Research on identifying key genes and mechanisms related to lymphangiogenesis in acute myocardial infarction via bioinformatics screening and experimental verification

Research on identifying key genes and mechanisms related to lymphangiogenesis in acute myocardial infarction via bioinformatics screening and experimental verification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on identifying key genes and mechanisms related to lymphangiogenesis in acute myocardial infarction via bioinformatics screening and experimental verification Lihong Zhang, Jingjing Zhang, Yinnan Qian, Xuanping Zhang, Yi Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7844178/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 Background In acute myocardial infarction (AMI), remodeling of the myocardial lymphatic system is crucial for infarct repair and inflammation control. This study used bioinformatics to identify genes related to lymphangiogenesis in AMI, hoping to elucidate the mechanisms of AMI and develop new targeted treatments. Methods GSE66360, GSE48060, and lymphangiogenesis-related genes ( LRGs ) were obtained from databases and the literature. Key genes associated with lymphangiogenesis were identified through machine learning, receiver operating characteristic (ROC) curve analysis, and expression verification. Gene set enrichment analysis (GSEA), immune infiltration analysis, and drug prediction were subsequently carried out. Finally, experimental verification of key gene expression was performed in clinical samples. Results Three PIM3, BMX, and ID1 signature genes were obtained by machine learning, and their regions under the curve showed significant differences in expression between groups, with consistent trends in both GSE66360 and GSE48060 datasets (p < 0.05). In addition, drug predictions showed PIM3 and BMX interacting with SGI-1776, vadimezan, canine, and gefitinib. Finally, genes in clinical samples also show the same expression trend. Conclusion This study identified three key genes ( PIM3, BMX, and ID1 ) as novel key genes in AMI, laying a foundation for clinical diagnosis and drug development. Health sciences/Biomarkers Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases acute myocardial infarction immune environment key genes lymphatic vessels Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Acute myocardial infarction (AMI) results from the rupture or erosion of vulnerable atherosclerotic plaques and is accompanied by thrombosis, which leads to coronary artery occlusion and progressive cell death within hypoperfused regions( 1 , 2 ). Over the past few decades, interventional and surgical bypass surgeries as well as pharmacological therapy have significantly improved the prognosis of patients with AMI, reducing the incidence of complications after acute myocardial infarction. However, the overall incidence and mortality rates remain high. According to statistics, ischemic heart disease accounted for 49.2% of cardiovascular disease-related deaths worldwide in 2019( 3 ). The American Heart Association (AHA) in the United States estimated that the overall prevalence of AMI reached 3%( 4 ). In hospitalized patients with ST-segment elevation myocardial infarction (STEMI), the annual mortality rate is approximately 1%( 5 , 6 ). Although early revascularization( 7 ) and pharmacotherapy( 8 ) can reduce the mortality rate and the occurrence of complications, a large proportion of patients progress to heart failure over time. Therefore, it is important to investigate and develop new treatment strategies to prevent or reverse HF after MI, which requires a deeper understanding of its underlying pathogenesis( 9 ). Research indicates that AMI triggers cardiomyocyte apoptosis via multiple pathways, with inflammation playing a central role. Certain genes influence AMI by modulating immune and inflammatory responses, as well as metabolic pathways. Thus, discovering genes associated with AMI offers insights into the underlying mechanisms of the disease( 10 ). The lymphatic system, as an important circulatory network in the human body, maintains the homeostasis of the body by transporting lymphocytes and participating in immune defense. Vascular endothelial growth factor (VEGF) and its receptors play pivotal roles in regulating lymphatic vessel functions( 11 ). Clinical studies have confirmed that human VEGF-C mutations are directly related to autosomal dominant Milroy-like primary lymphedema, further verifying the core role of this pathway( 12 ). This regulatory network is highly important for tissue repair and the maintenance of organ function. For example, in ischemic injury, the VEGF-C/VEGFR-3 pathway can protect myocardial function by maintaining tissue fluid balance( 11 ). Intervention targeting lymphangiogenesis has become a potential strategy for the treatment of cancer, neurological diseases and repair after myocardial infarction( 13 ). The heart itself has a complex lymphatic network, and its functional abnormalities are closely related to the imbalance of myocardial homeostasis: stimulating the generation of cardiac lymphatic vessels can significantly improve the efficiency of lymphatic transport, reduce myocardial edema, and simultaneously protect the function of the left ventricle by reducing inflammation and fibrosis( 11 ). The role of lymphangiogenesis is particularly crucial in the research of acute myocardial infarction. Given the close association between inflammation and the prognosis of AMI, lymphangiogenesis alleviates myocardial edema and improves cardiac function by activating VEGFR-3 transcription, expediting immune cell infiltration and the removal of necrotic debris in the infarcted region, and reducing the release of local inflammatory factors( 14 ). However, at present, an in-depth understanding of the specific molecular mechanisms of lymphangiogenesis in AMI, such as upstream regulatory factors and intercellular signal interactions, is lacking. Exploration in this field will provide a new theoretical basis for targeted therapy for myocardial infarction( 15 ). This study focused on the unclear mechanisms of lymphangiogenesis after AMI and the paucity of effective therapeutic targets and conducted an in-depth exploration by comprehensively applying multidimensional research methods. First, through transcriptome data screening, key genes closely related to lymphangiogenesis in AMI were precisely identified. Subsequently, via bioinformatics approaches, a systematic analysis was performed on the biological pathways involving these key genes, immune infiltration profiles, network regulatory patterns, and potential therapeutic agents, providing solid theoretical support and a theoretical foundation for the formulation of clinical treatment strategies. Finally, the expression levels of key genes were quantified in clinical samples and compared with and verified against the results of bioinformatics analysis to evaluate their clinical applicability. These findings are expected to facilitate the development of novel, precise treatment strategies for AMI. Materials and methods 2.1 Data sources This experiment first downloaded the training dataset GSE66360 (GPL570) from the GEO, which included circulating endothelial cells from 49 AMI patients and 50 controls. The test set consisted of the GSE48060 dataset (GPL570) also downloaded from GEO, featuring AMI peripheral blood samples versus control peripheral blood samples (31/21). The 660 LRGs were identified on the basis of a review of relevant references( 16 ) (Supplementary Table 1) . 2.2 Differential expression analysis Using the "limma" R package (v 3.54.0)( 17 ), we performed DEG analysis on the GSE66360 training dataset to compare the gene expression profiles of the AMI and control groups. The identification of DEGs was based on two thresholds: a p value below 0.05 and a |log2 FC| exceeding 0.5. The screening results for DEGs were visualized via the "ggplot2" R package (v 3.4.1)( 18 ) and the "ComplexHeatmap" R package (v 2.14.0)( 19 ) to generate a volcano plot and a heatmap. 2.3 Acquisition of candidate genes and enrichment analysis Differentially expressed LRGs in AMI were determined by finding the intersection between DEGs and LRGs via the "ggvenn" R package (v 0.1.9)( 20 ). The intersecting genes were named candidate genes. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted via the 'clusterProfiler' package (v 4.2.2)( 21 ), which identified the genes with the most significant enrichment. The significance threshold was set as p.adj 0.4) to predict protein‒protein interactions, and the resulting network was visualized via Cytoscape software (v 3.7.2)( 22 ). Using all samples from the training dataset GSE66360, the "glmnet" R package (v 4.1.8)( 23 ) was applied to build a LASSO regression model and perform 10-fold cross-validation. Simultaneously, the SVM-RFE algorithm, implemented via the "SVM" classifier from the "e1071" R package (v 1.7.16)( 24 ), was applied to perform classification analysis. The intersection of the two algorithms was determined via the "VennDiagram" R package (v 1.7.1)( 25 ), and the genes found at the intersection were selected as characteristic genes for subsequent studies. 2.6 Receiver operating characteristic (ROC) analysis To screen for candidate key genes with potential discriminatory power between AMI and control samples, the "pROC" R package (v 1.18.0)( 26 ) was used to conduct ROC analysis. Additionally, the area under the curve (AUC) values for each gene were computed across all samples in GSE66360 and GSE48060. Genes with an AUC > 0.7 were considered to have good discriminatory ability and were recognized as potential key genes for subsequent assessment. 2.7 Expression analysis To compare the expression levels of these genes across samples, the Wilcoxon test was subsequently employed (p < 0.05). Genes with reproducible expression patterns and significant differences between groups in both datasets were identified as key genes. 2.8 Nomogram To further examine the predictive performance of the combined key genes, the "rms" R package (v 6.5.0)( 27 ) was used to develop a nomogram model based on these genes from GSE66360. Each key gene was assigned a score (Point) within the nomogram, and the sum of these individual scores yielded a Total Point, which directly correlated with the predicted probability of AMI. Higher total scores indicate a greater likelihood of AMI. The "regplot" R package (v 1.1)( 26 ) was used to produce the calibration curve. Additionally, the "pROC" R package (v 1.18.0)( 26 ) was used to generate the ROC curve. 2.9 Correlation and localization analysis of key genes To assess the correlations between key genes, Spearman correlation analysis was performed via the "psych" R package (v 2.1.6)( 28 ) across all samples in the training dataset GSE66360 (|cor| >0.3 and p < 0.05). Chromosomal localization refers to the process of determining the specific position of a gene or DNA sequence on a chromosome. To establish where key genes are positioned on chromosomes, the "RCircos" R package (v 1.2.2)( 29 ) was used to analyze their distribution across chromosomes, and a chromosomal localization map of the key genes was generated. 2.10 Gene set enrichment analysis (GSEA) The key genes were subjected to GSEA to investigate the biological pathways they regulate in AMI. The reference gene set 'c2.cp.kegg.v7.4.symbols.gmt' was carefully selected from the MSigDB database. First, on the basis of the complete training set, pairwise Spearman correlations were determined between each key gene and all remaining genes to obtain the correlation coefficients. The genes were subsequently arranged in descending order according to these coefficients. The sorted data were then utilized to conduct GSEA (with thresholds of |NES| >1, FDR < 0.25, and p.adj < 0.05) via the 'clusterProfiler' package (v 4.2.2)( 21 ). 