
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12646-1
10.1016/j.heliyon.2024.e36615
e36615
Research Article
Identification of a novel histone acetylation-related long non-coding RNA model combined with qRT-PCR experiments for prognosis and therapy in gastric cancer
Wu Zhixuan ab
Yang Xuejia a
Yuan Ziwei a
Guo Yangyang yang984054863@163.com
b⁎
Wang Xiaowu wangxiaowu_email@163.com
cd⁎⁎
Qu Liangchen 1106083035@qq.com
b⁎⁎⁎
a Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
b Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, 318000, China
c Department of Burns and Skin Repair Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, China
d Department of Thyroid and Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, 315000, China
⁎ Corresponding author. yang984054863@163.com
⁎⁎ Corresponding author. Department of Burns and Skin Repair Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, China. wangxiaowu_email@163.com
⁎⁎⁎ Corresponding author. 1106083035@qq.com
22 8 2024
15 9 2024
22 8 2024
10 17 e3661528 10 2023
12 8 2024
19 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Gastric cancer (GC) is considered a global health crisis due to the scarcity of early diagnostic methods. Numerous studies have substantiated the involvement of histone acetylation imbalance in the progression of diverse tumor types. The potential roles of long non-coding RNA (lncRNA) in improving prognostic, predictive as well as therapeutic approaches in cancers have made it a major hotspot in recent years. Nevertheless, existent studies have never concerned the prognostic and clinical value of histone acetylation-related lncRNAs (HARlncs) in GC. Based on the aforementioned rationale, we developed a prognostic model incorporating four HARlncs—AC114730.1, AL445250.1, LINC01778, and AL163953.1—which demonstrated potential as an independent predictor of prognosis. Subsequently, GC patients were stratified into high-risk and low-risk groups. The low-risk group exhibited significantly higher overall survival (OS) compared to the high-risk group. Based on the analyses of the tumor microenvironment (TME) and immune responses, significant differences were observed between the two risk groups in terms of immune cell infiltration, immune checkpoint (ICP) expression, and other TME alterations. Furthermore, the sensitivity of GC patients to some chemotherapeutic drugs and the discrepant biological behaviors of three tumor clusters were studied in this model. In summary, we developed an effective HARlncs model with the objective of offering novel prognostic prediction methods and identifying potential therapeutic targets for GC patients.

Keywords

Histone acetylation
Long non-coding RNA
Gastric cancer (GC)
Prognostic
Predictive model
==== Body
pmc1 Introduction

Gastric cancer (GC) is globally recognized as the fifth most common malignancy and ranks third in terms of cancer-related mortality [1]. The prognosis of GC is profoundly influenced by the stage of the disease at the time of diagnosis [2,3]. Numerous research studies have suggested that early detection of GC can lead to a 5-year survival rate of over 90 % [4]. Nowadays, Endoscopy is widely applied to early diagnosis of GC because of its accuracy whereas its misdiagnosis occurs commonly. With the addition of the non-specific symptomatology in identifying early GC from other gastric diseases, more than 80 % of GCs miss the chance to be diagnosed at an early stage [5]. Hence, there is a pressing need for further research to identify reliable biomarkers for the diagnosis and treatment of GC.

Epigenetics pertains to alterations in gene expression without modifications to the DNA sequence [6]. The primary systems involved include histone modifications, DNA methylation, and RNA-related mechanisms [7], all of which have been recognized as regulators of gene expression associated with tumors and as potential therapeutic targets [8]. In eukaryotic cells, chromatin is constituted by approximately 147 base pairs of DNA wrapped around a histone octamer [9]. The post-translationally modification (PTM) on the terminal tails of histone proteins, such as acetylation, methylation and ubiquitination [10], could influence diverse cellular processes by changing the chromatin status and subsequent gene expression [11]. Specifically, histone acetylation is recognized as a dynamic histone modification [12,13]. There is a delicate equilibrium between the antagonistic actions of histone acetyltransferases (HATs) and histone deacetylases (HDACs) to effectively govern the process of histone acetylation [14]. HAT could transfer the acetyl groups to N-terminal lysine residues of histones, induce conformational changes in nucleosome and coordinate the recruitment as well as activation of transcription factors by histones [15,16]. HDACs exert a counteractive influence on lysine residue, impeding the functionality of the transcription machinery within the promoter region and suppressing the synthesis of mRNA [17]. Currently, a significant discrepancy exists in the activities of HAT and HDAC within various cancer cells, leading to the inhibition of gene transcription associated with vital processes such as control of differentiation, apoptosis, and cell cycle arrest [6]. Consequently, it is imperative to ascertain the prognostic significance of histone acetylation in cancer progression.

