Identification and validation of mitochondrial dynamics-related genes in patients with acute myocardial infarction-a bioinformatics analysis

Identification and validation of mitochondrial dynamics-related genes in patients with acute myocardial infarction-a bioinformatics analysis | 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 Research Article Identification and validation of mitochondrial dynamics-related genes in patients with acute myocardial infarction-a bioinformatics analysis Xiaolin Yue, Jinlei Wu, Xueyun Shi, Youshun Xu, Xiaowei Han, Ruijian Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5727986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent studies highlight the link between cardiovascular disease and mitochondrial dynamics. This study sought biomarkers of mitochondrial dynamics in acute myocardial infarction (AMI) to guide more precise clinical management. AMI-related datasets (GSE62646 and GSE59867) and 50 mitochondrial dynamics-related genes (MD-RGs) were derived from public databases. Firstly, based on MD-RGs, AMI samples in GSE62646 were classified into high- and low-scoring groups by single-sample gene set enrichment analysis. The differentially expressed genes (DEGs) were incorporated into machine learning algorithms. Subsequent expression level and receiver operating characteristic (ROC) analyses identified biomarkers. Furthermore, the relationship between biomarkers and AMI was analyzed by enrichment analysis, immune infiltration analysis, correlation analysis of m6A regulators. Finally, biomarker expression was verified by reverse transcription quantitative PCR (RT-qPCR). In this study, COX7B and SNORD54 were identified as biomarkers associated with mitochondrial dynamics in AMI. ROC curves showed that two biomarkers could better differentiate between AMI and control samples, and subsequent nomogram created by integrating two biomarkers were highly accurate in predicting AMI. Furthermore, enrichment analysis revealed that co-enrich pathways for COX7B and SNORD54 included “oxidative phosphorylation” and “Notch signaling pathway”. Notably, six m6A regulators (HNRNPC, KIAA1429, METTL3, WTAP, YTHDC1, and YTHDC2) were found to be significantly under-expressed in AMI samples. The RT-PCR demonstrated that the expression levels of COX7B and SNORD54 were significantly downregulated in AMI samples compared to controls. The study recognized COX7B and SNORD54 as biomarkers associated with mitochondrial dynamics in AMI, presenting potential clinical applications that could advance the understanding of AMI management. Acute myocardial infarction Mitochondrial dynamics Biomarkers Machine learning Regulatory network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Acute myocardial infarction (AMI) is one of the leading causes of mortality and the most common cause of chronic heart failure (HF) worldwide [ 1 ]. AMI, also known as myocardial cell death due to prolonged ischemia, leads to progressive deterioration of cardiac pump function, which might result in HF and potentially fatal arrhythmias. Atherothrombotic coronary artery disease is the most common underlying cause of AMI. When unstable atherosclerotic plaques become rupture or erode, thrombi will be formed which can obstruct the coronary lumen at the site of the plaque or lead to distal coronary embolization [ 2 ]. In the case of AMI, early and successful revascularization can effectively prevent loss of contractile myocardial muscle mass, decrease the infarct size and improve clinical outcomes [ 3 ]. However, reperfusion may paradoxically lead to exacerbated and accelerated injury in the myocardium, referred to as myocardial ischemia-reperfusion (I/R) injury [ 4 ]. Previous studies have shown that different types of cell death, including apoptosis, necrosis, pyroptosis and autophagy, can occur during I/R injury [ 5 ]. Therefore, new treatments are required to protect the myocardium against the detrimental effects of acute IR injury in order to reduce myocardial infarct (MI) size, preserve left ventricular (LV) function and prevent the onset of heart failure (HF) [ 6 ]. Mitochondrial dynamics is a biological process in order to maintain normal physiological functions, mainly including mitochondrial fusion, fission and autophagy [ 7 ]. As one of the most abundant organelles in myocardial cells, mitochondria play an important role in various physiological processes, including myocardial cell proliferation, apoptosis and signal transduction [ 8 ]. Mitochondrial dysfunction is a pivotal pathological basis for myocardial (I/R) injury and it represents a critical therapeutic target for the mitigation of myocardial damage [ 9 , 10 ]. However, the exact pathogenesis and molecular mechanism of mitochondrial dynamics in AMI have not been fully elucidated. In AMI, mitochondrial dysfunction occurs due to hypoxia and oxidative stress, leading to energy metabolism disturbances and cell death. Genes such as peroxisome proliferator-activated receptor gamma co-activator 1 alpha (PGC-1α), optic atrophy 1 (Opa1), mitofusin-2 (MFN2) and dynamin-related protein 1 (Drp1) regulate mitochondrial biogenesis, morphological changes, and autophagy in the heart, thereby affecting cardiac function. The increase in mitochondrial fission is accompanied by a decrease in mitochondrial fusion, as evidenced by the upregulation of Drp1 and downregulation of Opa1, both of which lead to cardiac damage [ 11 ]. Although existing research has highlighted the importance of mitochondria in AMI, certain aspects remain insufficiently addressed, including the specific gene regulatory mechanisms, the relationship between changes in mitochondrial function and myocardial injury. Moreover, the insufficient sensitivity of existing markers leads to delayed diagnosis or poor therapeutic effect. We will target specific genes for in-depth study of mitochondrial dynamics to provide new insights into the pathophysiology of AMI, which will help develop new therapeutic targets for AMI. To explore the potential involvement of mitochondrial dynamics in AMI, this study procured AMI-associated datasets and identified 50 mitochondrial dynamics-related genes (MD-RGs) from publicly available databases. Utilizing bioinformatics approaches, we conducted a screening for candidate biomarkers linked to AMI. Additionally, we performed Gene Set Enrichment Analysis (GSEA) for functional enrichment, alongside assessments of immune cell infiltration and single-sample Gene Set Enrichment Analysis (ssGSEA) of immune functions. This research offers a foundational reference for the identification of novel therapeutic targets in the context of AMI. Material and methods Data collection Data on AMI-related gene expression matrices were searched by accessing gene expression omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/ ). We selected patients with MI for 6 months. Specifically, GSE62646 dataset was based on GPL6244 platform, containing blood samples from 28 AMI patients (6 months) and 14 patients with stable coronary artery disease without a history of myocardial infarction (MI). On the other hand, GSE59867 dataset contained blood samples from 83 AMI patients (6 months) and 46 patients with stable coronary artery disease without a history of MI, and the sequencing platform was GPL6244. In the GSE62646 and GSE59867 datasets, blood samples collected on non-first-day-of-admission were excluded to ensure that the selected disease samples were not influenced by treatment. We considered patients with stable coronary artery disease without a history of MI as the control group for this study. Mitochondrial dynamics, which encompasses processes like mitochondrial fission, fusion, and division, is essential for crucial role in cellular energy metabolism and maintenance of homeostasis. To comprehensively analyze these processes, 15 genes closely related to mitochondrial fission and 9 genes associated with mitochondrial fusion were screened using the MitoCarta 3.0 database ( http://www.broadinstitute.org/mitocarta ). Additionally, 28 genes related to mitophagy were retrieved from the Reactome database ( https://reactome.org/ ). After merging and eliminating duplicates, a final set of 50 mitochondrial dynamics-related genes (MD-RGs) was identified (Table S1). These genes constitute a valuable resource for in-depth investigations into the roles of mitochondrial dynamics in cellular physiology and pathology. Differential expression analysis Based on the MD-RGs, single-sample gene set enrichment analysis (ssGSEA) method was utilized through GSVA-package (v 1.42.0) to calculate MD-RGs scores for AMI samples in the GSE62646 dataset, followed by dividing AMI samples into high- and low-scoring groups through the median of MD-RGs score [ 12 ]. Subsequent differential expression analysis was undertaken via limma package (v 3.54.1) with the thresholds of P 0.5, aiming to identify differentially expressed genes (DEGs) between AMI and control samples as well as between high- and low-scoring groups [ 13 ]. Volcano maps and heat maps were created with the ggplot2-package (v 3.3.6)[ 14 ] and heatmap-package (v 1.0.12)[ 15 ], respectively, to demonstrate DEGs expression patterns. Next, overlapping analysis of the two sets of DEGs was completed to determine the common DEGs. Functional annotation and protein-protein interactions (PPI) analyses To reveal the biological functions that common DEGs were involved in the development of AMI, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were undertaken via cluster Profiler-package (v 4.6.2)[ 16 ] and the org.Hs.eg.db-package (v 3.16.0)[ 17 ] (P 0.15) to elucidate the association between them at the protein level. Machine learning algorithms Machine learning, a branch of artificial intelligence, focuses on empowering computer systems to learn from data and improve performance through the use of statistical techniques, with a wide and diverse range of applications in the biomedical field [ 18 ]. In this study, we utilized two machine learning algorithms to identify feature genes closely associated with AMI from common DEGs in GSE62646 dataset. At first, the glmnet-package (v 4.1-4)[ 19 ] was utilized to perform least absolute shrinkage and selection operator (LASSO) analysis, with the parameter 'family' set to binomial and 'lambda' set to 0. This approach was considered as the optimal method for selecting feature genes. Secondly, Boruta algorithm, implemented through the Boruta-package (v 8.0.0), was based on the idea of random forests, where the importance of each feature was evaluated by comparing it with randomly generated “shadow” features [ 20 ]. During the screening process using the Boruta algorithm, the program defined DEGs, categorizing them into accepted and rejected groups. The accepted genes represented the feature genes selected by the Boruta model. After that, the intersection of two sets of feature genes was extracted with the help of Venndiagram-package (v 1.7.3), which were recorded as candidate genes [ 21 ]. Expression analysis and receiver operating characteristic (ROC) analysis In GSE62646 and GSE59867 datasets, the expression trends of candidate genes were further compared between AMI and control samples utilizing the Wilcoxon test. Our focus was on genes exhibiting stable expression, characterized by consistent trends across both datasets and significant group differences (P < 0.05). Further, to assess the diagnostic accuracy of the model, ROC curves were generated based on gene expression in both datasets with the use of the ROC-package (v 1.18.0), and genes with the area under curve (AUC) greater than 0.7 were defined as biomarkers associated with mitochondrial dynamics in AMI [ 22 ]. In general, the AUC greater than 0.7 indicated that the gene was more effective in diagnosing the disease. Subsequently, the biomarkers were input into Gene MANIA online website ( http://www.genemania.org/ ) to generate a