Exploring Histone Acetylation in Ischemic Stroke: The Role of CREBBP and CKAP4 as Key Biomarkers

Exploring Histone Acetylation in Ischemic Stroke: The Role of CREBBP and CKAP4 as Key Biomarkers | 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 Exploring Histone Acetylation in Ischemic Stroke: The Role of CREBBP and CKAP4 as Key Biomarkers Wang Yanni, Yang Fan, Tan Jiaojiao, Guo Zhentao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6336853/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 Ischemic stroke (IS) is a serious cerebrovascular disease. Excessive acetylated protein levels are linked to neuronal resistance to ischemia, making histone acetylation regulatory-related biomarkers crucial for IS. Methods To identify differentially expressed genes (DEGs) in GSE16561, differential expression analysis (normal versus IS) was performed. Key module genes linked to single-sample gene set enrichment analysis (ssGSEA) scores of histone acetylation regulatory-related genes (HARGs) were identified via weighted correlation network analysis (WGCNA). Overlapping DEGs and key module genes yielded histone acetylation regulatory-related DEGs (HAR-DEGs). Three machine learning algorithms, expression, and receiver operating characteristic (ROC) analysis screened histone acetylation regulatory-related biomarkers. Functional enrichment, immune microenvironment analysis, and disease association were conducted. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) assessed biomarker expression levels. Results A total of 550 differentially DEGs and 137 key module genes related to ssGSEA enrichment scores of HARGs were identified. Overlapping these yielded 44 HAR-DEGs. CREBBP and CKAP4 were identified as histone acetylation regulatory-related biomarkers in IS. Both biomarkers were linked to immune-related pathways, such as complement and coagulation cascades. CREBBP inversely correlated with CD8 + T cell scores, while CKAP4 positively correlated with M0 macrophage scores. Both were associated with brain injury and disease. RT-qPCR confirmed elevated expression of CREBBP and CKAP4 in IS samples compared to controls. Conclusion In summary, we identified two biomarkers ( CREBBP and CKAP4 ) linked to histone acetylation regulation in IS, providing a theoretical basis for its treatment. Ischemic stroke Histone acetylation regulatory genes Immune infiltration Biomarkers Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Stroke is globally recognized as the second leading cause of mortality and is characterized by a significant burden in terms of morbidity, mortality and disability, and far-reaching social and economic impacts [1] . The two primary classes of stroke are ischemic stroke and hemorrhagic stroke. Acute ischemic stroke (AIS) accounts for 87% of the overall incidence of stroke. Regrettably, the treatment options for mitigating stroke-related conditions are constrained in terms of available pharmaceuticals and the limited therapeutic intervention time window[ 2 , 3 ]. Currently, intravenous tissue plasminogen activator and emergency endovascular thrombectomy represent the primary therapeutic modalities for ischemic stroke. The treatment effects, however, failed to meet the desired criteria in both cases.Therefore, it is of paramount importance to investigate the pathogenesis of ischemic stroke and to identify potential biomarkers that can offer critical insights into the diagnosis and prevention of this condition. Histone acetylation refers to the addition of acetyl groups to the lysine residues of histones catalyzed by histone acetyltransferase (HAT), while histone deacetylase (HDAC) mediates the removal of acetyl groups from the lysine residues of histones. Numerous studies have demonstrated that the histones acetylation exerts regulatory control over multiple genes essential for diverse biological processes. The acetylation of histones induces a breakdown of the nuclear chromatin, rendering it more accessible to transcription factors and thus stimulating protein synthesis within the cell. Conversely, histone deacetylation promotes chromatin condensation, leading to inhibition of gene expression and suppression of protein synthesis[ 4 , 5 ]. Previous studies have demonstrated that histone acetylation can facilitate the activation of genes associated with tissue regeneration and axonal growth, while elevated levels of acetylated proteins are linked to neuronal resistance against ischemia, thereby influencing stroke outcomes and contributing to brain recovery[ 6 ]. Liao et al. have demonstrated that histone deacetylase 3 (HDAC3) mitigates neuroinflammation and brain injury resulting from ischemia-reperfusion (I/R) by modulating the cGAS-STING pathway, thereby establishing a novel therapeutic target for ischemic stroke[ 7 ]. Investigating biomarkers associated with histone acetylation may therefore provide a novel therapeutic avenue for the management of ischemic stroke. This study aimed to identify biomarkers related to histone acetylation regulation in IS through bioinformatics, shedding light on the underlying molecular mechanisms and uncovering potential therapeutic targets. We also included the relevant signature genes in receiver operating characteristic (ROC) curves to evaluate their effectiveness in diagnosing IS. Furthermore, we investigated the connection between these signature genes and immune cells, offering fresh insights for developing immunomodulatory treatment strategies for IS. 2. Materials and Methods 2.1 Data acquisition Two datasets of IS GSE16561 and GSE202518, which contained clinical features and gene expression profiles, were compiled Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). The GSE16561 dataset (platform: GPL6883), which included 39 normal and 24 IS samples from blood, was used as training set. The GSE202518 dataset (platform: GPL24676), which included 4 normal and 12 IS samples from blood, was regarded as validation set. A sum of 83 histone acetylation regulatory genes (HARGs) were obtained from existing literature research[ 8 ]. 2.2 Analysis of differential genes The differentially expressed genes (DEGs) from normal group to IS group were attained through "limma" package (version 3.52.4)[ 9 ] (p < 0.05, |log 2 FoldChange (FC)| ≥ 0.5). Volcano plot was utilized to display DEGs via ‘ggplot2’ (version 3.3.6)[ 10 ] package. Heat map was used to present top20 up and Top20 down DEGs by ‘ComplexHeatmap’ (version 2.18.0)[ 11 ] and ‘heatmap3’ (version 1.1.9)[ 12 ] packages. 2.3 Weighted correlation network analysis (WGCNA) The single-sample gene set enrichment analysis (ssGSEA) enrichment score of HARGs was taken as trait for WGCNA through ‘WGCNA’ (version 1.70-3)[ 13 ] package. First, to guarantee correctness of study, cluster analysis was carried out on all samples and outlier samples were removed. Then, optimal soft threshold was ascertained in line with the fact that R 2 was greater than 0.9. After ascertaining the likeness between genes according to their closeness to each other, a phylogenetic tree was built. The modules were partitioned by means of dynamic tree cutting algorithm. The modules genes (|GS| ≥ 0.2 ༆ |MM| ≥ 0.8) which had the strongest connection with the ssGSEA enrichment score of HARGs were viewed as key modules for the following analysis. 2.4 Gene enrichment analysis and protein-protein interaction network (PPI) network Then, by intersecting key module genes and DEGs, histone acetylation regulatory-related differential expression genes (HAR-DEGs) were secured. Moreover, HAR-DEGs were analyzed using ‘clusterProfiler’ (version 4.7.1)[ 14 ] package in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), with adjust. p < 0.05. In additional, a PPI network illustrating HAR-DEGs interactions was established through STRING website[ 15 ]. 2.5 Machine learning methods Support vector machine-recursive feature elimination (SVM-RFE) (e1071 package, version 1.7–11)[ 16 ], least absolute shrinkage and selection operator (LASSO) regression (glmnet package,version 4.1.8 )[ 17 ] and Boruta (glmnet package, version 8.0.0)[ 18 ] algorithms were employed to screen essential HAR-DEGs in training set. The candidate genes were gained through overlapping results of three algorithms. 2.6 Receiver operating characteristic (ROC) analysis Using ‘pROC’ tool (version 1.18.5)[ 19 ], diagnostic value of candidate genes was estimated by ROC curve[ 19 ]. Meanwhile, GSE202518 was taken as verification set to assess diagnostic value. Then, genes with significant diagnostic utility (area under curve (AUC) > 0.7) and same expression trend in training set and validation set were viewed as histone acetylation regulatory related biomarkers. 