2.11 Immune infiltration analysis To assess how immune cell infiltration differs between the AMI group and the control group, the CIBERSORT algorithm (v 0.1.0)( 30 ) was used to analyze the composition and abundance of 22 immune cell types( 31 ) in GSE66360, with samples with p > 0.05 excluded. The infiltration levels of the 22 immune cell types were subsequently compared between the AMI and control samples via the Wilcoxon test (p < 0.05). Box plots were generated to visualize significant differences. Spearman correlation analysis was carried out among the differentially infiltrated immune cells and key genes via the "psych" R package (v 2.1.6)( 28 ) (|cor| >0.3 and p < 0.05). 2.12 Proteomic classification and interaction analysis of key genes The PANTHER classification system categorizes proteins (and their corresponding genes). To further investigate the protein-level classification of key genes, this database was utilized to analyze the proteins of key genes and obtain classification information for the proteins corresponding to each key gene. The Biological Universal Repository for Interactive Datasets (BioGRID) database ( https://thebiogrid.org/ ) is a publicly accessible resource containing protein and genetic interactions across diverse species, including yeast, mice, and humans. To identify proteins that interact with key genes during AMI development, via the "network" module, we generated a protein–protein interaction (PPI) network for these genes, and its visualization was set to the "concentric circles" layout. 2.13 Establishment of the molecular regulatory network To dissect the complex regulatory landscape of key gene expression, we constructed a molecular network by predicting the target miRNAs from the mirWalk and MiRDB databases, generating mRNA‒miRNA interaction pairs. The "VennDiagram" R package (v 1.7.1)( 32 ) was used to identify overlapping miRNAs between the two databases, with the intersecting miRNAs designated key miRNAs. On the basis of these key miRNAs, potential lncRNA interactors were predicted via starBase and mirnet, and overlapping lncRNAs between the two databases were identified as key lncRNAs via the same Venn diagram approach. Using the obtained key miRNAs, key lncRNAs, and key genes, a molecular regulatory network was generated. 2.14 Drug prediction To explore potential targeted drugs for AMI treatment, key genes were input into the Drug–Gene Interaction Database (DGIdb) for drug prediction. The predicted drug‒gene interaction information was visualized to construct a key drug‒gene interaction network. 2.15 Reverse transcription‒quantitative PCR (RT‒qPCR) The study included 10 fresh peripheral blood samples (5 controls and 5 AMI patients), all of which were collected from the clinic at Yunnan Fuwai Cardiovascular Hospital. The Ethics Committee of Yunnan Fuwai Cardiovascular Hospital (IRB2022–BG-029) approved the study, All procedures were performed in accordance with the protocol approved by the Yunnan Fuwai Cardiovascular Hospital Ethics Committee and relevant guidelines and regulations, Informed consent has been obtained from all subjects, and all subjects have signed the informed consent form before enrollment. Total RNA extraction was performed via a modified TRIzol-based procedure, followed by cDNA synthesis via reverse transcription. The expression levels of the target genes and the reference gene were quantified via RT‒qPCR. Primer specificity was verified by melting curve analysis, with single-peak profiles indicating specific amplification. The materials and methods, including the experimental protocols and primer sequences, are detailed below, and the primer information is summarized in Table 1 . We utilized the 2 –△△Ct approach to compute relative gene expression. GraphPad Prism (v 10.1.2)( 33 ) was used to visualize the data and determine the P values. Differences between the experimental groups in the PCR analysis were assessed via a two-tailed t test (p < 0.05). Table 1 Primer information Primer Sequence PIM3 F CCTGGTGACCTTCGCTTTGA PIM3 R GGCGTCTGGGGTTCAAGTAT BMX F AGCAAGTCAGACGTATGGGC BMX R GGTAGATGGTGTCCGATGCC ID1 F CCAGCACGTCATCGACTACA ID1 R CATAGCCAGGTACCCGCAAG Housekeeping Gene H-GAPDH: F ATGGGCAGCCGTTAGGAAAG Housekeeping Gene H-GAPDH: R AGGAAAAGCATCACCCGGAG 2.16 Statistical analysis All bioinformatic analyses were performed via the R programming language (v 4.2.2). Differences between AMI patients and controls were assessed by Wilcoxon tests, and a p value < 0.05 was considered significant. Results 3.1 Screening of DEGs, functional enrichment and PPIs The analysis revealed 2,584 DEGs, including 963 upregulated genes and 1,621 downregulated genes, in the AMI samples ( Fig. 1 A-B ) . To identify lymphangiogenesis-related differentially expressed genes in AMI, this study performed intersection analysis between DEGs and LRGs, screening out 96 candidate genes ( Fig. 1 C ) . The top five GO categories included biological processes related to the regulation of vasculature development, muscle cell proliferation, etc.; cellular components related to the collagen-containing extracellular matrix; and molecular functions related to receptor‒ligand activity ( Fig. 1 D, Supplementary Table 2) . The top 10 KEGG pathways included lipid and atherosclerosis, the HIF-1 signaling pathway, etc( 34 , 35 ). ( Fig. 1 E, Supplementary Table 3) , providing multidimensional evidence for revealing the molecular mechanisms of LRGs in AMI. In this study, a PPI network of 96 candidate genes was constructed. Key interactions included the binding of TGFBI to TGFBR1, TNF to NFKBIA, TGFBR1 to SMAD4, and FGF2 to FRS2, among others ( Fig. 1 F ) . 3.2 Machine learning-based feature gene ROC analysis and expression validation results This study screened 24 genes via LASSO and 28 via SVM from 96 candidate genes. Venn diagram analysis identified 12 feature genes (LTB, PIM3, IL1B, SKIL, SOCS1, EDN1, MMP9, AREG, EPAS1, PLAU, ID1, and BMX) ( Fig. 2 A-C ) . Through ROC curve analysis of the training set (GSE66360) and test set (GSE48060), three genes—PIM3, BMX, and ID1—were identified with AUCs > 0.7 in both datasets ( Fig. 2 D-F ) . The Wilcoxon rank-sum test further confirmed the significantly upregulated expression of these three genes in the AMI samples across both datasets, confirming their efficacy as key AMI genes (p 0.7 in the two datasets ( Fig. 2 D-F ) . The Wilcoxon rank-sum test further confirmed the significantly upregulated expression of these three genes in the AMI samples across both datasets, confirming their efficacy as key AMI genes (p < 0.05) ( Fig. 2 G-H ) . 3.3 Nomogram-based key gene correlation and localization analysis A nomogram model constructed using all samples from the training set GSE66360 showed excellent predictive accuracy, with an AUC of 0.939 (AUC > 0.7). The calibration curve was essentially near the line with a slope of 1 (p = 0.709), indicating that the nomogram showed superior performance in predicting AMI incidence and could be applied in clinical practice ( Fig. 3 A-C ) . Spearman correlation analysis revealed notable positive associations between the key genes PIM3 and ID1 (cor = 0.35, p < 0.001) and between ID1 and BMX (cor = 0.44, p < 0.001) ( Fig. 3 D ) . Chromosomal localization analysis revealed that ID1, PIM3, and BMX were mapped to chromosomes 20, 22, and X, respectively ( Fig. 3 E ) . These findings not only underscore the robust predictive utility of the nomogram for AMI but also reveal potential genetic interactions and chromosomal clustering of key regulatory genes, providing novel targets for exploring the molecular mechanisms underlying AMI and guiding future therapeutic interventions. 3.4 Pathway enrichment and immune cell infiltration analysis of key genes The study analyzed key genes (PIM3, BMX, ID1) to gain deeper insights into their involved signaling pathways and biological mechanisms in AMI, with findings showing that PIM3, ID1, and BMX were enriched in 92, 47, and 66 pathways, respectively. Among them, PIM3 was enriched mainly in the B-cell receptor signaling pathway and phagocytosis; ID1 was closely related to inflammatory pathways such as complement and coagulation cascades and cytokine‒cytokine receptor interactions; and BMX was significantly enriched in pathways such as complement and coagulation and the hematopoietic cell lineage. These findings provide important clues for revealing the functional network of key genes involved in the pathological process of AMI ( Fig. 4 A-C and Supplementary Table 4–6) . Additionally, the study examined immune cell infiltration patterns and revealed that AMI patients had significantly lower proportions of CD4 + resting memory T cells than healthy controls did ( Fig. 4 D ) . A total of 13 differentially infiltrated immune cell types were identified (p < 0.05), among which neutrophils had a notable positive correlation with M2 macrophages (cor = 0.55), and the correlation analysis revealed that neutrophils had the most substantial negative association with CD4 + resting memory T cells (r = -0.57, p < 0.001) ( Fig. 4 E-F ) . Furthermore, PIM3 had notable positive relationships with monocytes (cor = 0.44) but negative relationships with M0 macrophages (cor = -0.40); BMX had notable positive relationships with neutrophils (cor = 0.49); and ID1 expression levels were positively associated with activated dendritic cells (r = 0.39, p < 0.001) ( Fig. 4 G ) . These results indicate that key genes could play a role in shaping the immune microenvironment of AMI patients, which could offer a valuable reference for clinical decision-making in AMI treatment. 3.5 Proteomic classification, interaction analysis, molecular regulatory network construction, and drug prediction of key genes This study explored the proteomic classification of key genes and revealed that BMX belongs to the cytoplasmic nonreceptor tyrosine protein kinase class, PIM3 belongs to the nonreceptor serine/threonine protein kinase class, and ID1 belongs to the DNA-binding transcription factor class ( Table 2 ) . Protein interaction analysis revealed that PIM3, ID1, and BMX interact with 59, 66, and 79 proteins, respectively (Fig. 5 A-C). For example, BMX was found to interact with HSP90AA1, CCT5, and other proteins; ID1 was associated with USP7, which in turn interacted with TP53 and other proteins. Notably, PIM3 was also shown to interact with TP53, further highlighting the potential regulatory networks involving these key genes ( Fig. 5 A-C ) . A molecular regulatory network was constructed, incorporating 10 key miRNAs and 65 key lncRNAs, providing new insights into AMI mechanisms. Specifically, BMX was found to interact with hsa-miR-4430, ID1 was associated with hsa-miR-3200-5p, PIM3 bound to hsa-miR-654-5p, etc., illustrating the extensive cross-talk between key genes and noncoding RNAs in the regulatory network ( Fig. 5 D ) . Drug prediction revealed that PIM3 had high-scoring interactions with unapproved anticancer drugs (e.g., SGI-1776, VADIMEZAN, and AZD-1208) and moderate interactions with approved gefitinib, whereas BMX strongly interacted with unapproved PD-168393/canertinib and approved ibrutinib/ritorycitinib-tosylate. No interacting drugs were found for ID1. The high-scoring interactions of PIM3 and BMX with candidate drugs offer critical clues for subsequent AMI-targeted drug research ( Fig. 5 E ) . Table 2 Proteomic classification of key genes ID Gene Name Gene ID PANTHER Family/Subfamily PANTHER Protein Class BMX Cytoplasmic tyrosine-protein kinase BMX; BMX; PTN002521548; orthologs HUMAN|HGNC = 1079|UniProtKB = P51813 CYTOPLASMIC TYROSINE-PROTEIN KINASE BMX (PTHR24418:SF91) nonreceptor tyrosine protein kinase (PC00168) PIM3 Serine_threonine-protein kinase pim-3; PIM3; PTN002506631; orthologs HUMAN|HGNC = 19310|UniProtKB = Q86V86 SERINE_THREONINE-PROTEIN KINASE PIM-3 (PTHR22984:SF26) nonreceptor serine/threonine protein kinase (PC00167) ID1 DNA-binding protein inhibitor ID- 1; ID1; PTN002482400; orthologs HUMAN|HGNC = 5360|UniProtKB = P41134 DNA-BINDING PROTEIN INHIBITOR ID-1 (PTHR11723:SF4) DNA-binding transcription factor (PC00218) 3.6 Validation of the expression of key genes via RT‒qPCR The experimental results revealed that PIM3, ID1, and BMX were expressed at significantly higher levels in AMI patients than in controls (p < 0.05). The upregulation of these three genes may collectively contribute to AMI-induced