Integrating GEO Database and Bioinformatics Analysis to Screen the Key Targets of Tanshinone IIA in Myocardial Infarction

Integrating GEO Database and Bioinformatics Analysis to Screen the Key Targets of Tanshinone IIA in Myocardial Infarction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating GEO Database and Bioinformatics Analysis to Screen the Key Targets of Tanshinone IIA in Myocardial Infarction GUAN Zhong-yi, Xu Wen-hua, Zheng Jing-hui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8185303/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 Objective: To systematically screen the core targets of Tanshinone IIA in the treatment of myocardial infarction (MI) and to explore its molecular mechanisms based on the Gene Expression Omnibus (GEO) database and bioinformatics methods. Methods: MI-related transcriptome datasets (GSE62646, GSE83500) were obtained from the GEO database. The "sva" package was used to correct batch effects, and the "limma" package was employed to identify differentially expressed genes (DEGs). Potential targets of Tanshinone IIA were retrieved by integrating the TCMSP, CTD, and SwissTargetPrediction databases. Intersecting genes were extracted via Venn diagrams, and a protein-protein interaction (PPI) network was constructed. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, was performed using DAVID. Molecular docking was conducted using CB-Dock2 to validate binding affinity. Results: A total of 8 core candidate targets were identified, including SCN9A, PIM1, GSK3A, RELA, HMGB1, FASN, ACAT1 , and MPI . Molecular docking showed that the binding energies of Tanshinone IIA with all targets were lower than -6.8 kcal/mol, with the strongest binding observed for SCN9A (-10.6 kcal/mol) and PIM1 (-10.2 kcal/mol). GO analysis revealed that the targets were significantly enriched in biological processes such as inflammatory response and interleukin regulation. KEGG analysis indicated that the main pathways involved were fatty acid metabolism, the AGE-RAGE signaling pathway, and the acute myeloid leukemia pathway. Conclusion: Tanshinone IIA may exert multi-dimensional protective effects in the treatment of myocardial infarction by acting on multiple targets such as SCN9A and PIM1, and by synergistically regulating key pathways including inflammatory response and fatty acid metabolism. This study provides a theoretical basis for elucidating its systematic mechanism of action and future clinical translation. Tanshinone IIA Myocardial Infarction Bioinformatics Molecular Docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Myocardial infarction (MI) is the most critical clinical type of coronary heart disease, with its primary pathological features being the rupture of coronary atherosclerotic plaques, secondary thrombus formation, and consequent persistent ischemic necrosis of the myocardium.With changes in lifestyle and an aging population in China, cardiovascular diseases have become the leading cause of death among urban and rural residents, among which the incidence and mortality of MI continue to show a persistent upward trend[ 1 – 3 ]. Although reperfusion therapy and percutaneous coronary intervention (PCI) have improved patient prognosis to some extent, the clinical management of MI still faces severe challenges due to issues such as the narrow therapeutic time window, reperfusion injury, and postoperative restenosis[ 4 – 6 ] .In-depth elucidation of the molecular mechanisms underlying the occurrence and development of MI and the identification of effective intervention targets hold significant scientific and clinical value. Tanshinone IIA is a lipophilic diterpenoid quinone extracted from the traditional Chinese medicine Salvia miltiorrhiza Bunge, and it is one of the core material bases for its efficacy in promoting blood circulation and removing blood stasis. Studies have confirmed that Tanshinone IIA possesses multiple cardiovascular protective effects, including anti-inflammatory, antioxidant, inhibition of cardiomyocyte apoptosis, and amelioration of myocardial fibrosis[ 7 , 8 ]. In recent years, several experimental studies have preliminarily revealed its potential in MI treatment, such as mitigating myocardial injury through mechanisms like inhibiting calpain-1 activity[ 9 ], regulating the NRF2/NLRP3 /pyroptosis axis [ 10 ], modulating mitophagy [ 11 ], and interfering with the STING signaling pathway[ 12 ]. However, most of these studies have focused on single signaling pathways or specific phenotypic observations, failing to integrate multi-omics data and network regulatory relationships at a systematic level. Consequently, the core target system and the holistic mechanism of action of Tanshinone IIA in MI treatment remain unclear. With the rapid development of bioinformatics and computational biology methods, integrating gene expression databases (such as GEO) with network pharmacology strategies has become an effective approach for deciphering the complex mechanisms of action of traditional Chinese medicine. Such methods can identify intersections between key disease genes and drug action targets at the whole-genome level, construct a "drug-target-pathway-disease" multidimensional interaction network, and thereby systematically elucidate the drug's mechanism of action[ 13 ]. Based on this, our study integrates MI-related transcriptome datasets from GEO and multiple drug target databases including TCMSP, CTD, and Swiss Target Prediction to systematically screen the potential core targets of Tanshinone IIA against MI. Furthermore, we employ differential expression analysis, protein-protein interaction (PPI) network construction, and GO/KEGG functional enrichment analysis to reveal the involved biological processes and signaling pathways. Molecular docking technology is also used to validate the binding affinity of core targets, assessing the stability of the interaction between Tanshinone IIA and key targets at the computational simulation level. This study aims to establish a systematic research framework of "multi-omics integration-network analysis-structural verification" to clarify the molecular mechanism by which Tanshinone IIA coordinately regulates MI-associated inflammation and metabolic disorders through multiple targets and pathways, thereby providing a theoretical basis and candidate targets for its clinical translation. 1. Methods 1.1 Myocardial Infarction-Related Gene Expression Data Myocardial Infarction (MI)-related transcriptome data were obtained from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI). Using "Myocardial infarct" as the search keyword and limiting the sample source to peripheral blood, we identified two eligible datasets: GSE62646 and GSE83500. 