Long non-coding RNA (lncRNA) constitutes a classification of RNA molecules that exceed a length of 200 nucleotides and lack the capacity for protein encoding. Nevertheless, these molecules substantially contribute to a range of critical physiological processes, such as metabolism and immunity [18,19]. Numerous studies have demonstrated that lncRNAs are involved in a variety of cellular processes and the pathogenesis of human diseases, particularly cancer [20]. For instance, an increasing number of lncRNAs have been identified as biomarkers for malignant tumors, including exosomal H19 [21]. Furthermore, lncRNAs influence the expression of downstream oncogenes and tumor suppressors, which may provide insights for cancer therapy [22]. In the related studies of GC, lncRNAs have also been shown to facilitate the initiation and progression of malignant tumors [23]. Hence, gaining a comprehensive understanding of the functional role of lncRNAs in the progression of GC would greatly contribute to the advancement of diagnostic techniques and systematic treatment strategies.

At present, most research efforts are directed towards examining the role of either protein acetylation or lncRNAs in the development of GC, with relatively few studies integrating both factors. Therefore, our aim is to develop a prognostic model that incorporates lncRNAs linked to histone acetylation in GC, which may provide valuable insights for early detection and therapeutic intervention. The workflow diagram for this study is illustrated in Fig. 1.Fig. 1 Identification of HARlncs in GC patients. (A, B) Co-expression network of histone acetylation-related regulators and lncRNAs. (C) Heatmap for expression of HARlncs in tumor and normal tissue. (D) Volcano map for differential expressed HARlncs. (E) 13 lncRNAs were identified correlated with the prognosis of patients by univariate Cox regression analysis. (F) LASSO-Cox regression and the coefficients of the 13 OS-related lncRNAs. (G) Cross-validation for LASSO regression. (H) Correlations between 4 lncRNAs involved in the prognostic model and histone acetylation regulators. (I) Interactions between 4 significant histone acetylation regulators and 13 OS-related lncRNAs.

Fig. 1

2 Materials and methods

2.1 Data collection

Firstly, we acquired the RNA-sequencing gene expression data for GC and relevant clinical datasheets from The Cancer Genome Atlas (TCGA) Data Portal. The expected read counts were downloaded as number of fragments per kilobase million (FPKM) for further analysis. A total of 337 patients with a follow-up period of more than 28 days were included for subsequent analysis and were divided into two sets at random: training set(n = 169) and validation set (n = 168) in the ratio of 1:1. Baseline clinical characteristics were presented in the Supplementary Table S1.

2.2 Identification of differentially expressed histone acetylation-related lncRNAs (HARlncs)

A total of 40 histone acetylation-related regulators presented in Supplementary Table S2 were obtained from a review [24]. In order to identify HARlncs, pearson's correlation analysis was performed. The cutoff criteria for identifying lncRNAs that exhibited differential expression between tumor and normal tissues were set at a correlation coefficient (|R|) greater than 0.4 and a P-value less than 0.001. The limma package was utilized to calculate the distinguishing expression levels of these lncRNAs. To determine the statistical significance of differential expression in HARlncs, a threshold of a | log2 fold change (FC) | greater than 1.0 and FDR-corrected P-value less than 0.05 was employed.

2.3 Construction and validation of a HARlncs prognostic model for GC

In order to investigate the prognostic significance of each lncRNA related to histone acetylation, we established a correlation between the expression levels of these lncRNAs and survival outcomes, including survival status and overall survival time. Prognosis-related lncRNAs were identified through univariate Cox analysis, with a significance level of P < 0.05 as the inclusion criterion. Subsequently, the variables were further filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, a well-known machine learning algorithm, implemented in the "glmnet" R package. Ultimately, 4 lncRNAs with their corresponding nonzero coefficients were identified by minimum criteria. Risk score for each GC patient was calculated using the following formula: risk score = ∑Coefi × lncRNAi where Coefi was the coefficient and lncRNAi was the corresponding expression of lncRNA. The GC patients were stratified into two groups, low-risk and high-risk, based on the median risk score. Kaplan-Meier analysis was employed to compare overall survival (OS) between these subgroups. The robustness of the risk model was assessed using receiver operating characteristic (ROC) curves. To confirm the reliability of the results, the same analytical procedure was applied to both the validation cohort and the entire cohort from TCGA.