cell proliferation, metabolic reprogramming, or inflammatory signaling pathway regulation. The experimental results are highly consistent with the bioinformatics analysis in terms of gene expression trends, supporting these genes as key genes in AMI ( Fig. 6 A-C ) . Discussion Existing treatments for acute myocardial infarction (AMI) remain limited in their efficacy in reducing the progression of heart failure after myocardial infarction( 36 , 37 ). Clinical evidence indicates that exacerbated inflammation can aggravate myocardial damage and promote heart failure( 14 ). The lymphatic system, through its lymphangiogenic processes, plays a crucial role in maintaining tissue homeostasis, transmitting injury signals and immune regulation( 15 ), although the VEGF-C/VEGFR-3 axis, etc.,, has been shown to promote cardiac lymphangiogenesis and improve AMI repair( 11 ); however, the molecular mechanism of lymphangiogenesis in AMI remains elusive. This study integrated AMI microarray data from the GEO database and applied a machine learning algorithm (LASSO/SVM-ROC), identified three key lymphangiogenesis-related genes (ID1, PIM3, BMX) and constructed a high-precision prediction model (AUC = 0.939). Through expression validation, functional enrichment and molecular network analysis, the enrichment of signaling pathways was characterized, and potential drug targets were revealed. Experimental validation further corroborated the reliability of the bioinformatics findings, provided a new perspective for lymphatic vessel regeneration after AMI, and offered new targets and theoretical bases for the precision treatment and drug development of AMI. ID1 (inhibitor of DNA binding 1) is a member of the helix-loop-helix (HLH) transcriptional regulator family. Lacking a basic DNA binding domain, it has no DNA-binding activity( 38 ). ID1, a transcriptional regulator identified in normal and acute myeloid leukemia (AML) stem cells( 39 ), regulates cytokine production in the bone marrow (BM) microenvironment( 40 ). It is characterized as an oncogenic factor and is involved in establishing the immunosuppressive tumor microenvironment( 41 ). The ID1 protein is pivotal in regulating lineage specification, cell fate determination, and differentiation timing during neurogenesis, lymphopoiesis, and neovascularization (angiogenesis). It is essential for embryogenesis and cell cycle progression and acts as a positive regulator of cell proliferation( 42 ). This study reports for the first time the role of ID1 in lymphangiogenesis in AMI. Previous studies have suggested that ID1 promotes tumor angiogenesis through the TGF-β pathway( 43 ). This study revealed that ID1 was significantly downregulated in AMI, possibly through the inhibition of endothelial cell migration, which is consistent with the mechanism by which ID1 deficiency in atherosclerosis leads to impaired vascular repair( 44 ). PIM3 (Pim-3 proto-oncogene, serine/threonine kinase) is a serine/threonine kinase involved in various carcinogenic processes and is usually overexpressed in solid tumors (such as pancreatic cancer, liver cancer, colon cancer, gastric cancer and breast cancer)( 45 ). It has been reported to play a crucial role in regulating cell proliferation, the cell cycle, and apoptosis signaling pathways. PIM3 upregulation is associated with poor patient prognosis, and its inhibition reduces cell proliferation, invasion, and tumor growth in vivo( 46 , 47 ). Therefore, PIM3 serves as an emerging therapeutic target in oncology( 48 ). Pim-3 is expressed in essential organs, including the heart, lung, and brain( 49 ). PIM3 is upregulated in response to cardiac ischemia. It enhances the function and survival of endothelial and progenitor cells, thereby promoting angiogenesis. This enhances blood flow to injured heart tissue, thereby promoting repair( 50 ). PIM3 directly phosphorylates and inhibits key proapoptotic proteins (such as BAD). It also activates prosurvival pathways (such as the AKT pathway), protecting cardiomyocytes and other cardiac cells from programmed cell death triggered by ischemic stress and reperfusion injury( 50 ). By enhancing vascular regeneration and reducing cell death, PIM3 mitigates myocardial damage and supports functional recovery after AMI, highlighting its potential as a therapeutic target( 51 ). High PIM3 expression is associated with poor AMI prognosis, which is consistent with its known role in promoting cardiomyocyte apoptosis( 52 ). However, its role in regulating lymphangiogenesis is reported here for the first time. BMX (bone marrow tyrosine kinase on chromosome X) is a member of the nonreceptor tyrosine kinase Tec family( 53 ), which contains Src homology (SH) 3 and SH2 domains( 54 ), as well as a carboxyl-terminal kinase domain, and plays a key role in multiple biological processes, including cytokine signaling and inflammation( 55 ). It has been implicated in angiogenesis( 56 ). Experimental studies have shown that BMX can be rapidly activated in response to hypoxia or inflammatory stimuli, and its upregulation may activate lymphatic endothelial cells through the VEGFR3 pathway( 57 ), suggesting that it may have important pathophysiological significance in ischemic cardiovascular disease. After acute myocardial infarction (AMI), myocardial cell necrosis, inflammatory cell infiltration, and tissue edema occur( 10 , 58 ). In the infarct core, the upregulation of BMX is mainly observed in residual vascular endothelial cells and infiltrating inflammatory cells; in the marginal area of infarction, the expression of BMX in lymphatic endothelial cells is significantly increased, and its expression is positively correlated with lymphatic vessel density. This expression pattern suggests that BMX may be involved in tissue repair and lymphatic remodeling after myocardial infarction( 59 ). ID1 is involved in vascular repair by inhibiting endothelial cell migration, which may indirectly affect the immunomodulatory function of lymphatic endothelial cells (LECs)( 39 ). High expression of PIM3, a proapoptotic kinase, may exacerbate monocyte infiltration by activating the NF-κB pathway (cor = 0.44)( 46 ). As the central signaling pathway governing inflammation and immune responses, the NF-κB pathway triggers the translocation of NF-κB dimers into the nucleus, where they activate the transcription of target genes such as proinflammatory cytokines (e.g., TNF-α and IL-6), adhesion molecules, and chemokines( 60 ). NF-κB drives the expression of monocyte chemokines (e.g., CCL2), promotes monocyte infiltration into the infarct area, and releases reactive oxygen species and proteases to exacerbate tissue destruction( 61 ); on the other hand, overactivated NF-κB exacerbates cardiomyocyte death by upregulating proapoptotic genes (e.g., Bax) and inhibiting antiapoptotic proteins (e.g., Bcl-2)( 62 ). NF-κB activity in the peripheral blood mononuclear cells of AMI patients is positively correlated with inflammatory marker levels, further supporting its potential as a therapeutic target. BMX was significantly positively correlated with neutrophils (cor = 0.49), suggesting that BMX may regulate the neutrophil-mediated inflammatory cascade through the PI3K/AKT pathway( 63 ). The analysis in this study revealed that the immune microenvironment of AMI patients exhibited imbalances, including reduced proportions of CD4 + resting memory T cells and enrichment of proinflammatory cells such as neutrophils and M1 macrophages. Decreased infiltration of resting CD4 + memory T cells, γδ T cells, M1 macrophages, and resting mast cells is associated with the onset and progression of AMI. During the proliferative repair phase, CD4 + and CD8 + T cells, regulatory T cells, and NKT cells infiltrate the infarcted myocardium, promoting its maturation( 64 ). Recent studies have shown that Treg infiltration drives lymphatic vessel proliferation after AMI( 65 ) and that ID1/PIM3/BMX may affect the microenvironment by regulating T-cell differentiation. Studies suggest that targeting the immunosuppressive microenvironment, regulating proinflammatory cell infiltration, or integrating differential immune cells and proportions with prognostic genes to construct predictive models is expected to enhance AMI treatment strategies( 66 ). However, it is still necessary to expand the sample size, conduct functional experiments, and combine multiomics techniques to further verify the causal relationships and potential regulatory mechanisms between the immune microenvironment and key genes. This study focused on AMI and revealed the distinct roles of PIM3, BMX and ID1 as potential therapeutic targets: PIM3 mediates inflammation and fibrosis by activating the NF-κB or STAT3 pathways. PIM3-targeted drugs such as SGI-1776 and gefitinib may balance myocardial inflammation and repair( 67 , 68 ). BMX modulates the immune microenvironment through the PI3K/AKT pathway. Inhibitors such as PD-168393 and ibrutinib may restore immune homeostasis( 69 ). BMX kinase inhibitors have shown antiangiogenic effects in lymphoma( 70 ), and this study proposes the potential for repurposing them for the treatment of AMI. Studies have shown that chloroquine (which targets ID1) inhibits pathological angiogenesis in rheumatoid arthritis( 71 ) and may mitigate abnormal lymphatic remodeling after AMI. However, owing to the unknown mechanism of ID1 and delays in drug development, no effective related drugs have been identified to date. The current research is limited by the coverage of the databases, and clinical translation faces challenges such as cardiotoxicity and a lack of functional verification. Future research should validate the safety and efficacy of PIM3- and BMX-targeted drugs via multiomics integration and functional assays, explore potential intervention strategies for ID1, and propose novel concepts for precision immunotherapy in AMI. Conclusion In this study, PIM3, BMX and ID1 were identified as key genes from AMI chip data, and the key gene profile of AMI lymphangiogenesis was established for the first time. The potential mechanism of their interaction through neural pathways and the immune microenvironment was revealed, and they form a regulatory network with miRNAs, providing new targets for AMI mechanism research and drug development. However, the results of this study are highly dependent on the data quality of the GEO database microarrays and the accuracy of the machine learning algorithm. Data noise, batch effects, etc., may lead to false positive results in the screening of differential genes. The experimental evidence for the regulation of lymphangiogenesis by neural pathways is still insufficient, and further analysis in in vivo and in vitro models is needed. In the future, key genes can be explored as biomarkers for the risk of heart failure after AMI. Abbreviations AMI acute myocardial infarction LRGs lymphangiogenesis-related genes ROC receiver operating characteristic GSEA gene set enrichment analysis AHA American Heart Association STEMI ST-segment elevation myocardial infarction VEGF vascular endothelial growth factor DEGs differentially expressed genes GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes ROC Receiver operating characteristic AUC area under the curve GSEA Gene set enrichment analysis DGIdb Drug-Gene Interaction Database RT‒qPCR Reverse transcription-quantitative PCR ID1 Inhibitor of DNA Binding 1 HLH Helix-Loop-Helix AML acute myeloid leukemia PIM3 Pim-3 proto-oncogene, serine/threonine kinase BMX Bone marrow tyrosine kinase on chromosome X SH Src homology LECs lymphatic endothelial cells Declarations E thics approval: This research was approved by Fuwai Cardiovascular Hospital, Yunnan Province, China. Approval No: 202103AC100004. Conflict of interest: The authors declare that this research was conducted without any commercial or financial relationships that could represent a potential conflict of interest. All the authors are required to disclose any financial or personal relationships that could be perceived as conflicts of interest. This includes any such activities within the three years prior to work, such as employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications, or travel grants. Author Contributions: Conceptionualization: Zhang Lihong; data curation: Zhang Jingjing; formal analysis: Guo Yi; investigation: Zhang Xuanping; resources: Qian Yinnan; software: Zhang Jingjing; writing—original draft: Zhang Lihong; writing—review and editing: Guo Yi. All the authors read and approved the final manuscript. Funding: This research received no external funding. Acknowledgments: We would like to express our sincere gratitude to all the individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Qian Yanna. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. Data availability statement: The datasets (ANALYZED) used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html). References Wu, X., Reboll, M. R., Korf-Klingebiel, M. & Wollert, K. C. Angiogenesis after acute myocardial infarction. Cardiovasc Res 117 , 1257-1273, doi:10.1093/cvr/cvaa287 (2021). Krittanawong, C., Khawaja, M., Tamis-Holland, J. E., Girotra, S. & Rao, S. V. Acute Myocardial Infarction: Etiologies and Mimickers in Young Patients. J Am Heart Assoc 12 , e029971, doi:10.1161/jaha.123.029971 (2023). Zhang, Q. et al. 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10:03:26","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74727,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/2fbddc6ab90ef1224f42eb41.png"},{"id":97028091,"identity":"c0b0f5bf-d04f-4da0-959d-d0cf8668fd25","added_by":"auto","created_at":"2025-11-29 00:52:12","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167185,"visible":true,"origin":"","legend":"","description":"","filename":"ae01a560cfff4472b1b433cc8f7e27231structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/16a2512490d95f53dae18bb7.xml"},{"id":97028089,"identity":"be75e00f-3935-4b1d-9d3f-e01b3ef4077e","added_by":"auto","created_at":"2025-11-29 00:52:12","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185029,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/2b142277ef2c3253b48dd404.html"},{"id":97139242,"identity":"346f9233-f1a9-4339-b1c6-80c8b1f35ccc","added_by":"auto","created_at":"2025-12-01 09:59:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1214054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of DEGs, functional enrichment and PPIs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Volcanic map of the distribution of differentially expressed genes between AMI patients and controls. The abscissa is Log2FC, the ordinate is -Log10 (adjusted P value), and each point represents a gene. Red represents upregulated DEGs, blue represents downregulated DEGs, and gray represents genes whose expression was not significantly different.\u003cstrong\u003e (B) \u003c/strong\u003eDifferential gene expression density heatmap. In the middle, yellow represents the AMI sample, and blue represents the healthy sample. The color of the density heatmap above represents the gene expression density of each sample, and the redder the color is, the greater the density. In the heatmap below, the ordinate represents genes, and yellow to blue represents gene expression from high to low. \u003cstrong\u003e(C) \u003c/strong\u003eCandidate gene Venn diagram. The yellow circles represent DEGs, the blue circles represent DEGs associated with LRG_IRGs, and the middle overlap indicates genes in the 2 gene sets at the same time.\u003cstrong\u003e (D) \u003c/strong\u003eGO enrichment analysis;\u003cstrong\u003e (E) \u003c/strong\u003eKEGG enrichment analysis. The size of the dot represents the number of enriched genes, and the larger the dot is, the greater the number of enriched genes. The abscissa represents -log10(p), a higher value represents a more significant enriched pathway, and the ordinate represents the name of the enriched pathway.\u003cstrong\u003e(F)\u003c/strong\u003e PPI protein interaction network results. Each rectangular node in the diagram represents a protein, and each connecting line represents a direct interaction between the two proteins.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/719ef356b55b20f6868a6307.png"},{"id":97028073,"identity":"1c856457-71e6-46b7-962e-1deca33ab2fc","added_by":"auto","created_at":"2025-11-29 00:52:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":618960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMachine learning-based feature geneROC analysis and expression validation results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eLASSO regression cross-validation (top) and coefficient spectra (bottom). The previous graph shows the trend of the cross-validation error (CVM) with log(λ), with red and blue dotted lines indicating λmin and λ1 se, respectively. The following figure shows the path of the coefficient of each candidate gene in the model with log(λ), and the color curve represents the change in thecoefficients of different genes. \u003cstrong\u003e(B) \u003c/strong\u003eSVM-RFE algorithm screening results. The horizontal axis represents the number of selected features, and the vertical axis represents the classification accuracy under 10-fold cross-validation (10 x CV). Line charts show trends in model accuracy as the number of features changes in the form of dots and lines. \u003cstrong\u003e(C) \u003c/strong\u003eVenn diagram to obtain key genes. \u003cstrong\u003e(D) (E) (F) \u003c/strong\u003eROC curves of the candidate biomarker training and validation sets. \u003cstrong\u003e(G) (H) \u003c/strong\u003eAMI validation set and training set expression level screening biomarkers. The figure shows the expression levels of candidate biomarkers in different groupings (Control and AMI) of the validation set and the training set. Each box plot shows the expression distribution of specific biomarkers in the two groups, with the horizontal axis as the group, the vertical axis as the expression level, the blue axis for the control group (Control), and the red axis for the AMI group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/2b00669207b9c76e59dd29b6.png"},{"id":97028075,"identity":"dd4b0bbb-b607-481c-b9e3-64337e5537d1","added_by":"auto","created_at":"2025-11-29 00:52:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":450879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram-based key gene correlation and localization analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eNomogram. The first part is points; the second part is the variable, and the scale on the line segment represents the different values of the variable; the third part is total points; and the fourth part is the probability prediction value of disease risk. \u003cstrong\u003e(B) \u003c/strong\u003eROC curve of the nomogram diagnostic model. The AUC value of the ROC curve was 0.939 (AUC\u0026gt;0.7). \u003cstrong\u003e(C) \u003c/strong\u003eNomogram diagnostic model calibration curve. The abscissa represents the probability of predicting the disease in the nomogram, and the ordinate represents the actual probability of disease. The standard diagonal dotted line represents a reference line that is exactly the same as the predicted probability of disease and the true probability of disease. The black dotted line represents the prediction of the nomogram, and the solid black line is corrected by bootstrapping(1000 repetitions). \u003cstrong\u003e(D) \u003c/strong\u003eCorrelation analysis of biomarkers. PIM3, ID1 and BMX were significantly positively correlated with each other.\u003cstrong\u003e (E) \u003c/strong\u003eDistribution of biomarkers on chromosomes. The ID1 gene is located on chromosome 20; thePIM3 gene is located on chromosome 22; and the BMX gene is located on the X chromosome.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/f287b4dfd76d8adc65a7c199.png"},{"id":97138300,"identity":"a6bb801a-15eb-4cba-8e60-18fc6978ab5e","added_by":"auto","created_at":"2025-12-01 09:58:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":683019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment and immune cell infiltration analysis of key genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) (B) (C) \u003c/strong\u003eGSEA enrichment analysis plot. The GSEA enrichment analysis plot for each biomarker is shown only as | NES|The top five enrichment pathways in descending numerical order.\u003cstrong\u003e (D) \u003c/strong\u003eHeatmap of immune cell infiltration in the AMI group vs the control group. The proportion of AMI brown samples was significantly lower than that of healthy samples, and the cumulative proportion of 12 immune cells above orange was significantly greater than that of healthy samples, indicating that there was a significant difference in the number of immune cells between AMI and healthy samples. \u003cstrong\u003e(E) \u003c/strong\u003eBox plot of the proportions of 22 immune cells in the AMI group vs. the control group. The horizontal axis represents the immune cell type, and the vertical axis representsthe level of infiltration. Blue indicates AMI samples,and yellow indicates control samples. \u003cstrong\u003e(F) \u003c/strong\u003eImmune cell-related heatmaps with significant differences. The abscissa and vertical coordinates are the names of the immune cells. The correlation coefficient size is expressed by the color shade of the square, with red indicating a positive correlation and blue indicating a negative correlation. \u003cstrong\u003e(G) \u003c/strong\u003eCorrelations between biomarker expression and differential immune cells. A scatter plot was drawn for visualization.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/1413a1b722694c7f412938a4.png"},{"id":97139101,"identity":"abf3ab0a-193d-4fc4-8180-7062111b577f","added_by":"auto","created_at":"2025-12-01 09:59:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1458555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic classification, interaction analysis, molecular regulatory network construction, and drug prediction of key genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) (B) (C)\u003c/strong\u003e Protein interaction networks of biomarkers. The overall shape of the figure is concentric circles, the brown component in the center is the target protein, and the blue component is the protein that interacts with the target protein. The yellow line represents physical interaction, the green line represents genetic interaction, and the purple line represents two forms of physical or genetic interaction. \u003cstrong\u003e(D)\u003c/strong\u003e Molecular regulatory networks. Red diamonds represent biomarkers, yellow ovals represent miRNAs, and blue rectangles represent lncRNAs.\u003cstrong\u003e (E)\u003c/strong\u003e Drug-biomarker networks. High-score interaction between PIM3 and BMX with drug candidates.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/81611619811396ec1171b26f.png"},{"id":97028076,"identity":"7d65e0a5-5fb1-423f-a5a9-a8f9355ed85b","added_by":"auto","created_at":"2025-11-29 00:52:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":315320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of key gene expression via RT‒qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) (B) (C) \u003c/strong\u003ePIM3, ID1, and BMX were significantly upregulated in patients with AMI compared with the control group (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/8e12aabe9c4e8bfd85d2543d.png"},{"id":98627319,"identity":"245170af-e3a1-4678-882c-e6b35fbba703","added_by":"auto","created_at":"2025-12-19 17:10:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6587492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/60a756fa-1aa7-4e69-a6b8-ca1a2d457d20.pdf"},{"id":97028074,"identity":"bad1a26d-9108-4c4a-bb9f-a01f2f054fd4","added_by":"auto","created_at":"2025-11-29 00:52:11","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":298826,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.zip","url":"https://assets-eu.researchsquare.com/files/rs-7844178/v1/3c6d08b07bdeb66bbb3c1e4f.