A gene feature based on histone modifications can predict the prognosis of prostate cancer

A gene feature based on histone modifications can predict the prognosis of prostate cancer | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A gene feature based on histone modifications can predict the prognosis of prostate cancer Xuee Zhou, Xiaolin Li, Jiahong Hong, Fuli Xie, Kuncai Liu, Yue Huang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3298585/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: Prostate cancer (PCa) is one of the most common malignant tumors in males, with a high recurrence rate and poor prognosis. Therefore, accurately predicting the prognosis of PCa patients and intervening as early as possible is of great significance. We aimed to establish a gene feature model based on histone modifications to predict the prognosis of patients with PCa after radical prostatectomy. Methods: Clinical data on PCa patients was obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases and was comprehensively evaluated. Expression subtypes of histone-modifying factors were identified by unsupervised clustering, and the molecular characteristics and functions of each subtype were explored. Subsequently, a risk-scoring model was constructed to characterize its impact on the prognosis of PCapatients. Results: Combined with histone modification factor signatures, we identified four PCa subtypes with different prognoses, biological functions, and mutational characteristics. Based on a series of analysis and screening, 21 characteristic genes ( MXD3 , CCDC28B , COL11A2 , SLC39A5 , GPT , DNASE1L2 , PIF1 , KRTAP5-9 , TTLL10 , KRTAP5-1 , KRTAP5-10 , HAGHL , MSLNL , AMH , NKAIN4 , CCDC114 , SLC9A3 , SULT1E1 , SLC6A14 , ALB , and RPE65 ) were used to establish a risk score model (HIS_score). Patients in the high-score group had worse outcomes than those in the low-score group. Additionally, we found that the HIS_score model can distinguish subgroups of PCa samples with different biological and immune characteristics. Conclusions: The HIS_score model with 21 genes as features is a promising tool that is of great significance for clinicians to predict the prognosis of PCa patients after radical prostatectomy and develop personalized treatment plans early. histone modification prognostic prediction prostate cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Preface Prostate cancer (PCa) is a highly heterogeneous disease and a prevalent malignancy among men worldwide. It is also a major cause of cancer-related deaths in men[ 1 ]. Management of PCa includes modalities such as radical prostatectomy (RP), radiotherapy, and androgen deprivation therapy, which are crucial components for treating organ-confined and androgen-dependent PCa[ 2 ]. Despite the benefits of different treatment options, biochemical recurrence (BCR) occurs in approximately one-third of patients after RP. This leads to rapid progression and development of castration-resistant PCa resulting in death within 2–4 years, with recurrence as a major cause of mortality[ 3 – 5 ]. Therefore, accurately identifying high-risk patients susceptible to disease progression after RP is crucial for developing personalized treatment plans. Previous studies have demonstrated that characteristics such as the Gleason score, prostate-specific antigen (PSA), and clinical T stage are associated with the survival of PCa patients and can predict prognosis after RP[ 6 ]. However, these characteristics have certain limitations that result in low predictive accuracy. In recent years, researchers have conducted a series of studies to better identify patients with poor prognosis and explored gene features based on gene expression and clinical data to predict PCa patient prognosis. For example, Lv et al. established and validated a seven-gene molecular feature based on immune-related genes to monitor immune status and evaluate the recurrence-free survival rate of PCa patients[ 7 ]. Zhang et al. identified an apoptosis-related gene feature and improved the risk stratification of recurrence-free survival by incorporating gene features into clinical parameters, thus identifying patients with poor prognosis[ 8 ]. Epigenetics plays an important role in the progression of PCa[ 9 , 10 ]. Epigenetic regulation can be achieved through DNA methylation, histone modifications and microRNA expression, and plays an important role in cancer[ 11 ]. Among these, histone modifications are common epigenetic approaches used to regulate chromatin structure, DNA repair, and gene expression, and they are diverse and complex. Increasing evidence suggests that histone modification is closely related to PCa onset and progression and can affect certain biological processes of tumor cells, such as proliferation, apoptosis, and metastasis[ 12 ]. Research has shown that global changes in histone modification in cancer cells can predict the risk of tumor recurrence in low-grade PCa patients independently of tumor stage, preoperative PSA, and capsule invasion[ 13 ]. However, current research on the role of histone modification in the clinical diagnosis and treatment of PCa remains limited[ 14 ]. There has been no exploration of whether the global transcriptional expression level of histone modification factors can distinguish high-risk patients, indicating that there remains exploration and development opportunities for the application of histone modifications in PCa. Therefore, this study aims to establish a histone modification-related prediction model based on the global transcriptional expression level of histone modification factors to improve the risk stratification of PCa patients after RP. These results may help provide better treatment for PCa patients and improve outcomes. Materials and methods Data acquisition and processing We conducted a retrospective search in publicly available datasets, including The Cancer Genome Atlas (TCGA) ( https://cancergenome.nih.gov/ ) and the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ), to obtain gene expression data and clinical information for PCa samples. Two independent cohorts, The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) and GSE70770, were included, which comprised RNA-seq data of 698 PCa samples and corresponding clinical information for further analysis. Specifically, the clinical information selected included patient age, tumor TNM stage, and Gleason score, and progression-free interval (PFI) and disease-free interval (DFI) were used as the clinical endpoints in our study. Genomic mutation data for TCGA-PRAD were obtained from the University of California, Santa Cruz (UCSC) Xena database ( https://xenabrowser.net ). The data in this study underwent preprocessing using the "voom" algorithm, and the "ComBat" R package[ 15 ] was used to adjust for batch effects caused by non-biological technical biases. Identification of histone modifier expression patterns by consensus clustering We employed unsupervised clustering (K-means) to identify distinct patterns of histone modification and to categorize PCa patients using the "ConsensusClusterPlus" R package[ 16 ]. The consensus clustering algorithm was utilized to assess the stability of clustering and determine the optimal number of clusters with the following parameters: 80% project resampling (pItem), 80% gene resampling (pFeature), maximum evaluation of k is ten (maxK), 1000 resamplings (reps), and K-means clustering algorithm based on euclidean distance (clusterAlg). Generation and validation of a histone modification-based prognostic model To establish a risk score, the linear model from the microarray data (limma) R package[ 17 ] was employed to identify differentially expressed genes between C1 and C3 patients, with a cutoff value of p 1. These differentially expressed genes were then subjected to a univariate Cox regression model and Kaplan-Meier analysis to identify genes associated with prognosis. The intersection of these genes was then included in a least absolute shrinkage and selection operator (Lasso) regression analysis (10-fold cross-validation), which was repeated 1000 times to ensure stability, resulting in the generation of the optimal gene model. Based on this model, a histone modification-related prognostic model was established. Data from 495 PCa patients from the TCGA-PRAD were used as the training set, and patients were categorized into high- and low-risk groups based on their scores, with the top and bottom 1/3 of patients being assigned to these groups, respectively. Subsequently, Kaplan-Meier curve analysis was performed to compare survival differences between the high- and low-risk groups. The same process was employed to validate the prognostic model in a validation set comprising 203 PCa patients from the GEO database. Furthermore, a nomogram was constructed using a multivariate COX regression analysis to visualize the model and enable clinical application. CNV mutation profile and methylation characteristics The copy number variation (CNV) data were downloaded using the "TCGAbioLink" R package[ 18 ], and the DNA methylation data (450k chip) of TCGA-PRAD were obtained from the UCSC Xena database ( https://xenabrowser.net ). The Genomic Identification of Significant Targets in Cancer 2.0 (GISTIC 2.0) was used to calculate the GISTIC score and copy number variation frequency of all samples in the TCGA-PRAD cohort. In terms of methylation analysis, we filtered out probes located on the XY chromosome and SNPs, retained probes located in promoter regions such as "TSS1500", "TSS200", "1stExone", and "5'UTR" on CpG sites, and selected the top 1000 probes with high variation after filtering. The "champ.DMP" R package was used to perform methylation differential analysis. Analysis of biological processes and tumor microenvironment features The gene set variation analysis (GSVA) was achieved by using the "GSVA" R package[ 19 ], where "signature gene sets" (HALLMRAK gene sets) were downloaded from the MSigDB database to understand the enrichment of pathways for different subtypes. In terms of immune cell infiltration, we utilized the CIBERSORT [ 20 ] method to explore the cellular composition of samples based on gene expression profiles. The parameter settings used were: signature genes: LM22, permutations: 1000, and Quantile normalization of input mixture = True. Prediction of immunotherapy and chemotherapy response To predict the clinical response of PCa patients to immunotherapy and chemotherapy, we conducted the Tumor Immune Dysfunction and Exclusion (TIDE)[ 21 ] evaluation. We calculated the IC50 values for 10 commonly used drugs in PCa patients in the TCGA dataset using the Cancer Drug Sensitivity Genomics (GDSC, https://www.cancerrxgene.org ) and executed the "pRRophetic" R package. Statistical analysis In terms of statistical analysis, we conducted data analysis using R software (version 4.0.5) and IBM SPSS Statistics 26.0 software. For all annotations in the figures, "ns" indicates non-significant, "*p < 0.05", "**p < 0.01", "***p < 0.001", and "****p < 0.0001". Unless otherwise specified, all statistical tests were two-sided. To clarify the survival differences between different subtypes, the "survival" R package and Kaplan-Meier analysis were used to generate survival curves, and the Log-Rank test was used to compare differences between groups. For the HIS_score, we divided the samples into high and low score groups based on the top and bottom 1/3 of the scores. Receiver operating characteristic curves were used to validate the effectiveness of the model, and the area under it was calculated. Decision curve analysis[ 22 , 23 ] was used to evaluate the performance of each indicator. The "limma" R package was used to determine the expression differences of histone modification regulatory factors in tumor and normal samples. Other statistical methods used in this study included the Kruskal-Wallis test, one-way ANOVA, Cox regression analysis, and Pearson correlation analysis. Results Classification and characteristics of histone modification regulatory factors in PCa After systematically reviewing published articles related to histone modification, we identified 67 histone modification regulatory genes from the gene expression profiles of PCa patients, including 28 acetyltransferases, 11 deacetylases, 10 methyltransferases, and 18 demethylases (Table S1 ). To clarify the role of histone modification regulatory factors in PCa patients, we collected data from 495 PCa and 52 normal samples from TCGA public database (Table S2 ). Analysis revealed that the distribution of these genes and their prognostic impact on patients in the TCGA-PRAD cohort varied. We found that 28 genes were upregulated in tumor tissue and were mainly deacetylases (35.71%) and methyltransferases (46.43%). Among the demethyltransferases, 66.67% were downregulated in tumor tissues. Regarding prognosis, the upregulation of the expression of many differentially expressed histone genes in tumor tissue suggested that they were related to poor prognosis. Among them, 60% of these genes belonged to the deacetylase family, accounting for the largest proportion of the four enzyme families (Fig. 1 A–D, Table S3 ). Additionally, a correlation heatmap showed that these genes had good correlation within their respective sets, with mostly positive correlations (Fig. 1 E–H). The strongest correlation was found among the acetyltransferase regulatory factors. Interestingly, the expression of KAT2A was negatively correlated with other factors in the methyltransferase set and was identified as a specific transcriptional activation target of PCa by Simmonds et al.