2.7 Immune infiltration analysis The analysis of 22 kinds of immune cells was carried out by CIBERSORT algorithm, and relative abundances of immune cells were calculated. The Wilcoxon test was utilized to contrast infiltration level of per kind of immune cell in two groups, and the differential immune cells were obtained. The Spearman correlation analysis was accomplished among biomarkers and enrichment scores of 22 immune cells, as well as between biomarkers the differential immune cells (|cor| > 0.3, p < 0.05). 2.8 Gene set enrichment analysis (GSEA) Based on C2 in KEGG gene set, GSEA analysis of biomarkers was carried out via "clusterProfiler" to probe latent pathways of biomarkers (adjust. p < 0.05). The correlation coefficients among expression levels of all genes and biomarkers were calculated as ranking criteria. 2.9 Construction of ‘lncRNA-miRNA-mRNA’ network The miRDB ( http://mirdb.org ) and Targetscan ( http://www.targetscan.org ) databases were utilized to forecast miRNAs associated with biomarkers, respectively. The key miRNAs were obtained by overlapping results of two databases. The Starbase database was applied to forecast lncRNAs (pancancerNum ≥ 10) linked to key miRNAs. Moreover, lncRNA-miRNA-mRNA interaction network was drawn by Cytoscape software[ 20 ]. 2.10 Disease and drug prediction analysis Through common technical document (CTD) database ( http://ctdbase.org ), diseases associated with biomarkers and drugs targeting biomarkers were discovered to study potential therapeutic strategies for biomarkers in IS. 2.11 The expression validation of biomarkers To verify analysis outcomes of transcriptome data, reverse transcription quantitative polymerase chain reaction (RT-qPCR) verification was carried out. Five blood samples from normal subjects and five blood samples from IS patients were attained from clinical patients. First, total RNA of samples was drawn out via TRIzol (Ambion, Austin, USA) method in accordance with manufacturer's instructions. Then, total RNA was reverse transcribed to cDNA through First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China) in accordance with the producer's tutorials. After that, RT-qPCR analysis was accomplished by 2xUniversal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China) under guidance of instruction manual. The PCR primer sequences were presented in Online Resource 1 . The expression levels of biomakers were calculated by 2 −ΔΔCq method, with GAPDH as internal reference[ 21 ]. 3. Results 3.1 Recognition of DEGs and key module genes in IS A total of 550 DEGs were attained, comprising 248 down and 302 up regulated genes in IS (Fig. 1 a). Top20 up and down regulated DEGs were shown in Fig. 1 b. To identify crucial modules associated with ssGSEA enrichment score of HARGs, we carried out WGCNA. No abnormal samples were detected in all samples ( Online Resource 2 ). The optimal soft threshold was ascertained to be 8, when R 2 was getting close to 0.9 (red line). Meanwhile, the average connectivity of co-expression network approached 0 (Fig. 1 c), and ultimately a total of 10 co-expression modules were successfully identified (Fig. 1 d). The result suggested that MEyellow (|cor| = 0.52 & p ≤ 0.05) was substantially connected with ssGSEA enrichment score of HARGs (Fig. 1 e). Therefore, 137 key module genes (|GS| ≥ 0.2 & |MM| ≥ 0.8) were acquired for further analyse. 3.2 Identification and function enrichment of HAR-DEGs Then, 44 HAR-DEGs were attained by taking intersection of DEGs and key module genes (Fig. 2 a). The roles of HAR-DEGs in IS were investigated via enrichment analysis. GO results demonstrated that HAR-DEGs were mainly engaged in biological process of ‘regulation of MAP kinase activity’ and ‘regulation of B cell proliferation’ (Fig. 2 b). Beside, KEGG results demonstrated HAR-DEGs were principally implicated in ‘HIF-1 signaling pathway’ and ‘Glucagon signaling pathway’ (Fig. 2 c). In additional, The PPI network of these genes was established, in which TLR4 interacted intricately with numerous proteins (Fig. 2 d). 3.3 Screening histone acetylation regulatory related biomarkers in IS To further investigate essential genes, 11 feature genes whose regression coefficients were not penalized to 0 were attained by LASSO analysis (Fig. 3 a). Meanwhile, 15 and 25 feature genes were retained by SVM-RFE and Boruta, respectively (Fig. 3 b-c). Conclusively, CREBBP , SRPK1 , CKAP4 , SNRPN , CARD12 and DIRC2 were regards as candidate genes (Fig. 3 d). Among them, CREBBP and CKAP4 exhibited significant diagnostic utility for IS in validation set (AUC > 0.7), and expression pattern was entirely corresponding to training set, which were identified as histone acetylation regulatory-related biomarkers (Fig. 3 e-h). Notably, CREBBP and CKAP4 were up-regulated in IS group compared to normal group. 3.4 Immune microenvironment of biomarkers To investigate prospective functions of CREBBP and CKAP4 in IS, we executed GSEA analysis on biomarkers. The KEGG results presented that biomarkers were enriched in some immune linked to pathways, including ‘ribosome’ and ‘complement and ‘coagulation cascade pathways’ (Fig. 4 a-b; Online Resource 3 ). Then, we explored immune microenvironment in IS. The infiltration level of 22 immune cells was examined (Fig. 4 c). Significantly, 6 differential immune cells were attained among IS and normal groups (Fig. 4 d). In IS group, there were substantially overproduced monocytes, macrophages and neutrophils than in normal group, while expression of CD8 + T cells, CD4 + T cells, and NK cells in IS group was markedly lower than normal group. Then, CREBBP (cor = -0.63, p = 4.09e-08) and CKAP4 (cor = -0.55, p = 3e-06) were negatively linked with score of CD8 + T cells, while CREBBP (cor = 0.57, p = 1e-06) and CKAP4 (cor = 0.66, p = 4.92e-09) was positively connected with score of M0 macrophages (Fig. 4 e; Online Resource 4 ). In additional, these two immune cell scores were significantly different between IS group and normal group (Fig. 4 f). 3.5 Analysis of regulatory network and drug in IS The ‘lncRNA-miRNA-mRNA’ network was constructed to study regulatory mechanisms of CREBBP and CKAP4 , in which AL133355.1 might regulate expression of CREBBP by affecting MIR100HG (Fig. 5 a). In additional, SNHG25 might regulated expression of CKAP4 by affecting NR2F1-AS1. Finally, disease association analysis revealed that these two biomarkers were related to brain injuries and diseases (Fig. 5 b). In particular, CREBBP had a strong correlation with IS. Meanwhile, drugs targeting CREBBP and CKAP4 were predicted (Fig. 5 c). Notably, CREBBP has been predicted for eight drugs, including cypermethrin, cyhalothrin and dronabinol et al. And CKAP4 has been predicted for three drugs, including tobacco smoke pollution, acetaminophen and progesterone. 3.6The expression validation of CREBBP and CKAP4 in IS It was indicated by RT-qPCR outcomes that, in contrast to normal group, the expressions of CREBBP and CKAP4 were remarkably elevated in IS group, which accorded with results of transcriptome data, further verifying dependability of results (Fig. 6 ). 4. Discussion Ischemic stroke, as a major threat to human health, has gained increasing attention on a global scale. An abrupt obstruction of cerebral blood supply can result in neurological deficits and impose substantial social and economic burdens. Despite the advancements in drug and recanalization therapies, there is still a lack of timely and effective treatments[ 2 ]. Related research is of paramount importance in order to explore novel therapeutic strategies aimed at enhancing the clinical efficacy of ischemic stroke. Previous studies have demonstrated that histone acetylation can facilitate the activation of genes associated with tissue regeneration and axonal growth, while elevated levels of acetylated proteins are linked to neuronal resistance against ischemia, thereby influencing stroke outcomes and contributing to brain recovery[ 6 ], histone acetylation plays a regulatory role in the permeability of the blood-brain barrier, leading to a reduction in blood vessel leakage and lowering the risk of brain hemorrhage[ 22 ]. Investigating biomarkers associated with histone acetylation may therefore provide a novel therapeutic avenue for the management of ischemic stroke.However, there are few studies on histone acetylation related genes as biomarkers for early prevention of IS. The CREB-binding protein ( CREBBP , or CBP ) is a member of the KAT3 family of proteins, which is lysine acetyltransferases (KATs) known for its ability to modify histones and non-histone proteins. This modification regulates chromatin accessibility and transcription. Previous studies have shown that CBP has tumor suppressor function[ 23 ]. Recently, it has been found to acetylate key factors involved in DNA replication, and in different DNA repair processes, such as base excision repair, nucleotide excision repair, and non-homologous end joining[ 24 ]. Studies have shown that the levels of CBP and histone acetylation play a crucial role in determining the outcome of stroke. And inhibition of CBP’s HAT activity can exacerbate neurological damage in patients