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on identifying key genes and mechanisms related to lymphangiogenesis in acute myocardial infarction via bioinformatics screening and experimental verification","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myocardial infarction (AMI) results from the rupture or erosion of vulnerable atherosclerotic plaques and is accompanied by thrombosis, which leads to coronary artery occlusion and progressive cell death within hypoperfused regions(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Over the past few decades, interventional and surgical bypass surgeries as well as pharmacological therapy have significantly improved the prognosis of patients with AMI, reducing the incidence of complications after acute myocardial infarction. However, the overall incidence and mortality rates remain high. According to statistics, ischemic heart disease accounted for 49.2% of cardiovascular disease-related deaths worldwide in 2019(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The American Heart Association (AHA) in the United States estimated that the overall prevalence of AMI reached 3%(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In hospitalized patients with ST-segment elevation myocardial infarction (STEMI), the annual mortality rate is approximately 1%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although early revascularization(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and pharmacotherapy(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) can reduce the mortality rate and the occurrence of complications, a large proportion of patients progress to heart failure over time. Therefore, it is important to investigate and develop new treatment strategies to prevent or reverse HF after MI, which requires a deeper understanding of its underlying pathogenesis(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Research indicates that AMI triggers cardiomyocyte apoptosis via multiple pathways, with inflammation playing a central role. Certain genes influence AMI by modulating immune and inflammatory responses, as well as metabolic pathways. Thus, discovering genes associated with AMI offers insights into the underlying mechanisms of the disease(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The lymphatic system, as an important circulatory network in the human body, maintains the homeostasis of the body by transporting lymphocytes and participating in immune defense. Vascular endothelial growth factor (VEGF) and its receptors play pivotal roles in regulating lymphatic vessel functions(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Clinical studies have confirmed that human VEGF-C mutations are directly related to autosomal dominant Milroy-like primary lymphedema, further verifying the core role of this pathway(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This regulatory network is highly important for tissue repair and the maintenance of organ function. For example, in ischemic injury, the VEGF-C/VEGFR-3 pathway can protect myocardial function by maintaining tissue fluid balance(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Intervention targeting lymphangiogenesis has become a potential strategy for the treatment of cancer, neurological diseases and repair after myocardial infarction(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The heart itself has a complex lymphatic network, and its functional abnormalities are closely related to the imbalance of myocardial homeostasis: stimulating the generation of cardiac lymphatic vessels can significantly improve the efficiency of lymphatic transport, reduce myocardial edema, and simultaneously protect the function of the left ventricle by reducing inflammation and fibrosis(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The role of lymphangiogenesis is particularly crucial in the research of acute myocardial infarction. Given the close association between inflammation and the prognosis of AMI, lymphangiogenesis alleviates myocardial edema and improves cardiac function by activating VEGFR-3 transcription, expediting immune cell infiltration and the removal of necrotic debris in the infarcted region, and reducing the release of local inflammatory factors(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, at present, an in-depth understanding of the specific molecular mechanisms of lymphangiogenesis in AMI, such as upstream regulatory factors and intercellular signal interactions, is lacking. Exploration in this field will provide a new theoretical basis for targeted therapy for myocardial infarction(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study focused on the unclear mechanisms of lymphangiogenesis after AMI and the paucity of effective therapeutic targets and conducted an in-depth exploration by comprehensively applying multidimensional research methods. First, through transcriptome data screening, key genes closely related to lymphangiogenesis in AMI were precisely identified. Subsequently, via bioinformatics approaches, a systematic analysis was performed on the biological pathways involving these key genes, immune infiltration profiles, network regulatory patterns, and potential therapeutic agents, providing solid theoretical support and a theoretical foundation for the formulation of clinical treatment strategies. Finally, the expression levels of key genes were quantified in clinical samples and compared with and verified against the results of bioinformatics analysis to evaluate their clinical applicability. These findings are expected to facilitate the development of novel, precise treatment strategies for AMI.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources\u003c/h2\u003e\u003cp\u003eThis experiment first downloaded the training dataset GSE66360 (GPL570) from the GEO, which included circulating endothelial cells from 49 AMI patients and 50 controls. The test set consisted of the GSE48060 dataset (GPL570) also downloaded from GEO, featuring AMI peripheral blood samples versus control peripheral blood samples (31/21). The 660 LRGs were identified on the basis of a review of relevant references(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e\u003cp\u003eUsing the \"limma\" R package (v 3.54.0)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), we performed DEG analysis on the GSE66360 training dataset to compare the gene expression profiles of the AMI and control groups. The identification of DEGs was based on two thresholds: a p value below 0.05 and a |log2 FC| exceeding 0.5. The screening results for DEGs were visualized via the \"ggplot2\" R package (v 3.4.1)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and the \"ComplexHeatmap\" R package (v 2.14.0)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) to generate a volcano plot and a heatmap.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Acquisition of candidate genes and enrichment analysis\u003c/h2\u003e\u003cp\u003eDifferentially expressed LRGs in AMI were determined by finding the intersection between DEGs and LRGs via the \"ggvenn\" R package (v 0.1.9)(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The intersecting genes were named candidate genes. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted via the 'clusterProfiler' package (v 4.2.2)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), which identified the genes with the most significant enrichment. The significance threshold was set as p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Protein‒protein interaction (PPI) network\u003c/h2\u003e\u003cp\u003eIn addition, the candidate genes were analyzed via the STRING database (confidence score cutoff\u0026thinsp;\u0026gt;\u0026thinsp;0.4) to predict protein‒protein interactions, and the resulting network was visualized via Cytoscape software (v 3.7.2)(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Using all samples from the training dataset GSE66360, the \"glmnet\" R package (v 4.1.8)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) was applied to build a LASSO regression model and perform 10-fold cross-validation. Simultaneously, the SVM-RFE algorithm, implemented via the \"SVM\" classifier from the \"e1071\" R package (v 1.7.16)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), was applied to perform classification analysis. The intersection of the two algorithms was determined via the \"VennDiagram\" R package (v 1.7.1)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and the genes found at the intersection were selected as characteristic genes for subsequent studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Receiver operating characteristic (ROC) analysis\u003c/h2\u003e\u003cp\u003eTo screen for candidate key genes with potential discriminatory power between AMI and control samples, the \"pROC\" R package (v 1.18.0)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) was used to conduct ROC analysis. Additionally, the area under the curve (AUC) values for each gene were computed across all samples in GSE66360 and GSE48060. Genes with an AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were considered to have good discriminatory ability and were recognized as potential key genes for subsequent assessment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Expression analysis\u003c/h2\u003e\u003cp\u003eTo compare the expression levels of these genes across samples, the Wilcoxon test was subsequently employed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Genes with reproducible expression patterns and significant differences between groups in both datasets were identified as key genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Nomogram\u003c/h2\u003e\u003cp\u003eTo further examine the predictive performance of the combined key genes, the \"rms\" R package (v 6.5.0)(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) was used to develop a nomogram model based on these genes from GSE66360. Each key gene was assigned a score (Point) within the nomogram, and the sum of these individual scores yielded a Total Point, which directly correlated with the predicted probability of AMI. Higher total scores indicate a greater likelihood of AMI. The \"regplot\" R package (v 1.1)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) was used to produce the calibration curve. Additionally, the \"pROC\" R package (v 1.18.0)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) was used to generate the ROC curve.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Correlation and localization analysis of key genes\u003c/h2\u003e\u003cp\u003eTo assess the correlations between key genes, Spearman correlation analysis was performed via the \"psych\" R package (v 2.1.6)(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) across all samples in the training dataset GSE66360 (|cor| \u0026gt;0.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eChromosomal localization refers to the process of determining the specific position of a gene or DNA sequence on a chromosome. To establish where key genes are positioned on chromosomes, the \"RCircos\" R package (v 1.2.2)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) was used to analyze their distribution across chromosomes, and a chromosomal localization map of the key genes was generated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Gene set enrichment analysis (GSEA)\u003c/h2\u003e\u003cp\u003eThe key genes were subjected to GSEA to investigate the biological pathways they regulate in AMI. The reference gene set 'c2.cp.kegg.v7.4.symbols.gmt' was carefully selected from the MSigDB database. First, on the basis of the complete training set, pairwise Spearman correlations were determined between each key gene and all remaining genes to obtain the correlation coefficients. The genes were subsequently arranged in descending order according to these coefficients. The sorted data were then utilized to conduct GSEA (with thresholds of |NES| \u0026gt;1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25, and p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05) via the 'clusterProfiler' package (v 4.2.2)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eTo assess how immune cell infiltration differs between the AMI group and the control group, the CIBERSORT algorithm (v 0.1.0)(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) was used to analyze the composition and abundance of 22 immune cell types(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) in GSE66360, with samples with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 excluded. The infiltration levels of the 22 immune cell types were subsequently compared between the AMI and control samples via the Wilcoxon test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Box plots were generated to visualize significant differences. Spearman correlation analysis was carried out among the differentially infiltrated immune cells and key genes via the \"psych\" R package (v 2.1.6)(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (|cor| \u0026gt;0.