2.4 Development and assessment of the predictive nomogram

To screen for prognostic-relevant features in GC patients, we implemented univariate Cox regression algorithm. Moreover, we employed the multivariable cox regression to identify independent predictors of prognosis. Both models included clinical features of age, sex, tumor grade, and tumor-node-metastasis (TNM) stage. On the basis of independent predictors, a nomogram was developed with the purpose of predicting an individual's OS of GC patient by “survival” as well as “rms” packages. In addition, the calibration curve and ROC curve were utilized to evaluate the deviation of the nomogram.

2.5 Tumour microenvironment and immunity analysis

The CIBESORT [25], MCPcounter [26], ssGSEA [27], ESTIMATE [28] as well as TIMER [29] were used in order to quantify the enrichment levels of cellular components or cellular immune responses between two risk groups on the basis of the HARlincs signature. A bubble chart was graphed to reveal variations in immune response under various algorithms. The expression level of ICPs was obtained from precious studies and was compared between the two subgroups by using the R packages “ggplot2” and “reshape2”. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was employed to evaluate the efficacy of immunotherapy [30].

2.6 Functional enrichment analysis

To identify differentially expressed genes (DEGs) between two risk subgroups, “limma” R package was utilized. Genes with |log2FC|≥1, FDR<0.05 were considered as DEGs. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were implemented by the “cluster Profiler” R package on the basis of these DEGs [31].

2.7 Prediction of drug sensitivity

The Genomics of Drugs Sensitivity in Cancer (GDSC) is the largest pharmacogenomics online database. To predict the chemotherapeutic sensitivity of the prognostic model, we assessed the IC50 values of BEZ235, CGP-60474, Dasatinib, Gefitinib, Gemcitabine, Tipifarnib, Mitomycin C, GSK19041904529A, XAV939 drugs from the GDSC database by using the pRRophetic algorithm with ridge regression.

2.8 Tumor classification based on HARlncs

To identify the correlation between HARlncs and GC subtypes, the R package “Consensus Cluster Plus” was implemented for clustering.

2.9 Mutation status analysis

The mutation status of somatic variants was processed by using R package “maftools”. Genes involved in tumor-specific mutations were used in the calculation of tumor mutation burden (TMB) [32].

2.10 Validation of the four lncRNAs

The GES-1 and HGC-27 cell lines were obtained from the Cell Bank of the Shanghai Institute of Biochemistry and Cell Biology. GES-1 cells were cultured in DMEM, while HGC-27 cells were cultured in RPMI-1640. The RT-qPCR procedure was carried out as follows: total RNA was extracted from the cells using RNAiso Plus reagent, and cDNA was synthesized using the PrimeScript RT reagent kit. The cDNA was then mixed with the corresponding primers, and qRT-PCR was performed using SYBR Green reagent. The specific primer sequences can be found in Supplementary Table S3.

2.11 Statistical analysis

All statistical calculations and visualizations were performed by R software. P ≤ 0.05 was statistically significant unless specified otherwise.

3 Results

3.1 Extraction of HARlncs with noteworthy prognostic value

Totally 40 histone acetylation-related regulators were obtained as previously described. A total of 678 HARlncs shown in Supplementary Table S4 were determined using Pearson correlation analyses for histone acetylation-associated regulators and lncRNAs. A co-expression network of histone acetylation-related regulators and lncRNAs was shown in Fig. 1A-B. The expression levels of 678 long non-coding RNAs (lncRNAs) associated with histone acetylation were compared between gastric cancer (GC) and normal tissues (Fig. 1C-D). As a result, 360 lncRNAs were identified as differentiallyexpressed and are listed in Supplementary Table S5. Of these lncRNAs, 13 lncRNAs exhibited a significant correlation with patient prognosis, as determined through univariate Cox regression analysis (Fig. 1E).