network, aiming to explore potential mechanisms of biomarkers in AMI. Creation and assessment of nomogram To predict the occurrence of AMI from perspective of the biomarkers as a whole, we integrated the expression of the biomarkers and subsequently constructed a nomogram in GSE62646 dataset applying the rms-package (v 6.5-0) [ 23 ]. Furthermore, calibration curve and ROC curve were created to estimate the accuracy of nomogram predictions, as well as decision curve analysis (DCA) was completed to determine the likelihood of clinical benefit from the nomogram. Enrichment analysis Gene Set Enrichment Analysis (GSEA) enrichment analysis was conducted to identify gene sets associated with specific biological processes, pathways, or functions related to biomarkers. The KEGG gene set (c2.cp.kegg.v7.4.symbols.gmt) retrieved from MsigDB database served as the background gene set, and gene set enrichment analysis (GSEA) was implemented with cluster Profiler-package. Briefly, based on all samples from GSE62646, Spearman correlation coefficients between each biomarker and the remaining genes were computed by the psych-package (v 2.1.6), followed by ranking the genes according to the coefficients (from highest to lowest). Then, the ranked genes were treated as the gene set to be detected, and the GSEA was then completed. The gene set with P < 0.05 and q < 0.25 was considered significant. Immunological characterization To explore the immune profile in AMI, CIBERSORT method was employed to infer the relative proportions of 22 immune-infiltrating cells in each sample for GSE62646 dataset [ 24 , 25 ]. Next, differences in proportions of immune-infiltrating cells between AMI and control groups were compared by Wilcoxon test (P < 0.05), followed by drawing box plot using ggplot2-package to visualize the results. Following this, Spearman correlation analysis was completed between the differential immune cells as well as between the differential immune cells and biomarkers to explore their associations, with thresholds set at |cor| > 0.3 and P < 0.05. Construction of molecular regulatory network To elucidate the molecular regulatory mechanisms of the biomarkers, microRNAs (miRNAs) regulating the biomarkers were predicted by applying the miRDB ( https://mirdb.org/ ) and TargetScan ( www.targetscan.org ) databases. Next, starBase ( http://starbase.sysu.edu.cn/ ) and miRNet ( https://www.mirnet.ca/ ) databases were utilized to retrieve lncRNAs that bound to the miRNAs obtained as described above. In addition, transcription factors (TFs) targeting biomarkers were collected through accessing University of California, Santa Cruz Genome Browser (UCSC, https://genome.ucsc.edu/ ). By integrating the relationship pairs obtained above, the regulatory networks of lncRNA-miRNA-mRNA and TF-biomarker were created via Cytoscape software (v 3.9.0) [ 26 ]. Correlation analysis of m6A regulators with biomarkers The m6A regulators are crucial for post-transcriptional gene expression regulation, which in turn affects a series of physiological and pathological functions. Related research has emphasized that m6A can participate in regulating the development of AMI [ 27 ]. Therefore, this study used the Wilcoxon test to compare expression differences of 13 widely reported m6A regulators between AMI and control groups in GSE62646 dataset (P < 0.05). The 13 m6A regulators were METTL3, METTL14, WTAP, KIAA1429, RAMI15, ZC3H13, YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC, FTO, and ALKBH5. Thereafter, Spearman correlation analysis was carried out for biomarkers and m6A regulators in AMI samples using the psych-package (P < 0.05). Disease and drug prediction and molecular docking AMI is the most common public health cardiovascular disease with a high number of complications. Therefore, diseases significantly associated with biomarkers (top 20) were analyzed using comparative toxicogenomics database (CTD, https://ctdbase.org/ ), and the results were visualized by Cytoscape. In addition, small molecule drugs targeting biomarkers were predicted by thorough analysis of the DGIdb database. Subsequent molecular docking was accomplished to investigate the feasibility of treating AMI. Specifically, it was possible to obtain the 3D structures of small-molecule medications applying PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). Next, Protein Data Bank (PDB) database ( https://www.rcsb.org/ ) was applied to acquire the protein crystal structures that corresponded to the biomarkers. Ultimately, molecular docking procedure was executed by applying CB-Dock2 ( https://cadd.labshare.cn/cb-dock2/index.php ), and docking binding energy was calculated. Docking results were generally considered feasible for docking binding energies less than − 5 kcal/mol. Reverse Transcription quantitative PCR (RT-qPCR) RT-qPCR was performed to detect and analyze the expression levels of specific mRNAs in blood samples. RNAs of 5 pairs of blood sample were collected, including 5 AMI gruops and 5 control gruops. Consistent with the previous data, we selected patients with myocardial infarction for 6 months. According to the corresponding age and gender, healthy people were selected as controls. The agency responsible for ethical approval was designated as Qilu hospital. The participants all completed and signed an informed consent form, and the ethical approval agency was Ethics Committee of Qilu Hospital of Shandong University (approval no. KYLL-2022(ZM)-082). Since patient genetic data are involved, we keep patient-specific information confidential. RNA concentrations were measured by NanoPhotometer N50. Secondly, mRNA was transcribed to synthesize cDNA using SweScript First Strand cDNA synthesis kit (Servicebio, Wuhan, China). For Quantitative RT-PCR analysis, each reaction mixture consisted of 3 µL cDNA, 5 µL 2x Universal Blue SYBR Green qPCR Master Mix (Servicebio), 1 µL forward primer (10 µM), and 1µL reverse primer (10 µM) (Table S4). The mRNA levels were normalized by GAPDH which was selected as an endogenous reference gene. The relative gene expression was calculated by 2-△△Ct, and p-value was calculated by Graphpad Prism 5. Statistical analysis Group comparisons were assessed using the Wilcoxon test. Statistical discrepancies were recognized as P-value below 0.05. In this work, bioinformatic statistical analyses were implemented employing R program (version 4.2.3). Results Identifying of 29 common DEGs associated with mitochondrial dynamics in AMI In the AMI sample for the GSE62646 dataset, there was a marked discrepancy in MD-RGs scores between high- and low-scoring groups divided based on the ssGSEAs algorithm (P < 0.05) (Fig. 1 A). By setting thresholds of P 0.5, 55 DEGs (43 up-regulated and 12 down-regulated in high-scoring groups) between high- and low-scoring groups (Fig. 1 B-C), as well as 157 DEGs (118 up-regulated and 39 down-regulated in AMI samples) were mined between AMI and control samples in GSE62646 dataset (Fig. 1 D-E). Following this, the Venn diagram displayed 29 common DEGs through by overlapping the two sets of DEGs (Fig. 1 F). Revealing the biological functions and PPI of 29 common DEGs These 29 common DEGs were enriched and analyzed, yielding 9 GO entries and 13 KEGG signaling pathways (P < 0.05). With respect to GO, the entries were mainly related to “cellular detoxification”, “detoxification”, “response to toxic substance”, “cellular oxidant detoxification”, “hydrogen peroxide catabolic process”, “peroxidase activity”, and so on (Fig. 2 A). KEGG analysis elucidated that candidate genes were engaged in “Chemical carcinogenesis - reactive oxygen species”, “Glutathione metabolism”, “Drug metabolism - cytochrome P450”, etc. (Fig. 2 B). In addition, the PPI network of the 29 common DEGs, containing 15 points and 17 edges, was illustrated in Fig. 2 C, and it could be noted that H4C6, PRDX1, and NAMPT seemed to have stronger interactions with the remaining genes. Identification of candidate genes associated with AMI These 29 common DEGs were incorporated into two machine learning algorithms to identify feature genes. The LASSO algorithm identified nine feature genes associated with AMI at lambda. min = 0.000299114, namely SNORA24, SNORD15B, CLC, RN7SL368P, SNORD54, COX7B, NAMPT, MGST3, and CCDC97 (Fig. 3 A). Furthermore, Boruta algorithm screened out 19 feature genes based on each feature’s importance, as shown in Fig. 3 B. We utilized a Venn diagram to identify common genes in the two algorithms, yielding a total of eight genes (SNORA24, SNORD15B, RN7SL368P, COX7B, NAMPT, MGST3, CCDC97, and SNORD54), which were recorded as candidate genes for further exploration (Fig. 3 C). Screening and diagnostic value of COX7B and SNORD54 in AMI The expression analyses of candidate genes were conducted in GSE62646 and GSE59867 datasets, which showed that six genes were markedly distinct between AMI and control groups (P < 0.05) (Fig. 3 D-E). Among them, CCDC97 was up-regulated in AMI samples, while the remaining five genes (COX7B, NAMPT, RN7SL368P, SNORD54, and SNORD15B) were down-regulated in AMI samples. What’s more, we could find that the AUC values of two (COX7B and SNORD54) of these six genes in both datasets were greater than 0.7 through the ROC curves (Fig. 3 F-G), implying that, they could better differentiate between AMI and control samples. Therefore, COX7B and SNORD54 were considered as biomarkers associated with mitochondrial dynamics in AMI. In addition, an interaction network between COX7B and 20 highly related genes was generated on the Gene MANIA platform, with 8.01% co-expression, 77.64% physical interactions, 5.37% prediction, 3.63% co-localization, 2.87% genetic interactions, 1.88% pathways, and 0.60% shared protein structural (Fig. 3 H). Unfortunately, the interaction network of SNORD54 was not predicted. Finally, we estimated the expression of 2 biomarkers by RT-qPCR. The expression levels of COX7B and SNORD54 were significantly downregulated in peripheral blood mononuclear cells (PBMCs) of AMI samples compared to control (P < 0.05), consistent with its expression in GSE2240 and GSE115574 (Fig. 3 I). Building an effective nomogram for diagnosing AMI We created a nomogram model in GSE62646 dataset so as to facilitate the clinical prediction of AMI using the selected two biomarkers (Fig. 4 A). In the nomogram, the lower the expression of the two biomarkers and the higher the total score, the higher the risk of developing AMI. The slope of the calibration curve was close to 1 and AUC value of ROC curve was 0.99, both of which indicated that the accuracy of the nomogram in predicting AMI was superior (Fig. 4 B-C). The DCA results revealed that the nomogram yielded a net benefit superior to that of the individual biomarker (Fig. 4 D). In conclusion, nomogram integrated by two biomarkers exhibited high potential clinical value. Elucidating the biological mechanisms of biomarkers In order to elucidate the biological mechanisms of two biomarkers in AMI, we completed GSEA in the GSE62646 dataset. Results indicated that the expression of two biomarkers was linked to “calcium signaling pathway”, “oxidative phosphorylation”, “MAPK signaling pathway”, “Wnt signaling pathway”, “cell cycle”, “Notch signaling pathway”, “cytokine-cytokine receptor interaction”, “ECM-Receptor interaction”, and other pathways (P < 0.05 and q < 0.25) (Table S2-3). The top 5 pathways were selected for visualization based on the order of significance (Fig. 5 A-B). These pathways and biological processes were intertwined and collectively participate in the onset, progression, and subsequent pathology of AMI. A deeper understanding of the role of these pathways could provide a theoretical basis for the discovery of novel therapeutic strategies in the future. Revealing the immune profile in AMI The development of AMI might be accompanied by abnormalities in the proportion and function of immune cells, so we mined the links between two