1.2 Collection of Tanshinone IIA Action Targets Using "Tanshinone IIA" as the search term, target collection was performed by combining multiple databases, including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the Comparative Toxicogenomics Database (CTD), and the Swiss Target Prediction database. The potential target gene set of Tanshinone IIA was obtained through integration. 1.3 Data Preprocessing The ComBat function from the "sva" R package was employed to correct the batch effects in the two selected MI datasets. By eliminating systematic biases between different datasets, the consistency and comparability of the data were ensured, providing a reliable foundation for subsequent differential expression analysis. 1.4 Identification of Differentially Expressed Genes Differential expression analysis was performed on the corrected and integrated dataset using the "limma" R package.The screening criteria were set as | logFC | >0.2 and P < 0.05 to identify MI-related differentially expressed genes (DEGs). Through Venn diagram analysis, the intersection genes between MI-related DEGs and Tanshinone IIA target genes were extracted and defined as the core candidate targets of Tanshinone IIA against MI. The protein-protein interaction (PPI) network of the core candidate targets was constructed using the GeneMANIA online tool ( http://genemania.org ) to explore the interactions between genes. 1.5 Functional Enrichment Analysis of Myocardial Infarction Differential Genes The DAVID database ( https://davidbioinformatics.nih.gov/home.jsp ) was used to perform Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the core candidate targets. A significance threshold of P < 0.05 was set to screen for statistically significant GO terms (including Biological Process [BP], Cellular Component [CC], and Molecular Function [MF]) and KEGG signaling pathways, thereby clarifying the biological functions and potential mechanisms of action of the core candidate targets. 1.6 Molecular Docking Validation The protein structures of the core candidate targets were downloaded from the Protein Data Bank (PDB) database, and the three-dimensional structure of Tanshinone IIA was obtained from the PubChem database. After preprocessing the protein structures (e.g., removing water molecules, adding hydrogen atoms) using AutoDockTools 1.5.6 software, protein-ligand molecular docking experiments were conducted via the CB-Dock2[ 14 , 15 ] online tool ( https://cadd.labshare.cn/cb-dock2/php/index.php ). The binding affinity was used to evaluate the binding capacity between Tanshinone IIA and the core candidate targets. 2 Results 2.1 Data Processing The detailed information of the two selected MI datasets is presented in Table 1 . The GSE62646 dataset comprises 14 normal samples and 84 MI samples, based on the GPL6244 platform; the GSE83500 dataset includes 20 normal samples and 17 MI samples, based on the GPL13667 platform. All samples were derived from peripheral blood. Principal Component Analysis (PCA) results showed that before batch effect correction, samples from different datasets exhibited dispersed clustering, indicating significant batch differences (Fig. 1 A). After correction using the ComBat function, samples from different datasets were mixed and clustered together, demonstrating a significant improvement in clustering (Fig. 1 B). This indicates that the batch effects were effectively controlled, and the data were suitable for subsequent analysis. Table 1 Sample Information of the MI Datasets ID GSE series Disease Samples Source types Platform 1 GSE62646 MI 14 normal and 84 MI [samples] Peripheral blood GPL6244 2 GSE83500 MI 20 normal and 17 MI [samples] Peripheral blood GPL13667 2.2 Screening of Differentially Expressed Genes and Identification of Core Candidate Targets The limma package analysis revealed a total of 1227 MI-related differentially expressed genes (DEGs), comprising 524 up-regulated and 703 down-regulated genes (Fig. 2 ). Venn diagram intersection analysis identified eight common genes between the MI-related DEGs and the Tanshinone IIA target genes (Fig. 3 ), namely MPI, GSK3A, PIM1, ACAT1, SCN9A, RELA, FASN , and HMGB1 . These genes were defined as the core candidate targets of Tanshinone IIA against MI.Using the GeneMANIA online tool (version 3.6.0) with the Homo sapiens genome as the reference background, a protein-protein interaction (PPI) network was constructed for the eight core candidate targets of Tanshinone IIA against MI ( ACAT1, GSK3A, PIM1, SCN9A, FASN, RELA, MPI, HMGB1 ). This systematically revealed the interaction relationships among the core genes and the potential regulatory network (Fig. 4 ). 2.3 Functional Enrichment Analysis Results of the Core Candidate Targets Using a threshold of P < 0.05 , the Gene Ontology (GO) functional annotation analysis identified a total of 34 significantly enriched terms, comprising 11 in Biological Process (BP), 1 in Cellular Component (CC), and 22 in Molecular Function (MF). At the BP level, the core candidate targets were primarily enriched in processes such as inflammatory response, positive regulation of amyloid-β, positive regulation of interleukin-12, negative regulation of insulin receptor signaling pathway, and positive regulation of interleukin-8 production. Among these, the inflammatory response exhibited the highest degree of enrichment. At the CC level, enrichment was observed solely in the term 'cytosol'. At the MF level, significant enrichment was mainly observed for DNA-binding transcription factor binding, cis-regulatory region sequence-specific DNA binding, and various histone kinase activities (e.g., histone H4 serine 1 (S1) kinase activity, histone H3 serine 10 (S10) kinase activity). Functions such as AMP-activated protein kinase activity and ribosomal protein S6 kinase activity were also involved. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis screened three significantly associated pathways: Fatty acid metabolism, Acute myeloid leukemia, and the AGE-RAGE signaling pathway in diabetic complications. Among these, the fatty acid metabolism pathway demonstrated the highest statistical significance, suggesting that the core candidate targets may participate in the therapeutic effect of Tanshinone IIA on MI by modulating this pathway. 