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Proteomic classification and interaction analysis of key genes\u003c/h2\u003e\u003cp\u003eThe PANTHER classification system categorizes proteins (and their corresponding genes). To further investigate the protein-level classification of key genes, this database was utilized to analyze the proteins of key genes and obtain classification information for the proteins corresponding to each key gene.\u003c/p\u003e\u003cp\u003eThe Biological Universal Repository for Interactive Datasets (BioGRID) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://thebiogrid.org/\u003c/span\u003e\u003cspan address=\"https://thebiogrid.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a publicly accessible resource containing protein and genetic interactions across diverse species, including yeast, mice, and humans. To identify proteins that interact with key genes during AMI development, via the \"network\" module, we generated a protein\u0026ndash;protein interaction (PPI) network for these genes, and its visualization was set to the \"concentric circles\" layout.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Establishment of the molecular regulatory network\u003c/h2\u003e\u003cp\u003eTo dissect the complex regulatory landscape of key gene expression, we constructed a molecular network by predicting the target miRNAs from the mirWalk and MiRDB databases, generating mRNA‒miRNA interaction pairs. The \"VennDiagram\" R package (v 1.7.1)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) was used to identify overlapping miRNAs between the two databases, with the intersecting miRNAs designated key miRNAs. On the basis of these key miRNAs, potential lncRNA interactors were predicted via starBase and mirnet, and overlapping lncRNAs between the two databases were identified as key lncRNAs via the same Venn diagram approach. Using the obtained key miRNAs, key lncRNAs, and key genes, a molecular regulatory network was generated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Drug prediction\u003c/h2\u003e\u003cp\u003eTo explore potential targeted drugs for AMI treatment, key genes were input into the Drug\u0026ndash;Gene Interaction Database (DGIdb) for drug prediction. The predicted drug‒gene interaction information was visualized to construct a key drug‒gene interaction network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Reverse transcription‒quantitative PCR (RT‒qPCR)\u003c/h2\u003e\u003cp\u003eThe study included 10 fresh peripheral blood samples (5 controls and 5 AMI patients), all of which were collected from the clinic at Yunnan Fuwai Cardiovascular Hospital. The Ethics Committee of Yunnan Fuwai Cardiovascular Hospital (IRB2022\u0026ndash;BG-029) approved the study, All procedures were performed in accordance with the protocol approved by the Yunnan Fuwai Cardiovascular Hospital Ethics Committee and relevant guidelines and regulations, Informed consent has been obtained from all subjects, and all subjects have signed the informed consent form before enrollment. Total RNA extraction was performed via a modified TRIzol-based procedure, followed by cDNA synthesis via reverse transcription. The expression levels of the target genes and the reference gene were quantified via RT‒qPCR. Primer specificity was verified by melting curve analysis, with single-peak profiles indicating specific amplification. The materials and methods, including the experimental protocols and primer sequences, are detailed below, and the primer information is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We utilized the 2\u003csup\u003e\u0026ndash;△△Ct\u003c/sup\u003e approach to compute relative gene expression. GraphPad Prism (v 10.1.2)(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) was used to visualize the data and determine the P values. Differences between the experimental groups in the PCR analysis were assessed via a two-tailed t test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSequence\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIM3 F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCCTGGTGACCTTCGCTTTGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIM3 R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGGCGTCTGGGGTTCAAGTAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMX F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGCAAGTCAGACGTATGGGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMX R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGTAGATGGTGTCCGATGCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID1 F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCAGCACGTCATCGACTACA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID1 R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCATAGCCAGGTACCCGCAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousekeeping Gene H-GAPDH: F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATGGGCAGCCGTTAGGAAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousekeeping Gene H-GAPDH: R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGGAAAAGCATCACCCGGAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.16 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll bioinformatic analyses were performed via the R programming language (v 4.2.2). Differences between AMI patients and controls were assessed by Wilcoxon tests, and a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Screening of DEGs, functional enrichment and PPIs\u003c/h2\u003e\u003cp\u003eThe analysis revealed 2,584 DEGs, including 963 upregulated genes and 1,621 downregulated genes, in the AMI samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B\u003cb\u003e)\u003c/b\u003e. To identify lymphangiogenesis-related differentially expressed genes in AMI, this study performed intersection analysis between DEGs and LRGs, screening out 96 candidate genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The top five GO categories included biological processes related to the regulation of vasculature development, muscle cell proliferation, etc.; cellular components related to the collagen-containing extracellular matrix; and molecular functions related to receptor‒ligand activity \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003eSupplementary Table\u0026nbsp;2)\u003c/b\u003e. The top 10 KEGG pathways included lipid and atherosclerosis, the HIF-1 signaling pathway, etc(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, \u003cb\u003eSupplementary Table\u0026nbsp;3)\u003c/b\u003e, providing multidimensional evidence for revealing the molecular mechanisms of LRGs in AMI. In this study, a PPI network of 96 candidate genes was constructed. Key interactions included the binding of TGFBI to TGFBR1, TNF to NFKBIA, TGFBR1 to SMAD4, and FGF2 to FRS2, among others \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Machine learning-based feature gene ROC analysis and expression validation results\u003c/h2\u003e\u003cp\u003eThis study screened 24 genes via LASSO and 28 via SVM from 96 candidate genes. Venn diagram analysis identified 12 feature genes (LTB, PIM3, IL1B, SKIL, SOCS1, EDN1, MMP9, AREG, EPAS1, PLAU, ID1, and BMX) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e. Through ROC curve analysis of the training set (GSE66360) and test set (GSE48060), three genes\u0026mdash;PIM3, BMX, and ID1\u0026mdash;were identified with AUCs\u0026thinsp;\u0026gt;\u0026thinsp;0.7 in both datasets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F\u003cb\u003e)\u003c/b\u003e. The Wilcoxon rank-sum test further confirmed the significantly upregulated expression of these three genes in the AMI samples across both datasets, confirming their efficacy as key AMI genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-H\u003cb\u003e)\u003c/b\u003e. Through ROC curve analysis of the training set (GSE66360) and test set (GSE48060), three genes\u0026mdash;PIM3, BMX, and ID1\u0026mdash;had AUCs\u0026thinsp;\u0026gt;\u0026thinsp;0.7 in the two datasets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F\u003cb\u003e)\u003c/b\u003e. The Wilcoxon rank-sum test further confirmed the significantly upregulated expression of these three genes in the AMI samples across both datasets, confirming their efficacy as key AMI genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-H\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nomogram-based key gene correlation and localization analysis\u003c/h2\u003e\u003cp\u003eA nomogram model constructed using all samples from the training set GSE66360 showed excellent predictive accuracy, with an AUC of 0.939 (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7). The calibration curve was essentially near the line with a slope of 1 (p\u0026thinsp;=\u0026thinsp;0.709), indicating that the nomogram showed superior performance in predicting AMI incidence and could be applied in clinical practice \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e. Spearman correlation analysis revealed notable positive associations between the key genes PIM3 and ID1 (cor\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between ID1 and BMX (cor\u0026thinsp;=\u0026thinsp;0.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Chromosomal localization analysis revealed that ID1, PIM3, and BMX were mapped to chromosomes 20, 22, and X, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. These findings not only underscore the robust predictive utility of the nomogram for AMI but also reveal potential genetic interactions and chromosomal clustering of key regulatory genes, providing novel targets for exploring the molecular mechanisms underlying AMI and guiding future therapeutic interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Pathway enrichment and immune cell infiltration analysis of key genes\u003c/h2\u003e\u003cp\u003eThe study analyzed key genes (PIM3, BMX, ID1) to gain deeper insights into their involved signaling pathways and biological mechanisms in AMI, with findings showing that PIM3, ID1, and BMX were enriched in 92, 47, and 66 pathways, respectively. Among them, PIM3 was enriched mainly in the B-cell receptor signaling pathway and phagocytosis; ID1 was closely related to inflammatory pathways such as complement and coagulation cascades and cytokine‒cytokine receptor interactions; and BMX was significantly enriched in pathways such as complement and coagulation and the hematopoietic cell lineage. These findings provide important clues for revealing the functional network of key genes involved in the pathological process of AMI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C \u003cb\u003eand Supplementary Table\u0026nbsp;4\u0026ndash;6)\u003c/b\u003e. Additionally, the study examined immune cell infiltration patterns and revealed that AMI patients had significantly lower proportions of CD4\u003csup\u003e+\u003c/sup\u003e resting memory T cells than healthy controls did \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. A total of 13 differentially infiltrated immune cell types were identified (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), among which neutrophils had a notable positive correlation with M2 macrophages (cor\u0026thinsp;=\u0026thinsp;0.55), and the correlation analysis revealed that neutrophils had the most substantial negative association with CD4\u003csup\u003e+\u003c/sup\u003e resting memory T cells (r = -0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F\u003cb\u003e)\u003c/b\u003e. Furthermore, PIM3 