[ 24 ]. Overall, these results indicate that histone modification factors have important effects on the development and prognosis of PCa. Construction of histone modifier expression pattern subtypes for PCa. We evaluated whether the transcriptome analysis of the 67 histone modification factors mentioned above could aid in classifying PCa patients. Therefore, we used the gene expression profiles of these genes to determine molecular subtypes. The optimal clustering was defined by the number of clusters (k) from 2 to 10 using the consensus clustering algorithm. When k = 4, stable clusters were identified (Fig. 2 A and 2 B), and four subtypes of PCa with different patterns of histone modification factor expression were constructed (referred to as PRAD clusters), including C1 (n = 142), C2 (n = 142), C3 (n = 155), and C4 (n = 56). They showed significant differences in gene expression profiles, with C3 patients exhibiting higher expression abundance of almost all histone modification factors, indicating that histone modification was more active in C3 patients, while the other three subtypes displayed enrichment of some regulatory factors (Fig. 2 C). Survival analysis showed significant differences in both progression-free survival (PFI) and disease-free survival (DFI) among patients with different subtypes. For PFI, the survival time of C3 patients was significantly shorter than that of C1, C2, and C4 patients, while C1 patients had the best survival (p = 6.63e-6, Fig. 2 D). The 3-year PFI rates of the four PRAD clusters were 92.96%, 88.03%, 76.77%, and 85.71%, respectively. Similarly, the DFI analysis based on 249 patients who underwent R0 resection showed that C1 patients had the best survival, with a 5-year DFI rate of 97.32%, whereas C3 patients showed a higher risk of relapse than other subtypes (p = 0.015, Fig. 2 E). Subsequently, a multivariate Cox regression analysis was performed on age, PSA, Gleason score, clinical T stage, and the four subtypes. This analysis revealed that the PRAD clusters was an independent prognostic factor for PFI in the TCGA-PRAD cohort (p = 0.017) and that C4 showed significantly higher progression (p = 0.006, hazard ratio (HR) = 3.166, 95% confidence interval (CI) = 1.397–7.714, Fig. 2 F) and relapse risks (p = 0.037, HR = 8.831, 95%CI = 1.139–68.476, Fig. 2 G) than C1. Finally, we calculated the half-maximal inhibitory concentration (IC50) of ten common chemotherapeutic drugs in each subtype using the GDSC database to compare differences in drug sensitivity among the different subtypes. The results showed that for the good-prognosis C1 patients, gemcitabine and cisplatin were more sensitive than in other subtypes (Fig. 2 H, Supplementary Fig. 1). Among the poor-prognosis C3 and C4 patients, the IC50 values of docetaxel, vinorelbine, and paclitaxel were the lowest in the C4 subtype. Compared to C4 patients, C3 patients showed a stronger sensitivity to doxorubicin, mitomycin C, etoposide, irinotecan, and methotrexate. In conclusion, our results suggest that patients with different patterns of histone modification regulator expression have significant differences in prognosis, which may have implications for treatment. Biological functions and tumor microenvironment immune cell infiltration of histone modification regulator expression pattern subtypes To further explore and understand the biological differences among PRAD clusters, we used GSVA for pathway analysis (Fig. 3 A, B). The results showed significant differences in biological pathways among the subtypes in the TCGA-PRAD cohort. Regarding metabolic pathways, we found that C1 and C3 patients had significant suppression of oxidative phosphorylation, fatty acid metabolism, and biosynthesis of amino acids, which are closely related to PCa, while C2 and C4 patients had high activation of these pathways. However, compared to patients with other subtypes, C3 patients showed a more pronounced inhibition of the androgen response pathway, while C1 and C2 patients had relatively higher scores in this pathway, and C1 patients also showed significant enrichment of the complement pathway. Additionally, C1 patients were enriched for carcinogenic pathways, such as the KRAS signaling pathway and the TGF-beta signaling pathway, which can promote PCa progression[ 25 ]. Interestingly, cell cycle-related pathways (G2M checkpoint, mitotic spindle) were highly activated in C1 and C3 patients but significantly inhibited in C2 and C4 patients. Regarding the tumor microenvironment (Fig. 3 C, D), heatmaps and box plots were used to show the distribution of 15 types of tumor-infiltrating immune cells. Some immune cells with important cytotoxic abilities, such as plasma cells, CD8 + T cells, T follicular helper cells, regulatory T cells (Tregs), and activated natural killer cells, were enriched in C4 patients. Although CD8 + T cell infiltration was the highest in the C4 patients, the proportion of Tregs (an immunosuppressive cell type) was significantly higher in C4 patients than patients in the other subtypes. Meanwhile, macrophages in C4 patients tended to differentiate towards the M2 type. Similar to C4 patients, C2 patients also showed a tendency towards M2 differentiation, while C1 and C3 patients showed a tendency towards M1 differentiation, with the relative proportion of monocytes in C1 patients being significantly higher than in the other subtypes. Additionally, we calculated the TIDE scores of each PRAD subtype and obtained the distribution of epithelial-mesenchymal transition (EMT) scores[ 26 ], stemness characteristics[ 27 ], tumor mutational burden (TMB)[ 27 ], and tumor purity[ 28 ] in the TCGA-PRAD cohort from the literature. The analysis showed that C1 patients had the lowest EMT and TIDE scores among all patient subtypes, suggesting that they have the lowest invasive and metastatic ability and are most likely to benefit from immune checkpoint inhibitor therapy. The highest TIDE score was found in C4 patients, indicating that these tumor cells are most likely to escape immune surveillance. There was no significant statistical difference in the performance of stemness features, TMB, and tumor purity among the subtypes (Fig. 3 E–J). These results emphasize the existence of four different histone modification factor expression patterns in PCa, which represent different biological behaviors and tumor microenvironment characteristics that lead to differences in prognosis. Mutation profiles and genetic alterations among subtypes As somatic mutations and epigenetic alterations are closely related to cancer, we also examined the copy number alterations and methylation differences among the four PCa subtypes. First, we calculated the GISTIC score and CNV frequency of each subtype, as shown in Fig. 4 A. Patients of each subtype are more likely to experience copy number amplification events, with a higher amplification frequency on chromosomes 7–9, while the C3 patients seem to have a higher deletion frequency than the other patients. Figure 4 B shows that compared to the patients with other subtypes, C1 patients have lower copy number amplification and deletion burdens at both the focal (less than half a chromosome arm) and broad (more than half a chromosome arm) levels (comparison of copy number amplification burdens at the focal level among the four subtypes: p = 0.42; copy number deletion burdens at the focal level: p = 0.022; copy number amplification burdens at the broad level: p = 0.056; copy number amplification and deletion burdens at the broad level: p = 0.0016). The C3 subtype has a relatively higher copy number gain and loss burden. These results suggest that patients with the C1 subtype have the best prognosis. With a waterfall plot (Fig. 4 C), we found 39 chromosomal loci with significant differences in amplification and deletion among the subtypes. To show this more intuitively, we displayed the top ten chromosomal loci in terms of amplification and deletion rates (amplified loci: AP_59:7p14.1, AP_64:7q34, AP_112:14q11.2, AP_113:14q11.2, AP_63:7q33, AP_72:8q23.1, AP_73:8q23.3; deleted loci: DP_48:8p23.1, DP_61:10q23.31, DP_33:5q11.2) (Table S5). DNA CpG methylation is not only related to gene expression control during development and homeostasis, but it is also a cancer-driving mechanism. Therefore, we explored the correlation between DNA methylation and transcriptome expression in the training set. We analyzed the level of methylation differences among the subtypes and performed differential analysis on the RNA-seq data of the TCGA-PRAD cohort. We identified 49 genes that showed a negative correlation; i.e., low methylation expression of some genes corresponded to high expression of transcriptome genes (Fig. 4 D, E). DNA methylation loss promotes immune evasion in tumors with high mutation and copy number burdens[ 29 ], and these changes in the TCGA-PRAD data provide insight into understanding the prognostic differences among the subtypes. Construction of a histone-related HIS_score and model validation Since we confirmed that different PCa prognosis can be distinguished based on the expression differences of histone modification factors, we attempted to establish a histone modification regulatory factor-related scoring model to predict the clinical prognosis of PCa patients. Figure 5 A shows the detailed process of constructing and validating the histone score model, with the obtained differentially expressed genes presented in Supplementary Table 6 (Table S6). Finally, we included 12 gene models for further screening, and among the 11 gene models, the 21-gene model had the highest frequency of 242 (Fig. 5 B). Therefore, based on the selected 21 genes (Table S7), we established the HIS_score prognosis model, and its calculation formula is: HIS_score = ( MXD3 + CCDC28B + COL11A2 + SLC39A5 + GPT + DNASE1L2 + PIF1 + KRTAP5-9 + TTLL10 + KRTAP5-1 + KRTAP5-10 + HAGHL + MSLNL + AMH + NKAIN4 + CCDC114 + SLC9A3 + SULT1E1 + ALB ) / 19 - ( SLC6A14 + RPE65 ) / 2. The box plot shows that the C3 patients have a higher HIS_score than the other patients, while patients with the C1 subtype have a relatively lower HIS_score (Fig. 5 C). We also applied common clinical characteristics to group the patients and observed differences in the distribution of the HIS_score among the groups (Supplementary Fig. 2A–F). Additionally, we defined the top third and bottom third of the samples with the highest and lowest HIS_scores, respectively, as the high-score group and low-score group. A heat map showing the expression of genes in the different subtypes is presented in Fig. 5 D. The heat map shows that the model could discriminate patients in the TCGA-PRAD cohort (Fig. 5 D). A Kaplan-Meyer plot showed that compared with the low-score group, the high-score group had significantly shortened PFI (p = 1.48e − 09, HR = 2.996, 95% CI = 1.904–4.715, Fig. 5 E) and DFI (p = 0.00022, HR = 3.415, 95% CI = 1.340–8.706, Fig. 5 F). Additionally, the area under the receiver operating characteristic curve for PFI at 1, 3, and 5 years was ≥ 0.7 (Fig. 5 G), indicating that the HIS_score is a reliable predictive model. Multivariate Cox analysis also showed that in the TCGA dataset, a high HIS_score was a predictive factor for the prognosis of TCGA-PRAD patients (p = 0.001, HR = 3.291, 95% CI = 1.593–6.800, Fig. 5 H). We then calculated the HIS_score of each sample in the validation set GSE70770 and divided it into groups. Slightly different from the TCGA-PRAD dataset, we used BCR, which is defined as two or more consecutively elevated PSA results greater than 0.2 ng/ml, as an indicator of prognosis for PCa patients in the GSE70770. As expected, survival analysis showed that BCR in the high-score group was significantly shorter than that in the low-score group (p = 1.34e − 05, HR = 2.483, 95% CI = 1.485–4.152, Fig. 5 I). The area under