2.4 Molecular Docking Validation Results The molecular docking results demonstrated that Tanshinone IIA exhibited good binding affinity with all eight core candidate targets, with binding energies all less than − 6.8 kcal/mol (Fig. 6 ).Among them, SCN9A had the lowest binding energy with Tanshinone IIA (-10.6 kcal/mol), indicating the strongest binding capability, followed by PIM1 (-10.2 kcal/mol). GSK3A and MPI shared identical binding energies (both − 8.8 kcal/mol). The binding energies for RELA , ACAT1 , HMGB1 , and FASN were − 8.3 kcal/mol, -7.8 kcal/mol, -7.0 kcal/mol, and − 6.8 kcal/mol, respectively, all demonstrating a strong potential for interaction. These results further verify the stable binding between Tanshinone IIA and the core candidate targets. 3 Discussion The pathological process following myocardial infarction (MI) involves complex dysregulation of molecular networks, including inflammatory cascades, metabolic disturbances, apoptosis, and fibrotic repair [ 16 ]. Tanshinone IIA, an active component extracted from Salvia miltiorrhiza, possesses effects such as alleviating inflammation, inhibiting myocardial fibrosis, and dilating blood vessels [ 17 ]. By integrating GEO transcriptome data and systems pharmacology approaches, this study identified eight core candidate targets ( MPI, GSK3A, PIM1, ACAT1, SCN9A, RELA, FASN, HMGB1 ) of Tanshinone IIA against MI, and constructed a "multi-target, multi-function, multi-pathway" regulatory network. This provides a new theoretical basis for systematically elucidating the cardioprotective mechanism of Tanshinone IIA. Molecular docking results showed that Tanshinone IIA had strong binding affinity with all eight core targets, among which SCN9A (-10.6 kcal/mol) and PIM1 (-10.2 kcal/mol) exhibited the lowest binding energies, suggesting they might be the key targets. SCN9A encodes a voltage-gated sodium channel protein. Beyond its role in pain conduction, recent studies indicate its potential involvement in cardiomyocyte electrical activity and cell fate regulation. Zhang et al. [ 18 ]found that SCN9A expression was significantly up-regulated in induced pluripotent stem cell-derived mesenchymal stem cells overexpressing cardiac proteins, suggesting its potential involvement in cardiomyocyte-like transdifferentiation. PIM1 , a serine/threonine kinase, not only promotes cell proliferation and survival but also plays a crucial role in cardiac repair. Kulandavelu et al. [ 19 ] demonstrated that PIM1 overexpression enhances the regenerative capacity of c-kit + cardiac stem cells, reduces infarct scar size, and improves cardiac function. GSK3A participates in cardiac stress response by regulating the β-adrenergic signaling pathway[ 20 ]; RELA , a key transcription factor in the NF-κB pathway, remains activated during myocardial regeneration [ 21 ]; and HMGB1 , as an important damage-associated molecular pattern ( DAMP ) molecule, coordinates inflammatory repair and tissue regeneration after MI [ 22 ]. These targets function at different levels—electrophysiological regulation, cell regeneration, inflammatory response, and metabolic balance—collectively forming the molecular basis for the multi-dimensional regulation of the MI pathological process by Tanshinone IIA. GO enrichment analysis results indicated that the core targets were significantly enriched in biological processes such as inflammatory response, interleukin regulation, and negative regulation of insulin signaling, with the "inflammatory response" being the most highly enriched. This aligns closely with the pathological features of post-MI immune cell infiltration, DAMP release, and secondary myocardial injury[ 23 ]. Studies have shown that Tanshinone IIA can regulate macrophage polarization by interfering with the PGK1-PDHK1 pathway, enhancing anti-inflammatory factor secretion, alleviating myocardial fibrosis, and promoting cardiac function recovery [ 24 ]. Our study, from a network perspective, confirms that the anti-inflammatory effect of Tanshinone IIA is not isolated but achieved through the synergistic regulation of multiple targets, providing systems biology evidence for its holistic effect in post-MI inflammation regulation. KEGG pathway analysis further identified three key pathways: fatty acid metabolism, acute myeloid leukemia, and the AGE-RAGE signaling pathway. The fatty acid metabolism pathway was the most significantly enriched, suggesting that metabolic reprogramming may be a core link in the therapeutic action of Tanshinone IIA. The heart, as a high-energy-demand organ, primarily relies on fatty acid β-oxidation for energy supply. After MI, myocardial energy metabolism is disrupted, fatty acid oxidation capacity declines, leading to insufficient ATP supply and accelerated cardiomyocyte death and dysfunction[ 25 ]. Among the core targets in this study, FASN (fatty acid synthase) and ACAT1 (acetyl-CoA acetyltransferase 1) are both directly involved in fatty acid synthesis and oxidation processes. Yu et al. [ 26 ] pointed out that free fatty acids are not only involved in atherosclerosis formation but also promote MI progression by inducing endothelial dysfunction and promoting thrombus formation. Tanshinone IIA may correct post-MI lipid metabolic disturbances in cardiomyocytes and restore energy supply by regulating FASN and ACAT1 , thereby exerting its cardioprotective effects. This finding shifts the research perspective on Tanshinone IIA from the traditional "anti-inflammatory, anti-apoptotic" focus to "metabolic regulation," providing a new direction for its mechanistic studies. This study employed an integrated strategy of "transcriptomics - target screening - network construction - functional enrichment - molecular docking," overcoming the limitations of previous single-pathway or single-target research and constructing a multi-dimensional regulatory network for Tanshinone IIA against MI. It not only computationally verified the binding feasibility with the core targets but also systematically elucidated its "multi-target, multi-pathway" synergistic mechanism from the perspectives of biological function and pathways, particularly revealing the potential value of "inflammation-metabolism" crosstalk in MI treatment. However, as this study is primarily based on bioinformatic predictions and computational simulations, its findings still require further experimental validation. Future research could focus on key targets such as SCN9A and PIM1 , utilizing gene editing, protein-protein interaction assays, and myocardial infarction animal models to validate the regulatory effects and functional mechanisms of Tanshinone IIA at the molecular, cellular, and whole-organism levels. Furthermore, the dynamic changes of the fatty acid metabolism pathway after MI and the interventional effects of Tanshinone IIA also warrant in-depth exploration. Declarations Author contributions Guan Zhong-yi: data curation, software, visualization, writing—original draft. Xu Wen-hua: conceptualization, methodology, writing—review & editing, funding acquisition. Zheng Jing-hui: supervision, project administration, writing—review & editing, funding acquisition. Conflicts of interest The authors declare no conflicts of interest. Ethics approval Not required; all data were downloaded from publicly available databases. Data availability statement The datasets analyzed during the current study are available in the GEO repository (GSE62646, GSE83500). Other data are available from the corresponding authors upon reasonable request. Author information Guangxi University of Chinese Medicine, Nanning 530200, Guangxi, China Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning 530011, Guangxi, China GUAN Zhong-yi（ [email protected] ） *Corresponding authors Xu Wen-hua ( [email protected] ) Zheng Jing-hui ( [email protected] ) Acknowledgments We thank the GEO, TCMSP, CTD, SwissTargetPrediction, DAVID and CB-Dock2 teams for providing free databases and tools. Funding This work was supported by the Guangxi Natural Science Foundation (2024GXNSFBA010300); Guangxi Higher-Education Young and Middle-Aged Teachers’ Basic Research Capacity Enhancement Project (2022KY0269); Young Scientists Fund of Guangxi University of Chinese Medicine (2021QN028); Self-funded Research Project of Guangxi Zhuang Autonomous Region Health Commission (Z20212460); Self-funded Research Projects of Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine (GXZYZ20210474, GXZYA20250123); and the 2023 National Undergraduate Innovation and Entrepreneurship Training Program (202310600031). Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Han YL, Jiang H, Yang JY, Chen J (2022) Chinese Society of Cardiology & Editorial Board of Chinese Journal of Cardiology. 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10:50:39","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73559,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/acc007f9b64650dbae2edf5a.html"},{"id":96717961,"identity":"22db03a2-348a-4957-b3bf-996dbea60db9","added_by":"auto","created_at":"2025-11-25 10:50:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":439601,"visible":true,"origin":"","legend":"\u003cp\u003ePCA analysis results of the MI datasets before and after batch effect correction. (A): Before correction; (B): After correction.\u003c/p\u003e","description":"","filename":"FIG.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/71e2ff67941338926a4aea3b.png"},{"id":96913284,"identity":"b9fbb22e-1240-44ca-aad3-3418c2b68cff","added_by":"auto","created_at":"2025-11-27 13:57:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":384454,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of the differentially expressed genes (DEGs) in myocardial infarction (MI).\u003c/p\u003e","description":"","filename":"FIG.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/b3f85197e7ac387e76776856.png"},{"id":96717965,"identity":"7d9207aa-3cfe-41ae-b261-94893ac27e7d","added_by":"auto","created_at":"2025-11-25 10:50:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":286317,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram illustrating the intersection between MI-related differentially expressed genes (DEGs) and Tanshinone IIA target genes.\u003c/p\u003e","description":"","filename":"FIG.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/8788fd8ced9b6f787605d1fd.png"},{"id":96717963,"identity":"9e8aa49f-ec91-437b-a6b1-a0f8fef196f8","added_by":"auto","created_at":"2025-11-25 10:50:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":907592,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction (PPI) network of the eight core candidate targets of Tanshinone IIA against myocardial infarction (MI).\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/dc861ff71cea9e801e92199b.png"},{"id":96717974,"identity":"f1e79aa5-1611-4920-b046-1867057ffe70","added_by":"auto","created_at":"2025-11-25 10:50:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":959328,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis results of the core candidate targets. (A) Gene Ontology (GO) analysis; (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.\u003c/p\u003e","description":"","filename":"FIG.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/d85f5f5a5f18c46edd0cc336.png"},{"id":96717972,"identity":"dd43f19d-44f4-46a2-bc88-8463557796bd","added_by":"auto","created_at":"2025-11-25 10:50:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2814291,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking validation results.\u003c/p\u003e","description":"","filename":"FIG.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/29b65108d30e7f4fe722de13.png"},{"id":97135416,"identity":"352e59f4-3f16-4846-a0a0-83e6442d34f4","added_by":"auto","created_at":"2025-12-01 09:44:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6337759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8185303/v1/5327ab64-8e21-4463-972e-541e108a4fc8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrating GEO Database and Bioinformatics Analysis to Screen the Key Targets of Tanshinone IIA in Myocardial Infarction\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyocardial infarction (MI) is the most critical clinical type of coronary heart disease, with its primary pathological features being the rupture of coronary atherosclerotic plaques, secondary thrombus formation, and consequent persistent ischemic necrosis of the myocardium.With changes in lifestyle and an aging population in China, cardiovascular diseases have become the leading cause of death among urban and rural residents, among which the incidence and mortality of MI continue to show a persistent upward trend[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although reperfusion therapy and percutaneous coronary intervention (PCI) have improved patient prognosis to some extent, the clinical management of MI still faces severe challenges due to issues such as the narrow therapeutic time window, reperfusion injury, and postoperative restenosis[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] .In-depth elucidation of the molecular mechanisms underlying the occurrence and development of MI and the identification of effective intervention targets hold significant scientific and clinical value.