3.2 Development and verification of the histone acetylation-related lncRNA model

A fivefold cross-validation analysis was used in LASSO-Cox regression to further increase the dependability and usability of signatures (Fig. 1F-G). Ultimately, 4 lncRNAs were involved in the model in accordance with the optimum λ value and the prognostic significance of each lncRNA. The correlations between 4 lncRNAs and acetylation-related regulators were shown in Fig. 1H-I. Based on each lncRNA’ s coefficient, the risk score was calculated by the following formula: risk score = −2.114*AC114730.1(exp) +1.101*AL445250.1(exp) +0.940*LINC01778(exp) −0.507*AL163953.1(exp). Our examination of the patient risk survival status maps demonstrated a notable negative correlation between risk scores and both survival rates and survival times of GC patients within the training cohort, as depicted in Fig. 2B. We employed a heatmap to illustrate the expression of four acetylation-related lncRNAs linked with prognostic patterns (Fig. 2E). The PCA analysis further highlighted a distinct separation between two disparate risk groups (Fig. 2H). To assess the model's accuracy, the distribution of risk scores among GC patients and their corresponding survival outcomes are illustrated in Fig. 2A, C, 2D, 2F, 2G, 2I, 2J, and 2L for both the validation cohorts and the entire dataset. Consistent with the results from the training cohort, patients categorized as low-risk demonstrated significantly better survival outcomes compared to those classified as high-risk across both cohorts.Fig. 2 Prognostic value analysis of the signature based on TCGA. (A, B, C) The distribution of GC patients and their survival status on the basis of the median risk score. (D, E, F) The heatmap for expression of the 4 OS-related lncRNAs between two risk groups. (G, H, I) PCA plot on the basis of the risk score. (J, K, L) Kaplan–Meier curves result.

Fig. 2

3.3 Identification of independent prognostic value

The forest plot demonstrated a significant association between the risk score and overall survival (p < 0.001, Fig. 3A-B). Subsequently, a nomogram was developed using the independent prognostic factor as a basis to predict survival rates at 1, 3, and 5 years (Fig. 3C). The calibration plots combining clinicopathological characteristics and acetylation-related lncRNA-based risk score showed that the nomogram had outstanding performance in predicting possibility of 1-, 3-, 5-years OS (Fig. 3D). Additionally, we assessed the accuracy of the nomogram in predicting OS using ROC curves. Compared to clinical characteristics, the nomogram revealed optimal predictive performance with AUCs of 0.721, 0.741, 0.744 for 1-, 3-, and 5-year survival, respectively (Fig. 3E–G). In order to establish the clinical relevance of the prognostic model, a subgroup survival analysis was conducted on the complete cohort, incorporating the clinicopathological variables of age, gender, grade, and TNM stage. The findings, as depicted in Fig. 4C, revealed a notably inferior prognosis among patients classified in the high-risk group compared to those in the low-risk group.Fig. 3 The independent prognostic value of the risk score. (A) Univariate (B) multivariate Cox regression analysis. (C) Nomogram model contained age, gender, grade, T, N, and M and risk to predict OS. (D) Calibration curves for nomogram in 1-, 3-, 5-year OS. (E, F, G) ROC analysis for predicting 1-, 3-, 5-year OS of the nomogram, risk, age, gender, grade as well as stage.

Fig. 3

Fig. 4 Somatic mutations landscape and K–M survival analysis of GC patients in different clinical subgroups. (A, B) Waterfall plots for highest mutation frequency of top 20 genes. (C) K–M curves for subgroup survival analysis.

Fig. 4

3.4 The mutation landscape

Based on the mutation data derived from the TCGA cohort, somatic hypermutation has been identified as a characteristic feature of GC. Consequently, the mutation landscapes and TMB between two risk groups were compared. The genes with the highest mutation frequencies in the top 20 were illustrated in Fig. 4A and B. Analysis of the waterfall chart revealed that TTN, TP53, and MUC16 were the most frequently mutated genes in patients from different risk groups, with mutation rates of 45 % vs. 49 %, 36 % vs. 47 %, and 26 % vs. 33 %, respectively. Additionally, missense mutations were identified as the most prevalent mutation type in GC.

3.5 Assessment of TME and immunotherapy response

Subsequently, we constructed a bubble chart to elucidate the disparities in immune cell infiltration between two risk groups, employing a variety of previously mentioned immune cell infiltration algorithms (Fig. 5B). Moreover, through the application of ESTIMATE algorithms, we discerned that the high-risk group manifested heightened stromal, immune, and ESTIMATE scores relative to the low-risk group (Fig. 5A). Given the importance of immunotherapy strategies based on checkpoint inhibitors, an investigation was conducted to examine disparities in the overall expression levels of ICPs. Notably, significant distinctions were identified in the expression of VTLA, CD160, CD244, TIGIT, CD96, and IL10 (Fig. 5C). Furthermore, we conducted calculations to determine the infiltrating scores of 16 prevalent immune cell types and the levels of enrichment for 13 immune-related pathways (Fig. 5D-E). The findings revealed a significant increase in both the quantity of immune cells and the enrichment scores of immune pathways, such as HLA, inflammation-promoting, T cell co-stimulation, Type I and II IFN Response, within the high-risk group. Fig. 5F demonstrates a significant negative correlation between the risk score and TMB. Furthermore, the integration of the risk score and TMB successfully distinguished patients with favorable and unfavorable prognoses, as depicted in Fig. 5G.Fig. 5 TME analysis in the whole TCGA patients. (A) Violin plots for the Stromal Score, Immune score, ESTIMATE score. (B) A bubble chart of different immune cell infiltration algorithms. (C, D, E) Boxplots of ICPs expression, immune cells infiltrating score, immune-related pathway enrichment score. (F) Distribution of TMB in different risk score groups and the correlation of risk score with TMB. (G) K–M curves for the prognostic value of TMB combined with risk score.

