The Histone Acetylation-Related Gene Signature for Prediction of Prognosis and Immunotherapy Efficacy in Stomach Adenocarcinoma and Verification in vitro

The Histone Acetylation-Related Gene Signature for Prediction of Prognosis and Immunotherapy Efficacy in Stomach Adenocarcinoma and Verification in vitro | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Histone Acetylation-Related Gene Signature for Prediction of Prognosis and Immunotherapy Efficacy in Stomach Adenocarcinoma and Verification in vitro Chen Dai, Rishun Su, Zhenzhen Zhao, Yangyang Guo, Songcheng Yin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4689949/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 Gastric cancer (GC) is a very aggressive, with extreme heterogeneity and rapid growth, most frequently manifested histologically as stomach adenocarcinoma (STAD). Current evidence suggests that histone acetylation is critical for the origin and development of tumors. However, the significance of histone acetylationrelated gene signatures for prognosis of STAD patients and mechanisms of histone acetylation in STAD therapy remains unclear. Methods We identified histone acetylationrelated genes in STAD from TCGA and constructed eight-gene signatures by utilizing a univariate Cox regression model with the Least Absolute Shrinkage and Selection Operator (LASSO). In addition, a nomogram was plotted to predict the prognostic significance of the established risk model. We examined associations between our gene signature and somatic mutation, immune subtype, clinicopathological features, tumor microenvironment, immune cell infiltration and immune activity, immunotherapy prediction and drug sensitivity. Cell-based assays were performed to determine the relationship between Doublecortin Like Kinase 1 (DCLK1) and the proliferation, migration and oxaliplatin resistance of GC cells in vitro . Results A prognostic model composed of eight histone acetylationrelated genes in STAD was developed. Based on median risk score, the STAD patients were equally assigned into two groups of high- and low-risk, where high-risk represented a less favorable prognosis than low-risk. The two groups showed significant differences with respect to somatic mutation, immune subtype, clinicopathological features, tumor microenvironment, immune cell infiltration and immune activity, immunotherapy prediction and drug sensitivity. The results generated during Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that Differentially Expressed Genes (DEGs) in the two groups were involved in cancer-related processes and pathways. Cell-based assays indicated that DCLK1 is a promoting factor in gastric cancer and can promote oxaliplatin resistance in gastric cancer cells. Conclusions A novel histone acetylationrelated gene signature, which possesses potential value in predicting the prognosis and immunotherapy effectiveness regarding STAD patients, was developed. This signature may serve as a reliable biomarker for prognosis of STAD and promote the identification of novel treatment targets for STAD. Furthermore, DCLK1 exhibited oncogenic roles and may be a new target for STAD therapy. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Gastric cancer (GC) is a very aggressive cancer, with extreme heterogeneity and rapid growth, and high incidence rate and mortality rates [ 1 , 2 ]. Annual diagnoses are over 1 million worldwide, with annual fatalities reaching 76,000 based on the Global Cancer Report 2020 [ 1 ]. The most frequent histological type of GC is stomach adenocarcinoma (STAD). Although substantial clinical advances including radical surgery, chemotherapy, radiotherapy, molecularly targeted drugs have been applied in early therapy of patients with GC over the past several decades, diagnostic efficiency and therapeutic effects of advanced GC remain unsatisfactory and the five-year survival rate of advanced GC has been slow to improve [ 3 – 5 ]. Therefore, reliable novel biomarkers and prognostic models that can be used to improve prognostic predictions and reasonable therapeutic strategies are urgently required for patients with STAD. Epigenetic alterations, including DNA methylation, histone modifications and chromatin remodeling refer to heritable phenotype changes in gene expression with the DNA sequence unchanged [ 6 , 7 ]. As one of epigenetic alterations, histone modifications vary, including methylation, acetylation, ubiquitination and phosphorylation [ 8 ]. Acetylation modification of histone is a reversible dynamic process that achieves a balance between histone acetyltransferases (HATs) and histone deacetylases (HDACs) [ 9 , 10 ]. HATs catalyze the transfer of acetyl from acetyl coenzyme-A to N-terminal lysine in histone, which facilitates the binding of transcription factors to promoters and promotes the transcription of genes [ 11 – 13 ]. In contrast, HDAC catalyze hydrolysis of acetyl from lysine, generating opposite results of HAT [ 14 ]. Current research indicates that histone acetylation are important for tumor origin and progression [ 15 , 16 ]. For example, Wisnieski et al. showed that acetylation levels increased in some regions of CDKN1A and these modifications were significantly associated with gastric cancer [ 17 ]. Tani et al. noted that histone H3 and H4 acetylation play an important role in the abnormal expression of MYO18B, contributing to the origin and progression of lung cancer. [ 18 ]. Falahi et al. showed that decrease of H4K16 (monoacetylated lysine 16 of histone H4) acetylation is closely associated with the early process of breast cancer [ 19 ]. Other studies have shown that HAT/HDAC inhibitors, as a class of newly developing antitumor drugs, may alter the expression of oncogenes or tumor suppressor genes by changing histone acetylation levels. However, the role of histone acetylation-related gene signature in prognosis of patients with STAD and mechanisms of histone acetylation in STAD therapy remain unclear. Therefore, we performed a systematic study to classify TCGA-STAD patients based on histone acetylation-related genes and screen prognostic significance of histone acetylation-related DEGs between two clusters. We also established histone acetylation-related gene signature to obtain the risk score for evaluating prognosis and conducted functional enrichment analysis to investigate the underlying mechanisms. The association between our gene signature and somatic mutation, tumor microenvironment, immune cell infiltration, immune activity and drug sensitivity were explored to determine novel strategies for targeted treatment of STAD. We selected the gene with the largest correlation coefficient in the prognostic model for subsequent experimental analysis and assessment. Materials and Methods Data Collection Transcriptome sequencing data of STAD patients and matching clinical features were sourced from the TCGA database. Validation cohorts were acquired from GSE84437. Tumor Classification Based on Histone Acetylationrelated DEGs We used the R package “ConsensusClusterPlus” to further investigate the associations between histone acetylationrelated genes and STAD subtypes and determine number and stability of clusters. Based on the cumulative distribution functions, the optimal cluster number of k-value was obtained [20]. The KM survival analysis was conducted using the R package “survival.” Differential genes between the two clusters and histone acetylationrelated genes are shown in Supplementary Table 1 and Table 2. Establishment and Verification of Prognostic Histone AcetylationRelated Gene Signature The univariate Cox regression algorithm was employed to filter out histone acetylationrelated genes independently related to prognosis, and LASSO Cox regression was carried out to further narrow the range of candidate genes using the R package “glmnet.” The risk scores were calculated for STAD patients as Risk Score= (X: coefficients, Y: gene expression level), and based on the middle value, the STAD patients were equally assigned into low- and high-risk groups. Then, Kaplan-Meier analysis was conducted, and PCA, t-SNE and ROC curves were plotted. The R package “pheatmap” was applied to draw the heatmap and applied the GSE84437 cohort to validate the risk signature. The prognostic significance of the signature was analyzed with univariate and multivariable Cox regression. The Eight-Gene Prognostic Model is shown in Supplementary Table 3. Establishment of Nomogram A nomogram based on independent prognostic parameters was developed using R package “rms” and “regplot” for STAD patients to predict the survival probability. In addition, calibration curve and AUC were applied to evaluate the nomogram regarding accuracy, and decision curves analysis was also performed to confirm the model with respect to its clinical effectiveness. Somatic Mutation, Tumor Immunity, Immunotherapy Prediction and Drug Susceptibility Analysis The TCGA database was used to retrieve sample mutation data, and the R package “maftools” was used to estimate the mutation status between the two groups. Immune, Stromal and ESTIMATE scores were applied to evaluate association between the risk score and tumor microenvironment (TME) with “ESTIMATE” R package. Immune cell subtype infiltration values of TCGA-STAD dataset samples were based on seven algorithms: XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS and CIBERSORT [ 21 – 30 ]. The correlation between infiltration immune cell subtypes and risk scores was established through the application of Spearman correlation analysis with R packages “ggpubr.” In addition, different immune-checkpoint genes in our two risk groups were confirmed with respect to their expression by using R packages “limma.” MSI, TMB, TIDE scores and the response to CTLA4 inhibitor were performed to assess and predict immunotherapy effectiveness of our prognostic signature. IC50 (Half-maximal inhibitory concentration) was used to determine the relationship between our prognostic signature and various drug susceptibility. Functional Enrichment Analysis GO and KEGG analyses using “clusterProfiler” R package were performed to examine the mechanisms of the signature, and GSEA analysis was used to elucidate signaling pathways. Cell Lines and Cell Culture The human immortalized normal gastric epithelial cell (GES1) and gastric adenocarcinoma cell lines (MKN-28 and HGC-27) were provided by the Chinese Academy of Sciences (Shanghai). These cells were maintained at 37°C and in 5% CO 2 in RPMI-1640 (Gibco, USA) medium with 10% fetal bovine serum (Gibco, USA) and 1% penicillin-streptomycin. Over one year, Oxaliplatin-resistant GC cell line HGC-27R was established stepwise through constant exposure to increasing quantities of Oxaliplatin and culture in RPMI-1640 (Gibco, USA) medium with oxaliplatin (1 uM). Cell Transfection The small interfering RNAs (siRNAs) targeting DCLK1 and scrambled siRNA nontarget control (Si-NC) were supplied by TRANSHEEPBIO (China), and their sequences are as follows: Si-DCLK1-1: sense UCUGUCGGAUAACGUGAAUUU and antisense AAAUUCACGUUAUCCGACAGA; Si-DCLK1-2: sense ACCCGAACUCUGUCGGAUAAC and antisense GUUAUCCGACAGAGUUCGGGU. Six-well plates were seeded with GC cell at a density of 2.0 × 10 5 /well and then Namipo™ was used to assist transfection in accordance with the instructions from manufacturer. Isolation of Total RNA With Quantitative Reverse Transcription RealTime Polymerase Chain Reaction (QRTPCR) Total RNA from cell lines was obtained with RNA Isolater Total RNA Extraction Reagent (Vazyme, Nanjing, China) in accordance with the instructions from manufacturer. Subsequently, cDNA was generated utilizing the Evo M-MLV RT Premix kit (Accurate Biotechnology). Then, qRT-PCR assay was performed using SYBR Green Master Mix (Vazyme), with beta-actin as the normalization control and the 2 − ΔΔCt method for the quantification of RNA expression levels. The primer sequences (5′-3′) were as follows: DCLK1 forward, ACTTCGACGAGCGGGATAAG and reverse, GGGCCTCAAAAGATCGGAACC; β-Actin forward, CACCATTGGCAATGAGCGGTTC and reverse, AGGTCTTTGCGGATGTCCACGT. Cell Proliferation Assay Cell viability and proliferation were assayed by Cell Counting Kit-8 kit (MeilunBio, Guangzhou, China) following the instructions from manufacturer. On the first day, 2 × 10 3 cells per well were seeded into 96-well plates for culture. Then on day 1, 2, 3, 4 and 5 follow cell seeding, the plates were added with 10 ul of CCK-8 reagent into each of wells and incubated for 1 h at 37°C to detect cell viability. The 450-nm optical absorption values were recorded using a microplate reader (Thermo, U.S.A.) on days 1, 2, 3, 4, and 5. Cytotoxicity of Oxaliplatin In Vitro Each well of a 96 well plate was seeded with 10,000 cells and treated with different concentrations of oxaliplatin (Aladdin, Shanghai) after 24 h. The Cell Counting Kit-8 (CCK8) (MeilunBio, Guangzhou, China) was employed to assess cell viability 48 h following the addition of oxaliplatin. The growth-inhibitory curves of oxaliplatin-resistant HGC-27 cells versus the parental cells were drawn, and the IC50 of oxaliplatin of each cell line was calculated. Colony-formation Assay The indicated cells (1000 cells/well) were plated into six-well plates and the plates were then cultured at 37°C for 10 to 14 days. After 15-min fixation with formaldehyde (10%), cells were stained with 1.0% crystal violet for 30 s before counting the colonies. Finally, cell clones (> 50 cells) were counted and analyzed. Wound Healing Assay For wound healing assay, cells were inoculated into 6-well plates for culture to form a confluent layer and wounds were created by scraping using sterile tips. The cells were then rinsed thoroughly with PBS and further cultured in serum-free medium. Images were acquired at 0 and 24 h with microscope and the rate of cell migration was obtained by Image J. Apoptosis Assay For apoptosis analysis, the cells were seeded into plates of six wells and then treated with oxaliplatin (Aladdin, Shanghai) for 24 h; afterward, they were digested by trypsin without addition of EDTA and washed for two times using PBS; after addition of AnnexinV- FITC and PI staining solution, they were incubated at ambient temperature in dark place for 15 min. Then, the apoptotic rate was determined with use of the flow cytometry. Statistical Analysis All statistical analyses were completed using Prism (Version 10.1) and R (Version 4.2.1) software. One-way analysis of variance (ANOVA) and unpaired Student’s t-test were adopted to assess differences between groups. A two-tailed P < 0.05 was set to determine statistical significance. Results STAD Classification Pattern Based on Histone AcetylationRelated Genes To analyze the connections between the histone acetylationrelated genes and STAD, consensus clustering analysis was performed in the TCGA-STAD cohort for STAD patients. The results of the CDF curves and tracking plot showed that the optimal cluster number was K = 2 (Fig. 1 A, B). Figure 1 C demonstrated that the maximum intra-group correlations with low inter-group correlations were observed when K = 2 and STAD patients could be split into two clusters according to differential expression patterns. K-M analysis was performed for comparison of overall survival advantage between two clusters. As indicated in Fig. 1 D, the survival probability was higher in cluster 1 (C1) than in cluster 2 (C2) (P = 0.021). Then, the immune checkpoint-related genes between the two clusters were examined with respect to their expression, and there were 12 DEGs (CD244, CSF1R, TGFBR1, BTLA, CTLA4, IL10, CD160, PDCD1LG2, KDR, CD96, CD274 and HAVCR2) between C1 and C2 (Fig. 1 E). These 12 genes showed higher expression levels in C2 (Fig. 1 E).In addition, we investigated the difference of TMB and MSI between two clusters and results indicated that C1 had higher TMB and MSI than C2 (Fig. 1 F, G). We also analyzed different immune cell infiltration level between C1 and C2. The results in Fig. 2 A-C showed that C2 have higher ImmuneScore, StromalScore and ESTIMATEScore. Furthermore, we found that significant differences exist in some immune cell types including CD8 T cells, Cytotoxic lymphocytes, Endothelial cells, Monocytic