Novel Histone Acetylation Regulators: Mediators of Tumor Microenvironment Infiltration and Prognostic Model in Cervical Cancer Patients

Novel Histone Acetylation Regulators: Mediators of Tumor Microenvironment Infiltration and Prognostic Model in Cervical Cancer Patients | 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 Novel Histone Acetylation Regulators: Mediators of Tumor Microenvironment Infiltration and Prognostic Model in Cervical Cancer Patients Dan Mo, Zhonghong Zeng, Mingmei Lin, Yongjin Luo, Yuxin Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4911165/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: Cervical cancer (CC) is the second most prevalent mortality rate for women’ cancer globally and the fourth most prevalent gynecological tumor. Dysregulation of histone acetylation (HA) influences the pathogenesis of cancer. However, there is a dearth of comprehensive research on HA in CC. Methods: We conducted univariate and multivariate Cox and LASSO regression analyses to assess the prognostic relevance of 36 HA-related genes identified in previous studies. A prognostic model was developed by utilizing the TCGA cohort as the training dataset and the screened HA genes. The model was subsequently validated on GSE68339 dataset. In order to confirm the accuracy of the model, Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) were implemented. The study also investigated the associations between immune cell infiltration characteristics, immune checkpoint genes, and drug sensitivity. Lastly, the essential genes were verified through qRT-PCR and immunohistochemistry. Results: KAT2B , HDAC5 , and HDAC10 were identified as pivotal for prognosis among the 36 HA genes that were analyzed. The prognostic model classified TCGA patients into high- and low-risk groups based on risk scores, revealing significantly reduced overall survival (OS) in the high-risk group. High-risk patients demonstrated decreased immune infiltration and checkpoint gene expression. KAT2B , HDAC5 , and HDAC10 were downregulated in CC compared to normal tissues, which was correlated with poorer 5-year OS rates. qRT-PCR and immunohistochemistry confirmed reduced expression of HDAC5 and HDAC10 in clinical samples. Conclusions: We propose a prognostic model based on three HA genes that demonstrates a well predictive effect on CC patients, offering predictive value and potential application in clinical treatments. Histone acetylation Cervical cancer Prognostic model Immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Cervical cancer (CC) is the second leading cause of cancer-related mortality among women worldwide and the fourth most prevalent gynecological tumor, particularly in industrialized and developing countries (1). In 2020, more than 600,000 new cases were reported, with over 341,000 deaths globally (2). In recent year, there have been substantial improvements in molecular diagnostics, targeted therapies, and immunotherapies for CC (3–5). For instance, GRP78 has demonstrated anticancer effects in CC by regulating cell apoptosis and autophagy, while immune CD8 + T cells, mediated by the immune system, play a crucial role in regulating tumor immunity, which is crucial for the effectiveness of the HPV vaccine (6). Additionally, several tumor biomarkers and molecular markers are currently available to predict overall survival (OS) in CC patients (7). Despite improvements in diagnostic and therapeutic approaches, the prognosis for advanced-stage CC remains poor, with a recurrence rate of approximately 35% (8). Consequently, new prognostic indicators are urgently need to reliably predict the OS of CC patients. Research has revealed that CC is significantly influenced by epigenetic modifications, in addition to genomic alteration (9). Histone modification and DNA methylation are the primary epigenetic modifications that regulate the structure of chromatin (10). While DNA methylation has been extensively investigated and has the potential to facilitate the advancement of biomarkers and targeted biological agents in CC (11), research on histone modification, especially histone acetylation, remains restricted. Histone acetylation modulator proteins (HAMPs), which include HATs (writers), HDACs (erasers), and bromodomain-containing proteins (readers), regulate histone acetylation and influence transcriptionally active euchromatin (12). Aberrant activity of HAMPs has been observed in various malignancies, suggesting that certain HAMPs may act as driver genes in the development of cancer (13). The role of histone acetylation in CC has been highlighted by previous studies. For example, the prognosis of CC patients was significantly affected by histone H3 acetyl K9 and histone H3 trimethyl K4 (14). Furthermore, HDAC1 and HDAC2 were overexpressed in cervical dysplasia and invasive carcinoma (15). Nevertheless, their precise role in CC remains uncertain. In this study, we evaluated the correlation between histone acetylation genes and the prognosis of CC patients. Furthermore, we identified independent prognostic markers to develop a predictive nomogram and a risk score model. We also assessed their correlation with immune checkpoints, immune cells, and drug sensitivity. Our findings support the potential use of this new predictive model and potential biomarkers for CC. Materials and methods Data sources for research We obtained the gene expression data, somatic mutation data, and related clinical information for CC patients from the Cancer Genome Atlas (TCGA) database. A pathological diagnosis of CC was used to filter these specimens, and patients without full clinical and follow-up data were excluded. Finally, 306 CC samples and 13 normal cervical samples were employed for the subsequent analysis. Furthermore, in order to validate the dataset, normalized matrix files from GSE68339 were obtained from the Gene Expression Omnibus (GEO) database. Analysis of copy cumber variations (CNVs) and mutations in histone acetylation genes We conducted a comprehensive review of literature on histone acetylation modifications, identifying and analyzing 36 established histone acetylation genes to uncover distinct modification patterns (16). Expression data for these genes were obtained from the TCGA database, and their chromosomal distribution was visualized using the RCircos tool. CNVs analysis utilized Perl (5.32.1.1) and R (4.1.2), while mutations were mapped with maftools. Student’s t-test was employed to assess the impact of individual mutations on gene expression levels, with findings visualized using ggplot2. Construction of a risk score model Following univariate Cox regression analysis in the TCGA cohort, we conducted minimum absolute shrinkage, absolute shrinkage and selection operator (LASSO) regression, and multivariate stepwise Cox regression analyses. Ultimately, three histone acetylated genes were identified as risk factors. The risk score was calculated using the algorithm: \(\:Risk\:score={\sum\:}_{i}^{n}{X}_{i}\times\:{Y}_{i}\) (X: coefficient of each gene, Y: gene expression level). Based on the median risk scores, the cohort were classified into high- and low-risk groups for both training and validation datasets. At the same time, we also used the GSE68339 external data set as a validation set to verify the superiority of the risk model. Prognosis analysis A prognostic nomogram was generated using the "RMS" package in R to predict 1-,3-, and 5- year OS for CC patients. The nomogram integrated the clinical stage and baseline patient information derived from the training dataset. To assess the predictive accuracy of the nomogram model in CC patients, calibration curve (using the KM analysis) and concordance index (C-index) curves were constructed. Receiver operating characteristics (ROC) and area under the curve (AUC) were conducted using the ‘timeROC’ package to evaluate the specificity and sensitivity of the risk score. Sing-cell transcriptional landscape We retrieved single-cell transcriptome files (GSE168652) from the TISCH database ( http://tisch.comp-genomics.org/ ). Gene set variation analysis (GSVA) GSVA, an unsupervised and non-parametric approach, is employed to assess the enrichment of transcriptomic gene sets. It is capable of assessing the biological functions of samples and calculating comprehensive scores for sets of interest by converting changes at the gene level into changes at the pathway level. The GSVA algorithm was employed in this study to assess potential changes in biological function across samples by utilizing gene sets that were obtained from molecular feature databases. The algorithm generated comprehensive scores for each gene set. Immunogenomic landscape analyses First, we compared the expression levels of immune checkpoint-related genes between the two groups. Subsequently, to investigate correlations with the risk score and immune cell infiltration, we assessed the infiltration of distinct immune cells and their related functions using the R program and CIBERSORT R packege (17). Drug Sensitivity Analysis Gene expression and drug sensitivity data were sourced from the CellMiner database ( https://discover.nci.nih.gov/cellminer/home.do ). Drug sensitivity data underwent screening based on clinical laboratory validation and certification standards set by the Food and Drug Administration. Subsequently, we integrated the expression data of three core prognostic genes with drug sensitivity data. Pearson correlation tests were performed to assess their associations. Clinical sample collection CC tissues and normal adjacent tissues were collected from the First Affiliated Hospital of Guangxi Medical University from January to Jun 2024. Tumor tissues and normal adjacent tissues were quickly collected after surgical removal and immediately frozen in liquid nitrogen for fast freezing. They were then stored at -80°C. A total of 6 CC samples and 6 normal adjacent tissues were collected for analysis. None of the patients did receive any anticancer treatment, such as radiation, chemotherapy, or biological therapies, either before or after surgery. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) The TRIzol™ reagent (Invitrogen) was used to extract total RNA from tissues, following the manufacturer's instructions. The concentration and purity of the collected RNA were verified with the Nano-300 microspectrophotometer. The RNA was subsequently converted into complementary DNA (cDNA) using the Goscript™ Reverse Transcription System (Promega). The synthesized cDNA was subsequently analyzed using the SYBR Green Realtime PCR Master Mix. Each sample from every group underwent three rounds of testing. GAPDH was used as the internal control. The primer sequences used in this study were provided in Table S3 . Immunohistochemistry Tissue samples that had been preserved in 4% PFA were put in blocks of paraffin and sliced with a microtome (Macroteck, Ilsansi, Korea). The sections were submerged in xylene for ten minutes in each of two distinct containers to accomplish deparaffinization. Ethanol solutions with escalating concentrations (100%, 95%, 90%, 80%, and 70%) were used for sequential dehydration for five minutes each. To retrieve the antigen, the sections were boiled in a sodium citrate solution for 15 minutes at 99°C and then cooled to RT. The sections were treated with 3% hydrogen peroxide (Polymer-HRP Detection System (Rabbit) (PV-1023), Bioss, China) for a duration of 15 minutes to inhibit endogenous peroxidases. They were then incubated at RT for 20 minutes with a standard goat serum blocking solution (Polymer-HRP Detection System (Rabbit) (PV-1023), Bioss, China). Subsequently, the sections were treated with a primary rabbit anti-HDCA5 (Bioss, bs-2810R, 1:200), anti-HDCA10 (Bioss, bs-2893R, 1:200), and anti-KAT2B (Bioss, bs-5130R, 1:200) antibody overnight at 4°C. Afterward, they were incubated for 20 minutes at RT with an anti-rabbit secondary antibody (Polymer-HRP Detection System (Rabbit) (PV-1023), Bioss, China). Using a DAB substrate kit (AC11043-2, Acmec, China), peroxidase signals were seen. The slides were cleaned, cleared in xylene, counterstained with hematoxylin, and mounted for imaging under a Primo Star microscope (Carl Zeiss, Oberkochen, Germany). Statistical analysis The Wilcox test was used to compare groups of samples in this study. Survival times of patients in high- and low-risk groups were compared using KM analysis, and the differences were investigated using the log-rank test. Spearman's correlation analysis was conducted to examine the correlations between quantitative variables with non-normal distributions. A p-value of less than 0.05 was considered significant. The PCR findings were visualized using GraphPad Prism. The majority of analyses were conducted using R software (version 4.1.1). Results Expression of histone acetylation regulated genes in CC We identified 36 genes involved in the regulation of histone acetylation, including 15 readers, 9 writers, and 12 erasers ( Table S1 ). The expression data for these genes were obtained from the TCGA-CC cohort ( Table S2 ). We conducted an analysis of the mRNA expression levels of these 36 genes in CC tissues in comparison to adjacent normal tissues, which revealed significant expression differences in 16 genes (Fig. 1 ). These findings suggest that the aberrant expression patterns of histone acetylation regulators in CC may play an important role in carcinogenesis and disease progression. Genetic polymorphisms of the histone acetylation regulators in CC CNVs were prevalent among the 36 regulators. Notable amplifications were observed in KAT6A , SIRT2 , HDAC9 , and YEATS4 , while CREB3L2 , CREB3L3 , and BATF were frequently deleted (Fig. 2 A). The chromosomal locations of CNVs alterations in histone acetylation regulators were shown in Fig. 2 B. Additionally, mutations were detected in all histone acetylation regulation genes, with a somatic mutation frequency of 38.75% (112 out of 289 samples). Missense and nonsense mutations were the two most prevalent types of mutations (Fig. 2 C). The elevated mutation rate suggested genomic instability of histone acetylation regulators in the TCGA-CC cohort, implicating their potential involvement in pathogenesis of CC. Furthermore, univariate Cox proportional hazards regression analysis of the 36 histone acetylation regulation genes indicated that KAT2B , HDAC5 , and HDAC10 were protective factors in patients with CC (p < 0.05, HR < 1) (Fig. 2 D). Risk score model based on histone acetylation regulated genes After performing LASSO-Cox regression analysis, the selected histone acetylation regulated genes were used to build a risk score model using the Cox regression method, with TCGA cohort serving as the training dataset and GSE68339 serving as the validating dataset. The risk score was determined in the following manner: Risk score = (-0.255* HDAC5 exp.) + (-0.650* HDAC10 exp.) + (-0.419* HAT2B exp.) (Fig. 3 A, B). The median risk score was used to categorize samples from both groups into high- and low-risk categories. The high-risk group exhibited substantially higher mortality rates and a worse survival rate in both the training and validation groups (Fig. 3 C, D; Fig. 4 A-D). The model's predictive accuracy for 1-year, 3-year, and 5-year survival was further validated by receiver operating characteristic (ROC) curve analysis ( Fig. 3 E, F). The training group (Fig. 4 E) and the validation group (Fig. 4 F) were presented with heat maps that illustrated the expression patterns of the three main genes. The high-risk score group in the training group exhibited down-regulation of these hub genes in comparison to the low-risk score group. In particular, HDAC5 and HDAC10 exhibited consistent down-regulation tendencies. Nevertheless, the validation group exhibited a contrasting tendency in HAT2B , which implied that there may be potential variability in gene expression patterns among various patient cohorts. In general, the prognosis of CC can be somewhat predicted by these three histone acetylation characteristics. Development and verification of a Nomogram To assess the clinical efficacy of the histone acetylation-based risk model, a nomogram was developed. In this model, a higher total score was associated with worse survival outcomes (Fig. 5 A). The nomogram's accuracy was underscored by the calibration plots, which showed a high degree of consistency between predicted and observed OS at 1-, 3-, and 5-year intervals (Fig. 5 B). Additionally, the nomogram's robust predictive performance was confirmed by ROC curve analysis, which yielded the AUC values of 0.773, 0.625, and 0.654 for 1-, 3-, and 5-year OS risk scores, respectively (Fig. 5 C-E). Furthermore, the risk score, T stage, and stage were identified as significant prognostic indicators for CC in univariate Cox regression analysis. The risk score and T stage were further emphasized as independent predictors of OS in the multivariate analysis (Fig. 5 F). These results emphasize the nomogram's usefulness in evaluating the prognosis and making clinical decisions of patients with CC. Single-cell atlas of human uterus cells in CC patients To generate a comprehensive single-cell atlas of human uterus cells in CC patients, we analyzed the scRNA-seq dataset GSE168652. We categorized 22 clusters, including seven distinct cell types: CD8T cells, endometrial stromal cells. endothelial cells, fibroblasts, malignant cells, mono/macrophages, and smooth muscle cells (SMCs) (Fig. 6 A, B). The differential patterns of HDAC5 , HDAC10 , and KAT2B were revealed by the expression correlations among these cell types (Fig. 6 C-E). These findings suggested that these core genes had predominantly positive correlations with various cell types, indicating their potential roles in the pathophysiology of CC. These discoveries provide insights into the potential regulatory roles and cellular heterogeneity of these core genes in CC. Pathway enrichment analysis GSVA revealed 85 enriched pathways among the low risk-score and high risk-score groups. These pathways included metabolic, immune and cancer-related pathways, such as folate biosynthesis, glyoxylate and dicatboxylate metabolism, primary immunodeficiency, and cancer pathway (Fig. 7 ) . Immunological landscape of CC To further understand the relationship between risk scores and patients' immune status, we analyzed 46 immune cell checkpoint genes and infiltration scores for each patient. The developed risk score was significantly negatively correlated with the assessment of immune checkpoint genes, indicating that histone acetylation regulators play a regulatory function in the immune microenvironment of CC patients (Fig. 8 A, B). Furthermore, there was a negative correlation between the risk score and the expression of PD1 and TIM-3 (Fig. 8 C-F). Immune cell infiltration according to risk score classifications To investigate the correlation between CC and the dynamics of immune cells, we used diverse algorithms to evaluate the proportions and functions of immune cells according to the risk model. We evaluated correlations with three major prognostic genes across 22 categories of tumor-infiltrating immune cells (TIICs) and linked the risk model to immune cell infiltration using the CIBERSORT technique (Fig. 9 A). KAT2B expression was discovered to be adversely correlated with activated NK cells, activated mast cells, M0 macrophages, and eosinophils, while positively correlated with CD8T cells, resting mast cells, M1 macrophages, and resting dendritic cells (p < 0.05). HDAC5 expression was discovered to be inversely connected to Tregs, resting memory CD4 T cells, plasma cells, resting mast cells, resting dendritic cells, and naive B cells, while positively correlated with activated memory CD4 T cells, resting NK cells, and neutrophils (p < 0.05). HDAC10 expression was found to be negatively correlated with resting memory CD4 T cells, whereas it was positively correlated with Tregs and memory B cells (p < 0.05) (Fig. 9 A). The distribution of TIIC subtypes between low- and high-risk groups was shown in Fig. 9 B. The low-risk group exhibited higher proportions of naive B cells, CD8 T cells, Tregs, resting dendritic cells, resting mast cells, and eosinophils, as well as lower proportions of M0 macrophages, activated mast cells, and neutrophils. Furthermore, the low-risk group exhibited a substantial activation of human leukocyte antigen (HLA), B cells, CD8 + T cells, immature dendritic cells (iDCs), neutrophils, and T helper cells, as evidenced by additional ssGSEA analysis (Fig. 9 C, D). Examination of drug sensitivity in the three core prognostic genes We investigated the relationship between drug sensitivity and the expression of the three core genes. As shown in Fig. 10 , KAT2B expression was positively correlated with