\u003c/p\u003e\u003cp\u003eTanshinone IIA is a lipophilic diterpenoid quinone extracted from the traditional Chinese medicine Salvia miltiorrhiza Bunge, and it is one of the core material bases for its efficacy in promoting blood circulation and removing blood stasis. Studies have confirmed that Tanshinone IIA possesses multiple cardiovascular protective effects, including anti-inflammatory, antioxidant, inhibition of cardiomyocyte apoptosis, and amelioration of myocardial fibrosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, several experimental studies have preliminarily revealed its potential in MI treatment, such as mitigating myocardial injury through mechanisms like inhibiting calpain-1 activity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], regulating the \u003cem\u003eNRF2/NLRP3\u003c/em\u003e/pyroptosis axis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], modulating mitophagy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and interfering with the STING signaling pathway[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, most of these studies have focused on single signaling pathways or specific phenotypic observations, failing to integrate multi-omics data and network regulatory relationships at a systematic level. Consequently, the core target system and the holistic mechanism of action of Tanshinone IIA in MI treatment remain unclear.\u003c/p\u003e\u003cp\u003eWith the rapid development of bioinformatics and computational biology methods, integrating gene expression databases (such as GEO) with network pharmacology strategies has become an effective approach for deciphering the complex mechanisms of action of traditional Chinese medicine. Such methods can identify intersections between key disease genes and drug action targets at the whole-genome level, construct a \"drug-target-pathway-disease\" multidimensional interaction network, and thereby systematically elucidate the drug's mechanism of action[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on this, our study integrates MI-related transcriptome datasets from GEO and multiple drug target databases including TCMSP, CTD, and Swiss Target Prediction to systematically screen the potential core targets of Tanshinone IIA against MI. Furthermore, we employ differential expression analysis, protein-protein interaction (PPI) network construction, and GO/KEGG functional enrichment analysis to reveal the involved biological processes and signaling pathways. Molecular docking technology is also used to validate the binding affinity of core targets, assessing the stability of the interaction between Tanshinone IIA and key targets at the computational simulation level. This study aims to establish a systematic research framework of \"multi-omics integration-network analysis-structural verification\" to clarify the molecular mechanism by which Tanshinone IIA coordinately regulates MI-associated inflammation and metabolic disorders through multiple targets and pathways, thereby providing a theoretical basis and candidate targets for its clinical translation.\u003c/p\u003e"},{"header":"1. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Myocardial Infarction-Related Gene Expression Data\u003c/h2\u003e\u003cp\u003eMyocardial Infarction (MI)-related transcriptome data were obtained from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI). Using \"Myocardial infarct\" as the search keyword and limiting the sample source to peripheral blood, we identified two eligible datasets: GSE62646 and GSE83500.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Collection of Tanshinone IIA Action Targets\u003c/h2\u003e\u003cp\u003eUsing \"Tanshinone IIA\" as the search term, target collection was performed by combining multiple databases, including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the Comparative Toxicogenomics Database (CTD), and the Swiss Target Prediction database. The potential target gene set of Tanshinone IIA was obtained through integration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Data Preprocessing\u003c/h2\u003e\u003cp\u003eThe ComBat function from the \"sva\" R package was employed to correct the batch effects in the two selected MI datasets. By eliminating systematic biases between different datasets, the consistency and comparability of the data were ensured, providing a reliable foundation for subsequent differential expression analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Identification of Differentially Expressed Genes\u003c/h2\u003e\u003cp\u003eDifferential expression analysis was performed on the corrected and integrated dataset using the \"limma\" R package.The screening criteria were set as \u003cem\u003e| logFC | \u0026gt;0.2\u003c/em\u003e and \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e to identify MI-related differentially expressed genes (DEGs). Through Venn diagram analysis, the intersection genes between MI-related DEGs and Tanshinone IIA target genes were extracted and defined as the core candidate targets of Tanshinone IIA against MI. The protein-protein interaction (PPI) network of the core candidate targets was constructed using the GeneMANIA online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org\u003c/span\u003e\u003cspan address=\"http://genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to explore the interactions between genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.5 Functional Enrichment Analysis of Myocardial Infarction Differential Genes\u003c/h2\u003e\u003cp\u003eThe DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the core candidate targets. A significance threshold of \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e was set to screen for statistically significant GO terms (including Biological Process [BP], Cellular Component [CC], and Molecular Function [MF]) and KEGG signaling pathways, thereby clarifying the biological functions and potential mechanisms of action of the core candidate targets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.6 Molecular Docking Validation\u003c/h2\u003e\u003cp\u003eThe protein structures of the core candidate targets were downloaded from the Protein Data Bank (PDB) database, and the three-dimensional structure of Tanshinone IIA was obtained from the PubChem database. After preprocessing the protein structures (e.g., removing water molecules, adding hydrogen atoms) using AutoDockTools 1.5.6 software, protein-ligand molecular docking experiments were conducted via the CB-Dock2[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2/php/index.php\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock2/php/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The binding affinity was used to evaluate the binding capacity between Tanshinone IIA and the core candidate targets.\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Processing\u003c/h2\u003e\u003cp\u003eThe detailed information of the two selected MI datasets is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The GSE62646 dataset comprises 14 normal samples and 84 MI samples, based on the GPL6244 platform; the GSE83500 dataset includes 20 normal samples and 17 MI samples, based on the GPL13667 platform. All samples were derived from peripheral blood.