biomarkers and 22 immune cells. The infiltration content of 22 immune cells in each sample of the GSE62646 dataset was calculated by the CIBERSORT algorithm, as demonstrated in Fig. 5 C. After removing the cells that turned out to be 0 in 30% of the samples, the expression of the remaining 16 immune cells in AMI and control samples was compared applying the Wilcoxon test, which showed that five immune cells were markedly distinct between groups (P < 0.05) (Fig. 5 D). Among them, M0 macrophages, naive CD4 T cells, and regulatory T cells (Tregs) were more abundant in AMI, while monocytes and neutrophils showed the opposite trend. In addition, the strongest positive association was presented between neutrophils and monocytes (cor = 0.53), and the highest negative association was presented between monocytes and M0 macrophages (cor = -0.43) (Fig. 5 E). Notably, a marked positive association was observed between SNORD54 and Tregs (cor = 0.307, P < 0.05) (Fig. 5 F). Exploration of potential regulatory mechanisms The mRNA-miRNA-lncRNA relationship network was composed of one mRNA (COX7B), five miRNAs, and 13 lncRNAs (Fig. 6 A). All five miRNAs ((hsa-mir-558, hsa-mir-520f-3p, hsa-mir-556-5p, hsa-mir-607, hsa-mir-192-3p) targeted COX7B; unfortunately, no miRNAs regulating SNORD54 were predicted. In addition, it could be found that lncRNAs (KCNQ1OT1, EBLN3P, LINC01235, etc.) regulation of COX7B was achieved through hsa-mir-520f-3p, as well as NEAT1 could simultaneously regulate COX7B through hsa-mir-520f-3p and hsa-mir-556-5p. In total, 35 TFs were retrieved, including 21 TFs regulating COX7B and 16 TFs regulating SNORD54, and the network of TF-biomarker was presented in Fig. 6 B. It could be observed that two TFs (KDM5A and CEBPG) could regulate two biomarkers simultaneously. Biomarkers were associated with m6A regulators The m6A modifications acted a pivotal role in the regulation of gene expression after AMI and might affect inflammatory responses, apoptosis, and cardiac regenerative repair processes. In this study, six m6A regulators (HNRNPC, KIAA1429, METTL3, WTAP, YTHDC1, and YTHDC2) were found to be significantly different between AMI and control samples in GSE62646 dataset (P < 0.05) (Fig. 7 A), and all of them were under-expressed in AMI samples. Notably, COX7B had the highest marked positive association with WTAP (cor = 0.62, P < 0.001), and SNORD54 had the strongest significant positive association with YTHDC1 (cor = 0.69, P < 0.001) (Fig. 7 B). Binding of biomarkers to potential drugs A comprehensive analysis of CTD database was conducted to retrieve diseases significantly linked to the two biomarkers, which revealed that chromosome-defective, nerve degeneration, and hyperplasia could be predicted by both biomarkers (Fig. 8 A). Additionally, SNORD54 could be found to be significantly associated with cardiovascular diseases such as ventricular dysfunction (left), heart diseases, and myocardial infarction. Further, the prediction of the DGIdb database showed that 25 drugs related to the biomarkers were retrieved, of which the top 3 drugs significantly related to COX7B were 1-Methyl-4-phenyl-2,3-dihydropyridinium, cube root extract, 3'-Azido-3'- deoxythymidine, and the top 3 drugs significantly associated with SNORD54 were chrysin, quercetin, and harmine (Fig. 8 B). The molecular docking results revealed that the binding energies of COX7B with 1-Methyl-4-phenyl-2,3-dihydropyridinium, cube root extract, and 3'-Azido-3'-deoxythymidine were − 8.1 kcal/mol, -7.5 kcal/mol, and − 10.9kcal/mol, accordingly, implying that COX7B had a strong affinity for all three drugs (Fig. 8 C-E). Because SNORD54 had no protein structure, molecular docking was not performed. Discussion In this study, we used the Lasso algorithm and Boruta algorithm to screen the DEGs, and COX7B and SNORD54 were finally identified as biomarkers related to mitochondrial dynamics in AMI by ROC curves, and both of them were lower expressed in AMI samples compared to control samples. GSEA, immune infiltration and drug prediction were further analyzed. These results provide a reference for the development of new therapeutic targets for AMI. COX7B (Cytochrome C Oxidase Subunit 7B) is one of the critical subunits of Complex IV in electron transport chain (ETC). Previous studies have identified COX7B as a critical gene in atherosclerosis by bioinformatics, and with the development of atherosclerosis, vessel wall thickening and lumen narrowing may induce AMI [ 28 ]. On the other hand, in cardiovascular disease, COX7B expression is positively correlated with cardiomyocyte size and may play a compensatory role in up-regulation during the early stages of cardiac hypertrophy, but it decreased in heart failure. COX7B plays an important role in cardiac hypertrophy [ 29 ]. Combining the results of previous studies and this study, the role of COX7B in cardiovascular diseases shows a certain degree of complexity and diversity. The results of the present study showed that COX7B was expressed at a lower level in AMI samples than in controls, which differs from some previous studies that suggested that COX7B might be upregulated in the early stage of cardiac hypertrophy. This discrepancy may be due to a variety of factors, including but not limited to different disease stages, individual differences, sample selection bias, and differences in experimental methods. Nevertheless, the results of the present study highlight the importance of COX7B as a potential biomarker and suggest the need for further exploration of its specific mechanism of action in different stages of cardiovascular disease and its potential use in clinical diagnosis or therapy. SNORD54 (Small Nucleolar RNA, C/D Box 54) is a non-protein-coding small nucleolar RNA that belongs to the C/D box class of small nucleolar RNAs (snoRNAs). These snoRNAs are primarily involved in the 2'-O-methylation modification of ribosomal RNA (rRNA), which in turn affects the maturation and function of rRNA [ 30 ]. Epidemiological evidence suggests that overweight and obesity have been associated with AMI (especially at a younger age) and that ribotoxic stress response underlies metabolic adaptation in obesity and aging [ 31 , 32 ]. SNORD54, as one of the key factors in ribosome biosynthesis, may play an important role in the occurrence and development of AMI. In the present study, we verified SNORD54 expression in AMI by analyzing clinical blood samples, and the results showed that SNORD54 expression level was lower in AMI samples than in controls. This finding suggests that abnormal expression of SNORD54 may be associated with cardiovascular diseases. However, specific to the level of expression, the results of this study may differ from some assumptions or expectations, which may be caused by differences in disease stage, individual differences, sample selection, or other experimental conditions. Nonetheless, the reduced expression level of SNORD54 in AMI still suggests us that it may serve as a novel biomarker for diagnosis, prognostic evaluation, or therapeutic monitoring of AMI. Exploring the clinical relevance of SNORD54 and its potential use in clinical diagnosis or treatment is of great significance for improving the diagnostic accuracy and treatment effect of AMI patients. This study completed the GSEA of two biomarkers in AMI from GSE62646 dataset and found were linked to some pathways associated with AMI, such as Oxidative Phosphorylation and the mitogen-activated protein kinase (MAPK) signaling pathway. Reactive oxygen species (ROS) are recognized as pivotal mediators in the regulatory pathways of endothelial cell (EC) viability and proliferation, exerting significant influence across the spectrum of cardiovascular health and pathology, particularly in the context of ischemic myocardial injury. Reduction in mitochondrial ROS improves oxidative phosphorylation and provides resilience to coronary endothelium in non-reperfused myocardial infarction [ 33 , 34 ]. In addition, Activation of MAPK signaling pathway in cardiomyocytes can promote intracellular oxidative stress and endoplasmic reticulum (ER) stress, thereby inhibit cell proliferation and induce apoptosis. such as Rich alkaloids fraction (RAF) exerts the cardioprotective effect against ischemic injury through inhibiting apoptosis underlying p38 MAPK signaling pathway [ 35 , 36 ]. In this study, we demonstrated that COX7B and SNORD54 had a significant association with oxidative phosphorylation and MAPK signaling pathway. Therefore, we speculate that two biomarkers could affect the occurrence and development of AMI by oxidative phosphorylation pathway or MAPK signaling pathway. It is well accepted that immune system plays a vital role in the pathophysiology of AMI. The CIBERSORT, a deconvolution algorithm, results suggested that there was a decrease in the infiltration of CD8 + T cells, gamma delta (γδ) T cells, and resting mast cells, along with an increase in the infiltration of neutrophils and M0 macrophages in AMI patients [ 37 ]. Through the analysis of the immune profile in AMI, we found that there were significant differences between AMI and control samples - the infiltration level of M0 macrophages, naive CD4 + T cells and Tregs is high in AMI, and a relatively low level of monocytes and neutrophils. The different infiltration of neutrophils may be related to the different stages of the disease, and need to further explore. Furthermore, COX7B was positively correlated with M0 Macrophages, and SNORD54 was positively correlated with Tregs. These two biomarkers may affect AMI by influence the expression of M0 Macrophages and Tregs. These results enrich the understanding of the immune mechanism of AMI. In recent years, LncRNA has been found to be an important regulator of cell growth, differentiation, proliferation, and apoptosis in cardiovascular diseases [ 38 ]. It binds to miRNAs and acts as a miRNA sponge in cells, thus reducing miRNA activity and indirectly upregulating miRNA-related target gene expression. A previous study of PBMCs from early onset MI patients analyzed comprehensive immune cell transcriptomes and observed distinctive anomalies encompassing several lncRNAs. Among the deregulated lncRNAs, nuclear enriched abundant transcript (NEAT1) was the most highly expressed and the only one significantly suppressed in AMI patients [ 39 , 40 ]. Moreover, KCNQ1OT1/miR-26a-5p/ATG12 axis regulated cardiomyocyte autophagy and apoptosis, both in vivo and in vitro [ 41 ]. By predicting the regulatory network, COX7B and SNORD54 might affect the progress of the AMI through NEAT1 or KCNQ1OT1. Recent studies have disclosed a critical role of m6A modification in regulating the homeostasis of metabolic processes and cardiovascular function. Meanwhile, m6A as a candidate of biomarker and therapeutic target for metabolic abnormality and cardiovascular diseases (CVD) is widely concerned [ 42 ]. Through the analysis of AMI and control samples, we found some typical m6A regulators. Existing multi-omics studies have helped to identify many potential biomarkers, but not all of them can be translated into clinical practice to provide diagnostic tools or therapeutic strategies. Biomarkers that are more sensitive and generalizable to disease progression are needed to replace and complement existing diagnostic and therapeutic methods. Of greatest concern, however, COX7B had the highest marked positive association with Wilms' tumor 1-associating protein (WTAP). It has been found that WTAP promoted myocardial I/R injury through promoting ER stress and cell apoptosis by regulating m6A modification of transcription factor 4 (ATF4) mRNA [ 43 ]. Thus, WTAP may be a bridge between COX7B and AMI, which provides new ideas for the translation of COX7B. Myocardium depends heavily on aerobic metabolism; hence, mitochondrial quality control plays a very important role in maintaining cardiac function. When mitochondrial dynamics is out of