Introduction

Myocardial infarction (MI) remains a leading global health challenge, significantly contributing to mortality and long-term disability worldwide1. 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 delayed2. 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 damage4,5. Conversely, the cTnI and CK-MB, while comprehensive, tend to manifest later and therefore do not support the most timely diagnosis of AMI6,7. Moreover, in addition to AMI, elevations in cTnI could occur in a number of conditions, diminishing its specificity for diagnosis8. 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 wide range of physiological, mental, and behavioral processes. This regulation is achieved through the tightly coordinated modulation of gene expression and biochemical functions. Key genes involved in circadian rhythms include CLOCK (Clock Circadian Regulator), BMAL1 (Basic Helix-Loop-Helix ARNT Like 1), PER1/2/3 (Period Circadian Regulator1/2/3), CRY1/2 (Cryptochrome Circadian Regulator1/2), and others such as TIM (Timeless Circadian Regulator), NR1D1 (Nuclear Receptor Subfamily 1 Group D Member 1), NR1D2 (Nuclear Receptor Subfamily 1 Group D Member 2), CSNK1D (Casein Kinase 1 Delta), and CSNK1E (Casein Kinase 1 Epsilon)9,10. Under physiological conditions, circadian rhythm-related genes (CRGs) are involved in regulating heart rate, cardiac electrophysiology, blood pressure, blood coagulability, and vascular tone11,12. 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 MI13,14,15. 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 thrombosis2,16. Furthermore, immune infiltration plays a vital role in the course of AMI by mediating injury and repair mechanisms17,18. 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 CRGs significantly influence inflammatory responses in heart failure19,20. 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 study aims to investigate the interaction between CRGs expression and immune infiltration in AMI, with the objective of identifying more precise diagnostic biomarkers and uncovering potential therapeutic targets.

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 package25,26,27,28. 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 package29 and visualized by heatmap using the “pheatmap” package30. 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 package31. 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 AMI32,33. 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% CO2. 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 h exposure of cells to the hypoxic environment (94% N2, 1% O2, and 5% CO2).

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 20,200,911, 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.
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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. The Benjamini–Hochberg procedure was used to correct for multiple comparisons. 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) from the GEO database. The expression data were normalized and visualized by box plots (Fig. 1A,B). The batch effect was corrected through the PCA algorithm (Fig. 1C,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. To control for multiple comparisons, the Benjamini–Hochberg method was applied, and genes with adjusted p-values < 0.05 were considered statistically significant. Based on the criterion |log2FC |> 1 and p < 0.05 (Benjamini–Hochberg corrected), differential CRGs were identified and visualized by the volcano plot and heatmap (Fig. 1E,F).

Fig. 1
figure 1

Identification the DEGs between healthy control and AMI patient samples. (A, B) Box plot shows pre- and post-normalization of two expression datasets (GSE48060 and GSE66360). (C, D) Principal Component Analysis (PCA) plots showing the chosen GEO datasets pre- and post- batch effect removal. (E) Volcano plot of the CRGs from combined database. Filtering criteria base on | log2FC |> 1 and adjusted p < 0.05 (Benjamini–Hochberg correction). (F) Heatmap of the CRGs from the combined database. Red indicates increased gene expression, while blue indicates decreased gene expression.

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Functional analysis of the differential CRGs

Functional enrichment analysis, with p-values adjusted using the Benjamini–Hochberg method, 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. 2A). The cellular component (CC) analysis enriched in “tertiary granule membrane”, “specific granule membrane”, “phagocytic vesicle membrane”, “endopeptidase complex”, and “azurophil granule lumen” (Fig. 2A). 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. 2A). Our analysis revealed significant enrichment of DEGs in several key biological pathways, indicating potential mechanisms underlying the disease process. Notably, enriched pathways (adjusted p < 0.05, Benjamini–Hochberg corrected) 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. 2B). A total of 78 differentially expressed genes (DEGs, blue) and 1475 CRGs (red) were identified, with 15 genes overlapping between the two groups (Fig. 2C). Through network analysis using the Cytoscape software and the “Cytohubba” plugin, isolated nodes were excluded from the network, and 15 hub CRGs were identified, including JUN, PTX3, LYZ, VCAN, S100A8, VNN1, PLAUR, SERPINA1, S100P, DDIT3, NFIL3, PPP1R15A, AQP9, ACSL1, and NAMPT (Fig. 2D).

Fig. 2
figure 2

Functional enrichment analysis of differential CRGs. (A) GO analysis of differential CRGs. (B) Combined GO and KEGG analysis with logFC of differential genes. (C) Venn diagram showing the overlap between DEGs and CRGs, highlighting 15 genes common in two specimens. (D) PPI network of the 15 hub CRGs.

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Identification of hub CRGs

The Venn diagram illustrates the overlap in gene identification among five different methods: Degree, BottleNeck, DMNC, EPC and MCC (Fig. 3A). 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. 3B). The BottleNeck method identified the top 10 hub CRGs as S100A8, SERPINA1, LYZ, VCAN, JUN, S100P, PTX3, DDIT3, NAMPT, and PPP1R15A (Fig. 3C). 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. 3D). The EPC method identified JUN, SERPINA1, S100A8, PLAUR, NAMPT, AQP9, VCAN, LYZ, ACSL1, and NFIL3 as the top 10 hub CRGs (Fig. 3E). According to the DMNC method, the top 10 hub CRGs were PTX3, VCAN, PLAUR, AQP9, JUN, ACSL1, SERPINA1, NAMPT, S100A8, and PPP1R15A (Fig. 3F).