suffering from ischemic stroke. The activation of CREB can serve as a neuroprotective function[ 25 ]. The study conducted by Ayden Gouveia demonstrated that the APKC-CBP pathway exerts regulatory control over neurovascular remodeling and functional recovery following stroke, with disruption of this pathway significantly impacting post-stroke neurovascular remodeling and recovery[ 26 ]. Hence, the involvement of CREBBP in repairing DNA damage and facilitating neurovascular remodeling may potentially enhance the prognosis of stroke patients. Cytoskeleton-associated protein 4 ( CKAP4 ) is a reversible type II transmembrane protein that is modified by palmitoylation and is found on multiple cellular membranes. Located in the rough endoplasmic reticulum, CKAP4 is crucial for facilitating the transport of certain proteins from the endoplasmic reticulum to the nucleus[ 27 ]. Neuronal cytoskeleton remodeling is associated with a variety of diseases related to the origin and development of the nervous system, including Alzheimer's disease, Parkinson's disease, muscle atrophy, amyotrophic lateral sclerosis, and ischemic cerebral apoplexy. Cyclin-dependent kinase-5(CDK5) plays a role in regulating neuronal cytoskeleton remodeling, which can enhance neurological function[ 28 ]. The remodeling of the cytoskeleton in platelets is associated with the thrombus perviousness. Treatments for highly permeable thrombi can be effective in thrombolysis[ 29 ]. Recent studies have demonstrated that CKAP4 exhibits potential as a diagnostic biomarker for IS[ 30 ].In summary, CKAP4 is essential for the remodeling of the neuronal cytoskeletal and is significantly linked to the development and progression of various neurological diseases. Further exploration of its regulatory mechanisms and role in ischemic stroke is necessary. To further investigate the potential roles of CREBBP and CKAP4 in IS, we performed single-gene GSEA on biomarkers. The GSEA results showed that these two biomarkers were enriched in some immune related to pathways, including ‘ribosome’ and ‘complement and the ‘coagulation cascade pathways’. Ribosome is the place where biological protein synthesis takes place. Ribosome biogenesis and protein synthesis are the basic rate-limiting steps for cell growth and proliferation. Ribosome-related genes downregulated lead to protein synthesis and secretion disorders. Studies have shown that ribosomal proteins S15(RPS15)may play an important role in the development of ischemic stroke[ 31 , 32 ]. The immuno-inflammatory responses, which occur throughout the whole process of ischemic stroke, are considered to be important modulating factors of disease progression, cerebral injury and repair after brain ischemia[ 33 ]. The coagulation cascade further exacerbates the occurrence of inflammation[ 34 ]. Thrombin can directly activate complement C3 and C5, disrupting the function of the endothelial barrier[ 35 ]. Following ischemic stroke, local vascular coagulation cascade can inhibit the expression of thrombomodulin (TM) on endothelial cells, leading to sustained activation of monocytes and the complement system, thus promoting deterioration in vascular immune-inflammatory response[ 36 ]. Therefore, additional research will be instrumental in elucidating its specific mechanisms and identifying novel targets for the diagnosis and treatment of ischemic stroke. Increasing evidence points to important role of the immune system in IS pathophysiology, so we explored the immune microenvironment in IS. In IS group, there were significantly higher monocytes, macrophages and neutrophils than in normal group, whereas the expression of CD4 + T cells, CD8 + T cells and NK cells in IS group was significantly lower than normal group. Yilmaz et al. discovered that knockout of CD8 + T cells significantly decreased cerebral infarct volume and improved neurological deficits in mice[ 37 ]. CD8 + T cells can enhance the production of TNF-α in macrophages and microglia by secreting IFN-γ, thereby exacerbating the inflammatory response following ischemic stroke[ 38 ]. These results indicate that CD8 + T cells can promote inflammatory cell response and aggravate brain injury after MCAO. M0 macrophages are in a non-activated state and can be activated into either classically activated M1 macrophages or alternatively activated M2 macrophages depending on the microenvironment. The M1 subtype secretes proinflammatory cytokines and contributes to the immune inflammatory response after a stroke, while the M2 subtype secretes anti-inflammatory cytokines and exhibits anti-inflammatory as well as neuroprotective effects[ 39 ]. The impact of macrophages on ischemic brain injury is largely determined by the different phenotypes of macrophages at various stages following a stroke. However, the conversion between different subtypes in the process is complex and the exact mechanism of action has not been fully revealed. In this study, we identified a significant relationship between CREBBP and CKAP4 in the context of immune cell pathology and ischemic stroke. Specifically, CREBBP exhibited a negative correlation with CD8 + T cell scores, whereas CKAP4 demonstrated a positive correlation with M0 macrophage scores. These findings suggest that CREBBP may exert a protective effect in ischemic stroke by inhibiting the function of CD8 + T cells. CKAP4 may facilitate the differentiation of M0 macrophages into M2-type macrophages through specific mechanisms, thereby contributing to protective responses following ischemic stroke. In summary, the role of the immune system in ischemic stroke is both intricate and critical. The insights gained from our investigation into CREBBP and CKAP4 enhance our understanding of the immune microenvironment, indicating that targeted interventions involving these molecules could pave new avenues for treating ischemic stroke. In recent years, there has been a growing focus on the regulation of lncRNA in cell cycle, cell differentiation, and epigenetics. LncRNA plays a crucial role in regulating gene expression across multiple processes, leading to either gene silencing or activation. Studies have identified that the abnormal expression of lncRNA is involved in multiple pathological processes of nerve cell damage and functional defects after IS[ 40 ]. The ‘lncRNA-miRNA-mRNA’ network was constructed and we found that the expression of CREBBP was associated with MIRG100H, while the expression of CKAP4 was associated with NR2F1-AS1. In summary, MIRG100H and NR2F1-AS1 may play a significant role in the pathogenesis following ischemic stroke, indicating their potential as novel therapeutic targets. CREBBP has been predicted for eight drugs, including cypermethrin, cyhalothrin and dronabinol et al. Cypermethrin and cyfluthrin exhibit neurotoxic properties, primarily attributed to their capacity to elevate oxidative stress levels. Cannabinoids have the potential to disrupt mitochondrial respiratory chain function in the brain and elevate oxidative stress levels, ultimately contributing to an increased risk of stroke. CKAP4 has been predicted for three drugs, including tobacco smoke pollution, acetaminophen and progesterone. Tobacco smoke pollution can activate macrophages to release inflammatory mediators, contributing to the body's inflammatory response[ 41 ]. Meanwhile, acetaminophen can stimulate macrophages in liver to produce reactive oxygen species (ROS), leading to an oxidative stress response[ 42 ]. Additionally, progesterone has the ability to regulate the function of macrophages[ 43 ], further supporting the notion that CKAP4 may play a protective role after IS by regulating macrophage function. In summary, the mechanisms of action of these drugs elucidate their potential risks and protective effects in ischemic stroke, offering significant insights for future research. 5. Conclusions This study aims to explore the relationship between biomarkers associated with histone acetylation regulation and IS through bioinformatics analysis. However, there are still some limitations in our study, including the expression verification of these markers in vitro and in vivo , the signaling pathways that these markers may affect, and the functional verification of these markers in immune cells. We will continue to closely monitor such research in the future, with the hope of advancing the treatment of IS Declarations Acknowledgments We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Wang Yanni, Yang Fan, Tan Jiaojiao, Guo Zhentao. 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. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yang Fan, Tan Jiaojiao and Guo Zhentao. The first draft of the manuscript was written by Wang Yanni and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Shandong University Qilu Hospital(Date2025-1-23/KYLL-KS-2025052) Consent to participate Participants in this study were provided with a clear and understandable explanation of the research objectives, procedures, potential risks, and benefits. They were informed that their participation is voluntary and that they have the right to withdraw from the study at any time. Participants were given the opportunity to ask questions and provided written informed consent prior to their involvement in the study. Consent to publish N/A Data availability statement The datasets (GSE16561, GSE202518) analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, (https://www.ncbi.nlm.nih.gov/gds). miRDB database from the web (http://mirdb.org). Targetscan database from the web (http://www.targetscan.org). CTD database from the web (http://ctdbase.org). References Feigin VL, Stark BA, Johnson CO,et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology. 2021;20:795–820. Rabinstein AA. Update on treatment of acute ischemic stroke. Continuum: Lifelong Learning in Neurology. 2020;26:268–286. Herpich F and Rincon F. Management of Acute Ischemic Stroke. Crit Care Med. 2020;48:1654–1663. Wang Z, Zang C, Cui K,et al. Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell. 2009;138:1019–1031. 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Complex roles of microglial cells in ischemic stroke pathobiology: new insights and future directions. International journal of molecular sciences. 2017;18:496. Wang Z, Li X, Huang L,et al. Long non-coding RNAs (lncRNAs), a new target in stroke. Cellular and molecular neurobiology. 2020:1–19. Barnes PJ. Inflammatory mechanisms in patients with chronic obstructive pulmonary disease. Journal of Allergy and Clinical Immunology. 2016;138:16–27. Du K, Ramachandran A, and Jaeschke H. Oxidative stress during acetaminophen hepatotoxicity: Sources, pathophysiological role and therapeutic potential. Redox biology. 2016;10:148–156. Jones LA, Anthony JP, Henriquez FL,et al. Toll-like receptor‐4‐mediated macrophage activation is differentially regulated by progesterone via the glucocorticoid and progesterone receptors. Immunology. 2008;125:59–69. Additional Declarations No competing interests reported. Supplementary Files OnlineResource.pdf OnlineResource3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6336853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446641729,"identity":"417c667e-15c3-451b-9f92-086665e71cb8","order_by":0,"name":"Wang Yanni","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Yanni","suffix":""},{"id":446641730,"identity":"83450d30-cbdd-4df9-825f-0f101b1713f8","order_by":1,"name":"Yang Fan","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Fan","suffix":""},{"id":446641731,"identity":"58369302-099d-42e5-a7f3-3e737eead228","order_by":2,"name":"Tan Jiaojiao","email":"","orcid":"","institution":"Qingdao Huanghai University","correspondingAuthor":false,"prefix":"","firstName":"Tan","middleName":"","lastName":"Jiaojiao","suffix":""},{"id":446641732,"identity":"e8da6369-58cd-4f80-898f-f94d8ea7684e","order_by":3,"name":"Guo Zhentao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3RMQrCMBSA4cYHr0vU9S3SK0QKIghewStUhLo4OImTRARXVwUPUW8QCTqJroqL4uri5qBicHJKOwrmnxLIxyOJ57lcP5q6vaiOvmTKbJhMB8DOU6y2ilx5mQmEHPtsNo0ykqCxWxPnBOJwSXR+XCtJX68SGykrjWQqiGPcNaQdSh7HeysZjpCEMFOOHWGIbkriFTsZAVIUEUsOm4wkQAChFJnr84xEcCifh5LMI8fd5XzbDsdpdwkmy5N+yoH5Sr04XXu10sTXa/sU9bXO5RE8tB3/TJFfhN0fkAZcLpfrD3sDsuFOzbgysmIAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Guo","middleName":"","lastName":"Zhentao","suffix":""}],"badges":[],"createdAt":"2025-03-30 05:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6336853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6336853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81290811,"identity":"b0079653-40c4-4d60-8c0d-0399f18b9c83","added_by":"auto","created_at":"2025-04-24 11:56:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1257606,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed genes and module genes acquisition (\u003cstrong\u003ea\u003c/strong\u003e) Volcano plot of gene differential expression, red indicating significantly upregulated genes, blue indicating significantly downregulated genes, and gray indicating non-significant genes (\u003cstrong\u003eb\u003c/strong\u003e) Heatmap of differentially expressed genes (\u003cstrong\u003ec\u003c/strong\u003e) Soft threshold selection (\u003cstrong\u003ed\u003c/strong\u003e) Dynamic cutting tree (\u003cstrong\u003ee\u003c/strong\u003e) Heatmap of module-trait relationships, red represented positive correlation, blue represented negative correlation, and the deeper the color, the stronger the correlation\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/6b72837229b5f643fc3541be.png"},{"id":81290812,"identity":"f92059ea-b8bb-44cf-bc93-6b462946b55e","added_by":"auto","created_at":"2025-04-24 11:56:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":800046,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional enrichment of HAR-DEGs\u003cstrong\u003e (a)\u003c/strong\u003e Venn diagram of differentially expressed genes related to histone acetylation (\u003cstrong\u003eb\u003c/strong\u003e) GO functional enrichment results of HAR-DEGs (\u003cstrong\u003ec\u003c/strong\u003e) KEGG enrichment pathway diagram of HAR-DEGs (\u003cstrong\u003ed\u003c/strong\u003e) PPI network diagram\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/5771e14f43b15705ab169229.png"},{"id":81290817,"identity":"cd72de66-0007-4a5f-83e1-0a956101a0af","added_by":"auto","created_at":"2025-04-24 11:56:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":977468,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of biomarkers\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eLASSO regression analysis (\u003cstrong\u003eb\u003c/strong\u003e) Plot showing the relationship between SVM generalization error and the number of features (\u003cstrong\u003ec\u003c/strong\u003e) Feature scores from the Boruta algorithm (\u003cstrong\u003ed\u003c/strong\u003e) Venn diagram of selected features (\u003cstrong\u003ee\u003c/strong\u003e) ROC curve of selected features in the GSE16561 dataset (\u003cstrong\u003ef\u003c/strong\u003e) ROC curve of selected features in the GSE202518 dataset (\u003cstrong\u003eg\u003c/strong\u003e) Boxplot of differential expression of selected features in the GSE16561 dataset, “****” represented p \u0026lt; 0.0001 (\u003cstrong\u003eh\u003c/strong\u003e) Boxplot of differential expression of selected features in the GSE202518 dataset. “ns” represented no significance, “*” represented p \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/a1f4dc64bf0391cd2f5974eb.png"},{"id":81291339,"identity":"d82424a2-a772-4d0d-9d53-a13f946d47d9","added_by":"auto","created_at":"2025-04-24 12:04:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1218970,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA and immune infiltration analysis\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eGSEA enrichment trend plot for the CREBBP gene (\u003cstrong\u003eb\u003c/strong\u003e) GSEA enrichment trend plot for the CKAP4 gene (\u003cstrong\u003ec\u003c/strong\u003e) Immune cell infiltration proportions (\u003cstrong\u003ed\u003c/strong\u003e) Differences in immune cell enrichment scores between groups, “ns” indicated no significance, “*” indicated p \u0026lt; 0.05, “**” indicated p \u0026lt; 0.01, and “****” indicated p \u0026lt; 0.0001 (\u003cstrong\u003ee\u003c/strong\u003e) Scatter plot of biomarkers and the most relevant immune cells (\u003cstrong\u003ef\u003c/strong\u003e) Differences in immune cell enrichment scores between the most relevant immune cells,“****” indicated p \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/e7b3a70e0ff05643cb16c329.png"},{"id":81290816,"identity":"ff973118-43fa-433e-8e39-1a5f0ea8c537","added_by":"auto","created_at":"2025-04-24 11:56:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1122322,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA Network and Drug Prediction (\u003cstrong\u003ea\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eceRNA network diagram of biomarkers, Red represented biomarkers, purple represented miRNAs, and yellow represented lncRNAs (\u003cstrong\u003eb\u003c/strong\u003e) Disease relevance prediction (\u003cstrong\u003ec\u003c/strong\u003e) Gene-drug relationship network, orange represented biomarkers, and blue represented the related drugs\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/7ed6a093c566b06eeab77ad9.png"},{"id":81290819,"identity":"6a05a210-97ea-4e6d-a5ac-a07f3c4cc958","added_by":"auto","created_at":"2025-04-24 11:56:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118859,"visible":true,"origin":"","legend":"\u003cp\u003eRT-qPCR validation of biomarker expression\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eBar chart of CKAP4 gene expression, “***” indicated p \u0026lt; 0.001 (\u003cstrong\u003eb\u003c/strong\u003e) Bar chart of CREBBP gene expression, “*” indicated p \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/6220d94781bf5b9565bf88d9.png"},{"id":81912055,"identity":"bca238ab-d789-4731-95b8-b00d2646dfc7","added_by":"auto","created_at":"2025-05-04 15:16:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5634844,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/e237cb9a-b12d-44ef-95b4-0032b1b291f9.pdf"},{"id":81290814,"identity":"05947a40-2dd9-4892-98f0-97ad0122a960","added_by":"auto","created_at":"2025-04-24 11:56:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":476014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"OnlineResource.