balance, pathological processes including oxidative stress, inflammation and apoptosis will be initiated [ 44 ]. Restoring blood flow after AMI can not only supply oxygen and nutrition, but also bring certain pathological damage to the heart. Mitochondria also have different roles in different stages of myocardial infarction. For example, autophagy is thought to be cardioprotective during ischemia but detrimental during reperfusion [ 45 , 46 ]. Our study adds to the complement of biomarkers related to MI, which may help to enrich clinical diagnosis and treatment. In summary, the present study has identified COX7B and SNORD54 as biomarkers related to mitochondrial dynamics in AMI. However, although the biomarkers obtained in this study showed a consistent expression trend in both the training and validation sets, with AUC values greater than 0.7, indicating good diagnostic performance, we recognize that the limited sample size is a major limitation of this study. The limited sample size may have led to underpower, and even if a true effect existed, the sample size may have been too small to detect significance. Therefore, a larger sample size is needed in the future to further validate and consolidate our findings. In conclusion, this study provides a rationale for the clinical application of COX7B and SNORD54 as potential biomarkers in AMI. Future studies can further explore their application prospects in the early diagnosis and prognosis evaluation of AMI, and whether they may be candidate molecules for targeted therapy to provide more accurate diagnostic tools or innovative treatment strategies for AMI patients, so as to promote the progress of clinical diagnosis and treatment of AMI. Declarations Funding This study was supported by the Shandong Province Natural Science Foundation (grant number: ZR2021MH405). Disclosure The authors report no conflicts of interest in this work. Authors contributions Ruijian Li conceived the project; Xiaolin Yue designed analysis and wrote the manuscript; Jinlei Wu and Xueyun Shi performed bioinformatics analyses; Youshun Xu, Xiaowei Han performed RT-qPCR experiments. All authors read and approved the final manuscript. Ethics Statement The participants all completed and signed an informed consent form, and the ethical approval agency was Ethics Committee of Qilu Hospital of Shandong University (approval no. KYLL-2022(ZM)-082). When utilizing patient genetic data from public databases, strict adherence to ethical guidelines and legal regulations is imperative to ensure the legality and compliance of data acquisition, processing, and utilization. Data should only be collected from publicly accessible databases that have undergone ethical review and obtained patients' informed consent. Measures such as anonymization and de-identification should be implemented to protect privacy, and access to the data should be strictly limited. Regular audits of data usage should also be conducted. Efforts should be made to strike a balance between scientific research and ethics, ensuring the safe and reasonable use of patient genetic data. References Salari N, et al. 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Int J Biol Sci. 2023;19:3428-3440. Teixeira RB, et al. Reduction in mitochondrial ROS improves oxidative phosphorylation and provides resilience to coronary endothelium in non-reperfused myocardial infarction. Basic Res Cardiol. 2023;118:3. Yu CJ, et al. Circ_0004771 Promotes Hypoxia/Reoxygenation Induced Cardiomyocyte Injury via Activation of Mitogen-Activated Protein Kinase Signaling Pathway. Int Heart J. 2023;64:1125-1132. Ge F, et al. The alkaloids of Corydalis hendersonii Hemsl. contribute to the cardioprotective effect against ischemic injury in mice by attenuating cardiomyocyte apoptosis via p38 MAPK signaling pathway. Chin Med. 2023;18:29. Zheng PF, et al. Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohort. J Transl Med. 2022;20:321. Ni H, et al. A Smooth Muscle Cell-Enriched Long Noncoding RNA Regulates Cell Plasticity and Atherosclerosis by Interacting With Serum Response Factor. Arterioscler Thromb Vasc Biol. 2021;41:2399-2416. Chen Z, et al. Expression level and diagnostic value of exosomal NEAT1/miR-204/MMP-9 in acute ST-segment elevation myocardial infarction. IUBMB Life. 2020;72:2499-2507. Gast M, et al. Long noncoding RNA NEAT1 modulates immune cell functions and is suppressed in early onset myocardial infarction patients. Cardiovasc Res. 2019;115:1886-1906. Li J, et al. LncRNA KCNQ1OT1 as a miR-26a-5p sponge regulates ATG12-mediated cardiomyocyte autophagy and aggravates myocardial infarction. Int J Cardiol. 2021;338:14-23. Zhang B, et al. The critical roles of m6A modification in metabolic abnormality and cardiovascular diseases. Genes Dis. 2020;8:746-758. Wang J, et al. WTAP promotes myocardial ischemia/reperfusion injury by increasing endoplasmic reticulum stress via regulating m6A modification of ATF4 mRNA. Aging (Albany NY). 2021;13:11135-11149. Guo Z, et al. Mitochondrial Stress as a Central Player in the Pathogenesis of Hypoxia-Related Myocardial Dysfunction: New Insights. Int J Med Sci. 2024;21(13):2502-2509. Yang M, et al. Mitophagy and mitochondrial integrity in cardiac ischemia-reperfusion injury. Biochim Biophys Acta Mol Basis Dis. 2019;1865(9):2293-2302. Popov SV, et al. Regulation of autophagy of the heart in ischemia and reperfusion. Apoptosis. 2023;28(1-2):55-80. Supplementary Tables Supplementary tables are not included with this version Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-5727986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396161329,"identity":"3fd1c49b-0a56-40de-adf7-124c07958699","order_by":0,"name":"Xiaolin Yue","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Yue","suffix":""},{"id":396161330,"identity":"00ba54af-881c-41ea-b7bd-f61c4a55a37a","order_by":1,"name":"Jinlei Wu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jinlei","middleName":"","lastName":"Wu","suffix":""},{"id":396161331,"identity":"719ee0f5-25bd-4226-8bd2-cbdaf1b4841b","order_by":2,"name":"Xueyun Shi","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xueyun","middleName":"","lastName":"Shi","suffix":""},{"id":396161332,"identity":"3733d9c8-3d0b-4c8c-bb36-8592832e9c1a","order_by":3,"name":"Youshun Xu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Youshun","middleName":"","lastName":"Xu","suffix":""},{"id":396161333,"identity":"6fea08c0-90e6-4443-a943-e2eb30e03095","order_by":4,"name":"Xiaowei Han","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Han","suffix":""},{"id":396161334,"identity":"f78b823e-882d-4374-86b2-6ab384ecf65d","order_by":5,"name":"Ruijian Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACPmYgkcAjwcDA3thw8EMFA2MDUEACnxY2uBaewwcfS5whRgucJZGWbMDbRowWdt5nEg9kLOTNGXLMJCTn1cluOMB88DYPg10eboexm0kAHWa4s+GMmUThtsPGGw6wJVvzMCQX49bCxgbSwrjhYA/Qlm0HEjcc4DGT5mE4kNhAQIv9hsM8ZhK8c+qAWvi/EaUlccMxNqD3G5hBtrAR0sJsAdSSvOEMMzCQjx02nnmYzdhyjkEyTi38/McYb/7sqbPdcP8hMCpr6mT7jjc/vPGmwg6nFjBg7EHmgSKXwQCfehD4QUjBKBgFo2AUjGgAADmsT9uHCGMHAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ruijian","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-29 01:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5727986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5727986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72841419,"identity":"57e7957d-a450-45a5-a175-0285faaa8af1","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2290987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening for AMI mitochondrial dynamics-related differential genes. (A)\u003c/strong\u003e GSE62646 dataset analysed with ssGSEAs. \u003cstrong\u003e(B-C)\u003c/strong\u003e Volcano and heat maps of differential genes in the high scoring group vs. the scoring group. \u003cstrong\u003e(D-E)\u003c/strong\u003eVolcano and heat maps of differential genes in Volcano and heat maps of differential genes in AMI and control samples in the GSE62646 dataset. \u003cstrong\u003e(F)\u003c/strong\u003eVenn diagram of DEGs in high and low scoring groups and GSE62646 dataset.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/10e54fee01348791b5f45fff.png"},{"id":72841423,"identity":"2af18c14-4bf5-40cc-8c8a-0ddac7c8fc29","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1222637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe biological functions analysis DEGs. (A)\u003c/strong\u003e GO enrichment analyses on the DEGs; \u003cstrong\u003e(B)\u003c/strong\u003eKEGG enrichment analyses on the DEGs; \u003cstrong\u003e(C) \u003c/strong\u003eThe PPI network of the DEGs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/64f50d7c4414bf386b0e5f5d.png"},{"id":72841422,"identity":"db924ddb-4f76-42ab-8bbb-8c0bab8d27ff","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2182661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of candidate genes associated with AMI. (A)\u003c/strong\u003e Genes associated with AMI analyzed with LASSO algorithm; \u003cstrong\u003e(B)\u003c/strong\u003e Genes associated with AMI analyzed with Boruta algorithm; \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram of common genes in the LASSO algorithm and Boruta algorithms; \u003cstrong\u003e(D-E)\u003c/strong\u003e The markedly distinct genes between AMI and control groups in GSE62646 and GSE59867 datasets; \u003cstrong\u003e(F-G)\u003c/strong\u003e The ROC curve of candidate genes associated with AMI; \u003cstrong\u003e(H)\u003c/strong\u003eThe interaction network between COX7B and highly related genes; \u003cstrong\u003e(I)\u003c/strong\u003e Quantitative RT-PCR analysis of COX7B and SNORD54 RNA levels (n=5). **P\u0026lt;0.01 vs Control.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/5ba0717c159310fa1e814cab.png"},{"id":72841426,"identity":"2e86a4c4-35d7-4705-9cad-1b884cf554a5","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":638863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffective nomogram for diagnosing AMI. (A) \u003c/strong\u003eNomogram model in GSE62646 dataset; \u003cstrong\u003e(B-C)\u003c/strong\u003eThe calibration curve and ROC curve shows that the accuracy of the nomogram; \u003cstrong\u003e(D)\u003c/strong\u003eThe Decision Curve Analysis of nomogram.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/dd141a88407a335b4ba7fed7.png"},{"id":72841437,"identity":"a26db4e0-6dc2-4f71-859c-d9459d53c74f","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1400861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological mechanisms of AMI biomarkers. (A-B) \u003c/strong\u003eKEGG enrichment analyses on the two biomarkers in AMI and the top 5 pathways; \u003cstrong\u003e(C) \u003c/strong\u003eCIBERSORT analysis of immune cells in GSE62646 dataset; \u003cstrong\u003e(D)\u003c/strong\u003e Box plots of differential expression of immune cells in AD and controls in the training set; \u003cstrong\u003e(E)\u003c/strong\u003e Heatmap of the correlation between the 5 differential immune cells in the disease samples of the training set; \u003cstrong\u003e(F)\u003c/strong\u003e Correlation between biomarkers and differential immune cells.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/74a3b661ec50d13cc56d0cb2.png"},{"id":72841434,"identity":"b76527c1-620e-4035-a659-0706ce51f5e2","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":670512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential regulatory mechanisms of AMI biomarkers. (A)\u003c/strong\u003e The mRNA-miRNA-lncRNA relationship network of COX7B; \u003cstrong\u003e(B) \u003c/strong\u003eTF Interaction Network with Candidate Genes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/ce552bccad9092a6b514ba5a.png"},{"id":72841430,"identity":"4ef14a82-37fe-4697-b69f-e7c8a34d407f","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1380266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of m6A regulators. (A)\u003c/strong\u003e Box plots of m6A modification-related gene expression in AMI and control samples in the GSE62646 dataset; \u003cstrong\u003e(B) \u003c/strong\u003eHeatmap of correlations between m6A modification-related genes and two AMI biomarkers.