vemurafenib, dabrafenib, encorafenib, and selumetinib (p < 0.05), and negatively with dasatinib, brigatinib, digoxin, and acetalax (p < 0.05). HDAC10 exhibited positive correlations with decitabine, vemurafenib, nelarabine, vorinostat, temsirolimus, and tegafur (p < 0.05). HDAC5 showed a negative association with palbociclib and dexrazoxane (p < 0.05). Expression and prognostic impact of three core genes in CC and adjacent normal tissues Comparative analysis of mRNA expression profiles from 306 CC and 13 adjacent normal tissues in the TCGA-CC cohort revealed significantly reduced expressions of KAT2B , HDAC5 , and HDAC10 in CC tissues (p < 0.05) (Fig. 11 A-C). The prognoses of patients with high expression levels of these genes were superior to those with low expression (p < 0.05) (Fig. 11 D-F). These results highlighted the potential of KAT2B , HDAC5 , and HDAC10 as prognostic biomarkers for CC. Clinical samples validation of HDAC5 , HDAC10 , and KAT2B expression To enhance the reliability of our bioinformatic findings, we employed qRT-PCR to examine the expression levels of three key genes. The mRNA expression levels of HDAC5 (Fig. 12 A) and HDAC10 (Fig. 12 B) were consistent with our bioinformatic results (p 0.05) (Fig. 12 C), This could be attributed to the small sample size, additional validation with larger samples should be conducted in future studies. Additionally, we further investigated the proteins expression levels of HDAC10 and HDAC5 using immunohistochemistry (Fig. 12 D, E), which confirmed the predictions made by our bioinformatic analyses. These findings further confirmed that HDAC10 and HDAC5 may serve as potential biomarkers for predicting CC. Discussion CC is the fourth most frequently diagnosed gynecological tumor, characterized by its high incidence and mortality rate (2). CC exhibits notable variations in both molecular and histological characteristics. Despite advancements in surgical procedures and integrated therapies, the prognosis for advanced or recurrent CC remains unfavorable. This emphasizes the immediate necessity to identify biological and therapeutic targets to improve the prognosis of CC. Histone acetylation plays a pivotal role in cancer initiation and progression, with many small molecule drugs currently under clinical investigation (18). Exploring its relationship with CC may aid in the development of innovative approaches for its management. This study comprehensively analyzed histone acetylation in CC development and constructed a prognostic model based on histone acetylation genes. This study focused on 36 histone acetylation regulated genes as determined by the published literature (16). The expression of acetylated genes differed significantly between CC tissues and normal cervical tissues. Using bioinformatics and statistical tools, we evaluated the prognostic accuracy of these genes and selected KAT2B , HDAC5 , and HDAC10 to construct prognostic models and calculate risk scores. These three core genes expression in CC was inferior to normal cervical tissues, and decreased expression of these three core genes had worse prognosis. Clinical samples confirmed reduced levels of HDAC5 and HDAC10 in CC tissues, suggesting their potential as diagnostic biomarkers. Histone acetylation is a dynamic and reversible process regulated by HATs and HDACs. HATs enhance gene expression by altering the spatial organization of nucleosomes, leading to chromatin relaxation and increased gene transcription and replication.Conversely. HDACs enzymatically eliminate acetyl groups and bind to compact chromatin formations, therefore repressing transcription (19). Hence, the alteration of histones through acetylation is an essential epigenetic aspect in the biology of cancer (20). Currently, the specific function of HDAC proteins in cancer is not fully elucidated. Recent studies have shown that overexpression of numerous members of the HDAC family causes epigenetic silencing of tumor suppressor genes, which is a key step in carcinogenesis. Class I HDACs facilitate the regulation of the cell cycle, cell differentiation, and tissue development (21). HDAC6, one of Class II HDACs, can influence cell migration and other key biological processes (22). Therefore, HDAC inhibitors have been identified as supplementary therapeutic drugs for various types of human malignancies (23). On the other hand, HDACs have the potential to restrict tumor growth in certain cases. For example, data from the TCGA dataset revealed that deep deletions in HDAC10 were implicated in 5–10% of ovarian cancers. Furthermore, there is a correlation between low mRNA expression and susceptibility to platinum-based treatments (24). In this study, HDAC5 and HDAC10 , both class II HDACs, exhibited lower expression in CC tissues, consistent with previous studies (25, 26). The possible mechanism involved the decrease in expression of HDAC10 , which prevented the migration of CC cells and tumorigenesis by suppressing miR-223 expression through histone deacetylation (25). Additionally, it suppressed metastasis via inhibiting the expression of matrix metalloproteinase (MMP) 2 and 9 (27). Depletion of HDAC5 resulted in an elevation in acetylation of histone H3 lysine 27 (H3K27-ac) and bypassed the inhibitory effect of RB on cell-cycle-related pro-oncogenic genes (28). An increasing number of researches suggested that components of tumor microenvironment (TME) played crucial roles in the development of cancer (29, 30). Epigenetics, particularly histone acetylation, not only regulates gene expression in tumor cells but also modulates TME components (31). To better understand the immune response against CC, we investigated the correlation between histone acetylation modifications and TME cell infiltration. Our prognostic model highlighted a significant association between histone acetylation and the TME, pointing to key immune checkpoints such as anti-programmed cell death 1 (PD-1) and TIM-3. Consistent with previous studies, these checkpoints were crucial in restoring anti-tumor immunity, which allowed for the reversal of immune evasion and the promotion of tumor cell death (32, 33). Furthermore, our findings suggested immune-related functions were negatively correlated with our risk scores, indicating immune evasion susceptibility in high-risk groups. Further exploration into the therapeutic implications of histone acetylation identified potential sensitivities to chemotherapeutic drugs like vemurafenib and dabrafenib in CC. This discovery could play a significant role in advancing new treatments for CC and in preventing drug resistance. Despite these observations, our study has limitations. Firstly, we should expand our clinical sample size to validate the expression of these key genes in cervical cancer. Additionally, the mechanistic functions of crucial genes and prognostic factors need further confirmation through in vivo studies and controlled laboratory experiments (in vitro). Conclusion In conclusion, our study verified the potential of histone acetylation-related genes as molecular biomarkers for prognostic stratification and immune infiltration in CC. A nomogram based on KAT2B , HDAC5 , and HDAC10 could provide personalized survival assessments for newly diagnosed CC patients, guiding tailored therapeutic strategies. Abbreviations CC Cervical cancer HA Histone acetylation ROC Receiver operating characteristics OS Overall survival HAMPs Histone acetylation modulator proteins TCGA the Cancer Genome Atlas GEO Gene Expression Omnibus CNVs Copy cumber variations GSVA Gene set variation analysis qRT-PCR Quantitative Reverse Transcription Polymerase Chain Reaction MMP Matrix metalloproteinase H3K27-ac Acetylation of histone H3 lysine 27 PD-1 Anti-programmed cell death 1 Declarations Ethics approval and consent to participate All specimens were obtained with informed consent from patients and received approval from the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (approval number 2024-E541-01). Consent for publication Not applicable Competing interests The authors have no conflicts of interest to disclose. Funding This study was founded by the National Natural Science Foundation of China (NO.82360308); the Special Fund of the Maternal Fertility Preservation Innovation Team of the First Affiliated Hospital of Guangxi Medical University (NO. YYZS2021001); Clinical Key Incubation Project A of Peking University Third Hospital (NO. BYSYZD2023022). Author Contribution D.M. drafted the manuscript and performed the experiments. Z.H.Z. revised the manuscript and did data analysis. M.M.L. revised the manuscript and provided funding support. Y.J.L. revised the manuscript. Y.X.C. collated the samples. Y.H.Y, provided technical support, critical comments, and funding supports. M.Y.D. designed the study, performed bioinformatic analysis, and revising the manuscript. All authors gave their approval for the final version. Acknowledgement We appreciated all the patients who participated in our research. 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Cancer research. 2021;81(6):1486-99. 29. Liu Y, Zhou X, Wang X. Targeting the tumor microenvironment in B-cell lymphoma: challenges and opportunities. Journal of hematology & oncology. 2021;14(1):125. 30. Leivonen SK, Friman T, Autio M, Vaittinen S, Jensen AW, D'Amore F, et al. Characterization and clinical impact of the tumor microenvironment in post-transplant aggressive B-cell lymphomas. Haematologica. 2023;108(11):3044-57. 31. Lodewijk I, Nunes SP, Henrique R, Jerónimo C, Dueñas M, Paramio JM. Tackling tumor microenvironment through epigenetic tools to improve cancer immunotherapy. Clinical epigenetics. 2021;13(1):63. 32. Martins F, Sofiya L, Sykiotis GP, Lamine F, Maillard M, Fraga M, et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nature reviews Clinical oncology. 2019;16(9):563 − 80. 33. Rådestad E, Klynning C, Stikvoort A, Mogensen O, Nava S, Magalhaes I, et al. Immune profiling and identification of prognostic immune-related risk factors in human ovarian cancer. Oncoimmunology. 