had notable positive relationships with monocytes (cor\u0026thinsp;=\u0026thinsp;0.44) but negative relationships with M0 macrophages (cor = -0.40); BMX had notable positive relationships with neutrophils (cor\u0026thinsp;=\u0026thinsp;0.49); and ID1 expression levels were positively associated with activated dendritic cells (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. These results indicate that key genes could play a role in shaping the immune microenvironment of AMI patients, which could offer a valuable reference for clinical decision-making in AMI treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Proteomic classification, interaction analysis, molecular regulatory network construction, and drug prediction of key genes\u003c/h2\u003e\u003cp\u003eThis study explored the proteomic classification of key genes and revealed that BMX belongs to the cytoplasmic nonreceptor tyrosine protein kinase class, PIM3 belongs to the nonreceptor serine/threonine protein kinase class, and ID1 belongs to the DNA-binding transcription factor class \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Protein interaction analysis revealed that PIM3, ID1, and BMX interact with 59, 66, and 79 proteins, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). For example, BMX was found to interact with HSP90AA1, CCT5, and other proteins; ID1 was associated with USP7, which in turn interacted with TP53 and other proteins. Notably, PIM3 was also shown to interact with TP53, further highlighting the potential regulatory networks involving these key genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e. A molecular regulatory network was constructed, incorporating 10 key miRNAs and 65 key lncRNAs, providing new insights into AMI mechanisms. Specifically, BMX was found to interact with hsa-miR-4430, ID1 was associated with hsa-miR-3200-5p, PIM3 bound to hsa-miR-654-5p, etc., illustrating the extensive cross-talk between key genes and noncoding RNAs in the regulatory network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Drug prediction revealed that PIM3 had high-scoring interactions with unapproved anticancer drugs (e.g., SGI-1776, VADIMEZAN, and AZD-1208) and moderate interactions with approved gefitinib, whereas BMX strongly interacted with unapproved PD-168393/canertinib and approved ibrutinib/ritorycitinib-tosylate. No interacting drugs were found for ID1. The high-scoring interactions of PIM3 and BMX with candidate drugs offer critical clues for subsequent AMI-targeted drug research \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProteomic classification of key genes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePANTHER Family/Subfamily\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePANTHER Protein Class\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytoplasmic tyrosine-protein kinase BMX; BMX; PTN002521548; orthologs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHUMAN|HGNC\u0026thinsp;=\u0026thinsp;1079|UniProtKB\u0026thinsp;=\u0026thinsp;P51813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCYTOPLASMIC TYROSINE-PROTEIN KINASE BMX (PTHR24418:SF91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003enonreceptor tyrosine protein kinase (PC00168)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSerine_threonine-protein kinase pim-3; PIM3; PTN002506631; orthologs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHUMAN|HGNC\u0026thinsp;=\u0026thinsp;19310|UniProtKB\u0026thinsp;=\u0026thinsp;Q86V86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSERINE_THREONINE-PROTEIN KINASE PIM-3 (PTHR22984:SF26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003enonreceptor serine/threonine protein kinase (PC00167)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDNA-binding protein inhibitor ID- 1; ID1; PTN002482400; orthologs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHUMAN|HGNC\u0026thinsp;=\u0026thinsp;5360|UniProtKB\u0026thinsp;=\u0026thinsp;P41134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDNA-BINDING PROTEIN INHIBITOR ID-1 (PTHR11723:SF4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDNA-binding transcription factor (PC00218)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Validation of the expression of key genes via RT‒qPCR\u003c/h2\u003e\u003cp\u003eThe experimental results revealed that PIM3, ID1, and BMX were expressed at significantly higher levels in AMI patients than in controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The upregulation of these three genes may collectively contribute to AMI-induced cell proliferation, metabolic reprogramming, or inflammatory signaling pathway regulation. The experimental results are highly consistent with the bioinformatics analysis in terms of gene expression trends, supporting these genes as key genes in AMI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eExisting treatments for acute myocardial infarction (AMI) remain limited in their efficacy in reducing the progression of heart failure after myocardial infarction(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Clinical evidence indicates that exacerbated inflammation can aggravate myocardial damage and promote heart failure(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The lymphatic system, through its lymphangiogenic processes, plays a crucial role in maintaining tissue homeostasis, transmitting injury signals and immune regulation(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), although the VEGF-C/VEGFR-3 axis, etc.,, has been shown to promote cardiac lymphangiogenesis and improve AMI repair(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e); however, the molecular mechanism of lymphangiogenesis in AMI remains elusive. This study integrated AMI microarray data from the GEO database and applied a machine learning algorithm (LASSO/SVM-ROC), identified three key lymphangiogenesis-related genes (ID1, PIM3, BMX) and constructed a high-precision prediction model (AUC\u0026thinsp;=\u0026thinsp;0.939). Through expression validation, functional enrichment and molecular network analysis, the enrichment of signaling pathways was characterized, and potential drug targets were revealed. Experimental validation further corroborated the reliability of the bioinformatics findings, provided a new perspective for lymphatic vessel regeneration after AMI, and offered new targets and theoretical bases for the precision treatment and drug development of AMI.\u003c/p\u003e\u003cp\u003eID1 (inhibitor of DNA binding 1) is a member of the helix-loop-helix (HLH) transcriptional regulator family. Lacking a basic DNA binding domain, it has no DNA-binding activity(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). ID1, a transcriptional regulator identified in normal and acute myeloid leukemia (AML) stem cells(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), regulates cytokine production in the bone marrow (BM) microenvironment(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). It is characterized as an oncogenic factor and is involved in establishing the immunosuppressive tumor microenvironment(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The ID1 protein is pivotal in regulating lineage specification, cell fate determination, and differentiation timing during neurogenesis, lymphopoiesis, and neovascularization (angiogenesis). It is essential for embryogenesis and cell cycle progression and acts as a positive regulator of cell proliferation(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This study reports for the first time the role of ID1 in lymphangiogenesis in AMI. Previous studies have suggested that ID1 promotes tumor angiogenesis through the TGF-β pathway(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This study revealed that ID1 was significantly downregulated in AMI, possibly through the inhibition of endothelial cell migration, which is consistent with the mechanism by which ID1 deficiency in atherosclerosis leads to impaired vascular repair(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePIM3 (Pim-3 proto-oncogene, serine/threonine kinase) is a serine/threonine kinase involved in various carcinogenic processes and is usually overexpressed in solid tumors (such as pancreatic cancer, liver cancer, colon cancer, gastric cancer and breast cancer)(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). It has been reported to play a crucial role in regulating cell proliferation, the cell cycle, and apoptosis signaling pathways. PIM3 upregulation is associated with poor patient prognosis, and its inhibition reduces cell proliferation, invasion, and tumor growth in vivo(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Therefore, PIM3 serves as an emerging therapeutic target in oncology(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Pim-3 is expressed in essential organs, including the heart, lung, and brain(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). PIM3 is upregulated in response to cardiac ischemia. It enhances the function and survival of endothelial and progenitor cells, thereby promoting angiogenesis. This enhances blood flow to injured heart tissue, thereby promoting repair(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). PIM3 directly phosphorylates and inhibits key proapoptotic proteins (such as BAD). It also activates prosurvival pathways (such as the AKT pathway), protecting cardiomyocytes and other cardiac cells from programmed cell death triggered by ischemic stress and reperfusion injury(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). By enhancing vascular regeneration and reducing cell death, PIM3 mitigates myocardial damage and supports functional recovery after AMI, highlighting its potential as a therapeutic target(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). High PIM3 expression is associated with poor AMI prognosis, which is consistent with its known role in promoting cardiomyocyte apoptosis(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). However, its role in regulating lymphangiogenesis is reported here for the first time.\u003c/p\u003e\u003cp\u003eBMX (bone marrow tyrosine kinase on chromosome X) is a member of the nonreceptor tyrosine kinase Tec family(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), which contains Src homology (SH) 3 and SH2 domains(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), as well as a carboxyl-terminal kinase domain, and plays a key role in multiple biological processes, including cytokine signaling and inflammation(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). It has been implicated in angiogenesis(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Experimental studies have shown that BMX can be rapidly activated in response to hypoxia or inflammatory stimuli, and its upregulation may activate lymphatic endothelial cells through the VEGFR3 pathway(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), suggesting that it may have important pathophysiological significance in ischemic cardiovascular disease. After acute myocardial infarction (AMI), myocardial cell necrosis, inflammatory cell infiltration, and tissue edema occur(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In the infarct core, the upregulation of BMX is mainly observed in residual vascular endothelial cells and infiltrating inflammatory cells; in the marginal area of infarction, the expression of BMX in lymphatic endothelial cells is significantly increased, and its expression is positively correlated with lymphatic vessel density. This expression pattern suggests that BMX may be involved in tissue repair and lymphatic remodeling after myocardial infarction(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eID1 is involved in vascular repair by inhibiting endothelial cell migration, which may indirectly affect the immunomodulatory function of lymphatic endothelial cells (LECs)(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). High expression of PIM3, a proapoptotic kinase, may exacerbate monocyte infiltration by activating the NF-κB pathway (cor\u0026thinsp;=\u0026thinsp;0.44)(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). As the central signaling pathway governing inflammation and immune responses, the NF-κB pathway triggers the translocation of NF-κB dimers into the nucleus, where they activate the transcription of target genes such as proinflammatory cytokines (e.g., TNF-α and IL-6), adhesion molecules, and chemokines(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). NF-κB drives the expression of monocyte chemokines (e.g., CCL2), promotes monocyte infiltration into the infarct area, and releases reactive oxygen species and proteases to exacerbate tissue destruction(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e); on the other hand, overactivated NF-κB exacerbates cardiomyocyte death by upregulating proapoptotic genes (e.g., Bax) and inhibiting antiapoptotic proteins (e.g., Bcl-2)(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). NF-κB activity in the peripheral blood mononuclear cells of AMI patients is positively correlated with inflammatory marker levels, further supporting its potential as a therapeutic target.