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/d57f407d6d3c6fabbdfdaa99.pdf"},{"id":81291340,"identity":"399e2f6b-6246-4b84-a7bd-613a4f111969","added_by":"auto","created_at":"2025-04-24 12:04:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":53608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"OnlineResource3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6336853/v1/fef6a9f2f3013e0fa53b03f1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Histone Acetylation in Ischemic Stroke: The Role of CREBBP and CKAP4 as Key Biomarkers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke is globally recognized as the second leading cause of mortality and is characterized by a significant burden in terms of morbidity, mortality and disability, and far-reaching social and economic impacts\u003csup\u003e[1]\u003c/sup\u003e. The two primary classes of stroke are ischemic stroke and hemorrhagic stroke. Acute ischemic stroke (AIS) accounts for 87% of the overall incidence of stroke. Regrettably, the treatment options for mitigating stroke-related conditions are constrained in terms of available pharmaceuticals and the limited therapeutic intervention time window[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, intravenous tissue plasminogen activator and emergency endovascular thrombectomy represent the primary therapeutic modalities for ischemic stroke. The treatment effects, however, failed to meet the desired criteria in both cases.Therefore, it is of paramount importance to investigate the pathogenesis of ischemic stroke and to identify potential biomarkers that can offer critical insights into the diagnosis and prevention of this condition.\u003c/p\u003e \u003cp\u003eHistone acetylation refers to the addition of acetyl groups to the lysine residues of histones catalyzed by histone acetyltransferase (HAT), while histone deacetylase (HDAC) mediates the removal of acetyl groups from the lysine residues of histones. Numerous studies have demonstrated that the histones acetylation exerts regulatory control over multiple genes essential for diverse biological processes. The acetylation of histones induces a breakdown of the nuclear chromatin, rendering it more accessible to transcription factors and thus stimulating protein synthesis within the cell. Conversely, histone deacetylation promotes chromatin condensation, leading to inhibition of gene expression and suppression of protein synthesis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have demonstrated that histone acetylation can facilitate the activation of genes associated with tissue regeneration and axonal growth, while elevated levels of acetylated proteins are linked to neuronal resistance against ischemia, thereby influencing stroke outcomes and contributing to brain recovery[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Liao et al. have demonstrated that histone deacetylase 3 (HDAC3) mitigates neuroinflammation and brain injury resulting from ischemia-reperfusion (I/R) by modulating the cGAS-STING pathway, thereby establishing a novel therapeutic target for ischemic stroke[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Investigating biomarkers associated with histone acetylation may therefore provide a novel therapeutic avenue for the management of ischemic stroke.\u003c/p\u003e \u003cp\u003eThis study aimed to identify biomarkers related to histone acetylation regulation in IS through bioinformatics, shedding light on the underlying molecular mechanisms and uncovering potential therapeutic targets. We also included the relevant signature genes in receiver operating characteristic (ROC) curves to evaluate their effectiveness in diagnosing IS. Furthermore, we investigated the connection between these signature genes and immune cells, offering fresh insights for developing immunomodulatory treatment strategies for IS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data acquisition\u003c/h2\u003e \u003cp\u003eTwo datasets of IS GSE16561 and GSE202518, which contained clinical features and gene expression profiles, were compiled Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE16561 dataset (platform: GPL6883), which included 39 normal and 24 IS samples from blood, was used as training set. The GSE202518 dataset (platform: GPL24676), which included 4 normal and 12 IS samples from blood, was regarded as validation set. A sum of 83 histone acetylation regulatory genes (HARGs) were obtained from existing literature research[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of differential genes\u003c/h2\u003e \u003cp\u003eThe differentially expressed genes (DEGs) from normal group to IS group were attained through \"limma\" package (version 3.52.4)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFoldChange (FC)| \u0026ge; 0.5). Volcano plot was utilized to display DEGs via \u0026lsquo;ggplot2\u0026rsquo; (version 3.3.6)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] package. Heat map was used to present top20 up and Top20 down DEGs by \u0026lsquo;ComplexHeatmap\u0026rsquo; (version 2.18.0)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and \u0026lsquo;heatmap3\u0026rsquo; (version 1.1.9)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Weighted correlation network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eThe single-sample gene set enrichment analysis (ssGSEA) enrichment score of HARGs was taken as trait for WGCNA through \u0026lsquo;WGCNA\u0026rsquo; (version 1.70-3)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] package. First, to guarantee correctness of study, cluster analysis was carried out on all samples and outlier samples were removed. Then, optimal soft threshold was ascertained in line with the fact that R\u003csup\u003e2\u003c/sup\u003e was greater than 0.9. After ascertaining the likeness between genes according to their closeness to each other, a phylogenetic tree was built. The modules were partitioned by means of dynamic tree cutting algorithm. The modules genes (|GS| \u0026ge; 0.2 ༆ |MM| \u0026ge; 0.8) which had the strongest connection with the ssGSEA enrichment score of HARGs were viewed as key modules for the following analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Gene enrichment analysis and protein-protein interaction network (PPI) network\u003c/h2\u003e \u003cp\u003eThen, by intersecting key module genes and DEGs, histone acetylation regulatory-related differential expression genes (HAR-DEGs) were secured.\u003c/p\u003e \u003cp\u003eMoreover, HAR-DEGs were analyzed using \u0026lsquo;clusterProfiler\u0026rsquo; (version 4.7.1)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] package in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), with adjust. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In additional, a PPI network illustrating HAR-DEGs interactions was established through STRING website[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Machine learning methods\u003c/h2\u003e \u003cp\u003eSupport vector machine-recursive feature elimination (SVM-RFE) (e1071 package, version 1.7\u0026ndash;11)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], least absolute shrinkage and selection operator (LASSO) regression (glmnet package,version 4.1.8 )[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Boruta (glmnet package, version 8.0.0)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] algorithms were employed to screen essential HAR-DEGs in training set. The candidate genes were gained through overlapping results of three algorithms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Receiver operating characteristic (ROC) analysis\u003c/h2\u003e \u003cp\u003eUsing \u0026lsquo;pROC\u0026rsquo; tool (version 1.18.5)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], diagnostic value of candidate genes was estimated by ROC curve[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Meanwhile, GSE202518 was taken as verification set to assess diagnostic value. Then, genes with significant diagnostic utility (area under curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and same expression trend in training set and validation set were viewed as histone acetylation regulatory related biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe analysis of 22 kinds of immune cells was carried out by CIBERSORT algorithm, and relative abundances of immune cells were calculated. The Wilcoxon test was utilized to contrast infiltration level of per kind of immune cell in two groups, and the differential immune cells were obtained. The Spearman correlation analysis was accomplished among biomarkers and enrichment scores of 22 immune cells, as well as between biomarkers the differential immune cells (|cor| \u0026gt; 0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eBased on C2 in KEGG gene set, GSEA analysis of biomarkers was carried out via \"clusterProfiler\" to probe latent pathways of biomarkers (adjust. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation coefficients among expression levels of all genes and biomarkers were calculated as ranking criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Construction of \u0026lsquo;lncRNA-miRNA-mRNA\u0026rsquo; network\u003c/h2\u003e \u003cp\u003eThe miRDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirdb.org\u003c/span\u003e\u003cspan address=\"http://mirdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Targetscan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.targetscan.org\u003c/span\u003e\u003cspan address=\"http://www.targetscan.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were utilized to forecast miRNAs associated with biomarkers, respectively. The key miRNAs were obtained by overlapping results of two databases. The Starbase database was applied to forecast lncRNAs (pancancerNum\u0026thinsp;\u0026ge;\u0026thinsp;10) linked to key miRNAs. Moreover, lncRNA-miRNA-mRNA interaction network was drawn by Cytoscape software[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Disease and drug prediction analysis\u003c/h2\u003e \u003cp\u003eThrough common technical document (CTD) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org\u003c/span\u003e\u003cspan address=\"http://ctdbase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), diseases associated with biomarkers and drugs targeting biomarkers were discovered to study potential therapeutic strategies for biomarkers in IS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 The expression validation of biomarkers\u003c/h2\u003e \u003cp\u003eTo verify analysis outcomes of transcriptome data, reverse transcription quantitative polymerase chain reaction (RT-qPCR) verification was carried out. Five blood samples from normal subjects and five blood samples from IS patients were attained from clinical patients. First, total RNA of samples was drawn out via TRIzol (Ambion, Austin, USA) method in accordance with manufacturer's instructions. Then, total RNA was reverse transcribed to cDNA through First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China) in accordance with the producer's tutorials. After that, RT-qPCR analysis was accomplished by 2xUniversal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China) under guidance of instruction manual. The PCR primer sequences were presented in \u003cb\u003eOnline Resource 1\u003c/b\u003e. The expression levels of biomakers were calculated by 2\u003csup\u003e\u0026minus;ΔΔCq\u003c/sup\u003e method, with GAPDH as internal reference[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Recognition of DEGs and key module genes in IS\u003c/h2\u003e \u003cp\u003eA total of 550 DEGs were attained, comprising 248 down and 302 up regulated genes in IS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Top20 up and down regulated DEGs were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. To identify crucial modules associated with ssGSEA enrichment score of HARGs, we carried out WGCNA. No abnormal samples were detected in all samples (\u003cb\u003eOnline Resource 2\u003c/b\u003e). The optimal soft threshold was ascertained to be 8, when R\u003csup\u003e2\u003c/sup\u003e was getting close to 0.9 (red line). Meanwhile, the average connectivity of co-expression network approached 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), and ultimately a total of 10 co-expression modules were successfully identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The result suggested that MEyellow (|cor| = 0.52 \u0026amp; p\u0026thinsp;\u0026le;\u0026thinsp;0.05) was substantially connected with ssGSEA enrichment score of HARGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Therefore, 137 key module genes (|GS| \u0026ge; 0.2 \u0026 |MM| \u0026ge; 0.8) were acquired for further analyse.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification and function enrichment of HAR-DEGs\u003c/h2\u003e \u003cp\u003eThen, 44 HAR-DEGs were attained by taking intersection of DEGs and key module genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The roles of HAR-DEGs in IS were investigated via enrichment analysis. GO results demonstrated that HAR-DEGs were mainly engaged in biological process of \u0026lsquo;regulation of MAP kinase activity\u0026rsquo; and \u0026lsquo;regulation of B cell proliferation\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Beside, KEGG results demonstrated HAR-DEGs were principally implicated in \u0026lsquo;HIF-1 signaling pathway\u0026rsquo; and \u0026lsquo;Glucagon signaling pathway\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In additional, The PPI network of these genes was established, in which TLR4 interacted intricately with numerous proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Screening histone acetylation regulatory related biomarkers in IS\u003c/h2\u003e \u003cp\u003eTo further investigate essential genes, 11 feature genes whose regression coefficients were not penalized to 0 were attained by LASSO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Meanwhile, 15 and 25 feature genes were retained by SVM-RFE and Boruta, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c). Conclusively, \u003cem\u003eCREBBP\u003c/em\u003e, \u003cem\u003eSRPK1\u003c/em\u003e, \u003cem\u003eCKAP4\u003c/em\u003e, \u003cem\u003eSNRPN\u003c/em\u003e, \u003cem\u003eCARD12\u003c/em\u003e and \u003cem\u003eDIRC2\u003c/em\u003e were regards as candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Among them, \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e exhibited significant diagnostic utility for IS in validation set (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7), and expression pattern was entirely corresponding to training set, which were identified as histone acetylation regulatory-related biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-h). Notably, \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e were up-regulated in IS group compared to normal group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Immune microenvironment of biomarkers\u003c/h2\u003e \u003cp\u003eTo investigate prospective functions of \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e in IS, we executed GSEA analysis on biomarkers. The KEGG results presented that biomarkers were enriched in some immune linked to pathways, including \u0026lsquo;ribosome\u0026rsquo; and \u0026lsquo;complement and \u0026lsquo;coagulation cascade pathways\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-b; \u003cb\u003eOnline Resource 3\u003c/b\u003e). Then, we explored immune microenvironment in IS. The infiltration level of 22 immune cells was examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Significantly, 6 differential immune cells were attained among IS and normal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In IS group, there were substantially overproduced monocytes, macrophages and neutrophils than in normal group, while expression of CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and NK cells in IS group was markedly lower than normal group. Then, \u003cem\u003eCREBBP\u003c/em\u003e (cor = -0.63, p\u0026thinsp;=\u0026thinsp;4.09e-08) and \u003cem\u003eCKAP4\u003c/em\u003e (cor = -0.55, p\u0026thinsp;=\u0026thinsp;3e-06) were negatively linked with score of CD8\u003csup\u003e+\u003c/sup\u003e T cells, while \u003cem\u003eCREBBP\u003c/em\u003e (cor\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;=\u0026thinsp;1e-06) and \u003cem\u003eCKAP4\u003c/em\u003e (cor\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;=\u0026thinsp;4.92e-09) was positively connected with score of M0 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee; \u003cb\u003eOnline Resource 4\u003c/b\u003e). In additional, these two immune cell scores were significantly different between IS group and normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of regulatory network and drug in IS\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;lncRNA-miRNA-mRNA\u0026rsquo; network was constructed to study regulatory mechanisms of \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e, in which AL133355.1 might regulate expression of \u003cem\u003eCREBBP\u003c/em\u003e by affecting MIR100HG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In additional, SNHG25 might regulated expression of \u003cem\u003eCKAP4\u003c/em\u003e by affecting NR2F1-AS1. Finally, disease