ASF1B acted as a prognostic biomarker for stomach adenocarcinoma

Abstract

Stomach adenocarcinoma (STAD) has a high mortality rate due to the lack of highly sensitive biomarkers. Therefore, the search for potential tumor markers is of great value. ASF1B is a prognostic marker for a variety of tumors, while the prognostic value and immune microenvironment of ASF1B in STAD remain unclear, and to be determined. Kaplan–Meier analysis was performed to analyze the prognostic role of ASF1B in STAD. Functional enrichment of ASF1B was explored with GO and KEGG pathway analysis. We also explored the correlation between ASF1B expression and immune infiltration in STAD. ASF1B was significantly upregulated in STAD tissues and high expression of ASF1B indicated a poor overall survival, progression-free survival, and first progression rate in STAD. The functional enrichment analysis of ASF1B and related genes showed high enrichment in the cell cycle and DNA repair, and the ASF1B high expression group was also mainly enriched in pathways such as the cell cycle. Analysis of tumor immune infiltration showed that ASF1B expression was significantly associated with the majority of immune cell infiltration in STAD. Moreover, STAD patients with high ASF1B expression had a higher tumor mutation burden score, microsatellite instability score, PD-1 immunophenoscore, and immune checkpoint expression. Our results suggest that ASF1B was an independent prognostic factor for STAD as well as a potential target for immunotherapy.

1. Introduction

Gastric cancer (GC) has become a huge challenge to people’s health, accounting for 5.7% of all diagnosed cancer cases and 8.2% of cancer-related mortality worldwide. GC is the fifth most common type of cancer worldwide and the third most deadly type of cancer after lung and colorectal cancer.^[1] With the improvement of endoscopic techniques, surgery and radiotherapy in recent years, there has been some improvement in the diagnosis and treatment of stomach adenocarcinoma (STAD). The incidence and mortality rates of STAD are slowly decreasing, but the overall survival rate is still poor.^[2] As most STAD patients have atypical clinical symptoms in the early stages, most patients are already at an advanced stage when first diagnosed. The 5-year survival rate for early-diagnosed STAD can be 90% or even higher after treatment and it drop to less than 30% for patients in advanced stage.^[3,4] The prognostic factors affecting gastric cancer include cell cycle regulators, microsatellite instability (MSI), apoptosis regulators, and Deoxyribonucleic acid (DNA) repair.^[5] Therefore, it is important to explore novel biomarkers to predict the prognosis of STAD patients and guide personalized treatment.

Anti-silencing function 1 (ASF1) is originally identified in yeast and belongs to the histone H3-H4 chaperone proteins.^[6] ASF1 plays a key role in the regulation of cell cycle and tumor progression. Moreover, it is involved in DNA replication, repair, and transcriptional regulation, as well as regulating chromatin functions.^[7,8] It is showed that ASF1 can promote tumorigenesis in a variety of tissues.^[9] There are 2 main isoforms of ASF1, Anti-silencing function 1A and Anti-silencing function 1B (ASF1B).^[10] Functionally, studies have shown that ASF1B is involved in tissue cell proliferation and is closely associated with the development and progression of many tumors.^[11] Recent studies have revealed the impact of ASF1B as an oncogene in cancer, and it can promote tumor cell growth in kidney cancers.^[12] ASF1B is as biomarker predicting poor prognosis and involved in tumorigenesis in pancreatic cancer.^[13] ASF1B enhance migration and invasion of lung cancers and predicted poor prognosis.^[14,15] Thus, ASF1B may also play a vital role in the development of STAD. However, few studies on ASF1B in STAD have been reported. This study aim to investigate the prognostic value and pathway of ASF1B in STAD based on the data from the Cancer Genome Atlas Program (TCGA) database. The accuracy of the results is verified using the Gene Expression Omnibus dataset. Reverse transcription polymerase chain reaction (RT-PCR) is performed to explore the expression of ASF1B in STAD.