lineage, Myeloid dendritic cells and NK cells with a p value < 0.05 for all, specifically, 0.047, 0.019, 0.036, 0.0019, 6e-05 and 0.0002, respectively, and C2 had more these types of immune cell (Fig. 2 D-M). Construction of Eight-Gene Prognostic Model We screened out the differentially expressed genes between C1 and C2 through the “limma” package. Then prognostic significance of histone acetylation-related DEGs was assessed by using univariate Cox regression analyses. The 10 genes (ASCL2, ADH1B, GPR87, F13A1, HDAC11, DCLK1, GCG, FABP4, AXIN2, OGN) were retained for subsequent analysis (Fig. 3 A). Using LASSO Cox regression, a signature of eight genes was generated based on optimum λ value (Fig. 3 B, C). These 8 genes (ASCL2, GPR87, F13A1, HDAC11, DCLK1, GCG, FABP4, AXIN2) were regarded as prognostic factors for STAD, and for which the calculation of risk score was: risk score = (− 0.016*ASCL2 exp.) + (0.108*GPR87 exp.) + (0.052*F13A1 exp.) + (− 0.085*HDAC11 exp.) + (0.111*DCLK1 exp.) + (0.088*GCG exp.) + (0.024*FABP4 exp.) + (− 0.088*AXIN2 exp.). In addition, division of STAD patients was conducted as two groups of high- and low-risk on the basis of their median risk score (Fig. 3 D). Patients with STAD in the high-risk one possessed a greater risk of death and less survival time (Fig. 3 E). The expression patterns of these eight prognostic genes between the groups were demonstrated by heatmap (Fig. 3 F). Patients with STAD in the high-risk group presented a lower overall survival (OS) than those in the low-risk group (Fig. 3 G). Figure 3 H indicated that the area under the ROC curve was 0.658, 0.699 and 0.632, respectively, at 1 year, 3 years and 5 years. The PCA and t-SNE analysis suggested that patients with varying risk levels in STAD can be effectively divided into two directions (Fig. 3 I, J). Validation of the Risk Signature The GSE84437 dataset was used for validation of robustness of our signature. Similar to the previously described results, STAD patients in GSE84437 dataset were also assigned into two groups of low- and high-risk in light of the median risk score (Fig. 3 K). The STAD patients with different risks could be assigned into two directions, which was confirmed by PCA and t-SNE analysis (Fig. 3 P, Q). The patients showed a greater risk of death, less survival time and decreased OS in the high-risk group than those in the low-risk group (Fig. 3 L, N). The expression patterns of eight prognostic genes between the two groups were shown by heatmap (Fig. 3 M). The area under the ROC curve reached 0.632 at 1 year, 0.624 at 3 years, and 0.680 at 5 years (Fig. 3 O). Independent Prognostic Value of the Risk Signature To determine the feasibility of this novel gene signature being used as a prognostic predictor independent of other factors, the Cox regression of univariate and multivariate was carried out. For the univariate analysis, we discovered that the identification of risk score as an independent prognostic predictor for STAD patients was demonstrated in both the TCGA cohort (HR = 4.015, 95% CI: 2.038–7.911, Fig. 4 A) and GEO cohort (HR = 1.998, 95% CI: 1.073–3.719, Fig. 4 C). For the multivariate analysis similar results were found, indicating that the risk score is an independent prognostic predictor (TCGA cohort: HR = 4.686, 95% CI: 2.223–9.875, Fig. 4 B; GEO cohort: HR = 2.238, 95% CI: 1.216–4.121, Fig. 4 D). A nomogram was established to predict 1, 3, and 5-year OS in TCGA-STAD cohort (Fig. 4 E). The calibration curve confirmed that the nomogram operated with an extremely high consistency with an ideal model (Fig. 4 F). The results of the ROC curves showed that the nomogram had the largest AUC (AUC = 0.694) than other clinicopathological features (Fig. 4 G). Decision curves of Fig. 4 H also showed the nomogram presented larger net benefits. Correlations Between Somatic Mutation, Immune Subtype, Clinicopathological Features and Risk Score We investigated different somatic mutation landscapes between TCGA-STAD patients defined as high- and low-risk (Fig. 5 A, B). Patients in the low-risk group had higher mutation rates than the high-risk group (94.59 vs 81.36%). In terms of gene mutation frequency, TTN, TP53 and MUC16 were the most altered gene. The most frequent mutation was missense mutation in patients with STAD. According to Fig. 5 C, risk score and different immune subtypes were conspicuously correlated with each other. In addition, a positive correlation was also present between the risk score and lymph node metastasis, distant metastasis, clinical stage and tumor grade except for age and T stage (Fig. 5 D-I). The K-M curves implied that high-risk patients exhibited a less favorable prognosis than those classified as low-risk in age > 65, age < = 65, Female, Male, G3, M0, N1-3, Stage III-IV and T3-4 (Fig. 6 ). Relationships Between Risk Score and Tumor Microenvironment, Immune Cell Infiltration and Immune Activity The relationships between risk score and TME were determined, using calculations of Immune, Stromal and ESTIMATE scores. As shown in Fig. 7 A-C, the Immune, Stromal and ESTIMATE scores were higher in the high-risk group. Based on seven known algorithms, the correlation of tumor-infiltrating immune cells to the risk score was analyzed (Fig. 7 D). Since tumor-associated macrophages (TAMs) are critical in TME, we explored the relationships between our risk score and TAMs. Figure 7 E implied that the level of M2 macrophages was higher in the high-risk group than in the low-risk group. Furthermore, results of functional analysis suggested that our histone acetylation-related gene signature was notably associated with immune activity and the high-risk subgroup exhibited elevated infiltrating levels of most immune cells except for Th2_cells (Fig. 7 F). Figure 7 G proved that the high-risk group showed more activity for most immune pathways excepting MHC_class_I, which showed less activity. The activity of APC_co_inhibition pathway revealed no significant difference between two subgroups (Fig. 7 G). Immunotherapy Prediction As shown in Fig. 8 A, 15 immune checkpoint-related genes (HAVCR2, CD274, ADORA2A, CD96, KDR, PDCD1LG2, TGFB1, CD160, IL10, TIGHT, BTLA, TGFBR1, CSF1R, CD244 and LAG3) were conspicuously overexpressed in the high-risk group, excepting one immune checkpoint-related gene (NECTIN2), which was obviously overexpressed in the low-risk group. Then, we investigated the different MSI status (MSS, MSI-H and MSI-L) between two subgroups. The data suggested that the MSI of high-risk group is lower (Fig. 8 B). We also observed that in the high-risk group, the TMB was lower, and there was a negative correlation between risk score and TMB (R=-0.29, P = 1.9e-08) (Fig. 8 C, D). Figure 8 E shows that the high-risk group had remarkably high TIDE scores than the low-risk group through the use of the TIDE algorithm, indicating that the low-risk group may have more immunotherapeutic effects than the high-risk group. Furthermore, TCIA immunotherapy prediction showed that the low-risk patients had a superior response to CTLA4 inhibitor (Fig. 8 F). Drug Sensitivity Based on the Prognostic Signature IC50 was used to estimate the difference between the two risk subgroups for 12 drugs. Our findings observed that patients from the high-risk group showed a higher estimated IC50 for five drugs including Doxorubicin, Etoposide, Gefitinib, GSK690693 and KIN001-266 but showed a lower estimated IC50 for the remaining seven drugs including AMG-706, CGP-60474, Cyclopamine, Dasatinib, JNK Inhibitor VIII, PIK-93 and PD-173074 (Fig. 9 ). Functional Analyses To analyze the differences between the two groups regarding gene functions and enrichment, we extracted DEGs between the two groups with use of R package (“limma”). With these DEGs, GO and KEGG analyses were performed, and as shown in Fig. 10 A-D. The results of GO analysis showed that the DEGs were related to muscle system process, etc. in BP, collagen-containing extracellular matrix, etc. in CC, receptor ligand activity, etc. in MF (Fig. 10 A, C). According to the data from KEGG, the DEGs were abundant in cytokine-cytokine receptor interaction, signaling pathways of cGMP-PKG and Wnt (Fig. 10 B, D). GSEA analysis was used to find which pathways were enriched in the two groups and the results are shown in Figs. 10 E, and 10 F. The cell adhesion molecules cams, cytokine-cytokine receptor interaction and dilated cardiomyopathy were conspicuously enriched in the high-risk group. Figure 10 F showed prominently enriched arginine and proline metabolism, cell cycle and DNA replication in the low-risk group. Effects of DCLK1 On The proliferation, Migration and Oxaliplatin Resistance Of gastric Cancer Cells In vitro Among the eight histone acetylation-related genes, DCLK1 had the largest correlation coefficient in the prognostic model. Therefore, we carried out in vitro analysis to determine the functions of DCLK1 in GC cells. The RT-qPCR experimental results suggested that the expression of DCLK1 was significantly higher in MKN-28 and HGC-27 of GC cell lines than in the normal human gastric epithelial cell line GES-1 (Fig. 11 A). To determine whether DCLK1 affected progression in gastric cancer cells, we designed siRNA to silence DCLK expression in MKN-28 and HGC-27 cells. The efficiency of DCLK1 silencing in MKN-28 and HGC-27 was confirmed through RT-qPCR (Fig. 11 B). Following the successful gene knockdown demonstrated previously, the CCK-8 assay was performed to validate the effects of DCLK1 on the proliferation and viability of MKN-28 and HGC-27 cells. The results proved that the proliferation of these two gastric cancer cell lines were impaired after knocking down DCLK1 compared to the Si-NC group (Fig. 11 C). Furthermore, colony-formation experiments showed that colony formation was lower in the DCLK1 down-regulation group (Fig. 11 D). Figure 11 E also manifested that the migration of HGC-27 was weakened in si-DCLK1 groups. Since oxaliplatin is commonly used as a first-line chemotherapeutic agent for gastric cancer, we proceeded to investigate the impact of DCLK1 expression on the effectiveness of this chemotherapy drug. To check the DCLK1 expression in Oxaliplatin (Oxa)-resistant GC cells, RT-PCR was performed (HGC-27/Oxa), and the findings manifested that DCLK1 was more expressed in Oxa-resistant GC cells than in the parental ones (Fig. 11 F). When treated with increasing levels of Oxaliplatin, HGC-27/Oxa cells exhibited greater viability (Fig. 11 G). In addition, when comparing the DCLK1 knockdown transfection group to the Si-NC group, the 50% maximal inhibitory concentration (IC50) value of oxaliplatin was lower in si-DCLK1 group (Fig. 11 H). The apoptotic rate of HGC-27 and MKN-28 cells treated with Oxaliplatin increased through the suppression of DCLK1. (Fig. 11 I). In summary, the knockdown of DCLK1 could repress gastric cancer cell in terms of cell proliferation, migration and oxaliplatin resistance. Discussion Stomach adenocarcinoma (STAD) accounts for the most gastric cancer cases. Current research indicates that histone acetylation is a pivotal factor in the occurrence and progression of STAD. Deng et al. suggested that over-expression of EGFL7 were found in gastric cancer cell lines and the potential mechanism was the hanging of histone acetylation levels in the EGFL7 promoter caused by MALAT1 [ 31 ]. Yamamura’s research demonstrated that hyperacetylated status occurred in histones H3 and H4 in the GATA4-positive gastric cancer cells, using CHIP assay [ 32 ]. Wisnieski et al. found that BMP8B had increased acetylated H3K9 and H4K16 levels in gastric cancer and abnormal expression of BMP8B was related to poorly differentiated gastric cancer [ 33 ]. However, current research generally focuses on the relationship between histone acetylation level of single or several genes and STAD, and neglects a systematic development of a prognostic model to explore the associations between histone acetylation-related genes and STAD. Therefore, a model derived from multiple histone acetylation-related genes is urgently required for STAD patients. In our study, we first used consensus clustering analysis to determine two molecular subtypes based on 40 known histone acetylationrelated genes [34]. The findings indicated that C2 had worse prognosis, lower TMB and MSI, higher and immune cell infiltration level versus C1. Then, we screened out DEGs between C1 and C2 and filtered out prognostic significance of these DEGs. Through utilization of the LASSO Cox regression model, we generated a signature composed of eight genes (ASCL2, GPR87, F13A1, HDAC11, DCLK1, GCG, FABP4, AXIN2). ASCL2 (Achaete scute-like 2) belongs to the basic helix-loop-helix (BHLH) family of transcription factors [35]. As an indispensable downstream element of Wnt/β-catenin signaling pathway, ASCL2 is closely associated with the occurrence and development of different cancers [36]. ASCL2 was an independent indicator in recurrent breast cancer patients, which can also estimate the risk of relapse in breast cancer [37]. ZUO et al. found that ASCL2 affected the process of metastasis in gastric cancer by regulating miR223 expression [38]. GPCRs (G protein-coupled receptors), a member of membrane signaling proteins in eukaryote, are encoded by about 800 genes and participate in different diseases[ 39 , 40 ]. GPR87 (G protein-coupled receptor 87) is one of the GPCR family and is in the chromosome 3q24 [41]. Recent studies have confirmed that GPR87 is overexpressed in several malignancies such as pancreatic cancer [42], lung cancer [43] and bladder cancer [44]. Bai’s research proved that GPR87 might participate in tumorigenesis and progression of lung cancer, which may also be related to immune infiltration [45].One study detected that GPR87, as an oncogene, is expressed in pancreatic cancer cells and promotes cancer proliferation and metastasis by driving the NF-κB signaling pathway [42]. F13A1 encodes coagulation factor XIII A subunit. This subunit has catalytic functions and acts as a plasma carrier molecule [46]. Research has demonstrated the presence of abnormal expression of F13A1 in tumors. In bladder cancer, over-expression of F13A1 was correlated with worse prognoses [47]. HDACs (histone deacetylases) are epigenetic regulators that are recruited by co-repressors or transcriptional complexes to gene promoters and participate in the regulation of gene transcription [48]. HDACs are grouped into four principal classes, among which, HDAC11, the newly discovered HDAC enzyme, has been classified into Class IV HDAC and was regarded as a key factor in metabolism, obesity and immune functions[ 49 , 50 – 52 ]. However, HDAC11 has been demonstrated to participate in tumor development and progression. The study from Bi et al showed that HDAC11 can regulate the process of glycolysis and stemness of hepatocellular carcinoma through the LKB1/ AMPK signaling pathway [53]. A pan-cancer analysis indicated that HDAC11 is aberrantly expressed in multiple cancers and markedly correlated with survival outcomes of patients with different cancers [54]. DCLK1 (doublecortin like kinase 1), which belongs to the protein kinase superfamily and the doublecortin family, is present on chromosome 13q13-q14.1 and was initially recognized as a crucial regulator of neurogenesis and neuronal migration [55]. Recent research suggests that DCLK1 displays the characteristics of cancer stem cells (CSC) and was highly expressed in several cancers [ 56 – 60 ]. Furthermore, research indicates that DCLK1 is essential for tumor progression, angiogenesis and epithelial-mesenchymal transition [61]. GCG (glucagon) is a protein that is included in four distinct mature peptides [62]. Research indicates that GCG and GLP-1 play a critical role in glucose metabolism and diabetes[ 63 , 64 ]. FABP4 belongs to the family of fatty acid-binding proteins (FABPs), which are small molecule proteins that can transport hydrophobic and bioactive fatty acids [65]. Studies of FABP4 have focused on patient obesity, because of its aberrant expression in adipose tissue and differentiated adipocytes and macrophages[ 66 , 67 ]. Recent research has focused on the relationship between FABP4 and