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/321526f8515b713c98267c2a.png"},{"id":72841427,"identity":"89244c8d-dc9d-47ec-88f0-236893049dfe","added_by":"auto","created_at":"2025-01-02 18:21:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1484401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological mechanisms of AMI biomarkers. (A)\u003c/strong\u003e Association analysis between two AMI biomarkers and disease;\u003cstrong\u003e (B)\u003c/strong\u003e Drug prediction network for two AMI biomarkers;\u003cstrong\u003e (C-E)\u003c/strong\u003e Molecular docking modelling between COX7B's and 3 drugs.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/6c928970c2d5ccc387326072.png"},{"id":72842872,"identity":"4681d264-5566-416e-83f9-37d5c6bec3ef","added_by":"auto","created_at":"2025-01-02 18:45:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11945403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5727986/v1/4ad59524-8cd6-4d65-91a3-11dd38175f7f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of mitochondrial dynamics-related genes in patients with acute myocardial infarction-a bioinformatics analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myocardial infarction (AMI) is one of the leading causes of mortality and the most common cause of chronic heart failure (HF) worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. AMI, also known as myocardial cell death due to prolonged ischemia, leads to progressive deterioration of cardiac pump function, which might result in HF and potentially fatal arrhythmias. Atherothrombotic coronary artery disease is the most common underlying cause of AMI. When unstable atherosclerotic plaques become rupture or erode, thrombi will be formed which can obstruct the coronary lumen at the site of the plaque or lead to distal coronary embolization [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the case of AMI, early and successful revascularization can effectively prevent loss of contractile myocardial muscle mass, decrease the infarct size and improve clinical outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, reperfusion may paradoxically lead to exacerbated and accelerated injury in the myocardium, referred to as myocardial ischemia-reperfusion (I/R) injury [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous studies have shown that different types of cell death, including apoptosis, necrosis, pyroptosis and autophagy, can occur during I/R injury [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, new treatments are required to protect the myocardium against the detrimental effects of acute IR injury in order to reduce myocardial infarct (MI) size, preserve left ventricular (LV) function and prevent the onset of heart failure (HF) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMitochondrial dynamics is a biological process in order to maintain normal physiological functions, mainly including mitochondrial fusion, fission and autophagy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As one of the most abundant organelles in myocardial cells, mitochondria play an important role in various physiological processes, including myocardial cell proliferation, apoptosis and signal transduction [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Mitochondrial dysfunction is a pivotal pathological basis for myocardial (I/R) injury and it represents a critical therapeutic target for the mitigation of myocardial damage [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the exact pathogenesis and molecular mechanism of mitochondrial dynamics in AMI have not been fully elucidated.\u003c/p\u003e \u003cp\u003eIn AMI, mitochondrial dysfunction occurs due to hypoxia and oxidative stress, leading to energy metabolism disturbances and cell death. Genes such as peroxisome proliferator-activated receptor gamma co-activator 1 alpha (PGC-1α), optic atrophy 1 (Opa1), mitofusin-2 (MFN2) and dynamin-related protein 1 (Drp1) regulate mitochondrial biogenesis, morphological changes, and autophagy in the heart, thereby affecting cardiac function. The increase in mitochondrial fission is accompanied by a decrease in mitochondrial fusion, as evidenced by the upregulation of Drp1 and downregulation of Opa1, both of which lead to cardiac damage [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although existing research has highlighted the importance of mitochondria in AMI, certain aspects remain insufficiently addressed, including the specific gene regulatory mechanisms, the relationship between changes in mitochondrial function and myocardial injury. Moreover, the insufficient sensitivity of existing markers leads to delayed diagnosis or poor therapeutic effect. We will target specific genes for in-depth study of mitochondrial dynamics to provide new insights into the pathophysiology of AMI, which will help develop new therapeutic targets for AMI.\u003c/p\u003e \u003cp\u003eTo explore the potential involvement of mitochondrial dynamics in AMI, this study procured AMI-associated datasets and identified 50 mitochondrial dynamics-related genes (MD-RGs) from publicly available databases. Utilizing bioinformatics approaches, we conducted a screening for candidate biomarkers linked to AMI. Additionally, we performed Gene Set Enrichment Analysis (GSEA) for functional enrichment, alongside assessments of immune cell infiltration and single-sample Gene Set Enrichment Analysis (ssGSEA) of immune functions. This research offers a foundational reference for the identification of novel therapeutic targets in the context of AMI.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData collection\u003c/h2\u003e\n \u003cp\u003eData on AMI-related gene expression matrices were searched by accessing gene expression omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e). We selected patients with MI for 6 months. Specifically, GSE62646 dataset was based on GPL6244 platform, containing blood samples from 28 AMI patients (6 months) and 14 patients with stable coronary artery disease without a history of myocardial infarction (MI). On the other hand, GSE59867 dataset contained blood samples from 83 AMI patients (6 months) and 46 patients with stable coronary artery disease without a history of MI, and the sequencing platform was GPL6244. In the GSE62646 and GSE59867 datasets, blood samples collected on non-first-day-of-admission were excluded to ensure that the selected disease samples were not influenced by treatment. We considered patients with stable coronary artery disease without a history of MI as the control group for this study. Mitochondrial dynamics, which encompasses processes like mitochondrial fission, fusion, and division, is essential for crucial role in cellular energy metabolism and maintenance of homeostasis. To comprehensively analyze these processes, 15 genes closely related to mitochondrial fission and 9 genes associated with mitochondrial fusion were screened using the MitoCarta 3.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/mitocarta\u003c/span\u003e\u003c/span\u003e). Additionally, 28 genes related to mitophagy were retrieved from the Reactome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003c/span\u003e). After merging and eliminating duplicates, a final set of 50 mitochondrial dynamics-related genes (MD-RGs) was identified (Table S1). These genes constitute a valuable resource for in-depth investigations into the roles of mitochondrial dynamics in cellular physiology and pathology.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDifferential expression analysis\u003c/h3\u003e\n\u003cp\u003eBased on the MD-RGs, single-sample gene set enrichment analysis (ssGSEA) method was utilized through GSVA-package (v 1.42.0) to calculate MD-RGs scores for AMI samples in the GSE62646 dataset, followed by dividing AMI samples into high- and low-scoring groups through the median of MD-RGs score [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSubsequent differential expression analysis was undertaken via limma package (v 3.54.1) with the thresholds of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 Fold Change (FC)| \u0026gt;0.5, aiming to identify differentially expressed genes (DEGs) between AMI and control samples as well as between high- and low-scoring groups [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Volcano maps and heat maps were created with the ggplot2-package (v 3.3.6)[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] and heatmap-package (v 1.0.12)[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], respectively, to demonstrate DEGs expression patterns. Next, overlapping analysis of the two sets of DEGs was completed to determine the common DEGs.\u003c/p\u003e\n\u003ch3\u003eFunctional annotation and protein-protein interactions (PPI) analyses\u003c/h3\u003e\n\u003cp\u003eTo reveal the biological functions that common DEGs were involved in the development of AMI, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were undertaken via cluster Profiler-package (v 4.6.2)[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] and the org.Hs.eg.db-package (v 3.16.0)[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, a PPI network between common DEGs was created through STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e) (confidence level\u0026thinsp;\u0026gt;\u0026thinsp;0.15) to elucidate the association between them at the protein level.\u003c/p\u003e\n\u003ch3\u003eMachine learning algorithms\u003c/h3\u003e\n\u003cp\u003eMachine learning, a branch of artificial intelligence, focuses on empowering computer systems to learn from data and improve performance through the use of statistical techniques, with a wide and diverse range of applications in the biomedical field [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, we utilized two machine learning algorithms to identify feature genes closely associated with AMI from common DEGs in GSE62646 dataset. At first, the glmnet-package (v 4.1-4)[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] was utilized to perform least absolute shrinkage and selection operator (LASSO) analysis, with the parameter \u0026apos;family\u0026apos; set to binomial and \u0026apos;lambda\u0026apos; set to 0. This approach was considered as the optimal method for selecting feature genes. Secondly, Boruta algorithm, implemented through the Boruta-package (v 8.0.0), was based on the idea of random forests, where the importance of each feature was evaluated by comparing it with randomly generated \u0026ldquo;shadow\u0026rdquo; features [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. During the screening process using the Boruta algorithm, the program defined DEGs, categorizing them into accepted and rejected groups. The accepted genes represented the feature genes selected by the Boruta model. After that, the intersection of two sets of feature genes was extracted with the help of Venndiagram-package (v 1.7.3), which were recorded as candidate genes [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eExpression analysis and receiver operating characteristic (ROC) analysis\u003c/h3\u003e\n\u003cp\u003eIn GSE62646 and GSE59867 datasets, the expression trends of candidate genes were further compared between AMI and control samples utilizing the Wilcoxon test. Our focus was on genes exhibiting stable expression, characterized by consistent trends across both datasets and significant group differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further, to assess the diagnostic accuracy of the model, ROC curves were generated based on gene expression in both datasets with the use of the ROC-package (v 1.18.0), and genes with the area under curve (AUC) greater than 0.7 were defined as biomarkers associated with mitochondrial dynamics in AMI [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. In general, the AUC greater than 0.7 indicated that the gene was more effective in diagnosing the disease. Subsequently, the biomarkers were input into Gene MANIA online website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org/\u003c/span\u003e\u003c/span\u003e) to generate a network, aiming to explore potential mechanisms of biomarkers in AMI.