association analysis revealed that these two biomarkers were related to brain injuries and diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In particular, \u003cem\u003eCREBBP\u003c/em\u003e had a strong correlation with IS. Meanwhile, drugs targeting \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e were predicted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Notably, \u003cem\u003eCREBBP\u003c/em\u003e has been predicted for eight drugs, including cypermethrin, cyhalothrin and dronabinol et al. And \u003cem\u003eCKAP4\u003c/em\u003e has been predicted for three drugs, including tobacco smoke pollution, acetaminophen and progesterone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6The expression validation of \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e in IS\u003c/h2\u003e \u003cp\u003eIt was indicated by RT-qPCR outcomes that, in contrast to normal group, the expressions of CREBBP and CKAP4 were remarkably elevated in IS group, which accorded with results of transcriptome data, further verifying dependability of results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIschemic stroke, as a major threat to human health, has gained increasing attention on a global scale. An abrupt obstruction of cerebral blood supply can result in neurological deficits and impose substantial social and economic burdens. Despite the advancements in drug and recanalization therapies, there is still a lack of timely and effective treatments[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Related research is of paramount importance in order to explore novel therapeutic strategies aimed at enhancing the clinical efficacy of ischemic stroke. Previous studies have demonstrated that histone acetylation can facilitate the activation of genes associated with tissue regeneration and axonal growth, while elevated levels of acetylated proteins are linked to neuronal resistance against ischemia, thereby influencing stroke outcomes and contributing to brain recovery[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], histone acetylation plays a regulatory role in the permeability of the blood-brain barrier, leading to a reduction in blood vessel leakage and lowering the risk of brain hemorrhage[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Investigating biomarkers associated with histone acetylation may therefore provide a novel therapeutic avenue for the management of ischemic stroke.However, there are few studies on histone acetylation related genes as biomarkers for early prevention of IS.\u003c/p\u003e \u003cp\u003eThe CREB-binding protein (\u003cem\u003eCREBBP\u003c/em\u003e, or \u003cem\u003eCBP\u003c/em\u003e) is a member of the KAT3 family of proteins, which is lysine acetyltransferases (KATs) known for its ability to modify histones and non-histone proteins. This modification regulates chromatin accessibility and transcription. Previous studies have shown that CBP has tumor suppressor function[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Recently, it has been found to acetylate key factors involved in DNA replication, and in different DNA repair processes, such as base excision repair, nucleotide excision repair, and non-homologous end joining[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Studies have shown that the levels of CBP and histone acetylation play a crucial role in determining the outcome of stroke. And inhibition of CBP\u0026rsquo;s HAT activity can exacerbate neurological damage in patients suffering from ischemic stroke. The activation of CREB can serve as a neuroprotective function[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The study conducted by Ayden Gouveia demonstrated that the APKC-CBP pathway exerts regulatory control over neurovascular remodeling and functional recovery following stroke, with disruption of this pathway significantly impacting post-stroke neurovascular remodeling and recovery[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hence, the involvement of \u003cem\u003eCREBBP\u003c/em\u003e in repairing DNA damage and facilitating neurovascular remodeling may potentially enhance the prognosis of stroke patients.\u003c/p\u003e \u003cp\u003eCytoskeleton-associated protein 4 (\u003cem\u003eCKAP4\u003c/em\u003e) is a reversible type II transmembrane protein that is modified by palmitoylation and is found on multiple cellular membranes. Located in the rough endoplasmic reticulum, \u003cem\u003eCKAP4\u003c/em\u003e is crucial for facilitating the transport of certain proteins from the endoplasmic reticulum to the nucleus[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Neuronal cytoskeleton remodeling is associated with a variety of diseases related to the origin and development of the nervous system, including Alzheimer's disease, Parkinson's disease, muscle atrophy, amyotrophic lateral sclerosis, and ischemic cerebral apoplexy. Cyclin-dependent kinase-5(CDK5) plays a role in regulating neuronal cytoskeleton remodeling, which can enhance neurological function[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The remodeling of the cytoskeleton in platelets is associated with the thrombus perviousness. Treatments for highly permeable thrombi can be effective in thrombolysis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Recent studies have demonstrated that \u003cem\u003eCKAP4\u003c/em\u003e exhibits potential as a diagnostic biomarker for IS[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].In summary, CKAP4 is essential for the remodeling of the neuronal cytoskeletal and is significantly linked to the development and progression of various neurological diseases. Further exploration of its regulatory mechanisms and role in ischemic stroke is necessary.\u003c/p\u003e \u003cp\u003eTo further investigate the potential roles of \u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e in IS, we performed single-gene GSEA on biomarkers. The GSEA results showed that these two biomarkers were enriched in some immune related to pathways, including \u0026lsquo;ribosome\u0026rsquo; and \u0026lsquo;complement and the \u0026lsquo;coagulation cascade pathways\u0026rsquo;. Ribosome is the place where biological protein synthesis takes place. Ribosome biogenesis and protein synthesis are the basic rate-limiting steps for cell growth and proliferation. Ribosome-related genes downregulated lead to protein synthesis and secretion disorders. Studies have shown that ribosomal proteins S15(RPS15)may play an important role in the development of ischemic stroke[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The immuno-inflammatory responses, which occur throughout the whole process of ischemic stroke, are considered to be important modulating factors of disease progression, cerebral injury and repair after brain ischemia[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The coagulation cascade further exacerbates the occurrence of inflammation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thrombin can directly activate complement C3 and C5, disrupting the function of the endothelial barrier[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Following ischemic stroke, local vascular coagulation cascade can inhibit the expression of thrombomodulin (TM) on endothelial cells, leading to sustained activation of monocytes and the complement system, thus promoting deterioration in vascular immune-inflammatory response[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, additional research will be instrumental in elucidating its specific mechanisms and identifying novel targets for the diagnosis and treatment of ischemic stroke.