tumors. Tian et al. suggested that upregulated FABP4 facilitates the migration and invasion of colon cancer cells by facilitating FAs transport and activating AKT pathway and EMT [68]. In addition, exogenous FABP4 played a vital role in breast cancer progression and regulated fatty acid transport proteins expression [69]. The AXIN2 gene, located on chromosome 17q24, has been reported to have an indispensable role in Wnt signaling pathway[ 70 , 71 ]. Research has found abnormally expressed AXIN2 in cancers such as hepatocellular-cholangiocarcinoma, colon cancer and lung cancer [ 72 – 74 ]. After our histone acetylationrelated gene signature was constructed, ROC analysis was conducted to confirm the prognostic signature with respect to the sensitivity and specificity. The signature enabled us to divide STAD-TCGA patients into two groups of high- and low-risk in light of their median risk score. We found that STAD patients in the high-risk group had significantly poorer survival outcomes than those in the low-risk group. Then, GEO database (GSE84437) was employed for validation of the risk signature. Furthermore, the results obtained from Cox regression of univariate and multivariable proved that our risk signature was a prognostic indicator independent from other factors for STAD patients. After that, a nomogram was constructed and calibration plots confirmed that this nomogram could accurately predict survival probability for patients with STAD. Recently, mutations identification is regarded as a critical step in cancer risk assessment and some published research implied somatic mutation plays pivotal roles in STAD [ 75 , 76 ]. Therefore, we investigated the mutation landscape of our gene signature, and then found that the mutation rate is higher in the low-risk group than in the high-risk one and TTN, TP53 and MUC16 are the most altered gene. In addition, missense mutation was the most frequent mutation in patients with STAD. These findings offer novel indicators for research on how STAD and somatic mutations are correlated with each other and for the formulation of more precise treatments in those STAD patients with gene mutations. In addition, there was a positive correlation between the risk score and lymph node metastasis, distant metastasis, clinical stage and tumor grade except for age and T stage. Tumor immunity is an important focus of cancer research, with some findings indicating strong relationships between histone acetylation and tumor immunity. Therefore, we examined the relationships between risk score and tumor microenvironment, immune cell infiltration and immune activity. Our findings suggested that the high-risk group had increased levels of infiltrating immune cells, enhanced activity of immune pathways and higher TME scores compared with the low-risk group. Tumor-associated macrophages (TAMs) were composed mostly of M0, M1 and M2 phenotypes, which have generally been found to promote cancer progression and development [ 77 , 78 ]. Research has defined M2 macrophages that play immunosuppressive and tumor promoting roles as TAMs. Our findings suggest that the level of macrophages M2 is higher in the high-risk group. This result could be a significant contributing factor to the unfavorable prognosis observed in the high-risk population. Cancer immunotherapy has made advances in the field of cancer treatments [ 79 ]. Thus, we analyzed the immunotherapy prediction of our histone acetylationrelated gene signature, and observed that high-risk group possessed lower MSI, lower TMB and higher TIDE scores. TCIA immunotherapy prediction revealed patients in the high-risk group had an inferior response to CTLA4 inhibitor. These findings confirmed that the low-risk group had a greater probability of responding effectively to immunotherapy than the high-risk group. The data regarding drug sensitivity provided the basis for the formulation of a more accurate treatment for STAD. We also performed functional analyses and found that DEGs in the two groups of high- and low-risk patients participated in cancer-related processes and pathways. For example, the Wnt signaling pathway and cGMP-PKG signaling pathway are strongly associated with the occurrence and progression of gastric cancer. Xiang et al. indicated that infection of H. pylori relies on ZEB1 to induce the over-expression of PRTG, which subsequently leads to the advancement of gastric cancer through activation of the cGMP/PKG signaling pathway [80]. Research has shown that the Wnt pathway is closely related to gastric cancer and its dysregulation has been observed in almost half of GC cases [81]. Finally, DCLK1 was selected for further experimental investigation to explore the molecular mechanism of the model genes, based on its highest correlation coefficient. In this research, we verified that down-regulation of DCLK1 significantly suppressed proliferation, colony forming ability, migration, and oxaliplatin resistance. This finding indicated that DCLK1 is a cancerpromoting factor in gastric cancer and can also promote oxaliplatin resistance in gastric cancer cells. Our study has some limitations. First, the main source of our data was publicly available datasets and further in-depth research, and in vivo experiments may be required for validation of our results. Second, larger and multicentric clinical samples and other clinical features (e.g., smoking, family background, BMI) must be included in such research. Finally, the specific mechanisms that determine histone acetylation regulation of STAD must be examined. Conclusions A novel histone acetylationrelated gene signature, which possesses potential value in predicting the prognosis and immunotherapy effectiveness regarding STAD patients, was developed. This signature may serve as a reliable biomarker for prognosis of STAD and promote the identification of novel treatment targets for STAD. Furthermore, DCLK1 exhibited oncogenic roles and may act as a new target for STAD therapy. Abbreviations GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; DEGs, Differentially Expressed Genes; CDF, cumulative distribution function; KM, Kaplan Meier; TME, Tumor Microenvironment; TMB, tumor mutational burden; MSI, microsatellite instability; MSS, microsatellite instability-stable; MSI-H, microsatellite instability-high; MSI-L, microsatellite instability-low; PCA, principal-component analysis; t-SNE, t-distributed stochastic neighbor embedding; Oxa, Oxaliplatin Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare no competing interests. Funding This study was supported by the National Natural Science Foundation of China (82073148), the Science and Technology Planning Project of Guangdong Province (No. 2021B1212040006), Sanming Project of Medicine in Shenzhen (No. SZSM201911010), Shenzhen Key Medical Discipline Construction Fund (No. SZXK016), Research start-up fund of part-time PI, SAHSYSU (ZSQYJZPI202001), and Guangdong–Hong Kong–Macau University Joint Laboratory of Digestive Cancer Research (2021LSYS003). Author Contribution Manuscript writing: CD, RS and ZZ; Conception and design: CD, RS and CZ; Data collection: ZZ and YG; Data analysis and interpretation: CD, RS and YG. Figure preparation:CD and RS. Experiments performation: CD and RS. Language polishing: SY and CZ. Manuscript editing: CZ, SY and JZ. Fund support: CZ and YH. All authors read and approved the final manuscript. Acknowledgement We thank all the members who contributed to this study. Data Availability The data analyzed in this study will be accessible upon reasonable request. GSE84437 was sourced from GEO database. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635–48. Jim MA, Pinheiro PS, Carreira H, Espey DK, Wiggins CL, Weir HK. Stomach cancer survival in the United States by race and stage (2001–2009): Findings from the CONCORD-2 study. Cancer. 2017;123(Suppl 24):4994–5013. Takahari D. Second-line chemotherapy for patients with advanced gastric cancer. Gastric Cancer. 2017;20(3):395–406. Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264–79. Deans C, Maggert KA. What do you mean. epigenetic? Genet. 2015;199(4):887–96. Gayon J. From Mendel to epigenetics: History of genetics. C R Biol. 2016;339(7–8):225–30. Bennett RL, Licht JD. Targeting Epigenetics in Cancer. Annu Rev Pharmacol Toxicol. 2018;58:187–207. Kaypee S, Sudarshan D, Shanmugam MK, Mukherjee D, Sethi G, Kundu TK. Aberrant lysine acetylation in tumorigenesis: Implications in the development of therapeutics. Pharmacol Ther. 2016;162:98–119. Raskov H, Soby JH, Troelsen J, Bojesen RD, Gogenur I. Driver Gene Mutations and Epigenetics in Colorectal Cancer. Ann Surg. 2020;271(1):75–85. Karsli-Ceppioglu S, Dagdemir A, Judes G, Ngollo M, Penault-Llorca F, Pajon A, Bignon YJ, Bernard-Gallon D. Epigenetic mechanisms of breast cancer: an update of the current knowledge. Epigenomics. 2014;6(6):651–64. Khan SI, Aumsuwan P, Khan IA, Walker LA, Dasmahapatra AK. Epigenetic events associated with breast cancer and their prevention by dietary components targeting the epigenome. Chem Res Toxicol. 2012;25(1):61–73. Legube G, Trouche D. Regulating histone acetyltransferases and deacetylases. EMBO Rep. 2003;4(10):944–7. Schneider A, Chatterjee S, Bousiges O, Selvi BR, Swaminathan A, Cassel R, Blanc F, Kundu TK, Boutillier AL. Acetyltransferases (HATs) as targets for neurological therapeutics. Neurotherapeutics. 2013;10(4):568–88. Ell B, Kang Y. Transcriptional control of cancer metastasis. Trends Cell Biol. 2013;23(12):603–11. Liu N, Li S, Wu N, Cho KS. Acetylation and deacetylation in cancer stem-like cells. Oncotarget. 2017;8(51):89315–25. Wisnieski F, Calcagno DQ, Leal MF, Santos LC, Gigek CO, Chen ES, Demachki S, Artigiani R, Assumpcao PP, Lourenco LG, et al. CDKN1A histone acetylation and gene expression relationship in gastric adenocarcinomas. Clin Exp Med. 2017;17(1):121–9. Tani M, Ito J, Nishioka M, Kohno T, Tachibana K, Shiraishi M, Takenoshita S, Yokota J. Correlation between histone acetylation and expression of the MYO18B gene in human lung cancer cells. Genes Chromosomes Cancer. 2004;40(2):146–51. Falahi F, van Kruchten M, Martinet N, Hospers GA, Rots MG. Current and upcoming approaches to exploit the reversibility of epigenetic mutations in breast cancer. Breast Cancer Res. 2014;16(4):412. Ben-Hur A, Elisseeff A, Guyon I. A stability based method for discovering structure in clustered data. Pac Symp Biocomput 2002:6–17. Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife 2017, 6. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77(21):e108–10. Plattner C, Finotello F, Rieder D. Deconvoluting tumor-infiltrating immune cells from RNA-seq data using quanTIseq. Methods Enzymol. 2020;636:261–85. Finotello F, Mayer C, Plattner C, Laschober G, Rieder D, Hackl H, Krogsdam A, Loncova Z, Posch W, Wilflingseder D, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 2019;11(1):34. Dienstmann R, Villacampa G, Sveen A, Mason MJ, Niedzwiecki D, Nesbakken A, Moreno V, Warren RS, Lothe RA, Guinney J. Relative contribution of clinicopathological variables, genomic markers, transcriptomic subtyping and microenvironment features for outcome prediction in stage II/III colorectal cancer. Ann Oncol. 2019;30(10):1622–9. Tamminga M, Hiltermann TJN, Schuuring E, Timens W, Fehrmann RS, Groen HJ. Immune microenvironment composition in non-small cell lung cancer and its association with survival. Clin Transl Immunol. 2020;9(6):e1142. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14. Aran D. Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell. Methods Mol Biol. 2020;2120:263–76. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018;1711:243–59. Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220. Deng QJ, Xie LQ, Li H. Overexpressed MALAT1 promotes invasion and metastasis of gastric cancer cells via increasing EGFL7 expression. Life Sci. 2016;157:38–44. Yamamura N, Kishimoto T. Epigenetic regulation of GATA4 expression by histone modification in AFP-producing gastric adenocarcinoma. Exp Mol Pathol. 2012;93(1):35–9. Wisnieski F, Leal MF, Calcagno DQ, Santos LC, Gigek CO, Chen ES, Artigiani R, Demachki S, Assumpcao PP, Lourenco LG, et al. BMP8B Is a Tumor Suppressor Gene Regulated by Histone Acetylation in Gastric Cancer. J Cell Biochem. 2017;118(4):869–77. Dai Q, Ye Y. Development and Validation of a Novel Histone Acetylation-Related Gene Signature for Predicting the Prognosis of Ovarian Cancer. Front Cell Dev Biol. 2022;10:793425. Massari ME, Murre C. Helix-loop-helix proteins: regulators of transcription in eucaryotic organisms. Mol Cell Biol. 2000;20(2):429–40. Wang C, Wang M, Arrington J, Shan T, Yue F, Nie Y, Tao WA, Kuang S. Ascl2 inhibits myogenesis by antagonizing the transcriptional activity of myogenic regulatory factors. Development. 2017;144(2):235–47. Xu H, Zhao XL, Liu X, Hu XG, Fu WJ, Li Q, Wang Y, Ping YF, Zhang X, Bian XW, et al. Elevated ASCL2 expression in breast cancer is associated with the poor prognosis of patients. Am J Cancer Res. 2017;7(4):955–61. Zuo Q, Wang J, Chen C, Zhang Y, Feng DX, Zhao R, Chen T. ASCL2 expression contributes to gastric tumor migration and invasion by downregulating miR223 and inducing EMT. Mol Med Rep. 2018;18(4):3751–9. Zhao Q, Wu BL. Ice breaking in GPCR structural biology. Acta Pharmacol Sin. 2012;33(3):324–34. Fidom K, Isberg V, Hauser AS, Mordalski S, Lehto T, Bojarski AJ, Gloriam DE. A new crystal structure fragment-based pharmacophore method for G protein-coupled receptors. Methods. 2015;71:104–12. Wittenberger T, Schaller HC, Hellebrand S. An expressed sequence tag (EST) data mining strategy succeeding in the discovery of new G-protein coupled receptors. J Mol Biol. 2001;307(3):799–813. Wang L, Zhou W, Zhong Y, Huo Y, Fan P, Zhan S, Xiao J, Jin X, Gou S, Yin T, et al. Overexpression of G protein-coupled receptor GPR87 promotes pancreatic cancer aggressiveness and activates NF-kappaB signaling pathway. Mol Cancer. 2017;16(1):61. Ahn HM, Choi EY, Kim YJ. GPR87 Promotes Metastasis through the AKT-eNOS-NO Axis in Lung Adenocarcinoma. Cancers (Basel) 2021, 14(1). Zhang X, Liu D, Hayashida Y, Okazoe H, Hashimoto T, Ueda N, Sugimoto M, Kakehi Y. G Protein-Coupled Receptor 87 (GPR87) Promotes Cell Proliferation in Human Bladder Cancer Cells. Int J Mol Sci. 2015;16(10):24319–31. Bai R, Zhang J, He F, Li Y, Dai P, Huang Z, Han L, Wang Z, Gong Y, Xie C. GPR87 promotes tumor cell invasion and mediates the immunogenomic landscape of lung adenocarcinoma. Commun Biol. 2022;5(1):663. De Paoli F, Eeckhoute J, Copin C, Vanhoutte J, Duhem C, Derudas B, Dubois-Chevalier J, Colin S, Zawadzki C, Jude B, et al. The neuron-derived orphan receptor 1 (NOR1) is induced upon human alternative macrophage polarization and stimulates the expression of markers of the M2 phenotype. Atherosclerosis. 2015;241(1):18–26. Lv J, Zhu Y, Ji A, Zhang Q, Liao G. Mining TCGA database for tumor mutation burden and their clinical significance in bladder cancer. Biosci Rep 2020, 40(4). Yang XJ, Seto E. Collaborative spirit of histone deacetylases in regulating chromatin structure and gene expression. Curr Opin Genet Dev. 2003;13(2):143–53. Gao L, Cueto MA, Asselbergs F, Atadja P. Cloning and functional characterization of HDAC11, a novel member of the human histone deacetylase family. J Biol Chem. 2002;277(28):25748–55. Bhaskara S. Histone deacetylase 11 as a key regulator of metabolism and obesity. EBioMedicine. 2018;35:27–8. Villagra A, Cheng F, Wang HW, Suarez I, Glozak M, Maurin M, Nguyen D, Wright KL, Atadja PW, Bhalla K, et al. The histone deacetylase HDAC11 regulates the expression of interleukin 10 and immune tolerance. Nat Immunol. 2009;10(1):92–100. Huang J, Wang L, Dahiya S, Beier UH, Han R, Samanta A, Bergman J, Sotomayor EM, Seto E, Kozikowski AP, et al. Histone/protein deacetylase 11 targeting promotes Foxp3 + Treg function. Sci Rep. 2017;7(1):8626. Bi L, Ren Y, Feng M, Meng P, Wang Q, Chen W, Jiao Q, Wang Y, Du L, Zhou F, et al. HDAC11 Regulates Glycolysis through the LKB1/AMPK Signaling Pathway to Maintain Hepatocellular Carcinoma Stemness. Cancer Res. 2021;81(8):2015–28. Li R, Wu X, Zhao P, Xue K, Li J. A pan-cancer analysis identifies HDAC11 as an immunological and prognostic biomarker. FASEB J. 2022;36(7):e22326. Lin PT, Gleeson JG, Corbo JC, Flanagan L, Walsh CA. DCAMKL1 encodes a protein kinase with homology to doublecortin that regulates microtubule polymerization. J Neurosci. 