2019;8(2):e1535730. Additional Declarations No competing interests reported. Supplementary Files TableS1SummaryofhistoneacetylationModificationregulators.xlsx TableS2Theexpressiondatafor36histoneacetylationgenesfromtheTCGACCAcohort..xlsx TableS3.PrimersforqRTPCR.docx 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4911165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351510508,"identity":"1fc6bdee-7293-4f1b-bb0b-e154b9e10ead","order_by":0,"name":"Dan Mo","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Mo","suffix":""},{"id":351510509,"identity":"49332c58-f6f5-4452-b5ff-367086fddb25","order_by":1,"name":"Zhonghong Zeng","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhonghong","middleName":"","lastName":"Zeng","suffix":""},{"id":351510510,"identity":"96e5519b-08b9-4158-bbc5-16d9cffcbcbe","order_by":2,"name":"Mingmei Lin","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mingmei","middleName":"","lastName":"Lin","suffix":""},{"id":351510511,"identity":"a6619913-eeaa-42fb-b6cc-75290779edf1","order_by":3,"name":"Yongjin Luo","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongjin","middleName":"","lastName":"Luo","suffix":""},{"id":351510515,"identity":"e17b1c9a-9309-46d3-b33a-e8ef5bf9586c","order_by":4,"name":"Yuxin Chen","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Chen","suffix":""},{"id":351510516,"identity":"2d91813e-bdbd-4e80-b41a-0de5a5bb9c45","order_by":5,"name":"Yihua Yang","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yihua","middleName":"","lastName":"Yang","suffix":""},{"id":351510517,"identity":"a5429812-5ba0-4be3-873d-966989d45314","order_by":6,"name":"Mingyou Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYHACAyC2gbLZiNeSRrqWwyRo0Z2RvPFxwa/ziWtn5B5g+FB2mIF/dgN+LWY30oqNZ/bdTtx2Iy+Bcca5wwwSdw4Q0pJjJs3bA9KSY8DM23aYwUAigSgt5yBa/hKthefHAYgWRqK0nHlWbMzbkGy87cwbg4M959J5JG4Q0nIcGGI8f+xktx3PMXzwo8xajn8GAS0MAkAFjG0Mjg1A9gEg5iGgHgj4Qer+MNgTVjkKRsEoGAUjFgAAzvZJPEs1Bs4AAAAASUVORK5CYII=","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mingyou","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2024-08-14 06:21:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4911165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4911165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66528738,"identity":"a382b4be-a2e5-43ad-b89b-ea98a512396a","added_by":"auto","created_at":"2024-10-14 05:30:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of histone acetylation regulated genes in cervical Cancer (CC)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/3cc90ae532e103451ff7fbf1.png"},{"id":66528735,"identity":"4e3ccf1e-73e0-4423-8692-f55b88e9cad4","added_by":"auto","created_at":"2024-10-14 05:30:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLandscape of genetic and expression variants of histone acetylation regulators in CC\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Copy number variations (CNVs) frequencies of histone acetylation regulators in TCGA-CC cohort. Blue dots represent amplification frequencies, orange dots represent deletion frequencies, and the height of the columns represents a change in frequency. (\u003cstrong\u003eB\u003c/strong\u003e) The position of CNV alteration of histone acetylation regulators on 23 chromosomes. (\u003cstrong\u003eC\u003c/strong\u003e) Mutation frequencies of 36 histone acetylation regulators in TCGA-CC cohort. (\u003cstrong\u003eD\u003c/strong\u003e) Univariate Cox regression analysis was used to study histone acetylation regulated genes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/d9f57696df5a19738e1bf73a.png"},{"id":66529173,"identity":"d0a6da97-9e10-4f2b-819c-39b3d474fc2c","added_by":"auto","created_at":"2024-10-14 05:38:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of a prognostic model based on histone acetylation regulation genes.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Ten-fold cross-validated error (the first vertical line equals the minimum error, whereas the second vertical line shows the cross-validated error within 1 standard error of the minimum).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) The Lasso coefficient profiles of the 3 genes selected for OS against the log (lambda)\u003c/p\u003e\n\u003cp\u003esequence. (\u003cstrong\u003eC \u003c/strong\u003eand \u003cstrong\u003eD\u003c/strong\u003e) Survival curves for the high-risk and low-risk groups. (E and F) ROC curves at 1, 3 and 5 years for prognostic models. OS, overall survival; AUC, area under the curve; ROC, receiver operating characteristic\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/c2c8e0a80be5c13ad2ea2646.png"},{"id":66529175,"identity":"f4670f29-3156-4b48-8e7f-e04509ffe1bf","added_by":"auto","created_at":"2024-10-14 05:38:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54954,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe risk score model of 3 histone acetylation regulated genes in training and validation cohorts.\u003c/strong\u003e (\u003cstrong\u003eA \u003c/strong\u003eand \u003cstrong\u003eB\u003c/strong\u003e) The scatter plot shows the correlation between survival time and risk score of patients in TCGA training (A) and testing cohort (B). (\u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eD\u003c/strong\u003e) The risk score distribution in the in TCGA training (C) and testing cohort (D). (\u003cstrong\u003eE \u003c/strong\u003eand \u003cstrong\u003eF\u003c/strong\u003e) Heatmaps of the risk score were based on histone acetylation regulated genes in in TCGA training (E) and testing cohort (F).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/dda853d49435bc1cb5c7481f.png"},{"id":66529172,"identity":"c8fc6fc2-694d-4a45-903c-9dd2e9469491","added_by":"auto","created_at":"2024-10-14 05:38:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of a Nomogram model and prognostic factor analysis. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) A nomogram model for predicting the 1-year, 3-year, and 5-year OS of CC. (\u003cstrong\u003eB\u003c/strong\u003e) Calibration curves for the nomogram model was used to assess the consistency between the predicted OS and the observed OS. (\u003cstrong\u003eC-E\u003c/strong\u003e) The AUC of survival ROC curves to assess the probability of the 1-year, 3-year, and 5-year OS of patients with CC. (\u003cstrong\u003eF\u003c/strong\u003e) The univariate and multivariate Cox regression analyses for identifying prognostic risk factors among the clinicopathological items and risk scores are shown in forest plots.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/44962837a39fe1ba2a60d096.png"},{"id":66528741,"identity":"d9ad057e-fb2a-46b8-8df8-40e9b9055c16","added_by":"auto","created_at":"2024-10-14 05:30:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":154764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution profile of histone acetylation in CC singe-cell samples depicted. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) t-SNE clustering of endometrial cells from CC patients colored by annotation of 22 Cluster and (\u003cstrong\u003eB\u003c/strong\u003e) seven cell subtypes (CD8Tcells, endometrial stromal cells. Endothelial, fibroblasts, malignant, mono/macro, and SMC). (\u003cstrong\u003eC-E\u003c/strong\u003e) Correlation heat map showing the expression relationships between 3 core gene and the uterus cells from GSE168152\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/76998b1880c7c21d70a71299.png"},{"id":66528747,"identity":"50a7b4fa-bdf3-42c1-a600-8543febe3190","added_by":"auto","created_at":"2024-10-14 05:30:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":165886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set variation analysis-Pathway enrichment in the high- and low- risk groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/3eb2e679987f39971aad3491.png"},{"id":66529176,"identity":"ac5ff2a7-cf68-4de8-859c-c38e715de5e8","added_by":"auto","created_at":"2024-10-14 05:38:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":95926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of risk score with 46 common immune checkpoint genes. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Boxplot showing significant differential expression of the immune checkpoint genes between high and low-risk score groups. (\u003cstrong\u003eB\u003c/strong\u003e) Correlation analysis between 46 immune checkpoint genes and three core genes and riskscore in CC. \u003cstrong\u003e(C–F\u003c/strong\u003e) Correlation of risk score with the expression of \u003cem\u003ePD1\u003c/em\u003e and \u003cem\u003eTIM-3\u003c/em\u003e in TCGA-CC dataset.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/d82b1fcb13403056ac6e43db.png"},{"id":66528739,"identity":"819384a5-d728-46d1-b661-13263dd89914","added_by":"auto","created_at":"2024-10-14 05:30:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":75753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune cell infiltration landscape in CC.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Heatmap of the correlation between three core genes and 22 type of immune cell infiltrates. (\u003cstrong\u003eB\u003c/strong\u003e) Bar plot of the differences in the proportion of 22 immune cells infiltrating tumor tissue in high- and low-risk groups. (\u003cstrong\u003eC\u003c/strong\u003e) Single sample gene set enrichment analysis (ssGSEA) for the immune functions between high- and low- risk groups. (\u003cstrong\u003eD\u003c/strong\u003e) ssGSEA for the immune cell abundances between the high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/825a9d69fca8c8100d9b9aba.png"},{"id":66528748,"identity":"5bd667e0-80f6-4ba2-8eb6-7a3a7764f9e0","added_by":"auto","created_at":"2024-10-14 05:30:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":174135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between the expression of the three core prognostic genes and drug sensitivity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/46a23f26a57328188fced523.png"},{"id":66528749,"identity":"dceb8feb-8841-48bc-b205-6f4fef0fd48c","added_by":"auto","created_at":"2024-10-14 05:30:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":85507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and prognostic significance of three core genes in CC and normal tissues. \u003c/strong\u003e(\u003cstrong\u003eA–C\u003c/strong\u003e) Gene expression levels (log2 transformed) of \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003ein CC normal tissues based on TCGA-CC cohort. (\u003cstrong\u003eD-E\u003c/strong\u003e) Survival curve showing the impact of \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e expressions on the OS in TCGA-CC dataset.