\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA) results showed that before batch effect correction, samples from different datasets exhibited dispersed clustering, indicating significant batch differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After correction using the ComBat function, samples from different datasets were mixed and clustered together, demonstrating a significant improvement in clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This indicates that the batch effects were effectively controlled, and the data were suitable for subsequent analysis.\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\u003eSample Information of the MI Datasets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eGSE series\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisease\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSource types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSE62646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 normal and 84 MI [samples]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeripheral blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGPL6244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSE83500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 normal and 17 MI [samples]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeripheral blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGPL13667\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=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Screening of Differentially Expressed Genes and Identification of Core Candidate Targets\u003c/h2\u003e\u003cp\u003eThe limma package analysis revealed a total of 1227 MI-related differentially expressed genes (DEGs), comprising 524 up-regulated and 703 down-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Venn diagram intersection analysis identified eight common genes between the MI-related DEGs and the Tanshinone IIA target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), namely \u003cem\u003eMPI, GSK3A, PIM1, ACAT1, SCN9A, RELA, FASN\u003c/em\u003e, and \u003cem\u003eHMGB1\u003c/em\u003e. These genes were defined as the core candidate targets of Tanshinone IIA against MI.Using the GeneMANIA online tool (version 3.6.0) with the Homo sapiens genome as the reference background, a protein-protein interaction (PPI) network was constructed for the eight core candidate targets of Tanshinone IIA against MI (\u003cem\u003eACAT1, GSK3A, PIM1, SCN9A, FASN, RELA, MPI, HMGB1\u003c/em\u003e). This systematically revealed the interaction relationships among the core genes and the potential regulatory network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Functional Enrichment Analysis Results of the Core Candidate Targets\u003c/h2\u003e\u003cp\u003eUsing a threshold of \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, the Gene Ontology (GO) functional annotation analysis identified a total of 34 significantly enriched terms, comprising 11 in Biological Process (BP), 1 in Cellular Component (CC), and 22 in Molecular Function (MF). At the BP level, the core candidate targets were primarily enriched in processes such as inflammatory response, positive regulation of amyloid-β, positive regulation of interleukin-12, negative regulation of insulin receptor signaling pathway, and positive regulation of interleukin-8 production. Among these, the inflammatory response exhibited the highest degree of enrichment. At the CC level, enrichment was observed solely in the term 'cytosol'. At the MF level, significant enrichment was mainly observed for DNA-binding transcription factor binding, cis-regulatory region sequence-specific DNA binding, and various histone kinase activities (e.g., histone H4 serine 1 (S1) kinase activity, histone H3 serine 10 (S10) kinase activity). Functions such as AMP-activated protein kinase activity and ribosomal protein S6 kinase activity were also involved.\u003c/p\u003e\u003cp\u003eThe Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis screened three significantly associated pathways: Fatty acid metabolism, Acute myeloid leukemia, and the AGE-RAGE signaling pathway in diabetic complications. Among these, the fatty acid metabolism pathway demonstrated the highest statistical significance, suggesting that the core candidate targets may participate in the therapeutic effect of Tanshinone IIA on MI by modulating this pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Molecular Docking Validation Results\u003c/h2\u003e\u003cp\u003eThe molecular docking results demonstrated that Tanshinone IIA exhibited good binding affinity with all eight core candidate targets, with binding energies all less than \u0026minus;\u0026thinsp;6.8 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).Among them, \u003cem\u003eSCN9A\u003c/em\u003e had the lowest binding energy with Tanshinone IIA (-10.6 kcal/mol), indicating the strongest binding capability, followed by \u003cem\u003ePIM1\u003c/em\u003e (-10.2 kcal/mol). \u003cem\u003eGSK3A\u003c/em\u003e and \u003cem\u003eMPI\u003c/em\u003e shared identical binding energies (both \u0026minus;\u0026thinsp;8.8 kcal/mol). The binding energies for \u003cem\u003eRELA\u003c/em\u003e, \u003cem\u003eACAT1\u003c/em\u003e, \u003cem\u003eHMGB1\u003c/em\u003e, and \u003cem\u003eFASN\u003c/em\u003e were \u0026minus;\u0026thinsp;8.3 kcal/mol, -7.8 kcal/mol, -7.0 kcal/mol, and \u0026minus;\u0026thinsp;6.8 kcal/mol, respectively, all demonstrating a strong potential for interaction. These results further verify the stable binding between Tanshinone IIA and the core candidate targets.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThe pathological process following myocardial infarction (MI) involves complex dysregulation of molecular networks, including inflammatory cascades, metabolic disturbances, apoptosis, and fibrotic repair [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Tanshinone IIA, an active component extracted from Salvia miltiorrhiza, possesses effects such as alleviating inflammation, inhibiting myocardial fibrosis, and dilating blood vessels [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By integrating GEO transcriptome data and systems pharmacology approaches, this study identified eight core candidate targets (\u003cem\u003eMPI, GSK3A, PIM1, ACAT1, SCN9A, RELA, FASN, HMGB1\u003c/em\u003e) of Tanshinone IIA against MI, and constructed a \"multi-target, multi-function, multi-pathway\" regulatory network. This provides a new theoretical basis for systematically elucidating the cardioprotective mechanism of Tanshinone IIA.