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCreation and assessment of nomogram\u003c/h2\u003e\n \u003cp\u003eTo predict the occurrence of AMI from perspective of the biomarkers as a whole, we integrated the expression of the biomarkers and subsequently constructed a nomogram in GSE62646 dataset applying the rms-package (v 6.5-0) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, calibration curve and ROC curve were created to estimate the accuracy of nomogram predictions, as well as decision curve analysis (DCA) was completed to determine the likelihood of clinical benefit from the nomogram.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEnrichment analysis\u003c/h3\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) enrichment analysis was conducted to identify gene sets associated with specific biological processes, pathways, or functions related to biomarkers. The KEGG gene set (c2.cp.kegg.v7.4.symbols.gmt) retrieved from MsigDB database served as the background gene set, and gene set enrichment analysis (GSEA) was implemented with cluster Profiler-package. Briefly, based on all samples from GSE62646, Spearman correlation coefficients between each biomarker and the remaining genes were computed by the psych-package (v 2.1.6), followed by ranking the genes according to the coefficients (from highest to lowest). Then, the ranked genes were treated as the gene set to be detected, and the GSEA was then completed. The gene set with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q\u0026thinsp;\u0026lt;\u0026thinsp;0.25 was considered significant.\u003c/p\u003e\n\u003ch3\u003eImmunological characterization\u003c/h3\u003e\n\u003cp\u003eTo explore the immune profile in AMI, CIBERSORT method was employed to infer the relative proportions of 22 immune-infiltrating cells in each sample for GSE62646 dataset [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Next, differences in proportions of immune-infiltrating cells between AMI and control groups were compared by Wilcoxon test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by drawing box plot using ggplot2-package to visualize the results. Following this, Spearman correlation analysis was completed between the differential immune cells as well as between the differential immune cells and biomarkers to explore their associations, with thresholds set at |cor| \u0026gt; 0.3 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of molecular regulatory network\u003c/h2\u003e\n \u003cp\u003eTo elucidate the molecular regulatory mechanisms of the biomarkers, microRNAs (miRNAs) regulating the biomarkers were predicted by applying the miRDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/\u003c/span\u003e\u003c/span\u003e) and TargetScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.targetscan.org\u003c/span\u003e\u003c/span\u003e) databases. Next, starBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/\u003c/span\u003e\u003c/span\u003e) and miRNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003c/span\u003e) databases were utilized to retrieve lncRNAs that bound to the miRNAs obtained as described above. In addition, transcription factors (TFs) targeting biomarkers were collected through accessing University of California, Santa Cruz Genome Browser (UCSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/\u003c/span\u003e\u003c/span\u003e). By integrating the relationship pairs obtained above, the regulatory networks of lncRNA-miRNA-mRNA and TF-biomarker were created via Cytoscape software (v 3.9.0) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation analysis of m6A regulators with biomarkers\u003c/h2\u003e\n \u003cp\u003eThe m6A regulators are crucial for post-transcriptional gene expression regulation, which in turn affects a series of physiological and pathological functions. Related research has emphasized that m6A can participate in regulating the development of AMI [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, this study used the Wilcoxon test to compare expression differences of 13 widely reported m6A regulators between AMI and control groups in GSE62646 dataset (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The 13 m6A regulators were METTL3, METTL14, WTAP, KIAA1429, RAMI15, ZC3H13, YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC, FTO, and ALKBH5. Thereafter, Spearman correlation analysis was carried out for biomarkers and m6A regulators in AMI samples using the psych-package (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eDisease and drug prediction and molecular docking\u003c/h2\u003e\n \u003cp\u003eAMI is the most common public health cardiovascular disease with a high number of complications. Therefore, diseases significantly associated with biomarkers (top 20) were analyzed using comparative toxicogenomics database (CTD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.org/\u003c/span\u003e\u003c/span\u003e), and the results were visualized by Cytoscape. In addition, small molecule drugs targeting biomarkers were predicted by thorough analysis of the DGIdb database. Subsequent molecular docking was accomplished to investigate the feasibility of treating AMI. Specifically, it was possible to obtain the 3D structures of small-molecule medications applying PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003c/span\u003e). Next, Protein Data Bank (PDB) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003c/span\u003e) was applied to acquire the protein crystal structures that corresponded to the biomarkers. Ultimately, molecular docking procedure was executed by applying CB-Dock2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2/index.php\u003c/span\u003e\u003c/span\u003e), and docking binding energy was calculated. Docking results were generally considered feasible for docking binding energies less than \u0026minus;\u0026thinsp;5 kcal/mol.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eReverse Transcription quantitative PCR (RT-qPCR)\u003c/h2\u003e\n \u003cp\u003eRT-qPCR was performed to detect and analyze the expression levels of specific mRNAs in blood samples. RNAs of 5 pairs of blood sample were collected, including 5 AMI gruops and 5 control gruops. Consistent with the previous data, we selected patients with myocardial infarction for 6 months. According to the corresponding age and gender, healthy people were selected as controls. The agency responsible for ethical approval was designated as Qilu hospital. The participants all completed and signed an informed consent form, and the ethical approval agency was Ethics Committee of Qilu Hospital of Shandong University (approval no. KYLL-2022(ZM)-082). Since patient genetic data are involved, we keep patient-specific information confidential. RNA concentrations were measured by NanoPhotometer N50. Secondly, mRNA was transcribed to synthesize cDNA using SweScript First Strand cDNA synthesis kit (Servicebio, Wuhan, China). For Quantitative RT-PCR analysis, each reaction mixture consisted of 3 \u0026micro;L cDNA, 5 \u0026micro;L 2x Universal Blue SYBR Green qPCR Master Mix (Servicebio), 1 \u0026micro;L forward primer (10 \u0026micro;M), and 1\u0026micro;L reverse primer (10 \u0026micro;M) (Table S4). The mRNA levels were normalized by GAPDH which was selected as an endogenous reference gene. The relative gene expression was calculated by 2-△△Ct, and p-value was calculated by Graphpad Prism 5.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eGroup comparisons were assessed using the Wilcoxon test. Statistical discrepancies were recognized as P-value below 0.05. In this work, bioinformatic statistical analyses were implemented employing R program (version 4.2.3).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying of 29 common DEGs associated with mitochondrial dynamics in AMI\u003c/h2\u003e \u003cp\u003eIn the AMI sample for the GSE62646 dataset, there was a marked discrepancy in MD-RGs scores between high- and low-scoring groups divided based on the ssGSEAs algorithm (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). By setting thresholds of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt;0.5, 55 DEGs (43 up-regulated and 12 down-regulated in high-scoring groups) between high- and low-scoring groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C), as well as 157 DEGs (118 up-regulated and 39 down-regulated in AMI samples) were mined between AMI and control samples in GSE62646 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E). Following this, the Venn diagram displayed 29 common DEGs through by overlapping the two sets of DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRevealing the biological functions and PPI of 29 common DEGs\u003c/h2\u003e \u003cp\u003eThese 29 common DEGs were enriched and analyzed, yielding 9 GO entries and 13 KEGG signaling pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). With respect to GO, the entries were mainly related to \u0026ldquo;cellular detoxification\u0026rdquo;, \u0026ldquo;detoxification\u0026rdquo;, \u0026ldquo;response to toxic substance\u0026rdquo;, \u0026ldquo;cellular oxidant detoxification\u0026rdquo;, \u0026ldquo;hydrogen peroxide catabolic process\u0026rdquo;, \u0026ldquo;peroxidase activity\u0026rdquo;, and so on (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). KEGG analysis elucidated that candidate genes were engaged in \u0026ldquo;Chemical carcinogenesis - reactive oxygen species\u0026rdquo;, \u0026ldquo;Glutathione metabolism\u0026rdquo;, \u0026ldquo;Drug metabolism - cytochrome P450\u0026rdquo;, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the PPI network of the 29 common DEGs, containing 15 points and 17 edges, was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, and it could be noted that H4C6, PRDX1, and NAMPT seemed to have stronger interactions with the remaining genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of candidate genes associated with AMI\u003c/h2\u003e \u003cp\u003eThese 29 common DEGs were incorporated into two machine learning algorithms to identify feature genes. The LASSO algorithm identified nine feature genes associated with AMI at lambda. min\u0026thinsp;=\u0026thinsp;0.000299114, namely SNORA24, SNORD15B, CLC, RN7SL368P, SNORD54, COX7B, NAMPT, MGST3, and CCDC97 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, Boruta algorithm screened out 19 feature genes based on each feature\u0026rsquo;s importance, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. We utilized a Venn diagram to identify common genes in the two algorithms, yielding a total of eight genes (SNORA24, SNORD15B, RN7SL368P, COX7B, NAMPT, MGST3, CCDC97, and SNORD54), which were recorded as candidate genes for further exploration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eScreening and diagnostic value of COX7B and SNORD54 in AMI\u003c/h2\u003e \u003cp\u003eThe expression analyses of candidate genes were conducted in GSE62646 and GSE59867 datasets, which showed that six genes were markedly distinct between AMI and control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). Among them, CCDC97 was up-regulated in AMI samples, while the remaining five genes (COX7B, NAMPT, RN7SL368P, SNORD54, and SNORD15B) were down-regulated in AMI samples. What\u0026rsquo;s more, we could find that the AUC values of two (COX7B and SNORD54) of these six genes in both datasets were greater than 0.7 through the ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G), implying that, they could better differentiate between AMI and control samples. Therefore, COX7B and SNORD54 were considered as biomarkers associated with mitochondrial dynamics in AMI.