2000;20(24):9152–61. Sureban SM, May R, Lightfoot SA, Hoskins AB, Lerner M, Brackett DJ, Postier RG, Ramanujam R, Mohammed A, Rao CV, et al. DCAMKL-1 regulates epithelial-mesenchymal transition in human pancreatic cells through a miR-200a-dependent mechanism. Cancer Res. 2011;71(6):2328–38. Sureban SM, May R, Mondalek FG, Qu D, Ponnurangam S, Pantazis P, Anant S, Ramanujam RP, Houchen CW. Nanoparticle-based delivery of siDCAMKL-1 increases microRNA-144 and inhibits colorectal cancer tumor growth via a Notch-1 dependent mechanism. J Nanobiotechnol. 2011;9:40. Sureban SM, Madhoun MF, May R, Qu D, Ali N, Fazili J, Weygant N, Chandrakesan P, Ding K, Lightfoot SA, et al. Plasma DCLK1 is a marker of hepatocellular carcinoma (HCC): Targeting DCLK1 prevents HCC tumor xenograft growth via a microRNA-dependent mechanism. Oncotarget. 2015;6(35):37200–15. Weygant N, Qu D, May R, Tierney RM, Berry WL, Zhao L, Agarwal S, Chandrakesan P, Chinthalapally HR, Murphy NT, et al. DCLK1 is a broadly dysregulated target against epithelial-mesenchymal transition, focal adhesion, and stemness in clear cell renal carcinoma. Oncotarget. 2015;6(4):2193–205. Wang YL, Li Y, Ma YG, Wu WY. DCLK1 promotes malignant progression of breast cancer by regulating Wnt/beta-Catenin signaling pathway. Eur Rev Med Pharmacol Sci. 2019;23(21):9489–98. Fesler A, Liu H, Ju J. Modified miR-15a has therapeutic potential for improving treatment of advanced stage colorectal cancer through inhibition of BCL2, BMI1, YAP1 and DCLK1. Oncotarget. 2018;9(2):2367–83. Jia Y, Liu Y, Feng L, Sun S, Sun G. Role of Glucagon and Its Receptor in the Pathogenesis of Diabetes. Front Endocrinol (Lausanne). 2022;13:928016. Nauck MA, Meier JJ. The incretin effect in healthy individuals and those with type 2 diabetes: physiology, pathophysiology, and response to therapeutic interventions. Lancet Diabetes Endocrinol. 2016;4(6):525–36. Campbell JE, Drucker DJ. Pharmacology, physiology, and mechanisms of incretin hormone action. Cell Metab. 2013;17(6):819–37. Zhou J, Deng Z, Chen Y, Gao Y, Wu D, Zhu G, Li L, Song W, Wang X, Wu K et al. Overexpression of FABP7 promotes cell growth and predicts poor prognosis of clear cell renal cell carcinoma. Urol Oncol 2015, 33(3):113 e119-117. Hotamisligil GS, Bernlohr DA. Metabolic functions of FABPs–mechanisms and therapeutic implications. Nat Rev Endocrinol. 2015;11(10):592–605. Storch J, Thumser AE. Tissue-specific functions in the fatty acid-binding protein family. J Biol Chem. 2010;285(43):32679–83. Tian W, Zhang W, Zhang Y, Zhu T, Hua Y, Li H, Zhang Q, Xia M. FABP4 promotes invasion and metastasis of colon cancer by regulating fatty acid transport. Cancer Cell Int. 2020;20:512. Guaita-Esteruelas S, Bosquet A, Saavedra P, Guma J, Girona J, Lam EW, Amillano K, Borras J, Masana L. Exogenous FABP4 increases breast cancer cell proliferation and activates the expression of fatty acid transport proteins. Mol Carcinog. 2017;56(1):208–17. Dong X, Seelan RS, Qian C, Mai M, Liu W. Genomic structure, chromosome mapping and expression analysis of the human AXIN2 gene. Cytogenet Cell Genet. 2001;93(1–2):26–8. Huang SM, Mishina YM, Liu S, Cheung A, Stegmeier F, Michaud GA, Charlat O, Wiellette E, Zhang Y, Wiessner S, et al. Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature. 2009;461(7264):614–20. Xu HT, Yang LH, Li QC, Liu SL, Liu D, Xie XM, Wang EH. Disabled-2 and Axin are concurrently colocalized and underexpressed in lung cancers. Hum Pathol. 2011;42(10):1491–8. Schaal U, Grenz S, Merkel S, Rau TT, Hadjihannas MV, Kremmer E, Chudasama P, Croner RS, Behrens J, Sturzl M, et al. Expression and localization of axin 2 in colorectal carcinoma and its clinical implication. Int J Colorectal Dis. 2013;28(11):1469–78. Joseph NM, Tsokos CG, Umetsu SE, Shain AH, Kelley RK, Onodera C, Bowman S, Talevich E, Ferrell LD, Kakar S, et al. Genomic profiling of combined hepatocellular-cholangiocarcinoma reveals similar genetics to hepatocellular carcinoma. J Pathol. 2019;248(2):164–78. Chen K, Yang D, Li X, Sun B, Song F, Cao W, Brat DJ, Gao Z, Li H, Liang H, et al. Mutational landscape of gastric adenocarcinoma in Chinese: implications for prognosis and therapy. Proc Natl Acad Sci U S A. 2015;112(4):1107–12. Cancer Genome Atlas Research N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517):202–9. De Palma M, Lewis CE. Macrophage regulation of tumor responses to anticancer therapies. Cancer Cell. 2013;23(3):277–86. Locati M, Curtale G, Mantovani A. Diversity, Mechanisms, and Significance of Macrophage Plasticity. Annu Rev Pathol. 2020;15:123–47. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017;18(1):248–62. Xiang T, Yuan C, Guo X, Wang H, Cai Q, Xiang Y, Luo W, Liu G. The novel ZEB1-upregulated protein PRTG induced by Helicobacter pylori infection promotes gastric carcinogenesis through the cGMP/PKG signaling pathway. Cell Death Dis. 2021;12(2):150. Koushyar S, Powell AG, Vincan E, Phesse TJ. Targeting Wnt Signaling for the Treatment of Gastric Cancer. Int J Mol Sci 2020, 21(11). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4689949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324383232,"identity":"9fb8033d-0ea6-41f4-827e-a38ac2d1a37c","order_by":0,"name":"Chen Dai","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Dai","suffix":""},{"id":324383233,"identity":"2cef9b30-7db1-4132-88df-3477788cf98d","order_by":1,"name":"Rishun Su","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rishun","middleName":"","lastName":"Su","suffix":""},{"id":324383234,"identity":"245e5f05-c0cc-49a4-8002-649ef2e6f396","order_by":2,"name":"Zhenzhen Zhao","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Zhao","suffix":""},{"id":324383235,"identity":"80472f0b-75ed-47e9-afd7-b24a85ce018e","order_by":3,"name":"Yangyang Guo","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yangyang","middleName":"","lastName":"Guo","suffix":""},{"id":324383236,"identity":"94cf8d8b-bca8-40d9-beb3-b757200b290b","order_by":4,"name":"Songcheng Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZiBmbKiRA5KNB2CCEkRoOWYMIonUwgDWwpzYAKSJ02JwnPfwh5872NLXth8G2vLnsL3BAeaDt3kY7PJwajnMlybZe0Ymd9uZxIYDjG2HEzccYEu25mFILsathceMmbGNLXfbAZCWhsMJBgd4zKR5GA6AnYpDi/FnxjbmdLPzD2EO4/9GSIuBNFBLgtkNoC0MbIcZNxzgYcOrRRLoMMnetmOG224AbUlsS0+ceZjN2HKOQTJOLXznzxh/+NlWI292Pv3hgw9/rO35jjc/vPGmwg6nFoUDyLwEhmZI5DIY4FAPBPJoZtXhVjoKRsEoGAUjFgAA9LVedcQZzoIAAAAASUVORK5CYII=","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Songcheng","middleName":"","lastName":"Yin","suffix":""},{"id":324383237,"identity":"703be0a9-edc2-42f9-a1e6-9b3e86e7c17c","order_by":5,"name":"Jingyao Zhou","email":"","orcid":"","institution":"Taizhou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingyao","middleName":"","lastName":"Zhou","suffix":""},{"id":324383238,"identity":"758d9dc5-3548-450a-afa2-1e16ec10fc6e","order_by":6,"name":"Yulong He","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"He","suffix":""},{"id":324383239,"identity":"fed5a7b6-b1a0-4d7a-a52b-83e82944e125","order_by":7,"name":"Changhua Zhang","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changhua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-05 06:06:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4689949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4689949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62127564,"identity":"76ddcf1e-e579-4b0f-8db3-d7e1b39940da","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":878082,"visible":true,"origin":"","legend":"\u003cp\u003eSTAD classification pattern based on histone acetylation‑related genes. (A) CDF curves (B) Tracking Plot (C) STAD patients were divided into two groups according to the consensus clustering matrix (K = 2). (D) Kaplan–Meier OS curves for the two clusters. (E) Expression of immune checkpoint-related genes between two clusters. (F) Difference of TMB between two clusters (G) Difference of MSI between two clusters. * represents p \u0026lt; 0.05; ** represents p \u0026lt; 0.01; *** represents p \u0026lt; 0.001; ns represents p ≥ 0.05.\u003c/p\u003e","description":"","filename":"Figure1revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/621d47b8044299db6a325962.png"},{"id":62128308,"identity":"d2e1624c-6e7a-471b-a2b4-5041e41361d6","added_by":"auto","created_at":"2024-08-09 15:07:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":724519,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between two clusters and different immune cell infiltration. (A) Immune Score (B) Stromal Score (C) ESTIMATE Score (D) CD8 T cells (E) Cytotoxic lymphocytes (F) Endothelial cells (H) Monocytic lineage (I) Fibroblasts (J) Myeloid dendritic cells (K) NK cells (L) Neutrophils (M) T cells\u003c/p\u003e","description":"","filename":"Figure2revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/ef1d90212fe160729b0d0cff.png"},{"id":62129005,"identity":"efbb6f12-72cb-4d90-a40e-3e156f851041","added_by":"auto","created_at":"2024-08-09 15:15:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1585834,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of histone acetylation-related gene prognostic model based on the STAD-TCGA cohort and validation of the risk signature based on GSE84437 dataset. (A) Univariate cox regression analysis revealed 10 acetylation-related genes correlated with prognosis. (B) Acetylation-related genes were penalized by LASSO Cox regression analysis. (C) Cross-validation for tuning parameter selection in the LASSO Cox regression. (D) Distribution of STAD-TCGA patients based on the risk score. (E) The survival status of each STAD-TCGA sample based on the risk score. (F) The expression patterns of 8 prognostic genes between high and low risk groups in STAD-TCGA. (G) Kaplan–Meier OS curves of patients in the high- and low-risk groups. (H) ROC curves (I) Principal component analysis (PCA) plot for STAD upon the basis of risk score. (J) t-SNE analysis (K) Distribution of STAD patients in GSE84437 dataset based on the risk score. (L) The survival status of each STAD patients in GSE84437 dataset (M) The expression patterns of 8 prognostic genes between high and low risk groups in GSE84437 dataset. (N) Kaplan–Meier OS curves of patients in the high and low-risk groups. (O) ROC curves (P) PCA plot for STAD upon the basis of risk score. (Q) t-SNE analysis\u003c/p\u003e","description":"","filename":"Figure3revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/9c3909f846d735722a85b2cf.png"},{"id":62129007,"identity":"2feba3e4-ce9c-414d-a579-68836a4a898e","added_by":"auto","created_at":"2024-08-09 15:15:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1065465,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognostic value of the risk Signature and construction of a prognostic nomogram. (A) The forest plot of univariate analysis for the TCGA cohort (B) The forest plot of multivariate analysis for the TCGA cohort (C) The forest plot of univariate analysis for the GEO cohort (D) The forest plot of multivariate analysis for the GEO cohort (E) Nomogram of the TCGA cohort predicting the survival rate (F) Calibration plot analysis to evaluate nomogram accuracy (G) The AUC values of the nomogram and clinicopathological features (H) The decision curve analyses plot\u003c/p\u003e","description":"","filename":"Figure4revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/9fc0c800874b4132a2acea3a.png"},{"id":62128307,"identity":"8a844d0e-53dc-4de5-b290-ee444d73b4d6","added_by":"auto","created_at":"2024-08-09 15:07:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1463421,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between risk score and somatic mutation, immune subtype and clinicopathological features. (A) somatic mutation landscape in high-risk group (B) somatic mutation landscape in low-risk group (C) Relationship between risk score and immune subtypes. (D-I) The relationship between risk score and T stage (D), lymph node metastasis (E), distant metastasis (F), clinical stage (G), tumor grade (H) and age (I).\u003c/p\u003e","description":"","filename":"Figure5revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/a8bee9d6070959876e2f0326.png"},{"id":62128311,"identity":"ed30aabd-3d41-497a-a0a4-0fcdf470da78","added_by":"auto","created_at":"2024-08-09 15:07:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":944335,"visible":true,"origin":"","legend":"\u003cp\u003eThe KM curves difference of two risk groups in age \u0026gt;=65, age\u0026lt;= 65, Female, Male, G3, M0, N1-3, Stage III-IV and T3-4.\u003c/p\u003e","description":"","filename":"Figure6revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/529bee523692248148e1b5ad.png"},{"id":62127572,"identity":"d311db15-2699-442e-976a-73f2cb698324","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1381565,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between risk score and tumor microenvironment, immune cell infiltration and immune activity. (A) Immune score (B) Stromal score (C) ESTIMATE score (D) correlations between tumor-infiltrating immune cells and risk score based on 7 known algorithms (E) Different macrophages M2 level in two subgroups (F) The difference in enrichment scores of different types of immune cells between two risk subgroups. (G) The difference in enrichment scores of different types of immune pathways between two risk subgroups. * represents p \u0026lt; 0.05; ** represents p \u0026lt; 0.01; *** represents p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/4072d861df400950edabb68b.png"},{"id":62127569,"identity":"7bec55cc-2095-4c53-8dc6-15dcb4c64667","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":919684,"visible":true,"origin":"","legend":"\u003cp\u003eImmunotherapy prediction (A) Different expression of immune checkpoint-related genes between two risk subgroups. (B) Different MSI status (MSS, MSI-H and MSI-L) between two subgroups. (C) The difference of TMB was significant in STAD patients with different risk. (D) The correlations between risk score and TMB. (E) The correlations between risk score and TIDE score (F) Differences in immunophenoscores between patients in two risk groups received CTLA4 inhibitor.\u003c/p\u003e","description":"","filename":"Figure8revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/70f06823f41fb11a5699cf2a.png"},{"id":62127573,"identity":"e2661ed6-4196-475c-92e8-40353a0a56ba","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":606343,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity based on the prognostic signature.