\u003c/p\u003e\u003cp\u003eMolecular docking results showed that Tanshinone IIA had strong binding affinity with all eight core targets, among which \u003cem\u003eSCN9A\u003c/em\u003e (-10.6 kcal/mol) and \u003cem\u003ePIM1\u003c/em\u003e (-10.2 kcal/mol) exhibited the lowest binding energies, suggesting they might be the key targets. \u003cem\u003eSCN9A\u003c/em\u003e encodes a voltage-gated sodium channel protein. Beyond its role in pain conduction, recent studies indicate its potential involvement in cardiomyocyte electrical activity and cell fate regulation. Zhang et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]found that \u003cem\u003eSCN9A\u003c/em\u003e expression was significantly up-regulated in induced pluripotent stem cell-derived mesenchymal stem cells overexpressing cardiac proteins, suggesting its potential involvement in cardiomyocyte-like transdifferentiation. \u003cem\u003ePIM1\u003c/em\u003e, a serine/threonine kinase, not only promotes cell proliferation and survival but also plays a crucial role in cardiac repair. Kulandavelu et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] demonstrated that \u003cem\u003ePIM1\u003c/em\u003e overexpression enhances the regenerative capacity of c-kit\u0026thinsp;+\u0026thinsp;cardiac stem cells, reduces infarct scar size, and improves cardiac function. \u003cem\u003eGSK3A\u003c/em\u003e participates in cardiac stress response by regulating the β-adrenergic signaling pathway[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; \u003cem\u003eRELA\u003c/em\u003e, a key transcription factor in the NF-κB pathway, remains activated during myocardial regeneration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; and \u003cem\u003eHMGB1\u003c/em\u003e, as an important damage-associated molecular pattern (\u003cem\u003eDAMP\u003c/em\u003e) molecule, coordinates inflammatory repair and tissue regeneration after MI [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These targets function at different levels\u0026mdash;electrophysiological regulation, cell regeneration, inflammatory response, and metabolic balance\u0026mdash;collectively forming the molecular basis for the multi-dimensional regulation of the MI pathological process by Tanshinone IIA.\u003c/p\u003e\u003cp\u003eGO enrichment analysis results indicated that the core targets were significantly enriched in biological processes such as inflammatory response, interleukin regulation, and negative regulation of insulin signaling, with the \"inflammatory response\" being the most highly enriched. This aligns closely with the pathological features of post-MI immune cell infiltration, \u003cem\u003eDAMP\u003c/em\u003e release, and secondary myocardial injury[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies have shown that Tanshinone IIA can regulate macrophage polarization by interfering with the PGK1-PDHK1 pathway, enhancing anti-inflammatory factor secretion, alleviating myocardial fibrosis, and promoting cardiac function recovery [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study, from a network perspective, confirms that the anti-inflammatory effect of Tanshinone IIA is not isolated but achieved through the synergistic regulation of multiple targets, providing systems biology evidence for its holistic effect in post-MI inflammation regulation.\u003c/p\u003e\u003cp\u003eKEGG pathway analysis further identified three key pathways: fatty acid metabolism, acute myeloid leukemia, and the AGE-RAGE signaling pathway. The fatty acid metabolism pathway was the most significantly enriched, suggesting that metabolic reprogramming may be a core link in the therapeutic action of Tanshinone IIA. The heart, as a high-energy-demand organ, primarily relies on fatty acid β-oxidation for energy supply. After MI, myocardial energy metabolism is disrupted, fatty acid oxidation capacity declines, leading to insufficient ATP supply and accelerated cardiomyocyte death and dysfunction[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among the core targets in this study, \u003cem\u003eFASN\u003c/em\u003e (fatty acid synthase) and \u003cem\u003eACAT1\u003c/em\u003e (acetyl-CoA acetyltransferase 1) are both directly involved in fatty acid synthesis and oxidation processes. Yu et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] pointed out that free fatty acids are not only involved in atherosclerosis formation but also promote MI progression by inducing endothelial dysfunction and promoting thrombus formation. Tanshinone IIA may correct post-MI lipid metabolic disturbances in cardiomyocytes and restore energy supply by regulating \u003cem\u003eFASN\u003c/em\u003e and \u003cem\u003eACAT1\u003c/em\u003e, thereby exerting its cardioprotective effects. This finding shifts the research perspective on Tanshinone IIA from the traditional \"anti-inflammatory, anti-apoptotic\" focus to \"metabolic regulation,\" providing a new direction for its mechanistic studies.\u003c/p\u003e\u003cp\u003eThis study employed an integrated strategy of \"transcriptomics - target screening - network construction - functional enrichment - molecular docking,\" overcoming the limitations of previous single-pathway or single-target research and constructing a multi-dimensional regulatory network for Tanshinone IIA against MI. It not only computationally verified the binding feasibility with the core targets but also systematically elucidated its \"multi-target, multi-pathway\" synergistic mechanism from the perspectives of biological function and pathways, particularly revealing the potential value of \"inflammation-metabolism\" crosstalk in MI treatment. However, as this study is primarily based on bioinformatic predictions and computational simulations, its findings still require further experimental validation. Future research could focus on key targets such as \u003cem\u003eSCN9A\u003c/em\u003e and \u003cem\u003ePIM1\u003c/em\u003e, utilizing gene editing, protein-protein interaction assays, and myocardial infarction animal models to validate the regulatory effects and functional mechanisms of Tanshinone IIA at the molecular, cellular, and whole-organism levels. Furthermore, the dynamic changes of the fatty acid metabolism pathway after MI and the interventional effects of Tanshinone IIA also warrant in-depth exploration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuan Zhong-yi: data curation, software, visualization, writing\u0026mdash;original draft.\u003c/p\u003e\n\u003cp\u003eXu Wen-hua: conceptualization, methodology, writing\u0026mdash;review \u0026amp; editing, funding acquisition.