\u003c/p\u003e \u003cp\u003eIn addition, an interaction network between COX7B and 20 highly related genes was generated on the Gene MANIA platform, with 8.01% co-expression, 77.64% physical interactions, 5.37% prediction, 3.63% co-localization, 2.87% genetic interactions, 1.88% pathways, and 0.60% shared protein structural (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Unfortunately, the interaction network of SNORD54 was not predicted. Finally, we estimated the expression of 2 biomarkers by RT-qPCR. The expression levels of COX7B and SNORD54 were significantly downregulated in peripheral blood mononuclear cells (PBMCs) of AMI samples compared to control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with its expression in GSE2240 and GSE115574 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBuilding an effective nomogram for diagnosing AMI\u003c/h2\u003e \u003cp\u003eWe created a nomogram model in GSE62646 dataset so as to facilitate the clinical prediction of AMI using the selected two biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the nomogram, the lower the expression of the two biomarkers and the higher the total score, the higher the risk of developing AMI. The slope of the calibration curve was close to 1 and AUC value of ROC curve was 0.99, both of which indicated that the accuracy of the nomogram in predicting AMI was superior (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). The DCA results revealed that the nomogram yielded a net benefit superior to that of the individual biomarker (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In conclusion, nomogram integrated by two biomarkers exhibited high potential clinical value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eElucidating the biological mechanisms of biomarkers\u003c/h2\u003e \u003cp\u003eIn order to elucidate the biological mechanisms of two biomarkers in AMI, we completed GSEA in the GSE62646 dataset. Results indicated that the expression of two biomarkers was linked to \u0026ldquo;calcium signaling pathway\u0026rdquo;, \u0026ldquo;oxidative phosphorylation\u0026rdquo;, \u0026ldquo;MAPK signaling pathway\u0026rdquo;, \u0026ldquo;Wnt signaling pathway\u0026rdquo;, \u0026ldquo;cell cycle\u0026rdquo;, \u0026ldquo;Notch signaling pathway\u0026rdquo;, \u0026ldquo;cytokine-cytokine receptor interaction\u0026rdquo;, \u0026ldquo;ECM-Receptor interaction\u0026rdquo;, and other pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q\u0026thinsp;\u0026lt;\u0026thinsp;0.25) (Table S2-3). The top 5 pathways were selected for visualization based on the order of significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). These pathways and biological processes were intertwined and collectively participate in the onset, progression, and subsequent pathology of AMI. A deeper understanding of the role of these pathways could provide a theoretical basis for the discovery of novel therapeutic strategies in the future.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRevealing the immune profile in AMI\u003c/h2\u003e \u003cp\u003eThe development of AMI might be accompanied by abnormalities in the proportion and function of immune cells, so we mined the links between two biomarkers and 22 immune cells. The infiltration content of 22 immune cells in each sample of the GSE62646 dataset was calculated by the CIBERSORT algorithm, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. After removing the cells that turned out to be 0 in 30% of the samples, the expression of the remaining 16 immune cells in AMI and control samples was compared applying the Wilcoxon test, which showed that five immune cells were markedly distinct between groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Among them, M0 macrophages, naive CD4 T cells, and regulatory T cells (Tregs) were more abundant in AMI, while monocytes and neutrophils showed the opposite trend. In addition, the strongest positive association was presented between neutrophils and monocytes (cor\u0026thinsp;=\u0026thinsp;0.53), and the highest negative association was presented between monocytes and M0 macrophages (cor = -0.43) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Notably, a marked positive association was observed between SNORD54 and Tregs (cor\u0026thinsp;=\u0026thinsp;0.307, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eExploration of potential regulatory mechanisms\u003c/h2\u003e \u003cp\u003eThe mRNA-miRNA-lncRNA relationship network was composed of one mRNA (COX7B), five miRNAs, and 13 lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). All five miRNAs ((hsa-mir-558, hsa-mir-520f-3p, hsa-mir-556-5p, hsa-mir-607, hsa-mir-192-3p) targeted COX7B; unfortunately, no miRNAs regulating SNORD54 were predicted. In addition, it could be found that lncRNAs (KCNQ1OT1, EBLN3P, LINC01235, etc.) regulation of COX7B was achieved through hsa-mir-520f-3p, as well as NEAT1 could simultaneously regulate COX7B through hsa-mir-520f-3p and hsa-mir-556-5p.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn total, 35 TFs were retrieved, including 21 TFs regulating COX7B and 16 TFs regulating SNORD54, and the network of TF-biomarker was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB. It could be observed that two TFs (KDM5A and CEBPG) could regulate two biomarkers simultaneously.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eBiomarkers were associated with m6A regulators\u003c/h2\u003e \u003cp\u003eThe m6A modifications acted a pivotal role in the regulation of gene expression after AMI and might affect inflammatory responses, apoptosis, and cardiac regenerative repair processes. In this study, six m6A regulators (HNRNPC, KIAA1429, METTL3, WTAP, YTHDC1, and YTHDC2) were found to be significantly different between AMI and control samples in GSE62646 dataset (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), and all of them were under-expressed in AMI samples. Notably, COX7B had the highest marked positive association with WTAP (cor\u0026thinsp;=\u0026thinsp;0.62, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SNORD54 had the strongest significant positive association with YTHDC1 (cor\u0026thinsp;=\u0026thinsp;0.69, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eBinding of biomarkers to potential drugs\u003c/h2\u003e \u003cp\u003eA comprehensive analysis of CTD database was conducted to retrieve diseases significantly linked to the two biomarkers, which revealed that chromosome-defective, nerve degeneration, and hyperplasia could be predicted by both biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Additionally, SNORD54 could be found to be significantly associated with cardiovascular diseases such as ventricular dysfunction (left), heart diseases, and myocardial infarction. Further, the prediction of the DGIdb database showed that 25 drugs related to the biomarkers were retrieved, of which the top 3 drugs significantly related to COX7B were 1-Methyl-4-phenyl-2,3-dihydropyridinium, cube root extract, 3'-Azido-3'- deoxythymidine, and the top 3 drugs significantly associated with SNORD54 were chrysin, quercetin, and harmine (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The molecular docking results revealed that the binding energies of COX7B with 1-Methyl-4-phenyl-2,3-dihydropyridinium, cube root extract, and 3'-Azido-3'-deoxythymidine were \u0026minus;\u0026thinsp;8.1 kcal/mol, -7.5 kcal/mol, and \u0026minus;\u0026thinsp;10.9kcal/mol, accordingly, implying that COX7B had a strong affinity for all three drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-E). Because SNORD54 had no protein structure, molecular docking was not performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used the Lasso algorithm and Boruta algorithm to screen the DEGs, and COX7B and SNORD54 were finally identified as biomarkers related to mitochondrial dynamics in AMI by ROC curves, and both of them were lower expressed in AMI samples compared to control samples. GSEA, immune infiltration and drug prediction were further analyzed. These results provide a reference for the development of new therapeutic targets for AMI.\u003c/p\u003e \u003cp\u003eCOX7B (Cytochrome C Oxidase Subunit 7B) is one of the critical subunits of Complex IV in electron transport chain (ETC). Previous studies have identified COX7B as a critical gene in atherosclerosis by bioinformatics, and with the development of atherosclerosis, vessel wall thickening and lumen narrowing may induce AMI [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, in cardiovascular disease, COX7B expression is positively correlated with cardiomyocyte size and may play a compensatory role in up-regulation during the early stages of cardiac hypertrophy, but it decreased in heart failure. COX7B plays an important role in cardiac hypertrophy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Combining the results of previous studies and this study, the role of COX7B in cardiovascular diseases shows a certain degree of complexity and diversity. The results of the present study showed that COX7B was expressed at a lower level in AMI samples than in controls, which differs from some previous studies that suggested that COX7B might be upregulated in the early stage of cardiac hypertrophy. This discrepancy may be due to a variety of factors, including but not limited to different disease stages, individual differences, sample selection bias, and differences in experimental methods. Nevertheless, the results of the present study highlight the importance of COX7B as a potential biomarker and suggest the need for further exploration of its specific mechanism of action in different stages of cardiovascular disease and its potential use in clinical diagnosis or therapy. SNORD54 (Small Nucleolar RNA, C/D Box 54) is a non-protein-coding small nucleolar RNA that belongs to the C/D box class of small nucleolar RNAs (snoRNAs). These snoRNAs are primarily involved in the 2'-O-methylation modification of ribosomal RNA (rRNA), which in turn affects the maturation and function of rRNA [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Epidemiological evidence suggests that overweight and obesity have been associated with AMI (especially at a younger age) and that ribotoxic stress response underlies metabolic adaptation in obesity and aging [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. SNORD54, as one of the key factors in ribosome biosynthesis, may play an important role in the occurrence and development of AMI. In the present study, we verified SNORD54 expression in AMI by analyzing clinical blood samples, and the results showed that SNORD54 expression level was lower in AMI samples than in controls. This finding suggests that abnormal expression of SNORD54 may be associated with cardiovascular diseases. However, specific to the level of expression, the results of this study may differ from some assumptions or expectations, which may be caused by differences in disease stage, individual differences, sample selection, or other experimental conditions. Nonetheless, the reduced expression level of SNORD54 in AMI still suggests us that it may serve as a novel biomarker for diagnosis, prognostic evaluation, or therapeutic monitoring of AMI. Exploring the clinical relevance of SNORD54 and its potential use in clinical diagnosis or treatment is of great significance for improving the diagnostic accuracy and treatment effect of AMI patients.