Fig. 5

3.6 Function enrichment analysis

We utilized the GO and KEGG analysis to explore the potential functions of the DEGs between high- and low-risk groups. GO result showed that these DEGs were mainly correlated with cell substrate adhesion, contractile fiber and actin binding (Fig. 6A-B). Furthermore, KEGG analysis showed notably enrichment of vascular smooth muscle contraction, focal adhesion and cGMP-PKG pathway (Fig. 6C-D). Moreover, the GSEA algorithm was applied to perform KEGG pathway analysis between two risk subgroups and the result indicated that a major of DEGs were enriched in tumor-related pathways (Fig. 6E and F).Fig. 6 Function enrichment analysis on the basis of the DEGs between two different risk groups. (A) A bubble chart for GO enrichment. (B)The GO circle shows the specified GO enrichment terms. (C) A bubble chart for KEGG pathways. (D)The KEGG circle shows the specified GO enrichment terms. (E, F) GSEA analysis between two different risk groups.

Fig. 6

3.7 Identification of chemotherapy drug sensitivity on the basis of the prognostic model

Chemotherapy remains the primary treatment modality for gastric cancer. The prognosis is often poor due to the development of chemoresistance. Consequently, we aimed to predict the IC50 values of various chemotherapy drugs for two distinct risk subgroups. The graphical data presented in Fig. 7 indicate higher predicted IC50 values for six chemotherapy agents (gefitinib, gemcitabine, tipifarnib, mitomycin C, GSK1904529A, XAV939) in the high-risk subgroup, suggesting that these drugs may be more efficacious in low-risk patients. Additionally, it is apparent that patients classified as high-risk demonstrated increased sensitivity to BEZ235, CGP60474, and dasatinib.Fig. 7 Sensitivity to chemotherapeutics in different risk groups based on prognostic models.

Fig. 7

3.8 Tumor classification based on HARlncs

We performed consistent clustering analysis to assess the impact of HARlncs in GC patients. Based on the analysis of the Cumulative Distribution Function (CDF) curves, a cluster number of K = 3 was determined to be the optimal choice, as depicted in Fig. 8A–D. A clear distinction among the three clusters was observed by using PCA as well as the t-SNE analysis (Fig. 8E). Based on these results, GC patients could be divided into three clusters and significantly different OS and progression free survival (PFS) was observed among the three subtypes (Fig. 8F and Supplementary Fig. S1). In our study, we conducted an evaluation of the disparities in immune scores, immune cell infiltration, and the expression levels of ICPs among the three identified subgroups (Fig. 8, Fig. 9A–C). Our findings revealed significant differences among these groups. Utilizing the Tumor Immune Dysfunction and Exclusion (TIDE) database, we discerned that responses to immunotherapy varied significantly across the subgroups, as depicted in Fig. 9D. Additionally, we made predictions regarding the response of the three subgroups to chemotherapy (Fig. 9E).Fig. 8 Tumor classification based on HARlncs. (A) Consistent cumulative Distribution Function (CDF) Plot. (B) Delta area plot. (C) Tracking plot. (D) Consensus Clustering Matrix Plot (K = 3). (E) The PCA plot and the t-SNE analysis of the three clusters. (F) K-M curves of overall survival (OS) in three subgroups. (G) Violin plots for the Stromal Score, Immune score, ESTIMATE score of the three subgroups.

Fig. 8

Fig. 9 Differences in immune response and chemosensitivity among the three subgroups. (A)Violin plots for immune cell infiltration of the three subgroups. (B, C) Boxplots of ICPs expression (D) Prediction of immunotherapy response based on TIDE score. (E) Sensitivity to 8 chemotherapeutics in different subgroups.

Fig. 9

3.9 Validation of the four identified diff-lncRNAs

As shown in Supplementary Fig. S2, all lncRNAs (AC114730.1, AL445250.1, LINC01778, AL163953.1) involved in the model were remarkably up-regulated in HGC-27 in comparison with the GES-1 cell line. The results showed that the expression of the four lncRNAs could be validated in cell lines. What's exciting is that the four lncRNAs (AC114730.1, AL445250.1, LINC01778, AL163953.1) may be biomarkers to predict the prognosis of GC patients.