\u003c/p\u003e\n\u003cp\u003eZheng Jing-hui: supervision, project administration, writing\u0026mdash;review \u0026amp; editing, funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required; all data were downloaded from publicly available databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the GEO repository (GSE62646, GSE83500). Other data are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangxi University of Chinese Medicine, Nanning 530200, Guangxi, China\u003c/p\u003e\n\u003cp\u003eRuikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning 530011, Guangxi, China\u003c/p\u003e\n\u003cp\u003eGUAN Zhong-yi（[email protected]）\u003c/p\u003e\n\u003cp\u003e*Corresponding authors\u003c/p\u003e\n\u003cp\u003eXu Wen-hua ([email protected])\u003c/p\u003e\n\u003cp\u003eZheng Jing-hui ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the GEO, TCMSP, CTD, SwissTargetPrediction, DAVID and CB-Dock2 teams for providing free databases and tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Guangxi Natural Science Foundation (2024GXNSFBA010300); Guangxi Higher-Education Young and Middle-Aged Teachers\u0026rsquo; Basic Research Capacity Enhancement Project (2022KY0269); Young Scientists Fund of Guangxi University of Chinese Medicine (2021QN028); Self-funded Research Project of Guangxi Zhuang Autonomous Region Health Commission (Z20212460); Self-funded Research Projects of Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine (GXZYZ20210474, GXZYA20250123); and the 2023 National Undergraduate Innovation and Entrepreneurship Training Program (202310600031).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan YL, Jiang H, Yang JY, Chen J (2022) Chinese Society of Cardiology \u0026amp; Editorial Board of Chinese Journal of Cardiology. 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J Am Coll Cardiol 36:959\u0026ndash;969\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang M, An S, He LY (2024) Research progress on tanshinone ⅡA in improving myocardial infarction. Drugs Clin 39:3264\u0026ndash;3270\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Ho JC, Chan YC, Lian Q, Siu CW, Tse HF (2014) Overexpression of myocardin induces partial transdifferentiation of human-induced pluripotent stem cell-derived mesenchymal stem cells into cardiomyocytes. Physiol Rep 2:e00237\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKulandavelu S, Karantalis V, Fritsch J, Hatzistergos KE, Loescher VY, McCall F, Wang B, Bagno L, Golpanian S, Wolf A, Grenet J, Williams A, Kupin A, Rosenfeld A, Mohsin S, Sussman MA, Morales A, Balkan W, Hare JM (2016) Pim1 Kinase Overexpression Enhances ckit(+) Cardiac Stem Cell Cardiac Repair Following Myocardial Infarction in Swine. 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Cells ; 11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang LN, Lei JS, Li KB et al (2025) Research progress on inflammatory response in patients with acute myocardial infarction. Chin Gen Pract 28:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao S, Yang Z, Li D, Wang B, Zheng X, Li C, Fan G (2024) Intervention of Tanshinone IIA on the PGK1-PDHK1 Pathway to Reprogram Macrophage Phenotype After Myocardial Infarction. Cardiovasc Drugs Ther 38:1359\u0026ndash;1373\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan XY, Huang C, Jiang YJ, Wan CX (2025) Intermittent hypoxia intervention regulates p-STAT3/CPT-1 to influence myocardial fatty acid metabolism in mice after myocardial infarction. China J Mod Med 35:54\u0026ndash;62\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu JX, Xu YW, Xiao D, Chen QY, Mao CL (2013) Free fatty acids and cardiovascular disease. Prog Mod Biomed 13:1979\u0026ndash;1982\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Tanshinone IIA, Myocardial Infarction, Bioinformatics, Molecular Docking","lastPublishedDoi":"10.21203/rs.3.rs-8185303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8185303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo systematically screen the core targets of Tanshinone IIA in the treatment of myocardial infarction (MI) and to explore its molecular mechanisms based on the Gene Expression Omnibus (GEO) database and bioinformatics methods. \u003cstrong\u003eMethods: \u003c/strong\u003eMI-related transcriptome datasets (GSE62646, GSE83500) were obtained from the GEO database. The \"sva\" package was used to correct batch effects, and the \"limma\" package was employed to identify differentially expressed genes (DEGs). Potential targets of Tanshinone IIA were retrieved by integrating the TCMSP, CTD, and SwissTargetPrediction databases. Intersecting genes were extracted via Venn diagrams, and a protein-protein interaction (PPI) network was constructed. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, was performed using DAVID. Molecular docking was conducted using CB-Dock2 to validate binding affinity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 8 core candidate targets were identified, including \u003cem\u003eSCN9A, PIM1, GSK3A, RELA, HMGB1, FASN, ACAT1\u003c/em\u003e, and \u003cem\u003eMPI\u003c/em\u003e. Molecular docking showed that the binding energies of Tanshinone IIA with all targets were lower than -6.8 kcal/mol, with the strongest binding observed for\u003cem\u003e SCN9A\u003c/em\u003e (-10.6 kcal/mol) and \u003cem\u003ePIM1\u003c/em\u003e (-10.2 kcal/mol). GO analysis revealed that the targets were significantly enriched in biological processes such as inflammatory response and interleukin regulation. KEGG analysis indicated that the main pathways involved were fatty acid metabolism, the AGE-RAGE signaling pathway, and the acute myeloid leukemia pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Tanshinone IIA may exert multi-dimensional protective effects in the treatment of myocardial infarction by acting on multiple targets such as SCN9A and PIM1, and by synergistically regulating key pathways including inflammatory response and fatty acid metabolism. This study provides a theoretical basis for elucidating its systematic mechanism of action and future clinical translation.\u003c/p\u003e","manuscriptTitle":"Integrating GEO Database and Bioinformatics Analysis to Screen the Key Targets of Tanshinone IIA in Myocardial Infarction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 10:50:34","doi":"10.21203/rs.3.rs-8185303/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":"fdfeb6bd-71a4-4e8f-9c8e-19575ff155e2","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T10:50:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 10:50:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8185303","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8185303","identity":"rs-8185303","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}