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/7a7bca4d57e8fa6efa5f29a2.png"},{"id":66528745,"identity":"d6e5e0ef-f221-4a2a-bb5a-ab6dfc65cefc","added_by":"auto","created_at":"2024-10-14 05:30:15","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1376355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental verification of KAT2B, HDAC5, and HDAC10 expressions in CC tissues and adjacent normal tissues.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Comparison of the mRNA expressions of \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10 \u003c/em\u003ein CC tissues and adjacent normal tissues. (\u003cstrong\u003eB\u003c/strong\u003e) Immunohistochemistry analysis that HDAC5 and HDAC10 were significantly lower in CC tissues than in adjacent normal tissues. Statistical significance was determined by two-sided unpaired Student’s t-test: *, P \u0026lt; 0.05; **, P \u0026lt; 0.001; ***, P \u0026lt; 0.0001; ns, not significant.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/746e4e440be8fc9b52590ebc.png"},{"id":77966771,"identity":"99aac6b1-9e13-4a89-bd4d-f1d3aeb9dbd5","added_by":"auto","created_at":"2025-03-07 10:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3969202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/2a8abc9b-afa5-4fc5-896d-d9e904a4503d.pdf"},{"id":66528743,"identity":"4336d70b-7139-40d5-8b04-c19122d6c887","added_by":"auto","created_at":"2024-10-14 05:30:15","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":11713,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1SummaryofhistoneacetylationModificationregulators.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/8660331b84fec772037c784b.xlsx"},{"id":66528744,"identity":"1f0ea790-0c05-4f19-959b-a30af5643a4b","added_by":"auto","created_at":"2024-10-14 05:30:15","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":360870,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2Theexpressiondatafor36histoneacetylationgenesfromtheTCGACCAcohort..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/ec7e8d20cbe41e2a0b0627eb.xlsx"},{"id":66528750,"identity":"045fc9bb-f35b-46f3-a2d4-5ea85f87571a","added_by":"auto","created_at":"2024-10-14 05:30:16","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":16405,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.PrimersforqRTPCR.docx","url":"https://assets-eu.researchsquare.com/files/rs-4911165/v1/4a416d7c026e6bf5d3d494b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Histone Acetylation Regulators: Mediators of Tumor Microenvironment Infiltration and Prognostic Model in Cervical Cancer Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is the second leading cause of cancer-related mortality among women worldwide and the fourth most prevalent gynecological tumor, particularly in industrialized and developing countries (1). In 2020, more than 600,000 new cases were reported, with over 341,000 deaths globally (2). In recent year, there have been substantial improvements in molecular diagnostics, targeted therapies, and immunotherapies for CC (3\u0026ndash;5). For instance, GRP78 has demonstrated anticancer effects in CC by regulating cell apoptosis and autophagy, while immune CD8\u0026thinsp;+\u0026thinsp;T cells, mediated by the immune system, play a crucial role in regulating tumor immunity, which is crucial for the effectiveness of the HPV vaccine (6). Additionally, several tumor biomarkers and molecular markers are currently available to predict overall survival (OS) in CC patients (7). Despite improvements in diagnostic and therapeutic approaches, the prognosis for advanced-stage CC remains poor, with a recurrence rate of approximately 35% (8). Consequently, new prognostic indicators are urgently need to reliably predict the OS of CC patients.\u003c/p\u003e \u003cp\u003eResearch has revealed that CC is significantly influenced by epigenetic modifications, in addition to genomic alteration (9). Histone modification and DNA methylation are the primary epigenetic modifications that regulate the structure of chromatin (10). While DNA methylation has been extensively investigated and has the potential to facilitate the advancement of biomarkers and targeted biological agents in CC (11), research on histone modification, especially histone acetylation, remains restricted. Histone acetylation modulator proteins (HAMPs), which include HATs (writers), HDACs (erasers), and bromodomain-containing proteins (readers), regulate histone acetylation and influence transcriptionally active euchromatin (12). Aberrant activity of HAMPs has been observed in various malignancies, suggesting that certain HAMPs may act as driver genes in the development of cancer (13). The role of histone acetylation in CC has been highlighted by previous studies. For example, the prognosis of CC patients was significantly affected by histone H3 acetyl K9 and histone H3 trimethyl K4 (14). Furthermore, HDAC1 and HDAC2 were overexpressed in cervical dysplasia and invasive carcinoma (15). Nevertheless, their precise role in CC remains uncertain.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated the correlation between histone acetylation genes and the prognosis of CC patients. Furthermore, we identified independent prognostic markers to develop a predictive nomogram and a risk score model. We also assessed their correlation with immune checkpoints, immune cells, and drug sensitivity. Our findings support the potential use of this new predictive model and potential biomarkers for CC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources for research\u003c/h2\u003e \u003cp\u003eWe obtained the gene expression data, somatic mutation data, and related clinical information for CC patients from the Cancer Genome Atlas (TCGA) database. A pathological diagnosis of CC was used to filter these specimens, and patients without full clinical and follow-up data were excluded. Finally, 306 CC samples and 13 normal cervical samples were employed for the subsequent analysis. Furthermore, in order to validate the dataset, normalized matrix files from GSE68339 were obtained from the Gene Expression Omnibus (GEO) database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of copy cumber variations (CNVs) and mutations in histone acetylation genes\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive review of literature on histone acetylation modifications, identifying and analyzing 36 established histone acetylation genes to uncover distinct modification patterns (16). Expression data for these genes were obtained from the TCGA database, and their chromosomal distribution was visualized using the RCircos tool. CNVs analysis utilized Perl (5.32.1.1) and R (4.1.2), while mutations were mapped with maftools. Student\u0026rsquo;s t-test was employed to assess the impact of individual mutations on gene expression levels, with findings visualized using ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a risk score model\u003c/h2\u003e \u003cp\u003eFollowing univariate Cox regression analysis in the TCGA cohort, we conducted minimum absolute shrinkage, absolute shrinkage and selection operator (LASSO) regression, and multivariate stepwise Cox regression analyses. Ultimately, three histone acetylated genes were identified as risk factors.\u003c/p\u003e \u003cp\u003eThe risk score was calculated using the algorithm: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Risk\\:score={\\sum\\:}_{i}^{n}{X}_{i}\\times\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e(X: coefficient of each gene, Y: gene expression level). Based on the median risk scores, the cohort were classified into high- and low-risk groups for both training and validation datasets. At the same time, we also used the GSE68339 external data set as a validation set to verify the superiority of the risk model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePrognosis analysis\u003c/h2\u003e \u003cp\u003eA prognostic nomogram was generated using the \"RMS\" package in R to predict 1-,3-, and 5- year OS for CC patients. The nomogram integrated the clinical stage and baseline patient information derived from the training dataset. To assess the predictive accuracy of the nomogram model in CC patients, calibration curve (using the KM analysis) and concordance index (C-index) curves were constructed. Receiver operating characteristics (ROC) and area under the curve (AUC) were conducted using the \u0026lsquo;timeROC\u0026rsquo; package to evaluate the specificity and sensitivity of the risk score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSing-cell transcriptional landscape\u003c/h2\u003e \u003cp\u003eWe retrieved single-cell transcriptome files (GSE168652) from the TISCH database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene set variation analysis (GSVA)\u003c/h2\u003e \u003cp\u003eGSVA, an unsupervised and non-parametric approach, is employed to assess the enrichment of transcriptomic gene sets. It is capable of assessing the biological functions of samples and calculating comprehensive scores for sets of interest by converting changes at the gene level into changes at the pathway level. The GSVA algorithm was employed in this study to assess potential changes in biological function across samples by utilizing gene sets that were obtained from molecular feature databases. The algorithm generated comprehensive scores for each gene set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmunogenomic landscape analyses\u003c/h2\u003e \u003cp\u003eFirst, we compared the expression levels of immune checkpoint-related genes between the two groups. Subsequently, to investigate correlations with the risk score and immune cell infiltration, we assessed the infiltration of distinct immune cells and their related functions using the R program and CIBERSORT R packege (17).