\u003c/p\u003e\u003cp\u003eBMX was significantly positively correlated with neutrophils (cor\u0026thinsp;=\u0026thinsp;0.49), suggesting that BMX may regulate the neutrophil-mediated inflammatory cascade through the PI3K/AKT pathway(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). The analysis in this study revealed that the immune microenvironment of AMI patients exhibited imbalances, including reduced proportions of CD4\u0026thinsp;+\u0026thinsp;resting memory T cells and enrichment of proinflammatory cells such as neutrophils and M1 macrophages. Decreased infiltration of resting CD4\u003csup\u003e+\u003c/sup\u003e memory T cells, γδ T cells, M1 macrophages, and resting mast cells is associated with the onset and progression of AMI. During the proliferative repair phase, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, regulatory T cells, and NKT cells infiltrate the infarcted myocardium, promoting its maturation(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Recent studies have shown that Treg infiltration drives lymphatic vessel proliferation after AMI(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) and that ID1/PIM3/BMX may affect the microenvironment by regulating T-cell differentiation. Studies suggest that targeting the immunosuppressive microenvironment, regulating proinflammatory cell infiltration, or integrating differential immune cells and proportions with prognostic genes to construct predictive models is expected to enhance AMI treatment strategies(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). However, it is still necessary to expand the sample size, conduct functional experiments, and combine multiomics techniques to further verify the causal relationships and potential regulatory mechanisms between the immune microenvironment and key genes.\u003c/p\u003e\u003cp\u003eThis study focused on AMI and revealed the distinct roles of PIM3, BMX and ID1 as potential therapeutic targets: PIM3 mediates inflammation and fibrosis by activating the NF-κB or STAT3 pathways. PIM3-targeted drugs such as SGI-1776 and gefitinib may balance myocardial inflammation and repair(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). BMX modulates the immune microenvironment through the PI3K/AKT pathway. Inhibitors such as PD-168393 and ibrutinib may restore immune homeostasis(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). BMX kinase inhibitors have shown antiangiogenic effects in lymphoma(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), and this study proposes the potential for repurposing them for the treatment of AMI. Studies have shown that chloroquine (which targets ID1) inhibits pathological angiogenesis in rheumatoid arthritis(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e) and may mitigate abnormal lymphatic remodeling after AMI. However, owing to the unknown mechanism of ID1 and delays in drug development, no effective related drugs have been identified to date. The current research is limited by the coverage of the databases, and clinical translation faces challenges such as cardiotoxicity and a lack of functional verification. Future research should validate the safety and efficacy of PIM3- and BMX-targeted drugs via multiomics integration and functional assays, explore potential intervention strategies for ID1, and propose novel concepts for precision immunotherapy in AMI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, PIM3, BMX and ID1 were identified as key genes from AMI chip data, and the key gene profile of AMI lymphangiogenesis was established for the first time. The potential mechanism of their interaction through neural pathways and the immune microenvironment was revealed, and they form a regulatory network with miRNAs, providing new targets for AMI mechanism research and drug development. However, the results of this study are highly dependent on the data quality of the GEO database microarrays and the accuracy of the machine learning algorithm. Data noise, batch effects, etc., may lead to false positive results in the screening of differential genes. The experimental evidence for the regulation of lymphangiogenesis by neural pathways is still insufficient, and further analysis in in vivo and in vitro models is needed. In the future, key genes can be explored as biomarkers for the risk of heart failure after AMI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eAMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eacute myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eLRGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003elymphangiogenesis-related genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003ereceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003egene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eAHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eAmerican Heart Association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eST-segment elevation myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eVEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003evascular endothelial growth factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003edifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003earea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eGene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eDGIdb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eDrug-Gene Interaction Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eRT‒qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eReverse transcription-quantitative PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eID1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eInhibitor of DNA Binding 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eHLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eHelix-Loop-Helix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eacute myeloid leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003ePIM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003ePim-3 proto-oncogene, serine/threonine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eBMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eBone marrow tyrosine kinase on chromosome X\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003eSrc homology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eLECs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 388px;\"\u003e\n \u003cp\u003elymphatic endothelial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003ethics approval:\u0026nbsp;\u003c/strong\u003eThis research was approved by Fuwai Cardiovascular Hospital, Yunnan Province, China. Approval No: 202103AC100004.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that this research was conducted without any commercial or financial relationships that could represent a potential conflict of interest. All the authors are required to disclose any financial or personal relationships that could be perceived as conflicts of interest. This includes any such activities within the three years prior to work, such as employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications, or travel grants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptionualization: Zhang Lihong; data curation: Zhang Jingjing; formal analysis: Guo Yi; investigation: Zhang Xuanping; resources: Qian Yinnan; software: Zhang Jingjing; writing\u0026mdash;original draft: Zhang Lihong; writing\u0026mdash;review and editing: Guo Yi. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We would like to express our sincere gratitude to all the individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Qian Yanna. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003eavailability statement: The datasets (ANALYZED) used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu, X., Reboll, M. 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D.\u003cem\u003e et al.\u003c/em\u003e Effect of Neiyi Prescription of QIU on autophagy and angiogenic ability of endometriosis via the PPAR\u0026gamma;/NF-\u0026kappa;B signaling pathway. \u003cem\u003eArch Gynecol Obstet\u003c/em\u003e \u003cstrong\u003e306\u003c/strong\u003e, 533-545, doi:10.1007/s00404-022-06537-w (2022).\u003c/li\u003e\n\u003c/ol\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, immune environment, key genes, lymphatic vessels","lastPublishedDoi":"10.21203/rs.3.rs-7844178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7844178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn acute myocardial infarction (AMI), remodeling of the myocardial lymphatic system is crucial for infarct repair and inflammation control. This study used bioinformatics to identify genes related to lymphangiogenesis in AMI, hoping to elucidate the mechanisms of AMI and develop new targeted treatments.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eGSE66360, GSE48060, and lymphangiogenesis-related genes (\u003cem\u003eLRGs\u003c/em\u003e) were obtained from databases and the literature. Key genes associated with lymphangiogenesis were identified through machine learning, receiver operating characteristic (ROC) curve analysis, and expression verification. Gene set enrichment analysis (GSEA), immune infiltration analysis, and drug prediction were subsequently carried out. Finally, experimental verification of key gene expression was performed in clinical samples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThree PIM3, BMX, and ID1 signature genes were obtained by machine learning, and their regions under the curve showed significant differences in expression between groups, with consistent trends in both GSE66360 and GSE48060 datasets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, drug predictions showed PIM3 and BMX interacting with SGI-1776, vadimezan, canine, and gefitinib. Finally, genes in clinical samples also show the same expression trend.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study identified three key genes (\u003cem\u003ePIM3, BMX, and ID1\u003c/em\u003e) as novel key genes in AMI, laying a foundation for clinical diagnosis and drug development.\u003c/p\u003e","manuscriptTitle":"Research on identifying key genes and mechanisms related to lymphangiogenesis in acute myocardial infarction via bioinformatics screening and experimental verification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-29 00:52:06","doi":"10.21203/rs.3.rs-7844178/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":"7afdb6e7-249e-4e67-b8f9-66b6ee30e8af","owner":[],"postedDate":"November 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58713305,"name":"Health sciences/Biomarkers"},{"id":58713306,"name":"Health sciences/Cardiology"},{"id":58713307,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58713308,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-12-19T05:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-29 00:52:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7844178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7844178","identity":"rs-7844178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}