the receiver operating characteristic curve for BCR at 1, 3, and 5 years was 0.75, 0.76, and 0.75, respectively (Fig. 5 J), indicating that the HIS_score model was valid in the GSE70770 dataset. The results of the multivariate analysis also showed that the HIS_score was significantly correlated with BCR in PCa patients in the GSE70770 (p = 0.005, HR = 3.437, 95% CI = 1.449–8.153, Fig. 5 K). To test the performance of the scoring model and improve its predictive accuracy, we combined other prognostic factors (including T staging, PSA level, and Gleason score) with TCGA-PRAD data to establish a risk column chart for predicting the PFI probability of PCa patients at different time points. As shown in Fig. 5 L, the higher the total score based on the corresponding numbers of each factor in the column chart, the lower the 3-year and 5-year PFI rates. A calibration curve verified the reliability of the HIS_score model predicted survival probability (Fig. 5 M–O), and the observed results were consistent with the predicted survival probability of the model. Furthermore, decision curve analysis was used to compare the predictive accuracy of different predictive models. As shown in Fig. 5 P and Q, compared with model 1 and model 2, model 3 showed a better predictive accuracy. These results further confirm that the 21-gene model and HIS_score we established have superior predictive ability for determining the survival outcome of PCa patients. As before, we analyzed the biological functions and tumor immune infiltration levels of the HIS_score high-score and low-score groups. The high HIS_score group was mainly enriched for pathways such as oxidative phosphorylation, the coagulation process, and DNA repair (Fig. 6 A, B), and its enrichment scores in Treg cells and M2 macrophages were significantly higher than those in the low HIS_score group (Fig. 6 C, D). Additionally, the high HIS_score group had relatively higher dry scores, TMB, and TIDE (Fig. 6 E–I), indicating a higher malignancy and immunogenicity and a greater sensitivity to immune therapy. Based on these analyses, our HIS_score model can distinguish PCa subgroups with different biological and immune characteristics. Discussion Overall, PCa has a high incidence rate, and a subset of patients who receive local therapy have poor prognosis and high mortality rates, highlighting the need for early intervention. Currently, effective and reliable clinical risk stratification biomarkers for PCa are still being developed. Studies have shown that global changes in histone modifications are associated with patient prognosis, but no tool has been developed for clinical detection[ 13 , 30 ]. In our study, we classified PCa based on the transcriptional expression levels of 67 histone modification molecules and identified four expression patterns that were associated with significant differences in patient prognosis. Both PFI and DFI showed a significantly better prognosis in C1 patients than the patients in the other subtypes. Moreover, we observed that the histone modification genes SMYD5 , SUV39H1 , SIRT3 , SIRT6 , SIRT7 , HDAC10 , KAT2A , SIRT4 , and HDAC8 were relatively highly expressed in the poor prognostic C2–C4 patients compared to C1 patients, where they were all poorly expressed. This suggests that these histone modification genes may affect the prognosis of PCa. Among them, SUV39H1 [ 31 ], SIRT6 [ 32 ], and SIRT7 [ 33 ] have been found to participate in the proliferation and migration of PCa cells, and their overexpression can promote PCa progression. Notably, downregulation of SIRT7 expression can also increase the sensitivity of PCa cells to docetaxel and radiation therapy[ 33 ], which may provide a therapeutic target for PCa with drug resistance. This is consistent with the better prognosis of C1 patients. Importantly, the GDSC database was used to identify small molecule drugs for PCa. The classification based on histone modification genes could significantly distinguish the sensitivity of different subtypes of patients to various chemotherapy drugs, further supporting our findings and providing insights for the future development of new treatment strategies. Additionally, we observed differences in biological functions and tumor immunity among the different subtypes. Regarding biological characteristics, both the C1 and C3 patients exhibited inhibition of metabolic pathways and a high activation of cell proliferation/cycle-related pathways, while C2 and C4 patients showed the opposite pattern. This suggests that inhibition of the cell cycle pathway may be a potential therapeutic strategy for C1 and C3 patients, while inhibition of metabolism may be effective for C2 and C4 patients. Specifically, we found that the androgen response pathway was highly activated in both C1 and C2 patients, but the score in the C3 patients were significantly lower than the patients in other subtypes, suggesting that C3 patients may be an androgen-independent type of PCa patients that progresses after initial androgen ablation therapy (orchiectomy or luteinizing hormone-releasing hormone agonism), with the addition and subsequent discontinuation of anti-androgen drugs[ 34 ]. This has been confirmed by multiple studies as a highly malignant pathological type[ 35 , 36 ] that is currently incurable[ 37 ], which may also be the reason for the poor prognosis of the C3 patients. Notably, complement pathway activation was significantly higher in C1 patients than in the other subtypes. The complement system promotes phagocytosis of phagocytic cells[ 38 , 39 ]. Additionally, immune analysis revealed a high abundance of monocytes in C1 patients, which tended to differentiate into the M1 subtype. M1 macrophages are a subtype of macrophages with immunotoxicity and phagocytic functions[ 40 ]. Therefore, we hypothesize that the synergistic effect between the highly activated complement pathway and the aggregation of M1 macrophages may be the main factor resulting in the good prognosis of C1 patients. Furthermore, we found that although CD8 + T cell infiltration was abundant in C4 patients, immunosuppressive Treg cells were also significantly enriched in C4 patients, whereas they were very low in patients with the other subtypes. Meanwhile, macrophages in C4 patients also tended towards M2 differentiation. Combined with the high TIDE score in C4 patients, we speculate that the cytotoxic lymphocytes in C4 patients are in an exhausted state, allowing the tumor to evade immune surveillance, leading to a poor response to immunotherapy. Based on genomic analysis, we found that copy number gains and losses were relatively low in the C1 patients. Specific CNV can define the type and progression of tumors and are closely related to prognosis. In many malignant tumors, the higher the copy number burden, the worse the prognosis[ 41 , 42 ]. Additionally, previous studies have confirmed that higher copy number burden in PCa is associated with more disease deaths[ 42 ]. Therefore, this also suggests that C1 patients have the best prognosis. Considering the significant differences in PCa subtypes based on histone modification genes, we established a quantitative scoring model, the HIS_score, consisting of 21 genes for clinical translation. Compared to low-scoring patients, high-scoring patients had a significantly worse prognosis, with C1 patients generally having a low score, confirming the previous result that C1 patients have the best prognosis. Importantly, in a multivariate Cox regression analysis, we confirmed that the HIS_score is an independent prognostic factor for PCa. Incorporating the score into the construction of a column chart, as expected, predicted the PFI of PCa patients at 1, 3, and 5 years, indicating that our HIS_score scoring model could be a good clinical tool that can help clinicians make personalized clinical treatment decisions. Interestingly, some of the genes in our 21-gene feature are involved in histone modification regulation and are related to tumor development. SULT1E1, a cytoplasmic enzyme, is associated with various cancers. Studies have found that SULT1E1 mRNA is highly expressed in MCF10A cells (a non-tumorigenic proliferative breast disease cell line), but its expression is significantly suppressed in the MCF10A-derived cells used for basal-like human breast cancer progression research. Treatment of MCF10A-derived cell lines with histone deacetylase (HDAC) inhibitors can induce SULT1E1 mRNA synthesis, suggesting that histone deacetylation modification may inhibit SULT1E1 expression, which may be related to breast cancer progression[ 43 ]. Additionally, SULT1E1 may also act as a transcriptional mediator to regulate the mRNA expression of multiple genes in PCa cell lines and participate in PCa progression [ 44 ]. COL11A2 (type XI collagen alpha-2 chain) is a protein-coding gene, and Sakimura et al. demonstrated that HDAC inhibitors may effectively inhibit tumor growth by inducing chondrosarcoma cells to differentiate into hypertrophic and mineralized states in vivo. Dose-dependent upregulation of extracellular matrix genes (including COL2A11 ) may be one of the mechanisms[ 45 ]. These gene features may also drive us to search for meaningful drug targets. In conclusion, we constructed a HIS_score scoring model with 21 gene features, and our analysis showed that the model can effectively predict the prognosis of PCa patients after RP. This will help clinicians identify patients with poor prognosis early and facilitate the development or modification of personalized treatment plans. However, our study has certain limitations. First, this is a retrospective study based on public databases, and more reliable prospective studies are needed to support our results. Second, we did not investigate the functional roles and potential mechanisms of the 21 genes in PCa. Conclusion Our research indicates that the classification of PCa based on histone modifications is of significant importance. Furthermore, a HIS_score predictive model was established that can effectively predict the prognosis of patients with PCa after RP. Abbreviations BCR Biochemical recurrence CNV Copy number variation DFI Disease-free interval EMT Epithelial-mesenchymal transition GEO Gene Expression Omnibus GSVA Gene set variation analysis PCa Prostate cancer PFI Progression-free interval PRAD Prostate adenocarcinoma PSA Prostate-specific antigen RP Radical prostatectomy TCGA The Cancer Genome Atlas TIDE Tumor immune dysfunction and exclusion TMB Tumor mutational burden Declarations Availability of data and materials The public data used in this study are available at: TCGA-PRAD (https://xenabrowser.net/datapages/?cohort=TCGA Prostate Cancer (PRAD); GSE70770 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70770). Acknowledgements We thank all the databases involved in this study for their contribution. Funding This research was supported by the Natural Science Foundation of Guangdong Province (No. 2021A1515010700 to Zhenhua Huang), National Natural Science Foundation of China (No. 82073375 to Xiaoxiang Rong), China Postdoctoral Science Foundation (No. 2019M663001 to Xiaoxiang Rong) and the Science and Technology Planning Project of Guangzhou (No. 202102020532 to Xiaoxiang Rong). Author information Authors and Affililiations Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China Xuee Zhou, Xiaolin Li, Jiahong Hong, Fuli Xie, Kuncai Liu, Yue Huang, Ya Gao, Xiaoxiang Rong, Rui Zhou & Zhenhua Huang Contributions ZH, RZ, and XR contributed to the planning of the study. XZ and XL analyzed the data and wrote the manuscript. XZ, XL, JH, KL, FX, YH, YG prepared all the figures and tables. ZH, RZ, and XR revised the manuscript. All authors read and approved the final manuscript. 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Additional Declarations No competing interests reported. Supplementary Files S1.tif Figure S1. The drug sensitivity differs among PRAD clusters. A Heatmap demonstrating the difference in half inhibition concentration (IC50) of 50 chemotherapeutic drugs (P<0.05) in the GDSC database across the four PRAD clusters of the TCGA-PRAD cohort. S2.tif Figure S2. The association between HIS_score and clinical characteristics of PCa patients. A-E Distribution of HIS_score values for patients grouped by different clinical characteristics. These characteristics were Age (A), PSA (B), Gleason score (C), T stage (D), N stage (E), and Event (F) in order (YES means death event occurred, NO means no death event occurred). Supplementarytables.xlsx Table S1. Members of histone modification regulators. Table S2. Basic information of gene expression profiling series. Table S3. The survival impact and expression for histone modification regulators. Table S4. Patients’ basic characteristics of enrolled patients in each dataset. Table S5. Significant mutation site of CNV. Table S6. Differentially expressed genes in C1 VS C3 patients. Table S7. Gene signature identified that make up the HIS_score. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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The outermost part represents the names of the regulatory factors of each modifier enzyme, in which black fonts indicates genes that affect the prognosis of patients, and gray fonts indicates genes that have no effect on the prognosis. From the outside to the inside, the outermost ring represents histone modifying enzymes. The second circle represents the relationship between regulatory factors and patient prognosis. Red represents genes with a hazard ratio (HR) greater than 1, blue represents genes with an HR less than 1, and gray represents genes with no statistical significance. The third circle (Expression) represents the expression of genes in the TCGA-PRAD cohort. Green represents high expression in the tumor, yellow represents no higher expression in the tumor than normal tissue, and gray indicates no statistical significance. The next gray circle represents the HR value distribution of each factor, with the baseline HR=1. The innermost circle represents the statistical differences of Survival, with darker color indicating lower p-value. \u003cstrong\u003eE-H\u003c/strong\u003e Heatmaps of correlations between regulators of methyltransferase (\u003cstrong\u003eE\u003c/strong\u003e), demethylase (\u003cstrong\u003eF\u003c/strong\u003e), acetyltransferase (\u003cstrong\u003eG\u003c/strong\u003e), and deacetylase (\u003cstrong\u003eH\u003c/strong\u003e) of modifier histones. Negative correlations are marked in blue and positive correlations in red.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/d9e562a524443e4123e8b16f.png"},{"id":42445153,"identity":"b7c2643c-b313-4c68-803e-cec104f11eac","added_by":"auto","created_at":"2023-08-31 16:51:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":663389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFour PRAD clusters based on histone modification regulators. A\u003c/strong\u003e Consensus clustering matrix obtained when k = 4. The range of consistency values is from 0 to 1, with 0 indicating never clustered (white) and 1 indicating always clustered (dark blue). \u003cstrong\u003eB\u003c/strong\u003e Relative changes in the area under the curve of the cumulative distribution function (CDF) for different values of k. \u003cstrong\u003eC\u003c/strong\u003e Heatmap showing the expression of 67 histone modification regulatory factors in 495 patients from the TCGA-PRAD cohort classified into four consensus clusters. The colors for each cluster are dark blue for C1, sky blue for C2, orange for C3, and green for C4. \u003cstrong\u003eD\u003c/strong\u003eKaplan-Meier survival curves (PFI) according to four PRAD clusters based on 495 patients from the TCGA-PRAD cohort. \u003cstrong\u003eE\u003c/strong\u003e Kaplan-Meier survival curves (DFI) according to four PRAD clusters based on 249 patients from the TCGA-PRAD cohort. \u003cstrong\u003eF-G\u003c/strong\u003e Forest plots showing the hazard ratios (HR) for the effect of the four clusters and clinical characteristics on PFI (\u003cstrong\u003eF\u003c/strong\u003e) and DFI (\u003cstrong\u003eG\u003c/strong\u003e), including Age, PSA, Gleason score, T stage, and the four PRAD clusters. The length of the horizontal line represents the 95% confidence interval. \u003cstrong\u003eH\u003c/strong\u003eDifferences in the half-maximal inhibitory concentration (IC50) of 10 common chemotherapy drugs in each cluster. Differences between each pair of groups were compared by Kruskal-Wallis test.\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/f88ed11be5770ad933501a6f.png"},{"id":42444176,"identity":"b2f41a38-7ee5-41aa-ba81-e6133ae852f4","added_by":"auto","created_at":"2023-08-31 16:43:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":570227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of biological and tumor microenvironment characteristics in PCa patients with different PRAD clusters.\u003c/strong\u003e \u003cstrong\u003eA \u003c/strong\u003eHeatmap showing the GSVA enrichment analysis of representative hallmark pathways under different PRAD clusters. Red indicates pathway of activation and blue indicates pathway of suppression. PRAD clusters and clinical information of patients (Age, TNM stage, Gleason score) are annotated as sample information. \u003cstrong\u003eB\u003c/strong\u003eBox plots of GSVA scores for hallmark pathways based on four PRAD clusters in the TCGA-PRAD cohort. The boxes represent the values from the 25th to 75th percentile, the horizontal line inside the box represents the median, the whiskers represent 1.5 times the interquartile range, and the black dots represent outliers. \u003cstrong\u003eC\u003c/strong\u003e Enrichment distribution of tumor microenvironment-infiltrating cells under different histone modification patterns. PRAD clusters and clinical information of the TCGA-PRAD cohort (age, TNM stage, Gleason score) are annotated as sample information. \u003cstrong\u003eD \u003c/strong\u003eProportions of tumor microenvironment-infiltrating cells calculated by CIBERSORT algorithm in the four PRAD clusters. The boxes represent the values from the 25th to 75th percentile, the horizontal line inside the box represents the median, the whiskers represent 1.5 times the interquartile range, and the black dots represent outliers. \u003cstrong\u003eE-J\u003c/strong\u003e Distributions of EMT scores (\u003cstrong\u003eE\u003c/strong\u003e), TIDE scores (\u003cstrong\u003eF\u003c/strong\u003e), stemness scores (\u003cstrong\u003eG\u003c/strong\u003e), TMB scores (\u003cstrong\u003eH\u003c/strong\u003e), and tumor purity (\u003cstrong\u003eI\u003c/strong\u003e) in the four PRAD clusters in the TCGA-PRAD cohort. Differences between each two groups were compared by Kruskal-Wallis test. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ***: p \u0026lt; 0.001; ns: not significant, p \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/42540dd60c8aa59dd3e959f0.png"},{"id":42445154,"identity":"45b2ba82-f474-4cee-9570-7d7d3aa06e49","added_by":"auto","created_at":"2023-08-31 16:51:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":658371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCopy number variations and epigenetic changes among different PRAD clusters. A\u003c/strong\u003e Copy number profiles of the four PRAD clusters of the TCGA-PRAD cohort (left column is gistic score, right column is frequency). Red represents amplification, blue represents deletion. Gene fragments are placed according to their position on the chromosome, ranging from chromosome 1 to chromosome 22. \u003cstrong\u003eB\u003c/strong\u003e Distribution of focal and arm-level copy number changes in each PRAD cluster. Statistical differences between every two groups were compared by Kruskal-Wallis test. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ***: p \u0026lt; 0.001; ns: not significant, p \u0026gt; 0.05. \u003cstrong\u003eC\u003c/strong\u003eAmplification/deletion plots of the four PRAD clusters CNV significant mutation sites of the TCGA-PRAD cohort, with chromosomal loci sorted by decreasing frequency of mutations (amplifications or deletions). \u003cstrong\u003eD-E\u003c/strong\u003e Heatmaps showing the expression of genes significantly associated and all differentially expressed in the DNA methylation (\u003cstrong\u003eD\u003c/strong\u003e) and transcriptome (\u003cstrong\u003eE\u003c/strong\u003e) expression profiles of the four PRAD clusters in the cohort.\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/5d5c9ad1546306f6b2ebe8f2.png"},{"id":42444172,"identity":"4174ee3d-8b09-4ce1-b1d8-6a4db4fc25b7","added_by":"auto","created_at":"2023-08-31 16:43:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":964148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBuilding the HIS_score model for histone modification and exploring its clinical relevance.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Flow diagram of building the histone modification score model. \u003cstrong\u003eB\u003c/strong\u003e Generation of 12 gene models after 1000 iterations. \u003cstrong\u003eC\u003c/strong\u003e Distribution of HIS_score values of patients in four PRAD clusters in the TCGA-PRAD cohort. \u003cstrong\u003eD\u003c/strong\u003e Heatmap of gene expression in the TCGA-PRAD cohort by high and low HIS_score subgroups. HIS_score and four PRAD clusters are annotated for the samples. \u003cstrong\u003eE-F\u003c/strong\u003e Kaplan–Meier curves of PFI (\u003cstrong\u003eE\u003c/strong\u003e) and DFI (\u003cstrong\u003eF\u003c/strong\u003e) in the TCGA-PRAD cohort according to the HIS_score. \u003cstrong\u003eG\u003c/strong\u003e Predictive value of HIS_score for 1-year, 3-year, and 5-year survival rates of PCa patients in the TCGA-PRAD cohort. \u003cstrong\u003eH\u003c/strong\u003e Forest plot of multivariate Cox regression model analysis, including characteristics such as age, PSA value, Gleason score, T stage, and HIS_score in the TCGA-PRAD cohort. \u003cstrong\u003eI\u003c/strong\u003e Kaplan–Meier curves of BCR in the GSE70770 cohort according to the HIS_score. \u003cstrong\u003eJ\u003c/strong\u003e Predictive value of HIS_score for 1-year, 3-year, and 5-year survival rates of PCa patients in the GSE70770 cohort. \u003cstrong\u003eK\u003c/strong\u003e Forest plot of multivariate Cox regression model analysis, including characteristics such as age, Gleason score, T stage, and HIS_score in the GSE70770 cohort. \u003cstrong\u003eL\u003c/strong\u003eConstruction of a nomogram combining the HIS_score and clinical characteristics for predicting patients' PFI at 1, 3, and 5 years in the TCGA-PRAD cohort. \u003cstrong\u003eM-O\u003c/strong\u003eCalibration curves of the nomogram for predicting 1-year (\u003cstrong\u003eM\u003c/strong\u003e), 3-year (\u003cstrong\u003eN\u003c/strong\u003e), and 5-year (\u003cstrong\u003eO\u003c/strong\u003e) PFI of the TCGA-PRAD cohort. \u003cstrong\u003eP-Q\u003c/strong\u003e Decision curve analysis curve of the nomogram based on HIS_score for 5-year. Model1: HIS_score, Model2: Age + PSA + Gleason score + T stage, Model3: Age + PSA + Gleason score+ T stage + HIS_score.\u003c/p\u003e","description":"","filename":"FIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/2aaec824bcbe41a7761dd4eb.png"},{"id":42444175,"identity":"9ef27af4-de24-47dc-95c7-409b304a693b","added_by":"auto","created_at":"2023-08-31 16:43:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":537743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Biological and Immune Features in Prostate Cancer Patients Grouped by HIS_score.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Heatmap showing GSVA results of hallmark gene sets in patients with high and low HIS_score in the TCGA-PRAD cohort. Red represents pathway of activation, blue represents pathway of inhibition. HIS_score, clinical information (age, TNM stage, Gleason score) of the TCGA-PRAD cohort are annotated as sample information. \u003cstrong\u003eB\u003c/strong\u003e Box plots of GSVA results in high and low HIS_score subgroups. Boxes represent 25-75% values, horizontal lines in boxes represent the median, whiskers represent 1.5 interquartile ranges, and black dots represent outliers. \u003cstrong\u003eC\u003c/strong\u003e Results of cell type enrichment analysis in high and low HIS_score subgroups estimated by the CIBERSORT algorithm. HIS_score and clinical information (age, TNM staging, Gleason score) of the TCGA-PRAD cohort are annotated as sample information. \u003cstrong\u003eD\u003c/strong\u003e Box plots of CIBERSORT results in high and low HIS_score subgroups. Boxes represent 25-75% values, horizontal lines in boxes represent the median, whiskers represent 1.5 interquartile ranges, and black dots represent outliers. \u003cstrong\u003eE-J\u003c/strong\u003e Distribution of stemness scores (\u003cstrong\u003eE\u003c/strong\u003e), TIDE (\u003cstrong\u003eF\u003c/strong\u003e), TMB (\u003cstrong\u003eG\u003c/strong\u003e), EMT (\u003cstrong\u003eH\u003c/strong\u003e), and tumor purity (\u003cstrong\u003eI\u003c/strong\u003e) in high and low HIS_score subgroups of the TCGA-PRAD cohort. Statistical differences between each two groups were compared by Kruskal-Wallis test. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ***: p \u0026lt; 0.001; ns: not significant, p \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"FIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/6a85fe23cc0641b0a82c9f01.png"},{"id":42550146,"identity":"ce1c6fb0-ae66-43af-8218-26bce753ef67","added_by":"auto","created_at":"2023-09-03 12:52:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4384795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/d92fda5c-b94c-4088-a7c1-fb071dc477c5.pdf"},{"id":42444178,"identity":"0a1b1422-c396-4729-96c5-3f613123e3b7","added_by":"auto","created_at":"2023-08-31 16:43:35","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6907988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. The drug sensitivity differs among PRAD clusters.\u003c/strong\u003e \u003cstrong\u003eA \u003c/strong\u003eHeatmap demonstrating the difference in half inhibition concentration (IC50) of 50 chemotherapeutic drugs (P\u0026lt;0.05) in the GDSC database across the four PRAD clusters of the TCGA-PRAD cohort.\u003c/p\u003e","description":"","filename":"S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/982247cef8d4fd82068c4822.tif"},{"id":42444170,"identity":"18359bad-1361-4f53-a9b2-33935c3a6caf","added_by":"auto","created_at":"2023-08-31 16:43:34","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1429712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. The association between HIS_score and clinical characteristics of PCa patients.\u003c/strong\u003e \u003cstrong\u003eA-E\u003c/strong\u003e Distribution of HIS_score values for patients grouped by different clinical characteristics. These characteristics were Age (\u003cstrong\u003eA\u003c/strong\u003e), PSA (\u003cstrong\u003eB\u003c/strong\u003e), Gleason score (\u003cstrong\u003eC\u003c/strong\u003e), T stage (\u003cstrong\u003eD\u003c/strong\u003e), N stage (\u003cstrong\u003eE\u003c/strong\u003e), and Event (\u003cstrong\u003eF\u003c/strong\u003e) in order (YES means death event occurred, NO means no death event occurred).\u003c/p\u003e","description":"","filename":"S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/aa1f2732491357fde10b0b9b.tif"},{"id":42445155,"identity":"94df4266-5b57-4e2b-8f5c-7ef3404a6e7a","added_by":"auto","created_at":"2023-08-31 16:51:34","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":54160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eMembers of histone modification regulators. \u003cstrong\u003eTable S2. \u003c/strong\u003eBasic information of gene expression profiling series. \u003cstrong\u003eTable S3. \u003c/strong\u003eThe survival impact and expression for histone modification regulators. \u003cstrong\u003eTable S4. \u003c/strong\u003ePatients’ basic characteristics of enrolled patients in each dataset. \u003cstrong\u003eTable S5. \u003c/strong\u003eSignificant mutation site of CNV. \u003cstrong\u003eTable S6. \u003c/strong\u003eDifferentially expressed genes in C1 VS C3 patients. \u003cstrong\u003eTable S7. \u003c/strong\u003eGene signature identified that make up the HIS_score.\u003c/p\u003e","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3298585/v1/a6ba0fe37cc6c208ec9f86f5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A gene feature based on histone modifications can predict the prognosis of prostate cancer","fulltext":[{"header":"Preface","content":"\u003cp\u003eProstate cancer (PCa) is a highly heterogeneous disease and a prevalent malignancy among men worldwide. It is also a major cause of cancer-related deaths in men[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Management of PCa includes modalities such as radical prostatectomy (RP), radiotherapy, and androgen deprivation therapy, which are crucial components for treating organ-confined and androgen-dependent PCa[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the benefits of different treatment options, biochemical recurrence (BCR) occurs in approximately one-third of patients after RP. This leads to rapid progression and development of castration-resistant PCa resulting in death within 2\u0026ndash;4 years, with recurrence as a major cause of mortality[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, accurately identifying high-risk patients susceptible to disease progression after RP is crucial for developing personalized treatment plans. Previous studies have demonstrated that characteristics such as the Gleason score, prostate-specific antigen (PSA), and clinical T stage are associated with the survival of PCa patients and can predict prognosis after RP[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these characteristics have certain limitations that result in low predictive accuracy.\u003c/p\u003e \u003cp\u003eIn recent years, researchers have conducted a series of studies to better identify patients with poor prognosis and explored gene features based on gene expression and clinical data to predict PCa patient prognosis. For example, Lv et al. established and validated a seven-gene molecular feature based on immune-related genes to monitor immune status and evaluate the recurrence-free survival rate of PCa patients[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Zhang et al. identified an apoptosis-related gene feature and improved the risk stratification of recurrence-free survival by incorporating gene features into clinical parameters, thus identifying patients with poor prognosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpigenetics plays an important role in the progression of PCa[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Epigenetic regulation can be achieved through DNA methylation, histone modifications and microRNA expression, and plays an important role in cancer[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among these, histone modifications are common epigenetic approaches used to regulate chromatin structure, DNA repair, and gene expression, and they are diverse and complex. Increasing evidence suggests that histone modification is closely related to PCa onset and progression and can affect certain biological processes of tumor cells, such as proliferation, apoptosis, and metastasis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Research has shown that global changes in histone modification in cancer cells can predict the risk of tumor recurrence in low-grade PCa patients independently of tumor stage, preoperative PSA, and capsule invasion[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, current research on the role of histone modification in the clinical diagnosis and treatment of PCa remains limited[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There has been no exploration of whether the global transcriptional expression level of histone modification factors can distinguish high-risk patients, indicating that there remains exploration and development opportunities for the application of histone modifications in PCa.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to establish a histone modification-related prediction model based on the global transcriptional expression level of histone modification factors to improve the risk stratification of PCa patients after RP. These results may help provide better treatment for PCa patients and improve outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition and processing\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective search in publicly available datasets, including The Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), to obtain gene expression data and clinical information for PCa samples. Two independent cohorts, The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) and GSE70770, were included, which comprised RNA-seq data of 698 PCa samples and corresponding clinical information for further analysis. Specifically, the clinical information selected included patient age, tumor TNM stage, and Gleason score, and progression-free interval (PFI) and disease-free interval (DFI) were used as the clinical endpoints in our study. Genomic mutation data for TCGA-PRAD were obtained from the University of California, Santa Cruz (UCSC) Xena database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data in this study underwent preprocessing using the \"voom\" algorithm, and the \"ComBat\" R package[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was used to adjust for batch effects caused by non-biological technical biases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of histone modifier expression patterns by consensus clustering\u003c/h2\u003e \u003cp\u003eWe employed unsupervised clustering (K-means) to identify distinct patterns of histone modification and to categorize PCa patients using the \"ConsensusClusterPlus\" R package[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The consensus clustering algorithm was utilized to assess the stability of clustering and determine the optimal number of clusters with the following parameters: 80% project resampling (pItem), 80% gene resampling (pFeature), maximum evaluation of k is ten (maxK), 1000 resamplings (reps), and K-means clustering algorithm based on euclidean distance (clusterAlg).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGeneration and validation of a histone modification-based prognostic model\u003c/h2\u003e \u003cp\u003eTo establish a risk score, the linear model from the microarray data (limma) R package[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was employed to identify differentially expressed genes between C1 and C3 patients, with a cutoff value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (adjusted) and |log fold-change| \u0026gt; 1. These differentially expressed genes were then subjected to a univariate Cox regression model and Kaplan-Meier analysis to identify genes associated with prognosis. The intersection of these genes was then included in a least absolute shrinkage and selection operator (Lasso) regression analysis (10-fold cross-validation), which was repeated 1000 times to ensure stability, resulting in the generation of the optimal gene model. Based on this model, a histone modification-related prognostic model was established. Data from 495 PCa patients from the TCGA-PRAD were used as the training set, and patients were categorized into high- and low-risk groups based on their scores, with the top and bottom 1/3 of patients being assigned to these groups, respectively. Subsequently, Kaplan-Meier curve analysis was performed to compare survival differences between the high- and low-risk groups. The same process was employed to validate the prognostic model in a validation set comprising 203 PCa patients from the GEO database. Furthermore, a nomogram was constructed using a multivariate COX regression analysis to visualize the model and enable clinical application.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCNV mutation profile and methylation characteristics\u003c/h2\u003e \u003cp\u003eThe copy number variation (CNV) data were downloaded using the \"TCGAbioLink\" R package[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and the DNA methylation data (450k chip) of TCGA-PRAD were obtained from the UCSC Xena database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Genomic Identification of Significant Targets in Cancer 2.0 (GISTIC 2.0) was used to calculate the GISTIC score and copy number variation frequency of all samples in the TCGA-PRAD cohort. In terms of methylation analysis, we filtered out probes located on the XY chromosome and SNPs, retained probes located in promoter regions such as \"TSS1500\", \"TSS200\", \"1stExone\", and \"5'UTR\" on CpG sites, and selected the top 1000 probes with high variation after filtering. The \"champ.DMP\" R package was used to perform methylation differential analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of biological processes and tumor microenvironment features\u003c/h2\u003e \u003cp\u003eThe gene set variation analysis (GSVA) was achieved by using the \"GSVA\" R package[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], where \"signature gene sets\" (HALLMRAK gene sets) were downloaded from the MSigDB database to understand the enrichment of pathways for different subtypes. In terms of immune cell infiltration, we utilized the CIBERSORT [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] method to explore the cellular composition of samples based on gene expression profiles. The parameter settings used were: signature genes: LM22, permutations: 1000, and Quantile normalization of input mixture\u0026thinsp;=\u0026thinsp;True.