4 Discussion

Histone acetylation is integral to various cellular processes, including the cell cycle, differentiation, and apoptosis. Aberrations in acetylation levels can result in abnormal gene expression and disrupt critical physiological functions, thereby facilitating tumorigenesis [33]. Alterations in histone acetylation have been observed in multiple cancer types [34] and may hold promise as a prognostic indicator [35]. LncRNAs play a crucial role in the regulation of diverse biological processes in GC, encompassing DNA damage repair, proliferation, immune evasion, and resistance to therapeutic agents. Additionally, a significant number of lncRNAs identified in plasma or serum demonstrate considerable potential for prognostic prediction in GC [36]. Although extensive research has been conducted on histone acetylation and lncRNAs, there remains a paucity of comprehensive studies examining the role of HARlncs in the prognosis and treatment of gastric cancer.

In this study, we identified 13 HARlncs significantly relevant with the prognosis of GC. Of these, four lncRNAs (AC114730.1, AL445250.1, LINC01778, AL163953.1) were selected to construct the prognostic model. Notably, the functions of these lncRNAs in cancer development and prognosis have been scarcely reported. Therefore, further research is warranted to elucidate the clinical significance and underlying mechanisms of these lncRNAs in cancer.

In accordance with the model, the GC patients were categorized into two subgroups: high-risk and low-risk group. In the training cohort, the high-risk group exhibited lower OS rates and shorter survival times. The model's reliability in predicting patient survival was validated through PCA and ROC analysis. Subsequently, a nomogram was developed based on several prognostic factors, demonstrating robust performance in predicting OS compared to traditional clinicopathological features such as age, gender, grade, and TNM stage. As evidenced by our findings, patients in the high-risk group exhibited a significantly poorer prognosis. Furthermore, notable differences were observed between the two risk groups in terms of mutation landscape, TME, immunotherapy response, and functional enrichment analysis.

Somatic mutations are the key mechanism for the development of most tumors, including gastric cancer [37]. In this study, the most significantly altered genes (TTN, TP53, and MUC16) were identified between the two groups. The TTN gene is responsible for encoding muscle connectin, which plays a crucial role in regulating muscle's passive elasticity, as well as contributing to the development, structure formation, and functional regulation of cardiac and skeletal muscles [38]. Notably, TTN mutation ranks as the second most prevalent mutation across various cancer types. Furthermore, it has been observed that TTN mutation exhibits a favorable response to ICPs blockage in solid tumors [39]. However, the mechanisms underlying the role of TTN in cancer remain elusive; nonetheless, its significant potential for further investigation is evident due to its involvement in tumorigenesis and disease progression [40]. TP53, a gene frequently mutated in various types of cancer, has been extensively studied and established as a highly intricate biomarker and a target for pharmacological intervention [41]. MUC16, also known as CA125, is a cell surface glycoprotein that was initially discovered in 1981 [42]. It not only protects epithelial cells but also participates in oncogenic processes and serves as a biomarker for various tumor types, such as ovarian cancer [43]. Mutations in MUC16 are frequently observed in gastric cancer, contributing to improved prognosis and elevated tumor mutational burden (TMB) in this malignancy [44].

The development of GC is influenced by the TME, which plays a critical role in invasion and metastasis [45]. In our study, we observed a more pronounced immune cell infiltration in the TME of high-risk patients. Immune checkpoints and associated pathways, including HLA, proinflammatory responses, T-cell costimulation, and type I and II interferon (IFN) responses, exhibited differential behavior between the two risk groups. The TME of high-risk patients is likely indicative of a poor prognosis [46].

Finally, guided by our prognostic model, we investigated the differential responses to chemotherapy among patients with varying risk profiles. Besides, in TCGA cohort, GC patients were stratified into three distinct clusters based on histone acetylation-associated lncRNAs. The immune microenvironment and responses to both immunotherapy and chemotherapy were validated to differ across these clusters.

Despite this study presented many encouraging data for the prognosis of gastric cancer, there still exists some limitations. For instance, our study was not validated with clinical samples, and the four newly identified lncRNAs need more exploration. Moreover, the accuracy of our model requires validation in clinical cohorts and well-designed clinical trials to offer practical assistance in personalized treatment and clinical decision-making for patients with GC. In brief, our study established a prognosis model of histone acetylation related lncRNAs in GC, providing further information on the prognosis, immune status and chemotherapy sensitivity of GC. Perhaps it may offer more research ideas for early diagnosis and treatment in GC patients.