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eGene expression and drug sensitivity data were sourced from the CellMiner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/home.do\u003c/span\u003e\u003cspan address=\"https://discover.nci.nih.gov/cellminer/home.do\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Drug sensitivity data underwent screening based on clinical laboratory validation and certification standards set by the Food and Drug Administration. Subsequently, we integrated the expression data of three core prognostic genes with drug sensitivity data. Pearson correlation tests were performed to assess their associations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical sample collection\u003c/h2\u003e \u003cp\u003eCC tissues and normal adjacent tissues were collected from the First Affiliated Hospital of Guangxi Medical University from January to Jun 2024. Tumor tissues and normal adjacent tissues were quickly collected after surgical removal and immediately frozen in liquid nitrogen for fast freezing. They were then stored at -80\u0026deg;C. A total of 6 CC samples and 6 normal adjacent tissues were collected for analysis. None of the patients did receive any anticancer treatment, such as radiation, chemotherapy, or biological therapies, either before or after surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eThe TRIzol\u0026trade; reagent (Invitrogen) was used to extract total RNA from tissues, following the manufacturer's instructions. The concentration and purity of the collected RNA were verified with the Nano-300 microspectrophotometer. The RNA was subsequently converted into complementary DNA (cDNA) using the Goscript\u0026trade; Reverse Transcription System (Promega). The synthesized cDNA was subsequently analyzed using the SYBR Green Realtime PCR Master Mix. Each sample from every group underwent three rounds of testing. \u003cem\u003eGAPDH\u003c/em\u003e was used as the internal control. The primer sequences used in this study were provided in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003eTissue samples that had been preserved in 4% PFA were put in blocks of paraffin and sliced with a microtome (Macroteck, Ilsansi, Korea). The sections were submerged in xylene for ten minutes in each of two distinct containers to accomplish deparaffinization. Ethanol solutions with escalating concentrations (100%, 95%, 90%, 80%, and 70%) were used for sequential dehydration for five minutes each. To retrieve the antigen, the sections were boiled in a sodium citrate solution for 15 minutes at 99\u0026deg;C and then cooled to RT. The sections were treated with 3% hydrogen peroxide (Polymer-HRP Detection System (Rabbit) (PV-1023), Bioss, China) for a duration of 15 minutes to inhibit endogenous peroxidases. They were then incubated at RT for 20 minutes with a standard goat serum blocking solution (Polymer-HRP Detection System (Rabbit) (PV-1023), Bioss, China). Subsequently, the sections were treated with a primary rabbit anti-HDCA5 (Bioss, bs-2810R, 1:200), anti-HDCA10 (Bioss, bs-2893R, 1:200), and anti-KAT2B (Bioss, bs-5130R, 1:200) antibody overnight at 4\u0026deg;C. Afterward, they were incubated for 20 minutes at RT with an anti-rabbit secondary antibody (Polymer-HRP Detection System (Rabbit) (PV-1023), Bioss, China). Using a DAB substrate kit (AC11043-2, Acmec, China), peroxidase signals were seen. The slides were cleaned, cleared in xylene, counterstained with hematoxylin, and mounted for imaging under a Primo Star microscope (Carl Zeiss, Oberkochen, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Wilcox test was used to compare groups of samples in this study. Survival times of patients in high- and low-risk groups were compared using KM analysis, and the differences were investigated using the log-rank test. Spearman's correlation analysis was conducted to examine the correlations between quantitative variables with non-normal distributions. A p-value of less than 0.05 was considered significant. The PCR findings were visualized using GraphPad Prism. The majority of analyses were conducted using R software (version 4.1.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExpression of histone acetylation regulated genes in CC\u003c/h2\u003e \u003cp\u003eWe identified 36 genes involved in the regulation of histone acetylation, including 15 readers, 9 writers, and 12 erasers (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The expression data for these genes were obtained from the TCGA-CC cohort (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). We conducted an analysis of the mRNA expression levels of these 36 genes in CC tissues in comparison to adjacent normal tissues, which revealed significant expression differences in 16 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest that the aberrant expression patterns of histone acetylation regulators in CC may play an important role in carcinogenesis and disease progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGenetic polymorphisms of the histone acetylation regulators in CC\u003c/h2\u003e \u003cp\u003eCNVs were prevalent among the 36 regulators. Notable amplifications were observed in \u003cem\u003eKAT6A\u003c/em\u003e, \u003cem\u003eSIRT2\u003c/em\u003e, \u003cem\u003eHDAC9\u003c/em\u003e, and \u003cem\u003eYEATS4\u003c/em\u003e, while \u003cem\u003eCREB3L2\u003c/em\u003e, \u003cem\u003eCREB3L3\u003c/em\u003e, and \u003cem\u003eBATF\u003c/em\u003e were frequently deleted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The chromosomal locations of CNVs alterations in histone acetylation regulators were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. Additionally, mutations were detected in all histone acetylation regulation genes, with a somatic mutation frequency of 38.75% (112 out of 289 samples). Missense and nonsense mutations were the two most prevalent types of mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The elevated mutation rate suggested genomic instability of histone acetylation regulators in the TCGA-CC cohort, implicating their potential involvement in pathogenesis of CC. Furthermore, univariate Cox proportional hazards regression analysis of the 36 histone acetylation regulation genes indicated that \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e were protective factors in patients with CC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, HR\u0026thinsp;\u0026lt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRisk score model based on histone acetylation regulated genes\u003c/h2\u003e \u003cp\u003eAfter performing LASSO-Cox regression analysis, the selected histone acetylation regulated genes were used to build a risk score model using the Cox regression method, with TCGA cohort serving as the training dataset and GSE68339 serving as the validating dataset. The risk score was determined in the following manner: Risk score = (-0.255* HDAC5 exp.) + (-0.650* HDAC10 exp.) + (-0.419* HAT2B exp.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The median risk score was used to categorize samples from both groups into high- and low-risk categories. The high-risk group exhibited substantially higher mortality rates and a worse survival rate in both the training and validation groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). The model's predictive accuracy for 1-year, 3-year, and 5-year survival was further validated by receiver operating characteristic (ROC) curve analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F). The training group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) and the validation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) were presented with heat maps that illustrated the expression patterns of the three main genes. The high-risk score group in the training group exhibited down-regulation of these hub genes in comparison to the low-risk score group. In particular, \u003cem\u003eHDAC5\u003c/em\u003e and \u003cem\u003eHDAC10\u003c/em\u003e exhibited consistent down-regulation tendencies. Nevertheless, the validation group exhibited a contrasting tendency in \u003cem\u003eHAT2B\u003c/em\u003e, which implied that there may be potential variability in gene expression patterns among various patient cohorts. In general, the prognosis of CC can be somewhat predicted by these three histone acetylation characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and verification of a Nomogram\u003c/h2\u003e \u003cp\u003eTo assess the clinical efficacy of the histone acetylation-based risk model, a nomogram was developed. In this model, a higher total score was associated with worse survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The nomogram's accuracy was underscored by the calibration plots, which showed a high degree of consistency between predicted and observed OS at 1-, 3-, and 5-year intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, the nomogram's robust predictive performance was confirmed by ROC curve analysis, which yielded the AUC values of 0.773, 0.625, and 0.654 for 1-, 3-, and 5-year OS risk scores, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-E). Furthermore, the risk score, T stage, and stage were identified as significant prognostic indicators for CC in univariate Cox regression analysis. The risk score and T stage were further emphasized as independent predictors of OS in the multivariate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These results emphasize the nomogram's usefulness in evaluating the prognosis and making clinical decisions of patients with CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell atlas of human uterus cells in CC patients\u003c/h2\u003e \u003cp\u003eTo generate a comprehensive single-cell atlas of human uterus cells in CC patients, we analyzed the scRNA-seq dataset GSE168652. We categorized 22 clusters, including seven distinct cell types: CD8T cells, endometrial stromal cells. endothelial cells, fibroblasts, malignant cells, mono/macrophages, and smooth muscle cells (SMCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). The differential patterns of \u003cem\u003eHDAC5\u003c/em\u003e, \u003cem\u003eHDAC10\u003c/em\u003e, and \u003cem\u003eKAT2B\u003c/em\u003e were revealed by the expression correlations among these cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-E). These findings suggested that these core genes had predominantly positive correlations with various cell types, indicating their potential roles in the pathophysiology of CC. These discoveries provide insights into the potential regulatory roles and cellular heterogeneity of these core genes in CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePathway enrichment analysis\u003c/h2\u003e \u003cp\u003eGSVA revealed 85 enriched pathways among the low risk-score and high risk-score groups. These pathways included metabolic, immune and cancer-related pathways, such as folate biosynthesis, glyoxylate and dicatboxylate metabolism, primary immunodeficiency, and cancer pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImmunological landscape of CC\u003c/h2\u003e \u003cp\u003eTo further understand the relationship between risk scores and patients' immune status, we analyzed 46 