2. Materials and methods

2.1. Expression analysis and survival analysis

TCGA is the largest tumor database. Messenger Ribonucleic Acid (mRNA) expression data for 407 STAD samples (including 375 tumor tissues and 32 paracancer samples) and clinical data were downloaded from the TCGA (https://portal.gdc.cancer.gov/). GSE54129 included mRNA expression data from 21 para-cancer normal tissues and 111 STAD tissues. GSE79973 contained expression data from 10 pairs of matched STAD and para-cancer tissues. The Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) served as a validation set (from GSE54129 and GSE79973). The Tumor Immune Estimation Resource 2.0 database (http://timer.cistrome.org/) was used for the analysis of ASF1B expression in pan-cancer. Next, the GSE54129 and GSE79973 datasets were selected to validate the ASF1B expression in STAD. Finally, the Human Protein Atlas database (http://www.proteinatlas.org/) was used to validate the expression of ASF1B in STAD at the protein level.

2.2. Survival prognostic analysis

The Kaplan–Meier Plotter online tool (http://kmplot.com/) was used to analyze whether there was a difference in survival prognosis between high and low ASF1B expression group. We collected clinicopathological factors of STAD patients in terms of gender, age, clinical stage (Stage I, Stage II, Stage III, Stage IV), histologic grade (G1, G2, G3), H pylori infection, etc., and used COX regression to investigate the effect of ASF1B gene expression and other relevant clinicopathological factors on overall survival (OS). Nomogram was plotted to predict the prognosis of STAD patients. The accuracy was evaluated by calibration plots.

2.3. Functional enrichment analysis

Based on the expression data of mRNAs from STAD samples in the TCGA database, the top 25 genes positively and negatively associated with ASF1B were screened by the spearman algorithm for ASF1B co-expression patterns. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the above 50 genes. To further validate the role of ASF1B in STAD, differentially expressed genes (DEGs) between high and low ASF1B expression groups were screened, and Gene set enrichment analysis software was applied to explore the functions of these DEGs in KEGG and HALLMARK pathway.

2.4. Immune infiltration and immunotherapy analysis

The single sample gene set enrichment analysis immune infiltration algorithm was used to infer the abundance of tumor-infiltrating immune cells in STAD. The Wilcox test was used to further compare the differences in the abundance of tumor-infiltrating immune cells between the high and low ASF1B expression groups in STAD. Finally, a correlation scatter plot was plotted to understand the correlation between ASF1B expression and the abundance of each immune cell infiltration. The potential immune checkpoints were analyzed using the Wilcoxon test to determine differences between the two groups. Finally, immunophenoscore (IPS) was used as a predictor of Cytotoxic T lymphocyte associate protein-4 (CTLA-4) and Programmed Death Receptor 1 (PD-1) responsiveness for immunotherapy. The immune checkpoint inhibitor from the IPS dataset for STAD was obtained from The Cancer Immunome Database Website (https://tcia.at/home) to assess the efficacy of immunotherapy using CTLA-4 and PD-1 blockers between the two groups.

2.5. Validation of the expression and prognosis value of ARF1B in STAD

Approval of by ethics committee the First Affiliated Hospital of Hainan Medical University, we obtained a total of 48 cases of STAD and their paired tissues. All cases were diagnosed pathologically and total RNA was extracted from STAD tissues and normal gastric tissues using a TRIzol kit (Vazyme, Nanjing, China) with the informed consent of patients and their families. RT-qPCR experiments were used to verify the expression of the ASF1B in STAD. Glyceraldehyde-3-phosphate dehydrogenase was used as the loading control. The relative gene expression was generated using the 2^−ΔΔCT method (ΔCT target gene-ΔCT control gene). ASF1B primers were designed for the positive chain sequence CCAAGGTGTCGGTGCTGAA and the reverse chain sequence TCGAAGCTGATCTCGAACCG, and to design the glyceraldehyde-3-phosphate dehydrogenase primers, the positive chain sequence CAGGAGGCATTGCTGATGAT and the reverse chain sequence GAAGGCTGGGGCTCATTT.