5 Conclusion

In summary, we identified HARlncs and constructed a GC prognostic model on the basis of four histone acetylation-associated lncRNAs (AC114730.1, AL445250.1, LINC01778 and AL163953.1). In addition, the model not merely predicted chemotherapeutic drugs for adjuvant treatment of different risk groups but also determined the sensitivity of GC patients toward immunotherapy. Therefore, the model lays the root for further identifying potential biomarkers in GC and predicting the prognosis of GC patients.

Funding

This study was supported by the Ningbo Natural Science Fund (2019A610335 ), Zhejiang Province Medical Project (2020KY248 ), Ningbo Public Welfare Science and Technology Project (202002N3163 ) and Zhejiang Province Traditional Chinese Medicine Project (2020ZB214 ).

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Zhixuan Wu: Writing – original draft, Conceptualization. Xuejia Yang: Writing – original draft, Data curation, Conceptualization. Ziwei Yuan: Writing – original draft, Data curation. Yangyang Guo: Writing – original draft, Conceptualization. Xiaowu Wang: Writing – review & editing, Funding acquisition. Liangchen Qu: Writing – review & editing, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

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K-Mcurves of progression free survival (PFS) in three subgroups.

The expression levels of the four HARlncs in gastric cancer cells HGC-27 and normal gastric mucosal epithelial cells line GES-1 by qRT-PCR. *P < 0.05, **P < 0.01, ****P < 0.0001.

Acknowledgements

We thank HOME for Researchers (www.home-for-researchers.com) platform.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36615.
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References