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/6e4eea8cc5f070916f725205.png"},{"id":62129008,"identity":"0fa0f8a9-1d46-408e-a632-72106a767491","added_by":"auto","created_at":"2024-08-09 15:15:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3472377,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Analyses (A, C) GO enrichment analysis (B, D) KEGG pathway analysis (E, F) GSEA analysis\u003c/p\u003e","description":"","filename":"Figure10revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/0d2e1512b9a2b9efbc412820.png"},{"id":62127576,"identity":"84562f60-17e7-444b-93d6-c22b5a514adb","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":5744577,"visible":true,"origin":"","legend":"\u003cp\u003eBiological function of DCLK1 in GC cells and oxaliplatin-resistant GC cells. (A) The mRNA expression of DCLK1 in GC cell lines (HGC-27 and MKN-28) and normal gastric epithelial cell line (GES-1) were assessed by qRT-PCR. (B)DCLK1 RNA expression in HGC-27 and MKN-28 transfected with Si-DLCK1-1, SiDLCK1-2 and Si-NC were determined by qRT-PCR. (C) CCK-8 assays were performed to observe HGC-27 and MKN-28 cells viability after DCLK1 knockdown. (D) Colony formation assays were implemented in silenced DCLK1 gastric cancer cell lines (HGC-27 and MKN-28). (E) Wound‑healing assays were conducted to confirm HGC-27 cell migration with DCLK1 knockdown. (F) DCLK1 expression in oxaliplatin-resistant HGC-27(HGC-27/Oxa) gastric cancer cells compared with their parental cells. (G) Cell viability of HGC-27 and HGC-27/Oxa by CCK8 assay. (H) Cell viability in HGC-27/Oxa and MKN-28/Oxa transfected with DCLK1 SiRNAs and Si-NC were confirmed by CCK-8 assay. (I) Apoptotic ratio of HGC-27/Oxa and MKN-28/Oxa cells transfected with DCLK1 SiRNAs and Si-NC were estimated by Flow cytometry. Data was presented as mean ± SD. * represents p \u0026lt; 0.05; ** represents p \u0026lt; 0.01; *** represents p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure11revised.png","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/88762b0f50fc04dec8a1229a.png"},{"id":75014922,"identity":"5faf3bbe-0476-474e-be09-2930cde82906","added_by":"auto","created_at":"2025-01-29 12:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19372052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/c872a148-1d2b-4b9b-a09a-32854e3b3891.pdf"},{"id":62127567,"identity":"f0ca8660-8859-4c45-9e11-18485ffb8369","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32029,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/b8675a4003b098ca98d4718e.docx"},{"id":62129006,"identity":"d86af94a-da63-494f-92cb-6171f370bcb7","added_by":"auto","created_at":"2024-08-09 15:15:28","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19598,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/fcf3e7536789d82544efa426.docx"},{"id":62127563,"identity":"15238503-432b-40c6-abc1-1189254e54d2","added_by":"auto","created_at":"2024-08-09 14:59:28","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16202,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4689949/v1/9c5b7503c89fffc4a052929b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Histone Acetylation-Related Gene Signature for Prediction of Prognosis and Immunotherapy Efficacy in Stomach Adenocarcinoma and Verification in vitro","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is a very aggressive cancer, with extreme heterogeneity and rapid growth, and high incidence rate and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Annual diagnoses are over 1\u0026nbsp;million worldwide, with annual fatalities reaching 76,000 based on the Global Cancer Report 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most frequent histological type of GC is stomach adenocarcinoma (STAD). Although substantial clinical advances including radical surgery, chemotherapy, radiotherapy, molecularly targeted drugs have been applied in early therapy of patients with GC over the past several decades, diagnostic efficiency and therapeutic effects of advanced GC remain unsatisfactory and the five-year survival rate of advanced GC has been slow to improve [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, reliable novel biomarkers and prognostic models that can be used to improve prognostic predictions and reasonable therapeutic strategies are urgently required for patients with STAD.\u003c/p\u003e \u003cp\u003eEpigenetic alterations, including DNA methylation, histone modifications and chromatin remodeling refer to heritable phenotype changes in gene expression with the DNA sequence unchanged [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As one of epigenetic alterations, histone modifications vary, including methylation, acetylation, ubiquitination and phosphorylation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Acetylation modification of histone is a reversible dynamic process that achieves a balance between histone acetyltransferases (HATs) and histone deacetylases (HDACs) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. HATs catalyze the transfer of acetyl from acetyl coenzyme-A to N-terminal lysine in histone, which facilitates the binding of transcription factors to promoters and promotes the transcription of genes [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, HDAC catalyze hydrolysis of acetyl from lysine, generating opposite results of HAT [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Current research indicates that histone acetylation are important for tumor origin and progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, Wisnieski et al. showed that acetylation levels increased in some regions of CDKN1A and these modifications were significantly associated with gastric cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Tani et al. noted that histone H3 and H4 acetylation play an important role in the abnormal expression of MYO18B, contributing to the origin and progression of lung cancer. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Falahi et al. showed that decrease of H4K16 (monoacetylated lysine 16 of histone H4) acetylation is closely associated with the early process of breast cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Other studies have shown that HAT/HDAC inhibitors, as a class of newly developing antitumor drugs, may alter the expression of oncogenes or tumor suppressor genes by changing histone acetylation levels. However, the role of histone acetylation-related gene signature in prognosis of patients with STAD and mechanisms of histone acetylation in STAD therapy remain unclear.\u003c/p\u003e \u003cp\u003eTherefore, we performed a systematic study to classify TCGA-STAD patients based on histone acetylation-related genes and screen prognostic significance of histone acetylation-related DEGs between two clusters. We also established histone acetylation-related gene signature to obtain the risk score for evaluating prognosis and conducted functional enrichment analysis to investigate the underlying mechanisms. The association between our gene signature and somatic mutation, tumor microenvironment, immune cell infiltration, immune activity and drug sensitivity were explored to determine novel strategies for targeted treatment of STAD. We selected the gene with the largest correlation coefficient in the prognostic model for subsequent experimental analysis and assessment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eTranscriptome sequencing data of STAD patients and matching clinical features were sourced from the TCGA database. Validation cohorts were acquired from GSE84437.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTumor Classification Based on Histone Acetylationrelated DEGs\u003c/h2\u003e \u003cp\u003eWe used the R package \u0026ldquo;ConsensusClusterPlus\u0026rdquo; to further investigate the associations between histone acetylationrelated genes and STAD subtypes and determine number and stability of clusters. Based on the cumulative distribution functions, the optimal cluster number of k-value was obtained [20]. The KM survival analysis was conducted using the R package \u0026ldquo;survival.\u0026rdquo; Differential genes between the two clusters and histone acetylationrelated genes are shown in \u003cb\u003eSupplementary Table\u0026nbsp;1 and Table\u0026nbsp;2.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and Verification of Prognostic Histone AcetylationRelated Gene Signature\u003c/h2\u003e \u003cp\u003eThe univariate Cox regression algorithm was employed to filter out histone acetylationrelated genes independently related to prognosis, and LASSO Cox regression was carried out to further narrow the range of candidate genes using the R package \u0026ldquo;glmnet.\u0026rdquo; The risk scores were calculated for STAD patients as Risk Score= (X: coefficients, Y: gene expression level), and based on the middle value, the STAD patients were equally assigned into low- and high-risk groups. Then, Kaplan-Meier analysis was conducted, and PCA, t-SNE and ROC curves were plotted. The R package \u0026ldquo;pheatmap\u0026rdquo; was applied to draw the heatmap and applied the GSE84437 cohort to validate the risk signature. The prognostic significance of the signature was analyzed with univariate and multivariable Cox regression. The Eight-Gene Prognostic Model is shown in \u003cb\u003eSupplementary Table\u0026nbsp;3.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of Nomogram\u003c/h2\u003e \u003cp\u003eA nomogram based on independent prognostic parameters was developed using R package \u0026ldquo;rms\u0026rdquo; and \u0026ldquo;regplot\u0026rdquo; for STAD patients to predict the survival probability. In addition, calibration curve and AUC were applied to evaluate the nomogram regarding accuracy, and decision curves analysis was also performed to confirm the model with respect to its clinical effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSomatic Mutation, Tumor Immunity, Immunotherapy Prediction and Drug Susceptibility Analysis\u003c/h2\u003e \u003cp\u003eThe TCGA database was used to retrieve sample mutation data, and the R package \u0026ldquo;maftools\u0026rdquo; was used to estimate the mutation status between the two groups. Immune, Stromal and ESTIMATE scores were applied to evaluate association between the risk score and tumor microenvironment (TME) with \u0026ldquo;ESTIMATE\u0026rdquo; R package. Immune cell subtype infiltration values of TCGA-STAD dataset samples were based on seven algorithms: XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS and CIBERSORT [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The correlation between infiltration immune cell subtypes and risk scores was established through the application of Spearman correlation analysis with R packages \u0026ldquo;ggpubr.\u0026rdquo; In addition, different immune-checkpoint genes in our two risk groups were confirmed with respect to their expression by using R packages \u0026ldquo;limma.\u0026rdquo; MSI, TMB, TIDE scores and the response to CTLA4 inhibitor were performed to assess and predict immunotherapy effectiveness of our prognostic signature. IC50 (Half-maximal inhibitory concentration) was used to determine the relationship between our prognostic signature and various drug susceptibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGO and KEGG analyses using \u0026ldquo;clusterProfiler\u0026rdquo; R package were performed to examine the mechanisms of the signature, and GSEA analysis was used to elucidate signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell Lines and Cell Culture\u003c/h2\u003e \u003cp\u003eThe human immortalized normal gastric epithelial cell (GES1) and gastric adenocarcinoma cell lines (MKN-28 and HGC-27) were provided by the Chinese Academy of Sciences (Shanghai). These cells were maintained at 37\u0026deg;C and in 5% CO\u003csub\u003e2\u003c/sub\u003e in RPMI-1640 (Gibco, USA) medium with 10% fetal bovine serum (Gibco, USA) and 1% penicillin-streptomycin. Over one year, Oxaliplatin-resistant GC cell line HGC-27R was established stepwise through constant exposure to increasing quantities of Oxaliplatin and culture in RPMI-1640 (Gibco, USA) medium with oxaliplatin (1 uM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell Transfection\u003c/h2\u003e \u003cp\u003eThe small interfering RNAs (siRNAs) targeting DCLK1 and scrambled siRNA nontarget control (Si-NC) were supplied by TRANSHEEPBIO (China), and their sequences are as follows: Si-DCLK1-1: sense UCUGUCGGAUAACGUGAAUUU and antisense AAAUUCACGUUAUCCGACAGA; Si-DCLK1-2: sense ACCCGAACUCUGUCGGAUAAC and antisense GUUAUCCGACAGAGUUCGGGU. Six-well plates were seeded with GC cell at a density of 2.0 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e/well and then Namipo\u0026trade; was used to assist transfection in accordance with the instructions from manufacturer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of Total RNA With Quantitative Reverse Transcription RealTime Polymerase Chain Reaction (QRTPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA from cell lines was obtained with RNA Isolater Total RNA Extraction Reagent (Vazyme, Nanjing, China) in accordance with the instructions from manufacturer. Subsequently, cDNA was generated utilizing the Evo M-MLV RT Premix kit (Accurate Biotechnology). Then, qRT-PCR assay was performed using SYBR Green Master Mix (Vazyme), with beta-actin as the normalization control and the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method for the quantification of RNA expression levels. The primer sequences (5\u0026prime;-3\u0026prime;) were as follows: DCLK1 forward, ACTTCGACGAGCGGGATAAG and reverse, GGGCCTCAAAAGATCGGAACC; β-Actin forward, CACCATTGGCAATGAGCGGTTC and reverse, AGGTCTTTGCGGATGTCCACGT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell Proliferation Assay\u003c/h2\u003e \u003cp\u003eCell viability and proliferation were assayed by Cell Counting Kit-8 kit (MeilunBio, Guangzhou, China) following the instructions from manufacturer. On the first day, 2 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells per well were seeded into 96-well plates for culture. Then on day 1, 2, 3, 4 and 5 follow cell seeding, the plates were added with 10 ul of CCK-8 reagent into each of wells and incubated for 1 h at 37\u0026deg;C to detect cell viability. The 450-nm optical absorption values were recorded using a microplate reader (Thermo, U.S.A.) on days 1, 2, 3, 4, and 5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCytotoxicity of Oxaliplatin In Vitro\u003c/h2\u003e \u003cp\u003eEach well of a 96 well plate was seeded with 10,000 cells and treated with different concentrations of oxaliplatin (Aladdin, Shanghai) after 24 h. The Cell Counting Kit-8 (CCK8) (MeilunBio, Guangzhou, China) was employed to assess cell viability 48 h following the addition of oxaliplatin. The growth-inhibitory curves of oxaliplatin-resistant HGC-27 cells versus the parental cells were drawn, and the IC50 of oxaliplatin of each cell line was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eColony-formation Assay\u003c/h2\u003e \u003cp\u003eThe indicated cells (1000 cells/well) were plated into six-well plates and the plates were then cultured at 37\u0026deg;C for 10 to 14 days. After 15-min fixation with formaldehyde (10%), cells were stained with 1.0% crystal violet for 30 s before counting the colonies. Finally, cell clones (\u0026gt;\u0026thinsp;50 cells) were counted and analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWound Healing Assay\u003c/h2\u003e \u003cp\u003eFor wound healing assay, cells were inoculated into 6-well plates for culture to form a confluent layer and wounds were created by scraping using sterile tips. The cells were then rinsed thoroughly with PBS and further cultured in serum-free medium. Images were acquired at 0 and 24 h with microscope and the rate of cell migration was obtained by Image J.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eApoptosis Assay\u003c/h2\u003e \u003cp\u003eFor apoptosis analysis, the cells were seeded into plates of six wells and then treated with oxaliplatin (Aladdin, Shanghai) for 24 h; afterward, they were digested by trypsin without addition of EDTA and washed for two times using PBS; after addition of AnnexinV- FITC and PI staining solution, they were incubated at ambient temperature in dark place for 15 min. Then, the apoptotic rate was determined with use of the flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were completed using Prism (Version 10.1) and R (Version 4.2.1) software.\u003c/p\u003e \u003cp\u003eOne-way analysis of variance (ANOVA) and unpaired Student\u0026rsquo;s t-test were adopted to assess differences between groups. A two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set to determine statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSTAD Classification Pattern Based on Histone AcetylationRelated Genes\u003c/h2\u003e \u003cp\u003eTo analyze the connections between the histone acetylationrelated genes and STAD, consensus clustering analysis was performed in the TCGA-STAD cohort for STAD patients. The results of the CDF curves and tracking plot showed that the optimal cluster number was K\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC demonstrated that the maximum intra-group correlations with low inter-group correlations were observed when K\u0026thinsp;=\u0026thinsp;2 and STAD patients could be split into two clusters according to differential expression patterns. K-M analysis was performed for comparison of overall survival advantage between two clusters. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, the survival probability was higher in cluster 1 (C1) than in cluster 2 (C2) (P\u0026thinsp;=\u0026thinsp;0.021). Then, the immune checkpoint-related genes between the two clusters were examined with respect to their expression, and there were 12 DEGs (CD244, CSF1R, TGFBR1, BTLA, CTLA4, IL10, CD160, PDCD1LG2, KDR, CD96, CD274 and HAVCR2) between C1 and C2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These 12 genes showed higher expression levels in C2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).In addition, we investigated the difference of TMB and MSI between two clusters and results indicated that C1 had higher TMB and MSI than C2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also analyzed different immune cell infiltration level between C1 and C2. The results in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C showed that C2 have higher ImmuneScore, StromalScore and ESTIMATEScore. Furthermore, we found that significant differences exist in some immune cell types including CD8 T cells, Cytotoxic lymphocytes, Endothelial cells, Monocytic lineage, Myeloid dendritic cells and NK cells with a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all, specifically, 0.047, 0.019, 0.036, 0.0019, 6e-05 and 0.0002, respectively, and C2 had more these types of immune cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-M).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Eight-Gene Prognostic Model\u003c/h2\u003e \u003cp\u003eWe screened out the differentially expressed genes between C1 and C2 through the \u0026ldquo;limma\u0026rdquo; package. Then prognostic significance of histone acetylation-related DEGs was assessed by using univariate Cox regression analyses. The 10 genes (ASCL2, ADH1B, GPR87, F13A1, HDAC11, DCLK1, GCG, FABP4, AXIN2, OGN) were retained for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Using LASSO Cox regression, a signature of eight genes was generated based on optimum λ value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). These 8 genes (ASCL2, GPR87, F13A1, HDAC11, DCLK1, GCG, FABP4, AXIN2) were regarded as prognostic factors for STAD, and for which the calculation of risk score was: risk score = (\u0026minus;\u0026thinsp;0.016*ASCL2 exp.) + (0.108*GPR87 exp.) + (0.052*F13A1 exp.) + (\u0026minus;\u0026thinsp;0.085*HDAC11 exp.) + (0.111*DCLK1 exp.) + (0.088*GCG exp.) + (0.024*FABP4 exp.) + (\u0026minus;\u0026thinsp;0.088*AXIN2 exp.). In addition, division of STAD patients was conducted as two groups of high- and low-risk on the basis of their median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Patients with STAD in the high-risk one possessed a greater risk of death and less survival time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The expression patterns of these eight prognostic genes between the groups were demonstrated by heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Patients with STAD in the high-risk group presented a lower overall survival (OS) than those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH indicated that the area under the ROC curve was 0.658, 0.699 and 0.632, respectively, at 1 year, 3 years and 5 years. The PCA and t-SNE analysis suggested that patients with varying risk levels in STAD can be effectively divided into two directions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI, J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the Risk Signature\u003c/h2\u003e \u003cp\u003eThe GSE84437 dataset was used for validation of robustness of our signature. Similar to the previously described results, STAD patients in GSE84437 dataset were also assigned into two groups of low- and high-risk in light of the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK). The STAD patients with different risks could be assigned into two directions, which was confirmed by PCA and t-SNE analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eP, Q). The patients showed a greater risk of death, less survival time and decreased OS in the high-risk group than those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL, N). The expression patterns of eight prognostic genes between the two groups were shown by heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eM). The area under the ROC curve reached 0.632 at 1 year, 0.624 at 3 years, and 0.680 at 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eO).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Prognostic Value of the Risk Signature\u003c/h2\u003e \u003cp\u003eTo determine the feasibility of this novel gene signature being used as a prognostic predictor independent of other factors, the Cox regression of univariate and multivariate was carried out. For the univariate analysis, we discovered that the identification of risk score as an independent prognostic predictor for STAD patients was demonstrated in both the TCGA cohort (HR\u0026thinsp;=\u0026thinsp;4.015, 95% CI: 2.038\u0026ndash;7.911, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and GEO cohort (HR\u0026thinsp;=\u0026thinsp;1.998, 95% CI: 1.073\u0026ndash;3.719, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). For the multivariate analysis similar results were found, indicating that the risk score is an independent prognostic predictor (TCGA cohort: HR\u0026thinsp;=\u0026thinsp;4.686, 95% CI: 2.223\u0026ndash;9.875, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; GEO cohort: HR\u0026thinsp;=\u0026thinsp;2.238, 95% CI: 1.216\u0026ndash;4.121, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). A nomogram was established to predict 1, 3, and 5-year OS in TCGA-STAD cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The calibration curve confirmed that the nomogram operated with an extremely high consistency with an ideal model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The results of the ROC curves showed that the nomogram had the largest AUC (AUC\u0026thinsp;=\u0026thinsp;0.694) than other clinicopathological features (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Decision curves of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH also showed the nomogram presented larger net benefits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCorrelations Between Somatic Mutation, Immune Subtype, Clinicopathological Features and Risk Score\u003c/h2\u003e \u003cp\u003eWe investigated different somatic mutation landscapes between TCGA-STAD patients defined as high- and low-risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Patients in the low-risk group had higher mutation rates than the high-risk group (94.59 vs 81.36%). In terms of gene mutation frequency, TTN, TP53 and MUC16 were the most altered gene. The most frequent mutation was missense mutation in patients with STAD. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, risk score and different immune subtypes were conspicuously correlated with each other. In addition, a positive correlation was also present between the risk score and lymph node metastasis, distant metastasis, clinical stage and tumor grade except for age and T stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-I). The K-M curves implied that high-risk patients exhibited a less favorable prognosis than those classified as low-risk in age\u0026thinsp;\u0026gt;\u0026thinsp;65, age\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;65, Female, Male, G3, M0, N1-3, Stage III-IV and T3-4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eRelationships Between Risk Score and Tumor Microenvironment, Immune Cell Infiltration and Immune Activity\u003c/h2\u003e \u003cp\u003eThe relationships between risk score and TME were determined, using calculations of Immune, Stromal and ESTIMATE scores. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C, the Immune, Stromal and ESTIMATE scores were higher in the high-risk group. Based on seven known algorithms, the correlation of tumor-infiltrating immune cells to the risk score was analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Since tumor-associated macrophages (TAMs) are critical in TME, we explored the relationships between our risk score and TAMs. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE implied that the level of M2 macrophages was higher in the high-risk group than in the low-risk group. Furthermore, results of functional analysis suggested that our histone acetylation-related gene signature was notably associated with immune activity and the high-risk subgroup exhibited elevated infiltrating levels of most immune cells except for Th2_cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG proved that the high-risk group showed more activity for most immune pathways excepting MHC_class_I, which showed less activity. The activity of APC_co_inhibition pathway revealed no significant difference between two subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eImmunotherapy Prediction\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, 15 immune checkpoint-related genes (HAVCR2, CD274, ADORA2A, CD96, KDR, PDCD1LG2, TGFB1, CD160, IL10, TIGHT, BTLA, TGFBR1, CSF1R, CD244 and LAG3) were conspicuously overexpressed in the high-risk group, excepting one immune checkpoint-related gene (NECTIN2), which was obviously overexpressed in the low-risk group. Then, we investigated the different MSI status (MSS, MSI-H and MSI-L) between two subgroups. The data suggested that the MSI of high-risk group is lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). We also observed that in the high-risk group, the TMB was lower, and there was a negative correlation between risk score and TMB (R=-0.29, P\u0026thinsp;=\u0026thinsp;1.9e-08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE shows that the high-risk group had remarkably high TIDE scores than the low-risk group through the use of the TIDE algorithm, indicating that the low-risk group may have more immunotherapeutic effects than the high-risk group. Furthermore, TCIA immunotherapy prediction showed that the low-risk patients had a superior response to CTLA4 inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eDrug Sensitivity Based on the Prognostic Signature\u003c/h2\u003e \u003cp\u003eIC50 was used to estimate the difference between the two risk subgroups for 12 drugs. Our findings observed that patients from the high-risk group showed a higher estimated IC50 for five drugs including Doxorubicin, Etoposide, Gefitinib, GSK690693 and KIN001-266 but showed a lower estimated IC50 for the remaining seven drugs including AMG-706, CGP-60474, Cyclopamine, Dasatinib, JNK Inhibitor VIII, PIK-93 and PD-173074 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eFunctional Analyses\u003c/h2\u003e \u003cp\u003eTo analyze the differences between the two groups regarding gene functions and enrichment, we extracted DEGs between the two groups with use of R package (\u0026ldquo;limma\u0026rdquo;). With these DEGs, GO and KEGG analyses were performed, and as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-D. The results of GO analysis showed that the DEGs were related to muscle system process, etc. in BP, collagen-containing extracellular matrix, etc. in CC, receptor ligand activity, etc. in MF (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, C). According to the data from KEGG, the DEGs were abundant in cytokine-cytokine receptor interaction, signaling pathways of cGMP-PKG and Wnt (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, D). GSEA analysis was used to find which pathways were enriched in the two groups and the results are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF. The cell adhesion molecules cams, cytokine-cytokine receptor interaction and dilated cardiomyopathy were conspicuously enriched in the high-risk group. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF showed prominently enriched arginine and proline metabolism, cell cycle and DNA replication in the low-risk group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of DCLK1 On The proliferation, Migration and Oxaliplatin Resistance Of gastric Cancer Cells In vitro\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong the eight histone acetylation-related genes, DCLK1 had the largest correlation coefficient in the prognostic model. Therefore, we carried out \u003cem\u003ein vitro\u003c/em\u003e analysis to determine the functions of DCLK1 in GC cells. The RT-qPCR experimental results suggested that the expression of DCLK1 was significantly higher in MKN-28 and HGC-27 of GC cell lines than in the normal human gastric epithelial cell line GES-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). To determine whether DCLK1 affected progression in gastric cancer cells, we designed siRNA to silence DCLK expression in MKN-28 and HGC-27 cells. The efficiency of DCLK1 silencing in MKN-28 and HGC-27 was confirmed through RT-qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). Following the successful gene knockdown demonstrated previously, the CCK-8 assay was performed to validate the effects of DCLK1 on the proliferation and viability of MKN-28 and HGC-27 cells. The results proved that the proliferation of these two gastric cancer cell lines were impaired after knocking down DCLK1 compared to the Si-NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC). Furthermore, colony-formation experiments showed that colony formation was lower in the DCLK1 down-regulation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE also manifested that the migration of HGC-27 was weakened in si-DCLK1 groups. Since oxaliplatin is commonly used as a first-line chemotherapeutic agent for gastric cancer, we proceeded to investigate the impact of DCLK1 expression on the effectiveness of this chemotherapy drug. To check the DCLK1 expression in Oxaliplatin (Oxa)-resistant GC cells, RT-PCR was performed (HGC-27/Oxa), and the findings manifested that DCLK1 was more expressed in Oxa-resistant GC cells than in the parental ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eF). When treated with increasing levels of Oxaliplatin, HGC-27/Oxa cells exhibited greater viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eG). In addition, when comparing the DCLK1 knockdown transfection group to the Si-NC group, the 50% maximal inhibitory concentration (IC50) value of oxaliplatin was lower in si-DCLK1 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eH). The apoptotic rate of HGC-27 and MKN-28 cells treated with Oxaliplatin increased through the suppression of DCLK1. (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eI). In summary, the knockdown of DCLK1 could repress gastric cancer cell in terms of cell proliferation, migration and oxaliplatin resistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eStomach adenocarcinoma (STAD) accounts for the most gastric cancer cases. Current research indicates that histone acetylation is a pivotal factor in the occurrence and progression of STAD. Deng et al. suggested that over-expression of EGFL7 were found in gastric cancer cell lines and the potential mechanism was the hanging of histone acetylation levels in the EGFL7 promoter caused by MALAT1 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Yamamura\u0026rsquo;s research demonstrated that hyperacetylated status occurred in histones H3 and H4 in the GATA4-positive gastric cancer cells, using CHIP assay [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Wisnieski et al. found that BMP8B had increased acetylated H3K9 and H4K16 levels in gastric cancer and abnormal expression of BMP8B was related to poorly differentiated gastric cancer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, current research generally focuses on the relationship between histone acetylation level of single or several genes and STAD, and neglects a systematic development of a prognostic model to explore the associations between histone acetylation-related genes and STAD. Therefore, a model derived from multiple histone acetylation-related genes is urgently required for STAD patients.