\u003c/p\u003e \u003cp\u003eIncreasing evidence points to important role of the immune system in IS pathophysiology, so we explored the immune microenvironment in IS. In IS group, there were significantly higher monocytes, macrophages and neutrophils than in normal group, whereas the expression of CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells in IS group was significantly lower than normal group. Yilmaz et al. discovered that knockout of CD8\u0026thinsp;+\u0026thinsp;T cells significantly decreased cerebral infarct volume and improved neurological deficits in mice[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. CD8\u0026thinsp;+\u0026thinsp;T cells can enhance the production of TNF-α in macrophages and microglia by secreting IFN-γ, thereby exacerbating the inflammatory response following ischemic stroke[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These results indicate that CD8\u0026thinsp;+\u0026thinsp;T cells can promote inflammatory cell response and aggravate brain injury after MCAO. M0 macrophages are in a non-activated state and can be activated into either classically activated M1 macrophages or alternatively activated M2 macrophages depending on the microenvironment. The M1 subtype secretes proinflammatory cytokines and contributes to the immune inflammatory response after a stroke, while the M2 subtype secretes anti-inflammatory cytokines and exhibits anti-inflammatory as well as neuroprotective effects[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The impact of macrophages on ischemic brain injury is largely determined by the different phenotypes of macrophages at various stages following a stroke. However, the conversion between different subtypes in the process is complex and the exact mechanism of action has not been fully revealed. In this study, we identified a significant relationship between CREBBP and CKAP4 in the context of immune cell pathology and ischemic stroke. Specifically, CREBBP exhibited a negative correlation with CD8\u0026thinsp;+\u0026thinsp;T cell scores, whereas CKAP4 demonstrated a positive correlation with M0 macrophage scores. These findings suggest that CREBBP may exert a protective effect in ischemic stroke by inhibiting the function of CD8\u0026thinsp;+\u0026thinsp;T cells. CKAP4 may facilitate the differentiation of M0 macrophages into M2-type macrophages through specific mechanisms, thereby contributing to protective responses following ischemic stroke. In summary, the role of the immune system in ischemic stroke is both intricate and critical. The insights gained from our investigation into CREBBP and CKAP4 enhance our understanding of the immune microenvironment, indicating that targeted interventions involving these molecules could pave new avenues for treating ischemic stroke.\u003c/p\u003e \u003cp\u003eIn recent years, there has been a growing focus on the regulation of lncRNA in cell cycle, cell differentiation, and epigenetics. LncRNA plays a crucial role in regulating gene expression across multiple processes, leading to either gene silencing or activation. Studies have identified that the abnormal expression of lncRNA is involved in multiple pathological processes of nerve cell damage and functional defects after IS[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The \u0026lsquo;lncRNA-miRNA-mRNA\u0026rsquo; network was constructed and we found that the expression of \u003cem\u003eCREBBP\u003c/em\u003e was associated with MIRG100H, while the expression of \u003cem\u003eCKAP4\u003c/em\u003e was associated with NR2F1-AS1. In summary, MIRG100H and NR2F1-AS1 may play a significant role in the pathogenesis following ischemic stroke, indicating their potential as novel therapeutic targets.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCREBBP\u003c/em\u003e has been predicted for eight drugs, including cypermethrin, cyhalothrin and dronabinol et al. Cypermethrin and cyfluthrin exhibit neurotoxic properties, primarily attributed to their capacity to elevate oxidative stress levels. Cannabinoids have the potential to disrupt mitochondrial respiratory chain function in the brain and elevate oxidative stress levels, ultimately contributing to an increased risk of stroke. \u003cem\u003eCKAP4\u003c/em\u003e has been predicted for three drugs, including tobacco smoke pollution, acetaminophen and progesterone. Tobacco smoke pollution can activate macrophages to release inflammatory mediators, contributing to the body's inflammatory response[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Meanwhile, acetaminophen can stimulate macrophages in liver to produce reactive oxygen species (ROS), leading to an oxidative stress response[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, progesterone has the ability to regulate the function of macrophages[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], further supporting the notion that \u003cem\u003eCKAP4\u003c/em\u003e may play a protective role after IS by regulating macrophage function. In summary, the mechanisms of action of these drugs elucidate their potential risks and protective effects in ischemic stroke, offering significant insights for future research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study aims to explore the relationship between biomarkers associated with histone acetylation regulation and IS through bioinformatics analysis. However, there are still some limitations in our study, including the expression verification of these markers \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e, the signaling pathways that these markers may affect, and the functional verification of these markers in immune cells. We will continue to closely monitor such research in the future, with the hope of advancing the treatment of IS\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Wang Yanni, Yang Fan, Tan Jiaojiao, Guo Zhentao. 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\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yang Fan, Tan Jiaojiao and Guo Zhentao. The first draft of the manuscript was written by Wang Yanni and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Shandong University Qilu Hospital(Date2025-1-23/KYLL-KS-2025052)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in this study were provided with a clear and understandable explanation of the research objectives, procedures, potential risks, and benefits. They were informed that their participation is voluntary and that they have the right to withdraw from the study at any time. Participants were given the opportunity to ask questions and provided written informed consent prior to their involvement in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets (GSE16561, GSE202518) analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, (https://www.ncbi.nlm.nih.gov/gds). miRDB database from the web (http://mirdb.org). Targetscan database from the web (http://www.targetscan.org). CTD database from the web (http://ctdbase.org).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeigin VL, Stark BA, Johnson CO,et al. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. 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Immunology. 2008;125:59\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \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":"Ischemic stroke, Histone acetylation regulatory genes, Immune infiltration, Biomarkers, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-6336853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6336853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIschemic stroke (IS) is a serious cerebrovascular disease. Excessive acetylated protein levels are linked to neuronal resistance to ischemia, making histone acetylation regulatory-related biomarkers crucial for IS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo identify differentially expressed genes (DEGs) in GSE16561, differential expression analysis (normal \u003cem\u003eversus\u003c/em\u003e IS) was performed. Key module genes linked to single-sample gene set enrichment analysis (ssGSEA) scores of histone acetylation regulatory-related genes (HARGs) were identified via weighted correlation network analysis (WGCNA). Overlapping DEGs and key module genes yielded histone acetylation regulatory-related DEGs (HAR-DEGs). Three machine learning algorithms, expression, and receiver operating characteristic (ROC) analysis screened histone acetylation regulatory-related biomarkers. Functional enrichment, immune microenvironment analysis, and disease association were conducted. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) assessed biomarker expression levels.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 550 differentially DEGs and 137 key module genes related to ssGSEA enrichment scores of HARGs were identified. Overlapping these yielded 44 HAR-DEGs. CREBBP and CKAP4 were identified as histone acetylation regulatory-related biomarkers in IS. Both biomarkers were linked to immune-related pathways, such as complement and coagulation cascades. CREBBP inversely correlated with CD8\u003csup\u003e+\u003c/sup\u003e T cell scores, while CKAP4 positively correlated with M0 macrophage scores. Both were associated with brain injury and disease. RT-qPCR confirmed elevated expression of CREBBP and CKAP4 in IS samples compared to controls.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn summary, we identified two biomarkers (\u003cem\u003eCREBBP\u003c/em\u003e and \u003cem\u003eCKAP4\u003c/em\u003e) linked to histone acetylation regulation in IS, providing a theoretical basis for its treatment.\u003c/p\u003e","manuscriptTitle":"Exploring Histone Acetylation in Ischemic Stroke: The Role of CREBBP and CKAP4 as Key Biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 11:56:24","doi":"10.21203/rs.3.rs-6336853/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":"8d4c81ae-d769-4822-8681-8a7338810356","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-04T15:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 11:56:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6336853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6336853","identity":"rs-6336853","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}