1 Ferlay J. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012 Int. J. Cancer 136 5 2015 E359 E386 25220842
2 Voutilainen M.E. Juhola M.T. Evaluation of the diagnostic accuracy of gastroscopy to detect gastric tumours: clinicopathological features and prognosis of patients with gastric cancer missed on endoscopy Eur. J. Gastroenterol. Hepatol. 17 12 2005 1345 1349 16292088
3 Yie S.M. A protein fragment derived from DNA-topoisomerase I as a novel tumour-associated antigen for the detection of early stage carcinoma Br. J. Cancer 115 12 2016 1555 1564 27875523
4 Suzuki H. High rate of 5-year survival among patients with early gastric cancer undergoing curative endoscopic submucosal dissection Gastric Cancer 19 1 2016 198 205 25616808
5 Tan Y.K. Fielding J.W. Early diagnosis of early gastric cancer Eur. J. Gastroenterol. Hepatol. 18 8 2006 821 829 16825897
6 Lakshmaiah K.C. Epigenetic therapy of cancer with histone deacetylase inhibitors J Cancer Res Ther 10 3 2014 469 478 25313724
7 Zhang Y. Overview of histone modification Adv. Exp. Med. Biol. 1283 2021 1 16 33155134
8 Cao J. Yan Q. Cancer epigenetics, tumor immunity, and immunotherapy Trends Cancer 6 7 2020 580 592 32610068
9 Jenuwein T. Allis C.D. Translating the histone code Science 293 5532 2001 1074 1080 11498575
10 Strahl B.D. Allis C.D. The language of covalent histone modifications Nature 403 6765 2000 41 45 10638745
11 Kouzarides T. Chromatin modifications and their function Cell 128 4 2007 693 705 17320507
12 Clayton A.L. Histone acetylation and gene induction in human cells FEBS Lett. 336 1 1993 23 26 8262210
13 Pogo B.G. Allfrey V.G. Mirsky A.E. RNA synthesis and histone acetylation during the course of gene activation in lymphocytes Proc Natl Acad Sci U S A 55 4 1966 805 812 5219687
14 Durand-Dubief M. Specific functions for the fission yeast Sirtuins Hst2 and Hst4 in gene regulation and retrotransposon silencing EMBO J. 26 10 2007 2477 2488 17446861
15 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. 10 2022 793425
16 Jiang Y. Wang L. Role of histone deacetylase 3 in ankylosing spondylitis via negative feedback loop with microRNA-130a and enhancement of tumor necrosis factor-1α expression in peripheral blood mononuclear cells Mol. Med. Rep. 13 1 2016 35 40 26531724
17 Yoon S. Eom G.H. HDAC and HDAC inhibitor: from cancer to cardiovascular diseases Chonnam Med J 52 1 2016 1 11 26865995
18 Chen Y. Long non-coding RNAs: from disease code to drug role Acta Pharm. Sin. B 11 2 2021 340 354 33643816
19 Wang M. Long non-coding RNA DANCR in cancer: roles, mechanisms, and implications Front. Cell Dev. Biol. 9 2021 753706
20 Shi X. Long non-coding RNAs: a new frontier in the study of human diseases Cancer Lett. 339 2 2013 159 166 23791884
21 Wang X. Li X. Wang Z. lncRNA MEG3 inhibits pituitary tumor development by participating in cell proliferation, apoptosis and EMT processes Oncol. Rep. 45 4 2021
22 Gutschner T. Diederichs S. The hallmarks of cancer: a long non-coding RNA point of view RNA Biol. 9 6 2012 703 719 22664915
23 Wang Q. LINC00511 promotes gastric cancer progression by regulating SOX4 and epigenetically repressing PTEN to activate PI3K/AKT pathway J. Cell Mol. Med. 25 19 2021 9112 9127 34427967
24 Cheng Y. Targeting epigenetic regulators for cancer therapy: mechanisms and advances in clinical trials Signal Transduct Target Ther 4 2019 62 31871779
25 Kawada J.I. Immune cell infiltration landscapes in pediatric acute myocarditis analyzed by CIBERSORT J. Cardiol. 77 2 2021 174 178 32891480
26 Rooney M.S. Molecular and genetic properties of tumors associated with local immune cytolytic activity Cell 160 1–2 2015 48 61 25594174
27 Xiao B. Identification and verification of immune-related gene prognostic signature based on ssGSEA for osteosarcoma Front. Oncol. 10 2020 607622
28 Becht E. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression Genome Biol. 17 1 2016 218 27765066
29 Li T. TIMER2.0 for analysis of tumor-infiltrating immune cells Nucleic Acids Res. 48 W1 2020 W509 w514 32442275
30 Lebwohl D. Canetta R. Clinical development of platinum complexes in cancer therapy: an historical perspective and an update Eur. J. Cancer 34 10 1998 1522 1534 9893623
31 Yu G. clusterProfiler: an R package for comparing biological themes among gene clusters OMICS 16 5 2012 284 287 22455463
32 Wu Z. Identification of gene expression profiles and immune cell infiltration signatures between low and high tumor mutation burden groups in bladder cancer Int. J. Med. Sci. 17 1 2020 89 96 31929742
33 Farria A. Li W. Dent S.Y. KATs in cancer: functions and therapies Oncogene 34 38 2015 4901 4913 25659580
34 Fraga M.F. Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer Nat. Genet. 37 4 2005 391 400 15765097
35 Seligson D.B. Global levels of histone modifications predict prognosis in different cancers Am. J. Pathol. 174 5 2009 1619 1628 19349354
36 Xiao S. A ferroptosis-related lncRNAs signature predicts prognosis and therapeutic response of gastric cancer Front. Cell Dev. Biol. 9 2021 736682
37 Alexandrov L.B. Signatures of mutational processes in human cancer Nature 500 7463 2013 415 421 23945592
38 Vikhlyantsev I.M. Podlubnaya Z.A. New titin (connectin) isoforms and their functional role in striated muscles of mammals: facts and suppositions Biochemistry (Mosc.) 77 13 2012 1515 1535 23379526
39 Jia Q. Titin mutation associated with responsiveness to checkpoint blockades in solid tumors JCI Insight 4 10 2019
40 Kim N. Somatic mutaome profile in human cancer tissues Genomics Inform 11 4 2013 239 244 24465236
41 Olivier M. Hollstein M. Hainaut P. TP53 mutations in human cancers: origins, consequences, and clinical use Cold Spring Harb Perspect Biol 2 1 2010 a001008 20182602
42 Bast R.C. Jr. Reactivity of a monoclonal antibody with human ovarian carcinoma J. Clin. Invest. 68 5 1981 1331 1337 7028788
43 Gubbels J.A. MUC16 provides immune protection by inhibiting synapse formation between NK and ovarian tumor cells Mol. Cancer 9 2010 11 20089172
44 Li X. Association of MUC16 mutation with tumor mutation load and outcomes in patients with gastric cancer JAMA Oncol. 4 12 2018 1691 1698 30098163
45 Zhang R. E6/E7-P53-POU2F1-CTHRC1 axis promotes cervical cancer metastasis and activates Wnt/PCP pathway Sci. Rep. 7 2017 44744
46 Siegel R.L. Cancer statistics, 2021 CA Cancer J Clin 71 1 2021 7 33 33433946