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of immunotherapy and chemotherapy response\u003c/h2\u003e \u003cp\u003eTo predict the clinical response of PCa patients to immunotherapy and chemotherapy, we conducted the Tumor Immune Dysfunction and Exclusion (TIDE)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] evaluation. We calculated the IC50 values for 10 commonly used drugs in PCa patients in the TCGA dataset using the Cancer Drug Sensitivity Genomics (GDSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and executed the \"pRRophetic\" R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn terms of statistical analysis, we conducted data analysis using R software (version 4.0.5) and IBM SPSS Statistics 26.0 software. For all annotations in the figures, \"ns\" indicates non-significant, \"*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\", \"**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\", \"***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\", and \"****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\". Unless otherwise specified, all statistical tests were two-sided. To clarify the survival differences between different subtypes, the \"survival\" R package and Kaplan-Meier analysis were used to generate survival curves, and the Log-Rank test was used to compare differences between groups. For the HIS_score, we divided the samples into high and low score groups based on the top and bottom 1/3 of the scores. Receiver operating characteristic curves were used to validate the effectiveness of the model, and the area under it was calculated. Decision curve analysis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was used to evaluate the performance of each indicator. The \"limma\" R package was used to determine the expression differences of histone modification regulatory factors in tumor and normal samples. Other statistical methods used in this study included the Kruskal-Wallis test, one-way ANOVA, Cox regression analysis, and Pearson correlation analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClassification and characteristics of histone modification regulatory factors in PCa\u003c/h2\u003e \u003cp\u003eAfter systematically reviewing published articles related to histone modification, we identified 67 histone modification regulatory genes from the gene expression profiles of PCa patients, including 28 acetyltransferases, 11 deacetylases, 10 methyltransferases, and 18 demethylases (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To clarify the role of histone modification regulatory factors in PCa patients, we collected data from 495 PCa and 52 normal samples from TCGA public database (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Analysis revealed that the distribution of these genes and their prognostic impact on patients in the TCGA-PRAD cohort varied. We found that 28 genes were upregulated in tumor tissue and were mainly deacetylases (35.71%) and methyltransferases (46.43%). Among the demethyltransferases, 66.67% were downregulated in tumor tissues. Regarding prognosis, the upregulation of the expression of many differentially expressed histone genes in tumor tissue suggested that they were related to poor prognosis. Among them, 60% of these genes belonged to the deacetylase family, accounting for the largest proportion of the four enzyme families (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;D, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Additionally, a correlation heatmap showed that these genes had good correlation within their respective sets, with mostly positive correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u0026ndash;H). The strongest correlation was found among the acetyltransferase regulatory factors. Interestingly, the expression of \u003cem\u003eKAT2A\u003c/em\u003e was negatively correlated with other factors in the methyltransferase set and was identified as a specific transcriptional activation target of PCa by Simmonds et al.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Overall, these results indicate that histone modification factors have important effects on the development and prognosis of PCa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of histone modifier expression pattern subtypes for PCa.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe evaluated whether the transcriptome analysis of the 67 histone modification factors mentioned above could aid in classifying PCa patients. Therefore, we used the gene expression profiles of these genes to determine molecular subtypes. The optimal clustering was defined by the number of clusters (k) from 2 to 10 using the consensus clustering algorithm. When k\u0026thinsp;=\u0026thinsp;4, stable clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and four subtypes of PCa with different patterns of histone modification factor expression were constructed (referred to as PRAD clusters), including C1 (n\u0026thinsp;=\u0026thinsp;142), C2 (n\u0026thinsp;=\u0026thinsp;142), C3 (n\u0026thinsp;=\u0026thinsp;155), and C4 (n\u0026thinsp;=\u0026thinsp;56). They showed significant differences in gene expression profiles, with C3 patients exhibiting higher expression abundance of almost all histone modification factors, indicating that histone modification was more active in C3 patients, while the other three subtypes displayed enrichment of some regulatory factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Survival analysis showed significant differences in both progression-free survival (PFI) and disease-free survival (DFI) among patients with different subtypes. For PFI, the survival time of C3 patients was significantly shorter than that of C1, C2, and C4 patients, while C1 patients had the best survival (p\u0026thinsp;=\u0026thinsp;6.63e-6, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The 3-year PFI rates of the four PRAD clusters were 92.96%, 88.03%, 76.77%, and 85.71%, respectively. Similarly, the DFI analysis based on 249 patients who underwent R0 resection showed that C1 patients had the best survival, with a 5-year DFI rate of 97.32%, whereas C3 patients showed a higher risk of relapse than other subtypes (p\u0026thinsp;=\u0026thinsp;0.015, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Subsequently, a multivariate Cox regression analysis was performed on age, PSA, Gleason score, clinical T stage, and the four subtypes. This analysis revealed that the PRAD clusters was an independent prognostic factor for PFI in the TCGA-PRAD cohort (p\u0026thinsp;=\u0026thinsp;0.017) and that C4 showed significantly higher progression (p\u0026thinsp;=\u0026thinsp;0.006, hazard ratio (HR)\u0026thinsp;=\u0026thinsp;3.166, 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;1.397\u0026ndash;7.714, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) and relapse risks (p\u0026thinsp;=\u0026thinsp;0.037, HR\u0026thinsp;=\u0026thinsp;8.831, 95%CI\u0026thinsp;=\u0026thinsp;1.139\u0026ndash;68.476, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) than C1. Finally, we calculated the half-maximal inhibitory concentration (IC50) of ten common chemotherapeutic drugs in each subtype using the GDSC database to compare differences in drug sensitivity among the different subtypes. The results showed that for the good-prognosis C1 patients, gemcitabine and cisplatin were more sensitive than in other subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, Supplementary Fig.\u0026nbsp;1). Among the poor-prognosis C3 and C4 patients, the IC50 values of docetaxel, vinorelbine, and paclitaxel were the lowest in the C4 subtype. Compared to C4 patients, C3 patients showed a stronger sensitivity to doxorubicin, mitomycin C, etoposide, irinotecan, and methotrexate. In conclusion, our results suggest that patients with different patterns of histone modification regulator expression have significant differences in prognosis, which may have implications for treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBiological functions and tumor microenvironment immune cell infiltration of histone modification regulator expression pattern subtypes\u003c/h2\u003e \u003cp\u003eTo further explore and understand the biological differences among PRAD clusters, we used GSVA for pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The results showed significant differences in biological pathways among the subtypes in the TCGA-PRAD cohort. Regarding metabolic pathways, we found that C1 and C3 patients had significant suppression of oxidative phosphorylation, fatty acid metabolism, and biosynthesis of amino acids, which are closely related to PCa, while C2 and C4 patients had high activation of these pathways. However, compared to patients with other subtypes, C3 patients showed a more pronounced inhibition of the androgen response pathway, while C1 and C2 patients had relatively higher scores in this pathway, and C1 patients also showed significant enrichment of the complement pathway. Additionally, C1 patients were enriched for carcinogenic pathways, such as the KRAS signaling pathway and the TGF-beta signaling pathway, which can promote PCa progression[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Interestingly, cell cycle-related pathways (G2M checkpoint, mitotic spindle) were highly activated in C1 and C3 patients but significantly inhibited in C2 and C4 patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D), heatmaps and box plots were used to show the distribution of 15 types of tumor-infiltrating immune cells. Some immune cells with important cytotoxic abilities, such as plasma cells, CD8\u0026thinsp;+\u0026thinsp;T cells, T follicular helper cells, regulatory T cells (Tregs), and activated natural killer cells, were enriched in C4 patients. Although CD8\u0026thinsp;+\u0026thinsp;T cell infiltration was the highest in the C4 patients, the proportion of Tregs (an immunosuppressive cell type) was significantly higher in C4 patients than patients in the other subtypes. Meanwhile, macrophages in C4 patients tended to differentiate towards the M2 type. Similar to C4 patients, C2 patients also showed a tendency towards M2 differentiation, while C1 and C3 patients showed a tendency towards M1 differentiation, with the relative proportion of monocytes in C1 patients being significantly higher than in the other subtypes.\u003c/p\u003e \u003cp\u003eAdditionally, we calculated the TIDE scores of each PRAD subtype and obtained the distribution of epithelial-mesenchymal transition (EMT) scores[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], stemness characteristics[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], tumor mutational burden (TMB)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and tumor purity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] in the TCGA-PRAD cohort from the literature. The analysis showed that C1 patients had the lowest EMT and TIDE scores among all patient subtypes, suggesting that they have the lowest invasive and metastatic ability and are most likely to benefit from immune checkpoint inhibitor therapy. The highest TIDE score was found in C4 patients, indicating that these tumor cells are most likely to escape immune surveillance. There was no significant statistical difference in the performance of stemness features, TMB, and tumor purity among the subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u0026ndash;J). These results emphasize the existence of four different histone modification factor expression patterns in PCa, which represent different biological behaviors and tumor microenvironment characteristics that lead to differences in prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMutation profiles and genetic alterations among subtypes\u003c/h2\u003e \u003cp\u003eAs somatic mutations and epigenetic alterations are closely related to cancer, we also examined the copy number alterations and methylation differences among the four PCa subtypes. First, we calculated the GISTIC score and CNV frequency of each subtype, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Patients of each subtype are more likely to experience copy number amplification events, with a higher amplification frequency on chromosomes 7\u0026ndash;9, while the C3 patients seem to have a higher deletion frequency than the other patients. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB shows that compared to the patients with other subtypes, C1 patients have lower copy number amplification and deletion burdens at both the focal (less than half a chromosome arm) and broad (more than half a chromosome arm) levels (comparison of copy number amplification burdens at the focal level among the four subtypes: p\u0026thinsp;=\u0026thinsp;0.42; copy number deletion burdens at the focal level: p\u0026thinsp;=\u0026thinsp;0.022; copy number amplification burdens at the broad level: p\u0026thinsp;=\u0026thinsp;0.056; copy number amplification and deletion burdens at the broad level: p\u0026thinsp;=\u0026thinsp;0.0016). The C3 subtype has a relatively higher copy number gain and loss burden. These results suggest that patients with the C1 subtype have the best prognosis. With a waterfall plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), we found 39 chromosomal loci with significant differences in amplification and deletion among the subtypes. To show this more intuitively, we displayed the top ten chromosomal loci in terms of amplification and deletion rates (amplified loci: AP_59:7p14.1, AP_64:7q34, AP_112:14q11.2, AP_113:14q11.2, AP_63:7q33, AP_72:8q23.1, AP_73:8q23.3; deleted loci: DP_48:8p23.1, DP_61:10q23.31, DP_33:5q11.2) (Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDNA CpG methylation is not only related to gene expression control during development and homeostasis, but it is also a cancer-driving mechanism. Therefore, we explored the correlation between DNA methylation and transcriptome expression in the training set. We analyzed the level of methylation differences among the subtypes and performed differential analysis on the RNA-seq data of the TCGA-PRAD cohort. We identified 49 genes that showed a negative correlation; i.e., low methylation expression of some genes corresponded to high expression of transcriptome genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E). DNA methylation loss promotes immune evasion in tumors with high mutation and copy number burdens[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and these changes in the TCGA-PRAD data provide insight into understanding the prognostic differences among the subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a histone-related HIS_score and model validation\u003c/h2\u003e \u003cp\u003eSince we confirmed that different PCa prognosis can be distinguished based on the expression differences of histone modification factors, we attempted to establish a histone modification regulatory factor-related scoring model to predict the clinical prognosis of PCa patients. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA shows the detailed process of constructing and validating the histone score model, with the obtained differentially expressed genes presented in Supplementary Table\u0026nbsp;6 (Table S6). Finally, we included 12 gene models for further screening, and among the 11 gene models, the 21-gene model had the highest frequency of 242 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Therefore, based on the selected 21 genes (Table S7), we established the HIS_score prognosis model, and its calculation formula is:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHIS_score = (\u003cem\u003eMXD3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eCCDC28B\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eCOL11A2\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eSLC39A5\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eGPT\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eDNASE1L2\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePIF1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eKRTAP5-9\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eTTLL10\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eKRTAP5-1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eKRTAP5-10\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eHAGHL\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eMSLNL\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eAMH\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eNKAIN4\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eCCDC114\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eSLC9A3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eSULT1E1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eALB\u003c/em\u003e) / 19 - (\u003cem\u003eSLC6A14\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eRPE65\u003c/em\u003e) / 2.\u003c/p\u003e \u003cp\u003eThe box plot shows that the C3 patients have a higher HIS_score than the other patients, while patients with the C1 subtype have a relatively lower HIS_score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). We also applied common clinical characteristics to group the patients and observed differences in the distribution of the HIS_score among the groups (Supplementary Fig.\u0026nbsp;2A\u0026ndash;F). Additionally, we defined the top third and bottom third of the samples with the highest and lowest HIS_scores, respectively, as the high-score group and low-score group. A heat map showing the expression of genes in the different subtypes is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD. The heat map shows that the model could discriminate patients in the TCGA-PRAD cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). A Kaplan-Meyer plot showed that compared with the low-score group, the high-score group had significantly shortened PFI (p\u0026thinsp;=\u0026thinsp;1.48e\u0026thinsp;\u0026minus;\u0026thinsp;09, HR\u0026thinsp;=\u0026thinsp;2.996, 95% CI\u0026thinsp;=\u0026thinsp;1.904\u0026ndash;4.715, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) and DFI (p\u0026thinsp;=\u0026thinsp;0.00022, HR\u0026thinsp;=\u0026thinsp;3.415, 95% CI\u0026thinsp;=\u0026thinsp;1.340\u0026ndash;8.706, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Additionally, the area under the receiver operating characteristic curve for PFI at 1, 3, and 5 years was \u0026ge;\u0026thinsp;0.7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), indicating that the HIS_score is a reliable predictive model. Multivariate Cox analysis also showed that in the TCGA dataset, a high HIS_score was a predictive factor for the prognosis of TCGA-PRAD patients (p\u0026thinsp;=\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;3.291, 95% CI\u0026thinsp;=\u0026thinsp;1.593\u0026ndash;6.800, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). We then calculated the HIS_score of each sample in the validation set GSE70770 and divided it into groups. Slightly different from the TCGA-PRAD dataset, we used BCR, which is defined as two or more consecutively elevated PSA results greater than 0.2 ng/ml, as an indicator of prognosis for PCa patients in the GSE70770. As expected, survival analysis showed that BCR in the high-score group was significantly shorter than that in the low-score group (p\u0026thinsp;=\u0026thinsp;1.34e\u0026thinsp;\u0026minus;\u0026thinsp;05, HR\u0026thinsp;=\u0026thinsp;2.483, 95% CI\u0026thinsp;=\u0026thinsp;1.485\u0026ndash;4.152, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). The area under the receiver operating characteristic curve for BCR at 1, 3, and 5 years was 0.75, 0.76, and 0.75, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ), indicating that the HIS_score model was valid in the GSE70770 dataset. The results of the multivariate analysis also showed that the HIS_score was significantly correlated with BCR in PCa patients in the GSE70770 (p\u0026thinsp;=\u0026thinsp;0.005, HR\u0026thinsp;=\u0026thinsp;3.437, 95% CI\u0026thinsp;=\u0026thinsp;1.449\u0026ndash;8.153, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). To test the performance of the scoring model and improve its predictive accuracy, we combined other prognostic factors (including T staging, PSA level, and Gleason score) with TCGA-PRAD data to establish a risk column chart for predicting the PFI probability of PCa patients at different time points. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL, the higher the total score based on the corresponding numbers of each factor in the column chart, the lower the 3-year and 5-year PFI rates. A calibration curve verified the reliability of the HIS_score model predicted survival probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eM\u0026ndash;O), and the observed results were consistent with the predicted survival probability of the model. Furthermore, decision curve analysis was used to compare the predictive accuracy of different predictive models. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eP and Q, compared with model 1 and model 2, model 3 showed a better predictive accuracy. These results further confirm that the 21-gene model and HIS_score we established have superior predictive ability for determining the survival outcome of PCa patients.\u003c/p\u003e \u003cp\u003eAs before, we analyzed the biological functions and tumor immune infiltration levels of the HIS_score high-score and low-score groups. The high HIS_score group was mainly enriched for pathways such as oxidative phosphorylation, the coagulation process, and DNA repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B), and its enrichment scores in Treg cells and M2 macrophages were significantly higher than those in the low HIS_score group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D). Additionally, the high HIS_score group had relatively higher dry scores, TMB, and TIDE (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u0026ndash;I), indicating a higher malignancy and immunogenicity and a greater sensitivity to immune therapy. Based on these analyses, our HIS_score model can distinguish PCa subgroups with different biological and immune characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOverall, PCa has a high incidence rate, and a subset of patients who receive local therapy have poor prognosis and high mortality rates, highlighting the need for early intervention. Currently, effective and reliable clinical risk stratification biomarkers for PCa are still being developed. Studies have shown that global changes in histone modifications are associated with patient prognosis, but no tool has been developed for clinical detection[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we classified PCa based on the transcriptional expression levels of 67 histone modification molecules and identified four expression patterns that were associated with significant differences in patient prognosis. Both PFI and DFI showed a significantly better prognosis in C1 patients than the patients in the other subtypes. Moreover, we observed that the histone modification genes \u003cem\u003eSMYD5\u003c/em\u003e, \u003cem\u003eSUV39H1\u003c/em\u003e, \u003cem\u003eSIRT3\u003c/em\u003e, \u003cem\u003eSIRT6\u003c/em\u003e, \u003cem\u003eSIRT7\u003c/em\u003e, \u003cem\u003eHDAC10\u003c/em\u003e, \u003cem\u003eKAT2A\u003c/em\u003e, \u003cem\u003eSIRT4\u003c/em\u003e, and \u003cem\u003eHDAC8\u003c/em\u003e were relatively highly expressed in the poor prognostic C2\u0026ndash;C4 patients compared to C1 patients, where they were all poorly expressed. This suggests that these histone modification genes may affect the prognosis of PCa. Among them, \u003cem\u003eSUV39H1\u003c/em\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], \u003cem\u003eSIRT6\u003c/em\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and \u003cem\u003eSIRT7\u003c/em\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] have been found to participate in the proliferation and migration of PCa cells, and their overexpression can promote PCa progression. Notably, downregulation of \u003cem\u003eSIRT7\u003c/em\u003e expression can also increase the sensitivity of PCa cells to docetaxel and radiation therapy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which may provide a therapeutic target for PCa with drug resistance. This is consistent with the better prognosis of C1 patients. Importantly, the GDSC database was used to identify small molecule drugs for PCa. The classification based on histone modification genes could significantly distinguish the sensitivity of different subtypes of patients to various chemotherapy drugs, further supporting our findings and providing insights for the future development of new treatment strategies.