\u003c/p\u003e \u003cp\u003eIn our study, we first used consensus clustering analysis to determine two molecular subtypes based on 40 known histone acetylationrelated genes [34]. The findings indicated that C2 had worse prognosis, lower TMB and MSI, higher and immune cell infiltration level versus C1. Then, we screened out DEGs between C1 and C2 and filtered out prognostic significance of these DEGs. Through utilization of the LASSO Cox regression model, we generated a signature composed of eight genes (ASCL2, GPR87, F13A1, HDAC11, DCLK1, GCG, FABP4, AXIN2). ASCL2 (Achaete scute-like 2) belongs to the basic helix-loop-helix (BHLH) family of transcription factors [35]. As an indispensable downstream element of Wnt/β-catenin signaling pathway, ASCL2 is closely associated with the occurrence and development of different cancers [36]. ASCL2 was an independent indicator in recurrent breast cancer patients, which can also estimate the risk of relapse in breast cancer [37]. ZUO et al. found that ASCL2 affected the process of metastasis in gastric cancer by regulating miR223 expression [38]. GPCRs (G protein-coupled receptors), a member of membrane signaling proteins in eukaryote, are encoded by about 800 genes and participate in different diseases[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. GPR87 (G protein-coupled receptor 87) is one of the GPCR family and is in the chromosome 3q24 [41]. Recent studies have confirmed that GPR87 is overexpressed in several malignancies such as pancreatic cancer [42], lung cancer [43] and bladder cancer [44]. Bai\u0026rsquo;s research proved that GPR87 might participate in tumorigenesis and progression of lung cancer, which may also be related to immune infiltration [45].One study detected that GPR87, as an oncogene, is expressed in pancreatic cancer cells and promotes cancer proliferation and metastasis by driving the NF-κB signaling pathway [42]. F13A1 encodes coagulation factor XIII A subunit. This subunit has catalytic functions and acts as a plasma carrier molecule [46]. Research has demonstrated the presence of abnormal expression of F13A1 in tumors. In bladder cancer, over-expression of F13A1 was correlated with worse prognoses [47]. HDACs (histone deacetylases) are epigenetic regulators that are recruited by co-repressors or transcriptional complexes to gene promoters and participate in the regulation of gene transcription [48]. HDACs are grouped into four principal classes, among which, HDAC11, the newly discovered HDAC enzyme, has been classified into Class IV HDAC and was regarded as a key factor in metabolism, obesity and immune functions[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, HDAC11 has been demonstrated to participate in tumor development and progression. The study from Bi et al showed that HDAC11 can regulate the process of glycolysis and stemness of hepatocellular carcinoma through the LKB1/ AMPK signaling pathway [53]. A pan-cancer analysis indicated that HDAC11 is aberrantly expressed in multiple cancers and markedly correlated with survival outcomes of patients with different cancers [54]. DCLK1 (doublecortin like kinase 1), which belongs to the protein kinase superfamily and the doublecortin family, is present on chromosome 13q13-q14.1 and was initially recognized as a crucial regulator of neurogenesis and neuronal migration [55]. Recent research suggests that DCLK1 displays the characteristics of cancer stem cells (CSC) and was highly expressed in several cancers [\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Furthermore, research indicates that DCLK1 is essential for tumor progression, angiogenesis and epithelial-mesenchymal transition [61]. GCG (glucagon) is a protein that is included in four distinct mature peptides [62]. Research indicates that GCG and GLP-1 play a critical role in glucose metabolism and diabetes[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. FABP4 belongs to the family of fatty acid-binding proteins (FABPs), which are small molecule proteins that can transport hydrophobic and bioactive fatty acids [65]. Studies of FABP4 have focused on patient obesity, because of its aberrant expression in adipose tissue and differentiated adipocytes and macrophages[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Recent research has focused on the relationship between FABP4 and tumors. Tian et al. suggested that upregulated FABP4 facilitates the migration and invasion of colon cancer cells by facilitating FAs transport and activating AKT pathway and EMT [68]. In addition, exogenous FABP4 played a vital role in breast cancer progression and regulated fatty acid transport proteins expression [69]. The AXIN2 gene, located on chromosome 17q24, has been reported to have an indispensable role in Wnt signaling pathway[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Research has found abnormally expressed AXIN2 in cancers such as hepatocellular-cholangiocarcinoma, colon cancer and lung cancer [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter our histone acetylationrelated gene signature was constructed, ROC analysis was conducted to confirm the prognostic signature with respect to the sensitivity and specificity. The signature enabled us to divide STAD-TCGA patients into two groups of high- and low-risk in light of their median risk score. We found that STAD patients in the high-risk group had significantly poorer survival outcomes than those in the low-risk group. Then, GEO database (GSE84437) was employed for validation of the risk signature. Furthermore, the results obtained from Cox regression of univariate and multivariable proved that our risk signature was a prognostic indicator independent from other factors for STAD patients. After that, a nomogram was constructed and calibration plots confirmed that this nomogram could accurately predict survival probability for patients with STAD.\u003c/p\u003e \u003cp\u003eRecently, mutations identification is regarded as a critical step in cancer risk assessment and some published research implied somatic mutation plays pivotal roles in STAD [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Therefore, we investigated the mutation landscape of our gene signature, and then found that the mutation rate is higher in the low-risk group than in the high-risk one and TTN, TP53 and MUC16 are the most altered gene. In addition, missense mutation was the most frequent mutation in patients with STAD. These findings offer novel indicators for research on how STAD and somatic mutations are correlated with each other and for the formulation of more precise treatments in those STAD patients with gene mutations. In addition, there was a positive correlation between the risk score and lymph node metastasis, distant metastasis, clinical stage and tumor grade except for age and T stage.\u003c/p\u003e \u003cp\u003eTumor immunity is an important focus of cancer research, with some findings indicating strong relationships between histone acetylation and tumor immunity. Therefore, we examined the relationships between risk score and tumor microenvironment, immune cell infiltration and immune activity. Our findings suggested that the high-risk group had increased levels of infiltrating immune cells, enhanced activity of immune pathways and higher TME scores compared with the low-risk group. Tumor-associated macrophages (TAMs) were composed mostly of M0, M1 and M2 phenotypes, which have generally been found to promote cancer progression and development [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Research has defined M2 macrophages that play immunosuppressive and tumor promoting roles as TAMs. Our findings suggest that the level of macrophages M2 is higher in the high-risk group. This result could be a significant contributing factor to the unfavorable prognosis observed in the high-risk population. Cancer immunotherapy has made advances in the field of cancer treatments [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Thus, we analyzed the immunotherapy prediction of our histone acetylationrelated gene signature, and observed that high-risk group possessed lower MSI, lower TMB and higher TIDE scores. TCIA immunotherapy prediction revealed patients in the high-risk group had an inferior response to CTLA4 inhibitor. These findings confirmed that the low-risk group had a greater probability of responding effectively to immunotherapy than the high-risk group. The data regarding drug sensitivity provided the basis for the formulation of a more accurate treatment for STAD. We also performed functional analyses and found that DEGs in the two groups of high- and low-risk patients participated in cancer-related processes and pathways. For example, the Wnt signaling pathway and cGMP-PKG signaling pathway are strongly associated with the occurrence and progression of gastric cancer. Xiang et al. indicated that infection of H. pylori relies on ZEB1 to induce the over-expression of PRTG, which subsequently leads to the advancement of gastric cancer through activation of the cGMP/PKG signaling pathway [80]. Research has shown that the Wnt pathway is closely related to gastric cancer and its dysregulation has been observed in almost half of GC cases [81].\u003c/p\u003e \u003cp\u003eFinally, DCLK1 was selected for further experimental investigation to explore the molecular mechanism of the model genes, based on its highest correlation coefficient. In this research, we verified that down-regulation of DCLK1 significantly suppressed proliferation, colony forming ability, migration, and oxaliplatin resistance. This finding indicated that DCLK1 is a cancerpromoting factor in gastric cancer and can also promote oxaliplatin resistance in gastric cancer cells.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, the main source of our data was publicly available datasets and further in-depth research, and in vivo experiments may be required for validation of our results. Second, larger and multicentric clinical samples and other clinical features (e.g., smoking, family background, BMI) must be included in such research. Finally, the specific mechanisms that determine histone acetylation regulation of STAD must be examined.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA novel histone acetylationrelated gene signature, which possesses potential value in predicting the prognosis and immunotherapy effectiveness regarding STAD patients, was developed. This signature may serve as a reliable biomarker for prognosis of STAD and promote the identification of novel treatment targets for STAD. Furthermore, DCLK1 exhibited oncogenic roles and may act as a new target for STAD therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; DEGs, Differentially Expressed Genes; CDF, cumulative distribution function; KM, Kaplan Meier; TME, Tumor Microenvironment; TMB, tumor mutational burden; MSI, microsatellite instability; MSS, microsatellite instability-stable; MSI-H, microsatellite instability-high; MSI-L, microsatellite instability-low; PCA, principal-component analysis; t-SNE, t-distributed stochastic neighbor embedding; Oxa, Oxaliplatin\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82073148), the Science and Technology Planning Project of Guangdong Province (No. 2021B1212040006), Sanming Project of Medicine in Shenzhen (No. SZSM201911010), Shenzhen Key Medical Discipline Construction Fund (No. SZXK016), Research start-up fund of part-time PI, SAHSYSU (ZSQYJZPI202001), and Guangdong\u0026ndash;Hong Kong\u0026ndash;Macau University Joint Laboratory of Digestive Cancer Research (2021LSYS003).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eManuscript writing: CD, RS and ZZ; Conception and design: CD, RS and CZ; Data collection: ZZ and YG; Data analysis and interpretation: CD, RS and YG. Figure preparation:CD and RS. Experiments performation: CD and RS. Language polishing: SY and CZ. Manuscript editing: CZ, SY and JZ. Fund support: CZ and YH. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank all the members who contributed to this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data analyzed in this study will be accessible upon reasonable request. GSE84437 was sourced from GEO database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJim MA, Pinheiro PS, Carreira H, Espey DK, Wiggins CL, Weir HK. Stomach cancer survival in the United States by race and stage (2001\u0026ndash;2009): Findings from the CONCORD-2 study. Cancer. 2017;123(Suppl 24):4994\u0026ndash;5013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahari D. Second-line chemotherapy for patients with advanced gastric cancer. Gastric Cancer. 2017;20(3):395\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeans C, Maggert KA. What do you mean. epigenetic? Genet. 2015;199(4):887\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGayon J. From Mendel to epigenetics: History of genetics. C R Biol. 2016;339(7\u0026ndash;8):225\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBennett RL, Licht JD. Targeting Epigenetics in Cancer. Annu Rev Pharmacol Toxicol. 2018;58:187\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaypee S, Sudarshan D, Shanmugam MK, Mukherjee D, Sethi G, Kundu TK. Aberrant lysine acetylation in tumorigenesis: Implications in the development of therapeutics. Pharmacol Ther. 2016;162:98\u0026ndash;119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaskov H, Soby JH, Troelsen J, Bojesen RD, Gogenur I. Driver Gene Mutations and Epigenetics in Colorectal Cancer. Ann Surg. 2020;271(1):75\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarsli-Ceppioglu S, Dagdemir A, Judes G, Ngollo M, Penault-Llorca F, Pajon A, Bignon YJ, Bernard-Gallon D. Epigenetic mechanisms of breast cancer: an update of the current knowledge. Epigenomics. 2014;6(6):651\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan SI, Aumsuwan P, Khan IA, Walker LA, Dasmahapatra AK. Epigenetic events associated with breast cancer and their prevention by dietary components targeting the epigenome. Chem Res Toxicol. 2012;25(1):61\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegube G, Trouche D. Regulating histone acetyltransferases and deacetylases. EMBO Rep. 2003;4(10):944\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider A, Chatterjee S, Bousiges O, Selvi BR, Swaminathan A, Cassel R, Blanc F, Kundu TK, Boutillier AL. Acetyltransferases (HATs) as targets for neurological therapeutics. Neurotherapeutics. 2013;10(4):568\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEll B, Kang Y. Transcriptional control of cancer metastasis. Trends Cell Biol. 2013;23(12):603\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu N, Li S, Wu N, Cho KS. Acetylation and deacetylation in cancer stem-like cells. Oncotarget. 2017;8(51):89315\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWisnieski F, Calcagno DQ, Leal MF, Santos LC, Gigek CO, Chen ES, Demachki S, Artigiani R, Assumpcao PP, Lourenco LG, et al. CDKN1A histone acetylation and gene expression relationship in gastric adenocarcinomas. Clin Exp Med. 2017;17(1):121\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTani M, Ito J, Nishioka M, Kohno T, Tachibana K, Shiraishi M, Takenoshita S, Yokota J. Correlation between histone acetylation and expression of the MYO18B gene in human lung cancer cells. Genes Chromosomes Cancer. 2004;40(2):146\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalahi F, van Kruchten M, Martinet N, Hospers GA, Rots MG. Current and upcoming approaches to exploit the reversibility of epigenetic mutations in breast cancer. Breast Cancer Res. 2014;16(4):412.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen-Hur A, Elisseeff A, Guyon I. A stability based method for discovering structure in clustered data. Pac Symp Biocomput 2002:6\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRacle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife 2017, 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77(21):e108\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlattner C, Finotello F, Rieder D. Deconvoluting tumor-infiltrating immune cells from RNA-seq data using quanTIseq. Methods Enzymol. 2020;636:261\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinotello F, Mayer C, Plattner C, Laschober G, Rieder D, Hackl H, Krogsdam A, Loncova Z, Posch W, Wilflingseder D, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 2019;11(1):34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDienstmann R, Villacampa G, Sveen A, Mason MJ, Niedzwiecki D, Nesbakken A, Moreno V, Warren RS, Lothe RA, Guinney J. Relative contribution of clinicopathological variables, genomic markers, transcriptomic subtyping and microenvironment features for outcome prediction in stage II/III colorectal cancer. Ann Oncol. 