Fig. 3
figure 3

Determination of hub CRGs using multiple methods. (A) Venn diagram showing the overlap 5 methods in network including Degree, BottleNeck, MCC, EPC, and DMNC, highlighting 5 genes common in five methods. (B) Identification of top 10 hub CRGs with Degree method. (C) Identification of top 10 hub CRGs with BottleNeck method. (D) Identification of top 10 hub CRGs with MCC method. (E) Identification of top 10 hub CRGs with EPC method. (F) Identification of top 10 hub CRGs with DMNC method.

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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. 4A). 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. 4B). Nonetheless, the accuracy and reliability of this diagnostic model require further validation in future clinical trials.

Fig. 4
figure 4

Diagnostic model construction. (A) Construction of a nomogram model with 5 feature genes. (B) ROC curve for evaluating 5 genes signature model’s diagnostic performance.

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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. 5A). 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. 5B–5F). Among these, VALPROIC ACID CTD 00,006,977 and estradiol CTD 00,005,920 were associated with all five feature genes. Dexamethasone CTD 00,005,779 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.

Fig. 5
figure 5

MiRNA-mRNA and drug-gene network construction. (A) 13 Pivotal miRNAs targeting 5 feature genes. (B-F) Ten candidate drug molecules targeting the 5 feature genes JUN, NAMPT, S100A8, SERPINA1, and VACN.

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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. 6A). 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. 6E). 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. 6D). 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. 6F), 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. 6B,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.

Fig. 6
figure 6

Correlation among hub CRDEGs with differentially infiltrated immune cells and functions in AMI patients and healthy controls. (A) Heatmap of differential immune cells and functions. (B, C) Scatter diagram of the correlation between neutrophils and SERPINA1 and NAMPT. (D) Correlation matrix of 24 immune cells. (E) The ssGSEA scores of 24 immune cells. (F) Heatmap of correlation among 5 hub CRDEGs with immune cells and functions (***p < 0.001, ** p < 0.01, * p < 0.05).

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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 h 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. 7A–E). These findings indicate the potential of these genes as promising diagnostic targets.

Fig. 7
figure 7

Genes expression in control and 24 h hypoxia in AC16 cells. Control and 24 h hypoxia mRNA levels of JUN (A) NAMPT (B) S100A8 (C) SERPINA1 (D) VCAN (E) in AC16 cells (****p < 0.0001, ** p < 0.01, * p < 0.05).

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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 biomarkers34,35. Simultaneously, a study reveals a potentially complex relationship between CRGs and immune infiltration in heart failure19. 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 formation36,37,38. 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 remodeling39,40. 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 processes41. An in vivo study showed that neutrophils induce macrophages towards a reparative phenotype via neutrophil gelatinase-associated lipocalin in the MI42. The resolution of inflammation is marked by the efflux of macrophages via the lymphatic system41. 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 myofibroblasts43. In addition, eosinophils have shown potential as biomarkers for AMI and play a role in tissue repair processes44,45,46. 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 CRGs (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 suggest 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 were correlated with neutrophil counts47. Furthermore, S100A8 is associated with cardiac rupture, serving as a robust predictor and a potentially causal mediator48,49. 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 AMI50. In our study, the SERPINA1 was highly expressed under the 24 h 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 infarction51. Similarly, our study also confirmed the VCAN high expression in response to hypoxia. Studies showed that JUN involved in the MI process52,53. 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 infarction54,55. 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 cancer56. Hsa-miR-744, which regulates SERPINA1 and JUN, has been found to be elevated in various cancers, with significant implications for prognosis57,58,59. 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 disease60,61. 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 HNC62. 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 00,006,977 and estradiol CTD 00,005,920 ranked in the top two with targeting all 5 feature CRGs. Dexamethasone CTD 00,005,779 was linked to three feature genes, including S100A8, SERPINA1, and NAMPT. The VALPROIC ACID CTD 00,006,977, the drug identified in our screening, serves as a reverse agonist of the retinoic acid-related orphan receptor α (RORα)63. RORα is involved in the regulation of inflammatory macrophages under pathological conditions, such as myocardial infarction64. These suggest that VALPROIC ACID CTD 00,006,977 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 methods 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 00,006,977 and estradiol CTD 00,005,920 may be a potential targeted therapeutic drug. Hsa-miR-4506 plays an important role in regulating CRGs in AMI. Future research will prioritize large-scale cohorts encompassing diverse ethnic groups to further validate the generalizability of our findings across populations and address potential limitations of the present study. While our discoveries are notable, the study is constrained by limitations such as only in vitro validation and the absence of clinical specimens. Future research will focus on obtaining patient samples through collaborations with clinical teams to overcome the limitation of relying solely on in vitro cell experiments and to further strengthen the translational relevance of the identified biomarkers. Additionally, further experimentation is necessary to demonstrate the effects and underlying mechanisms of other miRNAs. By addressing these limitations, future research can provide novel insights into the diagnosis and management of AMI.

Conclusions

This study highlights the critical interplay between circadian rhythm-related genes and immune infiltration in the pathogenesis of acute myocardial infarction (AMI). Our findings underscore the potential of circadian rhythm-related biomarkers to enhance the early diagnosis of AMI, bridging a gap in the current diagnostic landscape. The identification of five key immune-related circadian rhythm genes and their validation through bioinformatics and experimental models lay the groundwork for future translational research. These insights provide a promising foundation for the development of more precise diagnostic tools and therapeutic strategies targeting AMI.