immune cell checkpoint genes and infiltration scores for each patient. The developed risk score was significantly negatively correlated with the assessment of immune checkpoint genes, indicating that histone acetylation regulators play a regulatory function in the immune microenvironment of CC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). Furthermore, there was a negative correlation between the risk score and the expression of \u003cem\u003ePD1\u003c/em\u003e and \u003cem\u003eTIM-3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eImmune cell infiltration according to risk score classifications\u003c/h2\u003e \u003cp\u003eTo investigate the correlation between CC and the dynamics of immune cells, we used diverse algorithms to evaluate the proportions and functions of immune cells according to the risk model. We evaluated correlations with three major prognostic genes across 22 categories of tumor-infiltrating immune cells (TIICs) and linked the risk model to immune cell infiltration using the CIBERSORT technique (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). \u003cem\u003eKAT2B\u003c/em\u003e expression was discovered to be adversely correlated with activated NK cells, activated mast cells, M0 macrophages, and eosinophils, while positively correlated with CD8T cells, resting mast cells, M1 macrophages, and resting dendritic cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eHDAC5\u003c/em\u003e expression was discovered to be inversely connected to Tregs, resting memory CD4 T cells, plasma cells, resting mast cells, resting dendritic cells, and naive B cells, while positively correlated with activated memory CD4 T cells, resting NK cells, and neutrophils (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eHDAC10\u003c/em\u003e expression was found to be negatively correlated with resting memory CD4 T cells, whereas it was positively correlated with Tregs and memory B cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The distribution of TIIC subtypes between low- and high-risk groups was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB. The low-risk group exhibited higher proportions of naive B cells, CD8 T cells, Tregs, resting dendritic cells, resting mast cells, and eosinophils, as well as lower proportions of M0 macrophages, activated mast cells, and neutrophils. Furthermore, the low-risk group exhibited a substantial activation of human leukocyte antigen (HLA), B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, immature dendritic cells (iDCs), neutrophils, and T helper cells, as evidenced by additional ssGSEA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eExamination of drug sensitivity in the three core prognostic genes\u003c/h2\u003e \u003cp\u003eWe investigated the relationship between drug sensitivity and the expression of the three core genes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, \u003cem\u003eKAT2B\u003c/em\u003e expression was positively correlated with vemurafenib, dabrafenib, encorafenib, and selumetinib (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and negatively with dasatinib, brigatinib, digoxin, and acetalax (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eHDAC10\u003c/em\u003e exhibited positive correlations with decitabine, vemurafenib, nelarabine, vorinostat, temsirolimus, and tegafur (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eHDAC5\u003c/em\u003e showed a negative association with palbociclib and dexrazoxane (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eExpression and prognostic impact of three core genes in CC and adjacent normal tissues\u003c/h2\u003e \u003cp\u003eComparative analysis of mRNA expression profiles from 306 CC and 13 adjacent normal tissues in the TCGA-CC cohort revealed significantly reduced expressions of \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e in CC tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA-C). The prognoses of patients with high expression levels of these genes were superior to those with low expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD-F). These results highlighted the potential of \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e as prognostic biomarkers for CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical samples validation of\u003c/b\u003e \u003cb\u003eHDAC5\u003c/b\u003e, \u003cb\u003eHDAC10\u003c/b\u003e, \u003cb\u003eand\u003c/b\u003e \u003cb\u003eKAT2B\u003c/b\u003e \u003cb\u003eexpression\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo enhance the reliability of our bioinformatic findings, we employed qRT-PCR to examine the expression levels of three key genes. The mRNA expression levels of \u003cem\u003eHDAC5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA) and \u003cem\u003eHDAC10\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB) were consistent with our bioinformatic results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the mRNA expression level of \u003cem\u003eHAT2B\u003c/em\u003e did not show a significant difference between normal and tumor tissues (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC), This could be attributed to the small sample size, additional validation with larger samples should be conducted in future studies. Additionally, we further investigated the proteins expression levels of HDAC10 and HDAC5 using immunohistochemistry (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD, E), which confirmed the predictions made by our bioinformatic analyses. These findings further confirmed that \u003cem\u003eHDAC10\u003c/em\u003e and \u003cem\u003eHDAC5\u003c/em\u003e may serve as potential biomarkers for predicting CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCC is the fourth most frequently diagnosed gynecological tumor, characterized by its high incidence and mortality rate (2). CC exhibits notable variations in both molecular and histological characteristics. Despite advancements in surgical procedures and integrated therapies, the prognosis for advanced or recurrent CC remains unfavorable. This emphasizes the immediate necessity to identify biological and therapeutic targets to improve the prognosis of CC. Histone acetylation plays a pivotal role in cancer initiation and progression, with many small molecule drugs currently under clinical investigation (18). Exploring its relationship with CC may aid in the development of innovative approaches for its management. This study comprehensively analyzed histone acetylation in CC development and constructed a prognostic model based on histone acetylation genes.\u003c/p\u003e \u003cp\u003eThis study focused on 36 histone acetylation regulated genes as determined by the published literature (16). The expression of acetylated genes differed significantly between CC tissues and normal cervical tissues. Using bioinformatics and statistical tools, we evaluated the prognostic accuracy of these genes and selected \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e to construct prognostic models and calculate risk scores. These three core genes expression in CC was inferior to normal cervical tissues, and decreased expression of these three core genes had worse prognosis. Clinical samples confirmed reduced levels of \u003cem\u003eHDAC5\u003c/em\u003e and \u003cem\u003eHDAC10\u003c/em\u003e in CC tissues, suggesting their potential as diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eHistone acetylation is a dynamic and reversible process regulated by HATs and HDACs. HATs enhance gene expression by altering the spatial organization of nucleosomes, leading to chromatin relaxation and increased gene transcription and replication.Conversely. HDACs enzymatically eliminate acetyl groups and bind to compact chromatin formations, therefore repressing transcription (19). Hence, the alteration of histones through acetylation is an essential epigenetic aspect in the biology of cancer (20). Currently, the specific function of HDAC proteins in cancer is not fully elucidated. Recent studies have shown that overexpression of numerous members of the HDAC family causes epigenetic silencing of tumor suppressor genes, which is a key step in carcinogenesis. Class I HDACs facilitate the regulation of the cell cycle, cell differentiation, and tissue development (21). HDAC6, one of Class II HDACs, can influence cell migration and other key biological processes (22). Therefore, HDAC inhibitors have been identified as supplementary therapeutic drugs for various types of human malignancies (23). On the other hand, HDACs have the potential to restrict tumor growth in certain cases. For example, data from the TCGA dataset revealed that deep deletions in \u003cem\u003eHDAC10\u003c/em\u003e were implicated in 5\u0026ndash;10% of ovarian cancers. Furthermore, there is a correlation between low mRNA expression and susceptibility to platinum-based treatments (24). In this study, \u003cem\u003eHDAC5\u003c/em\u003e and \u003cem\u003eHDAC10\u003c/em\u003e, both class II HDACs, exhibited lower expression in CC tissues, consistent with previous studies (25, 26). The possible mechanism involved the decrease in expression of \u003cem\u003eHDAC10\u003c/em\u003e, which prevented the migration of CC cells and tumorigenesis by suppressing miR-223 expression through histone deacetylation (25). Additionally, it suppressed metastasis via inhibiting the expression of matrix metalloproteinase (MMP) 2 and 9 (27). Depletion of \u003cem\u003eHDAC5\u003c/em\u003e resulted in an elevation in acetylation of histone H3 lysine 27 (H3K27-ac) and bypassed the inhibitory effect of RB on cell-cycle-related pro-oncogenic genes (28).\u003c/p\u003e \u003cp\u003eAn increasing number of researches suggested that components of tumor microenvironment (TME) played crucial roles in the development of cancer (29, 30). Epigenetics, particularly histone acetylation, not only regulates gene expression in tumor cells but also modulates TME components (31). To better understand the immune response against CC, we investigated the correlation between histone acetylation modifications and TME cell infiltration. Our prognostic model highlighted a significant association between histone acetylation and the TME, pointing to key immune checkpoints such as anti-programmed cell death 1 (PD-1) and TIM-3. Consistent with previous studies, these checkpoints were crucial in restoring anti-tumor immunity, which allowed for the reversal of immune evasion and the promotion of tumor cell death (32, 33). Furthermore, our findings suggested immune-related functions were negatively correlated with our risk scores, indicating immune evasion susceptibility in high-risk groups. Further exploration into the therapeutic implications of histone acetylation identified potential sensitivities to chemotherapeutic drugs like vemurafenib and dabrafenib in CC. This discovery could play a significant role in advancing new treatments for CC and in preventing drug resistance.