2019;30(10):1622\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamminga M, Hiltermann TJN, Schuuring E, Timens W, Fehrmann RS, Groen HJ. Immune microenvironment composition in non-small cell lung cancer and its association with survival. Clin Transl Immunol. 2020;9(6):e1142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAran D. Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell. Methods Mol Biol. 2020;2120:263\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018;1711:243\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng QJ, Xie LQ, Li H. Overexpressed MALAT1 promotes invasion and metastasis of gastric cancer cells via increasing EGFL7 expression. Life Sci. 2016;157:38\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamura N, Kishimoto T. Epigenetic regulation of GATA4 expression by histone modification in AFP-producing gastric adenocarcinoma. Exp Mol Pathol. 2012;93(1):35\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWisnieski F, Leal MF, Calcagno DQ, Santos LC, Gigek CO, Chen ES, Artigiani R, Demachki S, Assumpcao PP, Lourenco LG, et al. BMP8B Is a Tumor Suppressor Gene Regulated by Histone Acetylation in Gastric Cancer. J Cell Biochem. 2017;118(4):869\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai Q, Ye Y. Development and Validation of a Novel Histone Acetylation-Related Gene Signature for Predicting the Prognosis of Ovarian Cancer. Front Cell Dev Biol. 2022;10:793425.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassari ME, Murre C. Helix-loop-helix proteins: regulators of transcription in eucaryotic organisms. Mol Cell Biol. 2000;20(2):429\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Wang M, Arrington J, Shan T, Yue F, Nie Y, Tao WA, Kuang S. Ascl2 inhibits myogenesis by antagonizing the transcriptional activity of myogenic regulatory factors. Development. 2017;144(2):235\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Zhao XL, Liu X, Hu XG, Fu WJ, Li Q, Wang Y, Ping YF, Zhang X, Bian XW, et al. Elevated ASCL2 expression in breast cancer is associated with the poor prognosis of patients. Am J Cancer Res. 2017;7(4):955\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo Q, Wang J, Chen C, Zhang Y, Feng DX, Zhao R, Chen T. ASCL2 expression contributes to gastric tumor migration and invasion by downregulating miR223 and inducing EMT. Mol Med Rep. 2018;18(4):3751\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Q, Wu BL. Ice breaking in GPCR structural biology. Acta Pharmacol Sin. 2012;33(3):324\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFidom K, Isberg V, Hauser AS, Mordalski S, Lehto T, Bojarski AJ, Gloriam DE. A new crystal structure fragment-based pharmacophore method for G protein-coupled receptors. Methods. 2015;71:104\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWittenberger T, Schaller HC, Hellebrand S. An expressed sequence tag (EST) data mining strategy succeeding in the discovery of new G-protein coupled receptors. J Mol Biol. 2001;307(3):799\u0026ndash;813.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Zhou W, Zhong Y, Huo Y, Fan P, Zhan S, Xiao J, Jin X, Gou S, Yin T, et al. Overexpression of G protein-coupled receptor GPR87 promotes pancreatic cancer aggressiveness and activates NF-kappaB signaling pathway. Mol Cancer. 2017;16(1):61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn HM, Choi EY, Kim YJ. GPR87 Promotes Metastasis through the AKT-eNOS-NO Axis in Lung Adenocarcinoma. Cancers (Basel) 2021, 14(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Liu D, Hayashida Y, Okazoe H, Hashimoto T, Ueda N, Sugimoto M, Kakehi Y. G Protein-Coupled Receptor 87 (GPR87) Promotes Cell Proliferation in Human Bladder Cancer Cells. Int J Mol Sci. 2015;16(10):24319\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai R, Zhang J, He F, Li Y, Dai P, Huang Z, Han L, Wang Z, Gong Y, Xie C. GPR87 promotes tumor cell invasion and mediates the immunogenomic landscape of lung adenocarcinoma. Commun Biol. 2022;5(1):663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Paoli F, Eeckhoute J, Copin C, Vanhoutte J, Duhem C, Derudas B, Dubois-Chevalier J, Colin S, Zawadzki C, Jude B, et al. The neuron-derived orphan receptor 1 (NOR1) is induced upon human alternative macrophage polarization and stimulates the expression of markers of the M2 phenotype. Atherosclerosis. 2015;241(1):18\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv J, Zhu Y, Ji A, Zhang Q, Liao G. Mining TCGA database for tumor mutation burden and their clinical significance in bladder cancer. Biosci Rep 2020, 40(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang XJ, Seto E. Collaborative spirit of histone deacetylases in regulating chromatin structure and gene expression. Curr Opin Genet Dev. 2003;13(2):143\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao L, Cueto MA, Asselbergs F, Atadja P. Cloning and functional characterization of HDAC11, a novel member of the human histone deacetylase family. J Biol Chem. 2002;277(28):25748\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhaskara S. Histone deacetylase 11 as a key regulator of metabolism and obesity. EBioMedicine. 2018;35:27\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillagra A, Cheng F, Wang HW, Suarez I, Glozak M, Maurin M, Nguyen D, Wright KL, Atadja PW, Bhalla K, et al. The histone deacetylase HDAC11 regulates the expression of interleukin 10 and immune tolerance. Nat Immunol. 2009;10(1):92\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J, Wang L, Dahiya S, Beier UH, Han R, Samanta A, Bergman J, Sotomayor EM, Seto E, Kozikowski AP, et al. Histone/protein deacetylase 11 targeting promotes Foxp3\u0026thinsp;+\u0026thinsp;Treg function. Sci Rep. 2017;7(1):8626.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi L, Ren Y, Feng M, Meng P, Wang Q, Chen W, Jiao Q, Wang Y, Du L, Zhou F, et al. HDAC11 Regulates Glycolysis through the LKB1/AMPK Signaling Pathway to Maintain Hepatocellular Carcinoma Stemness. Cancer Res. 2021;81(8):2015\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Wu X, Zhao P, Xue K, Li J. A pan-cancer analysis identifies HDAC11 as an immunological and prognostic biomarker. FASEB J. 2022;36(7):e22326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin PT, Gleeson JG, Corbo JC, Flanagan L, Walsh CA. DCAMKL1 encodes a protein kinase with homology to doublecortin that regulates microtubule polymerization. J Neurosci. 2000;20(24):9152\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSureban SM, May R, Lightfoot SA, Hoskins AB, Lerner M, Brackett DJ, Postier RG, Ramanujam R, Mohammed A, Rao CV, et al. DCAMKL-1 regulates epithelial-mesenchymal transition in human pancreatic cells through a miR-200a-dependent mechanism. Cancer Res. 2011;71(6):2328\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSureban SM, May R, Mondalek FG, Qu D, Ponnurangam S, Pantazis P, Anant S, Ramanujam RP, Houchen CW. Nanoparticle-based delivery of siDCAMKL-1 increases microRNA-144 and inhibits colorectal cancer tumor growth via a Notch-1 dependent mechanism. J Nanobiotechnol. 2011;9:40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSureban SM, Madhoun MF, May R, Qu D, Ali N, Fazili J, Weygant N, Chandrakesan P, Ding K, Lightfoot SA, et al. Plasma DCLK1 is a marker of hepatocellular carcinoma (HCC): Targeting DCLK1 prevents HCC tumor xenograft growth via a microRNA-dependent mechanism. Oncotarget. 2015;6(35):37200\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeygant N, Qu D, May R, Tierney RM, Berry WL, Zhao L, Agarwal S, Chandrakesan P, Chinthalapally HR, Murphy NT, et al. DCLK1 is a broadly dysregulated target against epithelial-mesenchymal transition, focal adhesion, and stemness in clear cell renal carcinoma. Oncotarget. 2015;6(4):2193\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YL, Li Y, Ma YG, Wu WY. DCLK1 promotes malignant progression of breast cancer by regulating Wnt/beta-Catenin signaling pathway. Eur Rev Med Pharmacol Sci. 2019;23(21):9489\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFesler A, Liu H, Ju J. Modified miR-15a has therapeutic potential for improving treatment of advanced stage colorectal cancer through inhibition of BCL2, BMI1, YAP1 and DCLK1. Oncotarget. 2018;9(2):2367\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia Y, Liu Y, Feng L, Sun S, Sun G. Role of Glucagon and Its Receptor in the Pathogenesis of Diabetes. Front Endocrinol (Lausanne). 2022;13:928016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNauck MA, Meier JJ. The incretin effect in healthy individuals and those with type 2 diabetes: physiology, pathophysiology, and response to therapeutic interventions. Lancet Diabetes Endocrinol. 2016;4(6):525\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell JE, Drucker DJ. Pharmacology, physiology, and mechanisms of incretin hormone action. Cell Metab. 2013;17(6):819\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Deng Z, Chen Y, Gao Y, Wu D, Zhu G, Li L, Song W, Wang X, Wu K et al. Overexpression of FABP7 promotes cell growth and predicts poor prognosis of clear cell renal cell carcinoma. \u003cem\u003eUrol Oncol\u003c/em\u003e 2015, 33(3):113 e119-117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHotamisligil GS, Bernlohr DA. Metabolic functions of FABPs\u0026ndash;mechanisms and therapeutic implications. Nat Rev Endocrinol. 2015;11(10):592\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStorch J, Thumser AE. Tissue-specific functions in the fatty acid-binding protein family. J Biol Chem. 2010;285(43):32679\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian W, Zhang W, Zhang Y, Zhu T, Hua Y, Li H, Zhang Q, Xia M. FABP4 promotes invasion and metastasis of colon cancer by regulating fatty acid transport. Cancer Cell Int. 2020;20:512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuaita-Esteruelas S, Bosquet A, Saavedra P, Guma J, Girona J, Lam EW, Amillano K, Borras J, Masana L. Exogenous FABP4 increases breast cancer cell proliferation and activates the expression of fatty acid transport proteins. Mol Carcinog. 2017;56(1):208\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong X, Seelan RS, Qian C, Mai M, Liu W. Genomic structure, chromosome mapping and expression analysis of the human AXIN2 gene. Cytogenet Cell Genet. 2001;93(1\u0026ndash;2):26\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang SM, Mishina YM, Liu S, Cheung A, Stegmeier F, Michaud GA, Charlat O, Wiellette E, Zhang Y, Wiessner S, et al. Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature. 2009;461(7264):614\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu HT, Yang LH, Li QC, Liu SL, Liu D, Xie XM, Wang EH. Disabled-2 and Axin are concurrently colocalized and underexpressed in lung cancers. Hum Pathol. 2011;42(10):1491\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaal U, Grenz S, Merkel S, Rau TT, Hadjihannas MV, Kremmer E, Chudasama P, Croner RS, Behrens J, Sturzl M, et al. Expression and localization of axin 2 in colorectal carcinoma and its clinical implication. Int J Colorectal Dis. 2013;28(11):1469\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoseph NM, Tsokos CG, Umetsu SE, Shain AH, Kelley RK, Onodera C, Bowman S, Talevich E, Ferrell LD, Kakar S, et al. Genomic profiling of combined hepatocellular-cholangiocarcinoma reveals similar genetics to hepatocellular carcinoma. J Pathol. 2019;248(2):164\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen K, Yang D, Li X, Sun B, Song F, Cao W, Brat DJ, Gao Z, Li H, Liang H, et al. Mutational landscape of gastric adenocarcinoma in Chinese: implications for prognosis and therapy. Proc Natl Acad Sci U S A. 2015;112(4):1107\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCancer Genome Atlas Research N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517):202\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Palma M, Lewis CE. Macrophage regulation of tumor responses to anticancer therapies. Cancer Cell. 2013;23(3):277\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLocati M, Curtale G, Mantovani A. Diversity, Mechanisms, and Significance of Macrophage Plasticity. Annu Rev Pathol. 2020;15:123\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017;18(1):248\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang T, Yuan C, Guo X, Wang H, Cai Q, Xiang Y, Luo W, Liu G. The novel ZEB1-upregulated protein PRTG induced by Helicobacter pylori infection promotes gastric carcinogenesis through the cGMP/PKG signaling pathway. Cell Death Dis. 2021;12(2):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoushyar S, Powell AG, Vincan E, Phesse TJ. Targeting Wnt Signaling for the Treatment of Gastric Cancer. Int J Mol Sci 2020, 21(11).\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4689949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4689949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGastric cancer (GC) is a very aggressive, with extreme heterogeneity and rapid growth, most frequently manifested histologically as stomach adenocarcinoma (STAD). Current evidence suggests that histone acetylation is critical for the origin and development of tumors. However, the significance of histone acetylationrelated gene signatures for prognosis of STAD patients and mechanisms of histone acetylation in STAD therapy remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe identified histone acetylationrelated genes in STAD from TCGA and constructed eight-gene signatures by utilizing a univariate Cox regression model with the Least Absolute Shrinkage and Selection Operator (LASSO). In addition, a nomogram was plotted to predict the prognostic significance of the established risk model. We examined associations between our gene signature and somatic mutation, immune subtype, clinicopathological features, tumor microenvironment, immune cell infiltration and immune activity, immunotherapy prediction and drug sensitivity. Cell-based assays were performed to determine the relationship between Doublecortin Like Kinase 1 (DCLK1) and the proliferation, migration and oxaliplatin resistance of GC cells \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA prognostic model composed of eight histone acetylationrelated genes in STAD was developed. Based on median risk score, the STAD patients were equally assigned into two groups of high- and low-risk, where high-risk represented a less favorable prognosis than low-risk. The two groups showed significant differences with respect to somatic mutation, immune subtype, clinicopathological features, tumor microenvironment, immune cell infiltration and immune activity, immunotherapy prediction and drug sensitivity. The results generated during Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that Differentially Expressed Genes (DEGs) in the two groups were involved in cancer-related processes and pathways. Cell-based assays indicated that DCLK1 is a promoting factor in gastric cancer and can promote oxaliplatin resistance in gastric cancer cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA novel histone acetylationrelated gene signature, which possesses potential value in predicting the prognosis and immunotherapy effectiveness regarding STAD patients, was developed. This signature may serve as a reliable biomarker for prognosis of STAD and promote the identification of novel treatment targets for STAD. Furthermore, DCLK1 exhibited oncogenic roles and may be a new target for STAD therapy.\u003c/p\u003e","manuscriptTitle":"The Histone Acetylation-Related Gene Signature for Prediction of Prognosis and Immunotherapy Efficacy in Stomach Adenocarcinoma and Verification in vitro","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 14:59:23","doi":"10.21203/rs.3.rs-4689949/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":"79273087-90d3-45bb-bcba-74b4e2016d6a","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-29T12:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 14:59:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4689949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4689949","identity":"rs-4689949","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}