\u003c/p\u003e \u003cp\u003eAdditionally, we observed differences in biological functions and tumor immunity among the different subtypes. Regarding biological characteristics, both the C1 and C3 patients exhibited inhibition of metabolic pathways and a high activation of cell proliferation/cycle-related pathways, while C2 and C4 patients showed the opposite pattern. This suggests that inhibition of the cell cycle pathway may be a potential therapeutic strategy for C1 and C3 patients, while inhibition of metabolism may be effective for C2 and C4 patients. Specifically, we found that the androgen response pathway was highly activated in both C1 and C2 patients, but the score in the C3 patients were significantly lower than the patients in other subtypes, suggesting that C3 patients may be an androgen-independent type of PCa patients that progresses after initial androgen ablation therapy (orchiectomy or luteinizing hormone-releasing hormone agonism), with the addition and subsequent discontinuation of anti-androgen drugs[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This has been confirmed by multiple studies as a highly malignant pathological type[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] that is currently incurable[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which may also be the reason for the poor prognosis of the C3 patients. Notably, complement pathway activation was significantly higher in C1 patients than in the other subtypes. The complement system promotes phagocytosis of phagocytic cells[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, immune analysis revealed a high abundance of monocytes in C1 patients, which tended to differentiate into the M1 subtype. M1 macrophages are a subtype of macrophages with immunotoxicity and phagocytic functions[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, we hypothesize that the synergistic effect between the highly activated complement pathway and the aggregation of M1 macrophages may be the main factor resulting in the good prognosis of C1 patients. Furthermore, we found that although CD8\u0026thinsp;+\u0026thinsp;T cell infiltration was abundant in C4 patients, immunosuppressive Treg cells were also significantly enriched in C4 patients, whereas they were very low in patients with the other subtypes. Meanwhile, macrophages in C4 patients also tended towards M2 differentiation. Combined with the high TIDE score in C4 patients, we speculate that the cytotoxic lymphocytes in C4 patients are in an exhausted state, allowing the tumor to evade immune surveillance, leading to a poor response to immunotherapy.\u003c/p\u003e \u003cp\u003eBased on genomic analysis, we found that copy number gains and losses were relatively low in the C1 patients. Specific CNV can define the type and progression of tumors and are closely related to prognosis. In many malignant tumors, the higher the copy number burden, the worse the prognosis[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, previous studies have confirmed that higher copy number burden in PCa is associated with more disease deaths[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, this also suggests that C1 patients have the best prognosis.\u003c/p\u003e \u003cp\u003eConsidering the significant differences in PCa subtypes based on histone modification genes, we established a quantitative scoring model, the HIS_score, consisting of 21 genes for clinical translation. Compared to low-scoring patients, high-scoring patients had a significantly worse prognosis, with C1 patients generally having a low score, confirming the previous result that C1 patients have the best prognosis. Importantly, in a multivariate Cox regression analysis, we confirmed that the HIS_score is an independent prognostic factor for PCa. Incorporating the score into the construction of a column chart, as expected, predicted the PFI of PCa patients at 1, 3, and 5 years, indicating that our HIS_score scoring model could be a good clinical tool that can help clinicians make personalized clinical treatment decisions.\u003c/p\u003e \u003cp\u003eInterestingly, some of the genes in our 21-gene feature are involved in histone modification regulation and are related to tumor development. SULT1E1, a cytoplasmic enzyme, is associated with various cancers. Studies have found that \u003cem\u003eSULT1E1\u003c/em\u003e mRNA is highly expressed in MCF10A cells (a non-tumorigenic proliferative breast disease cell line), but its expression is significantly suppressed in the MCF10A-derived cells used for basal-like human breast cancer progression research. Treatment of MCF10A-derived cell lines with histone deacetylase (HDAC) inhibitors can induce \u003cem\u003eSULT1E1\u003c/em\u003e mRNA synthesis, suggesting that histone deacetylation modification may inhibit \u003cem\u003eSULT1E1\u003c/em\u003e expression, which may be related to breast cancer progression[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, SULT1E1 may also act as a transcriptional mediator to regulate the mRNA expression of multiple genes in PCa cell lines and participate in PCa progression [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003eCOL11A2\u003c/em\u003e (type XI collagen alpha-2 chain) is a protein-coding gene, and Sakimura et al. demonstrated that HDAC inhibitors may effectively inhibit tumor growth by inducing chondrosarcoma cells to differentiate into hypertrophic and mineralized states in vivo. Dose-dependent upregulation of extracellular matrix genes (including \u003cem\u003eCOL2A11\u003c/em\u003e) may be one of the mechanisms[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These gene features may also drive us to search for meaningful drug targets.\u003c/p\u003e \u003cp\u003eIn conclusion, we constructed a HIS_score scoring model with 21 gene features, and our analysis showed that the model can effectively predict the prognosis of PCa patients after RP. This will help clinicians identify patients with poor prognosis early and facilitate the development or modification of personalized treatment plans. However, our study has certain limitations. First, this is a retrospective study based on public databases, and more reliable prospective studies are needed to support our results. Second, we did not investigate the functional roles and potential mechanisms of the 21 genes in PCa.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research indicates that the classification of PCa based on histone modifications is of significant importance. Furthermore, a HIS_score predictive model was established that can effectively predict the prognosis of patients with PCa after RP.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiochemical recurrence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy number variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisease-free interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpithelial-mesenchymal transition\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\"\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\"\u003ePCa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgression-free interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate-specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadical prostatectomy\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\"\u003eTIDE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor immune dysfunction and exclusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor mutational burden\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe public data used in this study are available at:\u003c/p\u003e\n\u003cp\u003eTCGA-PRAD (https://xenabrowser.net/datapages/?cohort=TCGA Prostate Cancer\u003c/p\u003e\n\u003cp\u003e(PRAD);\u003c/p\u003e\n\u003cp\u003eGSE70770 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70770).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the databases involved in this study for their contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Natural Science Foundation of Guangdong Province (No. 2021A1515010700 to Zhenhua Huang), National Natural Science Foundation of China (No. 82073375 to Xiaoxiang Rong), China Postdoctoral Science Foundation (No. 2019M663001 to Xiaoxiang Rong) and the Science and Technology Planning Project of Guangzhou (No. 202102020532 to Xiaoxiang Rong).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affililiations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China\u003c/p\u003e\n\u003cp\u003eXuee Zhou, Xiaolin Li, Jiahong Hong, Fuli Xie, Kuncai Liu, Yue Huang, Ya Gao, Xiaoxiang Rong, Rui Zhou \u0026amp; Zhenhua Huang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZH, RZ, and XR contributed to the planning of the study. XZ and XL analyzed the data and wrote the manuscript. XZ, XL, JH, KL, FX, YH, YG prepared all the figures and tables. ZH, RZ, and XR revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xiaoxiang Rong, Rui Zhou and Zhenhua Huang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Research Ethics Committee of Nanfang Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. 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J Immunol (Baltimore Md: 1950). 2008;180(4):2011\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraf RP, Eskander R, Brueggeman L, Stupack DG. Association of Copy Number Variation Signature and Survival in Patients With Serous Ovarian Cancer. JAMA Netw open. 2021;4(6):e2114162.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHieronymus H, Murali R, Tin A, Yadav K, Abida W, Moller H, Berney D, Scher H, Carver B, Scardino P et al. Tumor copy number alteration burden is a pan-cancer prognostic factor associated with recurrence and death. \u003cem\u003eeLife\u003c/em\u003e 2018, 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu J, Weise AM, Falany JL, Falany CN, Thibodeau BJ, Miller FR, Kocarek TA, Runge-Morris M. Expression of estrogenicity genes in a lineage cell culture model of human breast cancer progression. Breast Cancer Res Treat. 2010;120(1):35\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerabe M, Matsui S, Noben-Trauth N, Chen H, Watson C, Donaldson DD, Carbone DP, Paul WE, Berzofsky JA. NKT cell-mediated repression of tumor immunosurveillance by IL-13 and the IL-4R-STAT6 pathway. Nat Immunol. 2000;1(6):515\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakimura R, Tanaka K, Yamamoto S, Matsunobu T, Li X, Hanada M, Okada T, Nakamura T, Li Y, Iwamoto Y. The effects of histone deacetylase inhibitors on the induction of differentiation in chondrosarcoma cells. Clin cancer research: official J Am Association Cancer Res. 2007;13(1):275\u0026ndash;82.\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 modification, prognostic prediction, prostate cancer","lastPublishedDoi":"10.21203/rs.3.rs-3298585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3298585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Prostate cancer (PCa) is one of the most common malignant tumors in males, with a high recurrence rate and poor prognosis. Therefore, accurately predicting the prognosis of PCa patients and intervening as early as possible is of great significance. We aimed to establish a gene feature model based on histone modifications to predict the prognosis of patients with PCa after radical prostatectomy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Clinical data on PCa patients was obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases and was comprehensively evaluated. Expression subtypes of histone-modifying factors were identified by unsupervised clustering, and the molecular characteristics and functions of each subtype were explored. Subsequently, a risk-scoring model was constructed to characterize its impact on the prognosis of PCapatients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Combined with histone modification factor signatures, we identified four PCa subtypes with different prognoses, biological functions, and mutational characteristics. Based on a series of analysis and screening, 21 characteristic genes (\u003cem\u003eMXD3\u003c/em\u003e, \u003cem\u003eCCDC28B\u003c/em\u003e, \u003cem\u003eCOL11A2\u003c/em\u003e, \u003cem\u003eSLC39A5\u003c/em\u003e, \u003cem\u003eGPT\u003c/em\u003e, \u003cem\u003eDNASE1L2\u003c/em\u003e, \u003cem\u003ePIF1\u003c/em\u003e, \u003cem\u003eKRTAP5-9\u003c/em\u003e, \u003cem\u003eTTLL10\u003c/em\u003e, \u003cem\u003eKRTAP5-1\u003c/em\u003e, \u003cem\u003eKRTAP5-10\u003c/em\u003e, \u003cem\u003eHAGHL\u003c/em\u003e, \u003cem\u003eMSLNL\u003c/em\u003e, \u003cem\u003eAMH\u003c/em\u003e, \u003cem\u003eNKAIN4\u003c/em\u003e, \u003cem\u003eCCDC114\u003c/em\u003e, \u003cem\u003eSLC9A3\u003c/em\u003e, \u003cem\u003eSULT1E1\u003c/em\u003e, \u003cem\u003eSLC6A14\u003c/em\u003e, \u003cem\u003eALB\u003c/em\u003e, and \u003cem\u003eRPE65\u003c/em\u003e) were used to establish a risk score model (HIS_score). Patients in the high-score group had worse outcomes than those in the low-score group. Additionally, we found that the HIS_score model can distinguish subgroups of PCa samples with different biological and immune characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The HIS_score model with 21 genes as features is a promising tool that is of great significance for clinicians to predict the prognosis of PCa patients after radical prostatectomy and develop personalized treatment plans early.\u003c/p\u003e","manuscriptTitle":"A gene feature based on histone modifications can predict the prognosis of prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-31 16:43:29","doi":"10.21203/rs.3.rs-3298585/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":"7dd216c3-e694-4be6-8c5c-baf13811135c","owner":[],"postedDate":"August 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-03T12:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-31 16:43:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3298585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3298585","identity":"rs-3298585","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}