responses. Immune infiltration assessments using ssGSEA indicated elevated levels of neutrophils, macrophages, and eosinophils in AMI patients. Additionally, we identified potential therapeutic implications with 13 pivotal miRNAs and 10 candidate drugs targeting these genes. RT-qPCR analysis further confirmed the upregulation of these five genes under hypoxic conditions, compared to controls. Collectively, our findings highlight the critical role of CRGs in AMI, offering new insights into its diagnosis and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Combining Circadian Rhythm-Related Gene Expression and Immune Infiltration to Identify Diagnostic Biomarkers in Acute Myocardial Infarction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 02:23:41","doi":"10.21203/rs.3.rs-4822907/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-27T14:36:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"136784203698472803907486668074364634406","date":"2024-11-05T18:21:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T11:13:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12662742472114394384146839173783682583","date":"2024-09-20T14:44:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-18T13:58:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-18T13:56:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-11T14:43:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-11T14:32:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-29T14:48:36+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":"b320f604-146e-4137-9b39-5093e54828e3","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36952594,"name":"Health sciences/Cardiology"},{"id":36952595,"name":"Health sciences/Cardiology/Cardiovascular biology"}],"tags":[],"updatedAt":"2025-02-03T16:01:55+00:00","versionOfRecord":{"articleIdentity":"rs-4822907","link":"https://doi.org/10.1038/s41598-025-88568-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-01 15:57:30","publishedOnDateReadable":"February 1st, 2025"},"versionCreatedAt":"2024-10-08 02:23:41","video":"","vorDoi":"10.1038/s41598-025-88568-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-88568-2","workflowStages":[]},"version":"v1","identity":"rs-4822907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4822907","identity":"rs-4822907","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}