\u003c/p\u003e \u003cp\u003eDespite these observations, our study has limitations. Firstly, we should expand our clinical sample size to validate the expression of these key genes in cervical cancer. Additionally, the mechanistic functions of crucial genes and prognostic factors need further confirmation through in vivo studies and controlled laboratory experiments (in vitro).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study verified the potential of histone acetylation-related genes as molecular biomarkers for prognostic stratification and immune infiltration in CC. A nomogram based on \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e could provide personalized survival assessments for newly diagnosed CC patients, guiding tailored therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHistone acetylation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAMPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHistone acetylation modulator proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy cumber variations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene set variation analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative Reverse Transcription Polymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMatrix metalloproteinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH3K27-ac\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcetylation of histone H3 lysine 27\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnti-programmed cell death 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eAll specimens were obtained with informed consent from patients and received approval from the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (approval number 2024-E541-01).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was founded by the National Natural Science Foundation of China (NO.82360308); the Special Fund of the Maternal Fertility Preservation Innovation Team of the First Affiliated Hospital of Guangxi Medical University (NO. YYZS2021001); Clinical Key Incubation Project A of Peking University Third Hospital (NO. BYSYZD2023022).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.M. drafted the manuscript and performed the experiments. Z.H.Z. revised the manuscript and did data analysis. M.M.L. revised the manuscript and provided funding support. Y.J.L. revised the manuscript. Y.X.C. collated the samples. Y.H.Y, provided technical support, critical comments, and funding supports. M.Y.D. designed the study, performed bioinformatic analysis, and revising the manuscript. All authors gave their approval for the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe appreciated all the patients who participated in our research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used in this study can be accessed through public databases （TCGA, GEO, and TISCH).. For more information, the corresponding author can provide the data and materials from this study upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Buskwofie A, David-West G, Clare CA. A Review of Cervical Cancer: Incidence and Disparities. Journal of the National Medical Association. 2020;112(2):229\u0026thinsp;\u0026minus;\u0026thinsp;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71(3):209\u0026thinsp;\u0026minus;\u0026thinsp;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e3. Liontos M, Kyriazoglou A, Dimitriadis I, Dimopoulos MA, Bamias A. Systemic therapy in cervical cancer: 30 years in review. 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Journal of thoracic disease. 2022;14(10):3886\u0026thinsp;\u0026minus;\u0026thinsp;902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e17. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods in molecular biology (Clifton, NJ). 2018;1711:243\u0026thinsp;\u0026minus;\u0026thinsp;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e18. Zaib S, Rana N, Khan I. Histone Modifications and their Role in Epigenetics of Cancer. Current medicinal chemistry. 2022;29(14):2399\u0026thinsp;\u0026minus;\u0026thinsp;411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e19. Shahbazian MD, Grunstein M. Functions of site-specific histone acetylation and deacetylation. Annual review of biochemistry. 2007;76:75\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e20. Barneda-Zahonero B, Parra M. Histone deacetylases and cancer. Molecular oncology. 2012;6(6):579\u0026thinsp;\u0026minus;\u0026thinsp;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e21. Reichert N, Choukrallah MA, Matthias P. Multiple roles of class I HDACs in proliferation, differentiation, and development. Cellular and molecular life sciences : CMLS. 2012;69(13):2173-87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e22. Valenzuela-Fern\u0026aacute;ndez A, Cabrero JR, Serrador JM, S\u0026aacute;nchez-Madrid F. HDAC6: a key regulator of cytoskeleton, cell migration and cell-cell interactions. Trends in cell biology. 2008;18(6):291-7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e23. Pan LN, Lu J, Huang B. HDAC inhibitors: a potential new category of anti-tumor agents. Cellular \u0026amp; molecular immunology. 2007;4(5):337\u0026thinsp;\u0026minus;\u0026thinsp;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e24. Islam MM, Banerjee T, Packard CZ, Kotian S, Selvendiran K, Cohn DE, et al. HDAC10 as a potential therapeutic target in ovarian cancer. Gynecologic oncology. 2017;144(3):613\u0026thinsp;\u0026minus;\u0026thinsp;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e25. Zhu J, Han S. Histone deacetylase 10 exerts antitumor effects on cervical cancer via a novel microRNA-223/TXNIP/Wnt/β-catenin pathway. IUBMB life. 2021;73(4):690\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e26. Ma X, Zhang Q, Du J, Tang J, Tan B. Integrated Analysis of ceRNA Regulatory Network Associated With Tumor Stage in Cervical Cancer. Frontiers in genetics. 2021;12:618753.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e27. Song C, Zhu S, Wu C, Kang J. Histone deacetylase (HDAC) 10 suppresses cervical cancer metastasis through inhibition of matrix metalloproteinase (MMP) 2 and 9 expression. 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Lodewijk I, Nunes SP, Henrique R, Jer\u0026oacute;nimo C, Due\u0026ntilde;as M, Paramio JM. Tackling tumor microenvironment through epigenetic tools to improve cancer immunotherapy. Clinical epigenetics. 2021;13(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e32. Martins F, Sofiya L, Sykiotis GP, Lamine F, Maillard M, Fraga M, et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nature reviews Clinical oncology. 2019;16(9):563\u0026thinsp;\u0026minus;\u0026thinsp;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e33. R\u0026aring;destad E, Klynning C, Stikvoort A, Mogensen O, Nava S, Magalhaes I, et al. Immune profiling and identification of prognostic immune-related risk factors in human ovarian cancer. Oncoimmunology. 2019;8(2):e1535730.\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":"Histone acetylation, Cervical cancer, Prognostic model, Immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-4911165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4911165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCervical cancer (CC) is the second most prevalent mortality rate for women’ cancer globally and the fourth most prevalent gynecological tumor. Dysregulation of histone acetylation (HA) influences the pathogenesis of cancer. However, there is a dearth of comprehensive research on HA in CC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted univariate and multivariate Cox and LASSO regression analyses to assess the prognostic relevance of 36 HA-related genes identified in previous studies. A prognostic model was developed by utilizing the TCGA cohort as the training dataset and the screened HA genes. The model was subsequently validated on GSE68339 dataset. In order to confirm the accuracy of the model, Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) were implemented. The study also investigated the associations between immune cell infiltration characteristics, immune checkpoint genes, and drug sensitivity. Lastly, the essential genes were verified through qRT-PCR and immunohistochemistry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003ewere identified as pivotal for prognosis among the 36 HA genes that were analyzed. The prognostic model classified TCGA patients into high- and low-risk groups based on risk scores, revealing significantly reduced overall survival (OS) in the high-risk group. High-risk patients demonstrated decreased immune infiltration and checkpoint gene expression. \u003cem\u003eKAT2B\u003c/em\u003e, \u003cem\u003eHDAC5\u003c/em\u003e, and \u003cem\u003eHDAC10\u003c/em\u003e were downregulated in CC compared to normal tissues, which was correlated with poorer 5-year OS rates. qRT-PCR and immunohistochemistry confirmed reduced expression of \u003cem\u003eHDAC5\u003c/em\u003e and \u003cem\u003eHDAC10\u003c/em\u003e in clinical samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWe propose a prognostic model based on three HA genes that demonstrates a well predictive effect on CC patients, offering predictive value and potential application in clinical treatments.\u003c/p\u003e","manuscriptTitle":"Novel Histone Acetylation Regulators: Mediators of Tumor Microenvironment Infiltration and Prognostic Model in Cervical Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 05:30:09","doi":"10.21203/rs.3.rs-4911165/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":"c71bc6ea-9c59-4c40-8c70-f2b46361afb2","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-07T09:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 05:30:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4911165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4911165","identity":"rs-4911165","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}