\u003c/p\u003e \u003cp\u003eThis study completed the GSEA of two biomarkers in AMI from GSE62646 dataset and found were linked to some pathways associated with AMI, such as Oxidative Phosphorylation and the mitogen-activated protein kinase (MAPK) signaling pathway. Reactive oxygen species (ROS) are recognized as pivotal mediators in the regulatory pathways of endothelial cell (EC) viability and proliferation, exerting significant influence across the spectrum of cardiovascular health and pathology, particularly in the context of ischemic myocardial injury. Reduction in mitochondrial ROS improves oxidative phosphorylation and provides resilience to coronary endothelium in non-reperfused myocardial infarction [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition, Activation of MAPK signaling pathway in cardiomyocytes can promote intracellular oxidative stress and endoplasmic reticulum (ER) stress, thereby inhibit cell proliferation and induce apoptosis. such as Rich alkaloids fraction (RAF) exerts the cardioprotective effect against ischemic injury through inhibiting apoptosis underlying p38 MAPK signaling pathway [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In this study, we demonstrated that COX7B and SNORD54 had a significant association with oxidative phosphorylation and MAPK signaling pathway. Therefore, we speculate that two biomarkers could affect the occurrence and development of AMI by oxidative phosphorylation pathway or MAPK signaling pathway.\u003c/p\u003e \u003cp\u003eIt is well accepted that immune system plays a vital role in the pathophysiology of AMI. The CIBERSORT, a deconvolution algorithm, results suggested that there was a decrease in the infiltration of CD8\u003csup\u003e+\u003c/sup\u003e T cells, gamma delta (γδ) T cells, and resting mast cells, along with an increase in the infiltration of neutrophils and M0 macrophages in AMI patients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Through the analysis of the immune profile in AMI, we found that there were significant differences between AMI and control samples - the infiltration level of M0 macrophages, naive CD4\u003csup\u003e+\u003c/sup\u003e T cells and Tregs is high in AMI, and a relatively low level of monocytes and neutrophils. The different infiltration of neutrophils may be related to the different stages of the disease, and need to further explore. Furthermore, COX7B was positively correlated with M0 Macrophages, and SNORD54 was positively correlated with Tregs. These two biomarkers may affect AMI by influence the expression of M0 Macrophages and Tregs. These results enrich the understanding of the immune mechanism of AMI.\u003c/p\u003e \u003cp\u003eIn recent years, LncRNA has been found to be an important regulator of cell growth, differentiation, proliferation, and apoptosis in cardiovascular diseases [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It binds to miRNAs and acts as a miRNA sponge in cells, thus reducing miRNA activity and indirectly upregulating miRNA-related target gene expression. A previous study of PBMCs from early onset MI patients analyzed comprehensive immune cell transcriptomes and observed distinctive anomalies encompassing several lncRNAs. Among the deregulated lncRNAs, nuclear enriched abundant transcript (NEAT1) was the most highly expressed and the only one significantly suppressed in AMI patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, KCNQ1OT1/miR-26a-5p/ATG12 axis regulated cardiomyocyte autophagy and apoptosis, both in vivo and in vitro [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. By predicting the regulatory network, COX7B and SNORD54 might affect the progress of the AMI through NEAT1 or KCNQ1OT1.\u003c/p\u003e \u003cp\u003eRecent studies have disclosed a critical role of m6A modification in regulating the homeostasis of metabolic processes and cardiovascular function. Meanwhile, m6A as a candidate of biomarker and therapeutic target for metabolic abnormality and cardiovascular diseases (CVD) is widely concerned [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Through the analysis of AMI and control samples, we found some typical m6A regulators. Existing multi-omics studies have helped to identify many potential biomarkers, but not all of them can be translated into clinical practice to provide diagnostic tools or therapeutic strategies. Biomarkers that are more sensitive and generalizable to disease progression are needed to replace and complement existing diagnostic and therapeutic methods. Of greatest concern, however, COX7B had the highest marked positive association with Wilms' tumor 1-associating protein (WTAP). It has been found that WTAP promoted myocardial I/R injury through promoting ER stress and cell apoptosis by regulating m6A modification of transcription factor 4 (ATF4) mRNA [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Thus, WTAP may be a bridge between COX7B and AMI, which provides new ideas for the translation of COX7B.\u003c/p\u003e \u003cp\u003eMyocardium depends heavily on aerobic metabolism; hence, mitochondrial quality control plays a very important role in maintaining cardiac function. When mitochondrial dynamics is out of balance, pathological processes including oxidative stress, inflammation and apoptosis will be initiated [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Restoring blood flow after AMI can not only supply oxygen and nutrition, but also bring certain pathological damage to the heart. Mitochondria also have different roles in different stages of myocardial infarction. For example, autophagy is thought to be cardioprotective during ischemia but detrimental during reperfusion [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our study adds to the complement of biomarkers related to MI, which may help to enrich clinical diagnosis and treatment.\u003c/p\u003e \u003cp\u003eIn summary, the present study has identified COX7B and SNORD54 as biomarkers related to mitochondrial dynamics in AMI. However, although the biomarkers obtained in this study showed a consistent expression trend in both the training and validation sets, with AUC values greater than 0.7, indicating good diagnostic performance, we recognize that the limited sample size is a major limitation of this study. The limited sample size may have led to underpower, and even if a true effect existed, the sample size may have been too small to detect significance. Therefore, a larger sample size is needed in the future to further validate and consolidate our findings. In conclusion, this study provides a rationale for the clinical application of COX7B and SNORD54 as potential biomarkers in AMI. Future studies can further explore their application prospects in the early diagnosis and prognosis evaluation of AMI, and whether they may be candidate molecules for targeted therapy to provide more accurate diagnostic tools or innovative treatment strategies for AMI patients, so as to promote the progress of clinical diagnosis and treatment of AMI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Shandong Province Natural Science Foundation (grant number: ZR2021MH405).\u003c/p\u003e\n\u003cp\u003eDisclosure\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003eAuthors contributions\u003c/p\u003e\n\u003cp\u003eRuijian Li conceived the project; Xiaolin Yue designed analysis and wrote the manuscript; Jinlei Wu and Xueyun Shi performed bioinformatics analyses;\u0026nbsp;Youshun Xu, Xiaowei Han performed\u0026nbsp;RT-qPCR\u0026nbsp;experiments. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics Statement\u003c/p\u003e\n\u003cp\u003eThe participants all completed and signed an informed consent form, and the ethical approval agency was Ethics Committee of Qilu Hospital of Shandong University (approval no. KYLL-2022(ZM)-082). When utilizing patient genetic data from public databases, strict adherence to ethical guidelines and legal regulations is imperative to ensure the legality and compliance of data acquisition, processing, and utilization. Data should only be collected from publicly accessible databases that have undergone ethical review and obtained patients\u0026apos; informed consent. Measures such as anonymization and de-identification should be implemented to protect privacy, and access to the data should be strictly limited. Regular audits of data usage should also be conducted. Efforts should be made to strike a balance between scientific research and ethics, ensuring the safe and reasonable use of patient genetic data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSalari N, et al. The global prevalence of myocardial infarction: a systematic review and meta-analysis. \u003cem\u003eBMC Cardiovasc Disord\u003c/em\u003e. 2023; 23:206.\u003c/li\u003e\n \u003cli\u003evan der Schoot GGF, et al. Acute myocardial infarction in adolescents: reappraisal of underlying mechanisms. Neth Heart J. 2020 Jun;28(6):301-308.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSaito Y, et al. Treatment strategies of acute myocardial infarction: updates on revascularization, pharmacological therapy, and beyond. J Cardiol. 2023;81(2):168-178.\u003c/li\u003e\n \u003cli\u003eWelt FGP, et al. Reperfusion Injury in Patients With Acute Myocardial Infarction: JACC Scientific Statement. 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Apoptosis. 2023;28(1-2):55-80. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary tables are not included with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute myocardial infarction, Mitochondrial dynamics, Biomarkers, Machine learning, Regulatory network","lastPublishedDoi":"10.21203/rs.3.rs-5727986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5727986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent studies highlight the link between cardiovascular disease and mitochondrial dynamics. This study sought biomarkers of mitochondrial dynamics in acute myocardial infarction (AMI) to guide more precise clinical management. AMI-related datasets (GSE62646 and GSE59867) and 50 mitochondrial dynamics-related genes (MD-RGs) were derived from public databases. Firstly, based on MD-RGs, AMI samples in GSE62646 were classified into high- and low-scoring groups by single-sample gene set enrichment analysis. The differentially expressed genes (DEGs) were incorporated into machine learning algorithms. Subsequent expression level and receiver operating characteristic (ROC) analyses identified biomarkers. Furthermore, the relationship between biomarkers and AMI was analyzed by enrichment analysis, immune infiltration analysis, correlation analysis of m6A regulators. Finally, biomarker expression was verified by reverse transcription quantitative PCR (RT-qPCR). In this study, COX7B and SNORD54 were identified as biomarkers associated with mitochondrial dynamics in AMI. ROC curves showed that two biomarkers could better differentiate between AMI and control samples, and subsequent nomogram created by integrating two biomarkers were highly accurate in predicting AMI. Furthermore, enrichment analysis revealed that co-enrich pathways for COX7B and SNORD54 included \u0026ldquo;oxidative phosphorylation\u0026rdquo; and \u0026ldquo;Notch signaling pathway\u0026rdquo;. Notably, six m6A regulators (HNRNPC, KIAA1429, METTL3, WTAP, YTHDC1, and YTHDC2) were found to be significantly under-expressed in AMI samples. The RT-PCR demonstrated that the expression levels of COX7B and SNORD54 were significantly downregulated in AMI samples compared to controls. The study recognized COX7B and SNORD54 as biomarkers associated with mitochondrial dynamics in AMI, presenting potential clinical applications that could advance the understanding of AMI management.\u003c/p\u003e","manuscriptTitle":"Identification and validation of mitochondrial dynamics-related genes in patients with acute myocardial infarction-a bioinformatics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 18:20:56","doi":"10.21203/rs.3.rs-5727986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecf091b7-4fb5-4ed4-a510-cc8dec9213fd","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T18:20:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-02 18:20:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5727986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5727986","identity":"rs-5727986","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}