3. Results

3.1. High expression of ASF1B in STAD

To explore the expression levels of ASF1B, we analyzed the differences in ASF1B mRNA expression in different tumors and corresponding normal tissues using the Tumor Immune Estimation Resource 2.0 database. The results showed that the expression of ASF1B was significantly higher in Bladder Urothelial Carcinoma, Breast invasive carcinoma, Cervical squamous cell carcinoma and endocervical adenocarcinoma, Cholangiocarcinoma, Colon adenocarcinoma, Esophageal, Glioblastoma multiforme carcinoma, Head and Neck squamous cell carcinoma, Kidney Chromophobe, Kidney renal clear cell carcinoma, Kidney renal papillary cell carcinoma, Liver hepatocellular carcinoma, Lung adenocarcinoma, Lung squamous cell carcinoma, Prostate adenocarcinoma, Rectum adenocarcinoma, STAD, Thyroid carcinoma, and Uterine Corpus Endometrial Carcinoma tissues compared to normal tissues (Figure S1, Supplemental Digital Content, http://links.lww.com/MD/K903). ASF1B expression levels in STAD patients were assessed by the TCGA database, which showed that ASF1B expression levels were also significantly elevated in STAD compared with matched and unmatched paracancerous tissues (Fig. 1A and B). Similar results were obtained from the GSE54129 and GSE79973 datasets (Fig. 1C and D). At the protein level, ASF1B expression was also higher in STAD compared with that in paracancerous tissues (Fig. 1E). RT-qPCR data also showed that ASF1B was also highly expressed in the STAD tissues (Fig. 1F).

Figure 1.

The elevated mRNA expression of ASF1B in stomach adenocarcinoma (STAD). (A and B) ASF1B was overexpressed in STAD [unmatched tissues (A) and matched tissues (B)]. (C and D) ASF1B was also overexpressed in GSE54129 and GSE79973. (E) Protein expression of ASF1B was elevated in STAD (The Human Protein Atlas database). (F) ASF1B was also highly expressed in the STAD tissues we collected. ASF1B = anti-silencing function 1B.

3.2. The prognostic significance of ASF1B in STAD patients

Kaplan–Meier survival curves were used to analyze whether ASF1B expression affected the OS, progression-free survival (PFS), and first progression rate in STAD. Patients in the high expression group were found to have lower survival rates (P < .05, Fig. 2A–C), with similar results in our collection of STAD cases (P < .05, Fig. 2D). The COX proportional risk model was applied to analyze the relationship between clinicopathological factors and the prognosis of STAD patients. The results showed that the prognosis was significantly correlated with ASF1B, age and clinical stage (P < .05, Fig. 3A and B; Table 1). The area under the curve was 0.925, and the results indicated that ASF1B performed well in predicting survival (Fig. 3C). We also compared the predictive value of ASF1B and clinical parameters. As shown in Figure S2, Supplemental Digital Content, http://links.lww.com/MD/K904, the combination of ASF1B expression, histologic grade and clinical stage had a best prognostic value. Combining gender, age, clinical stage, histologic grade, and H pylori infection, the nomogram was created to predict individualized survival times for patients (Fig. 3D). The calibration plot curve for predicted 1-year, 3-year, and 5-year survival rates and actual survival rates showed good agreement (Fig. 3E). Therefore, ASF1B expression was an independent risk factor for the prognosis of STAD.

Figure 2.

Differences in survival rates between high and low ASF1B expression groups. (A–C) Low expression of ASF1B predicts a good prognosis in stomach adenocarcinoma (STAD), including overall survival (OS), progression-free survival (PFS), and first progression (FP). (D) ASF1B was also associated with poor OS from the STAD tissues we collected. ASF1B = anti-silencing function 1B.

Figure 3.

Independent prognostic analysis of ASF1B. Univariate (A) and multivariate COX regression (B) were used to analyze the correlation between ASF1B expression level and clinicopathological factors and prognosis. (C) The predicted ROC curve for stomach adenocarcinoma (STAD) patients was 0.925. (D) Nomogram of ASF1B expression levels and clinicopathological factors used to predict 1-, 3-, and 5-year survival. (E) Calibration plot of a nomogram predicting 1, 3, and 5-year OS probability. ASF1B = anti-silencing function 1B.

Table 1.

Univariate and multivariate Cox proportional risk regression analysis of ASF1B.

Characteristics	Total (N)	Univariate analysis		Multivariate analysis
		Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
ASF1B	370	1.570 (1.320–1.867)	.018	1.771(1.611–1.892)	.047
Low	185
High	185
Age	367	1.620 (1.154–2.276)	.005	1.931 (1.336–2.791)	<.001
≤65	163
>65	204
Gender	370	1.267 (0.891–1.804)	.188	1.254 (0.867–1.815)	.230
Female	133
Male	237
Pathologic stage	347	1.947 (1.358–2.793)	<.001	1.818 (1.256–2.630)	.002
Stage I and Stage II	160
Stage III and Stage IV	187
Histologic grade	361	1.353 (0.957–1.914)	.087	1.363 (0.938–1.980)	.104
G1&G2	144
G3	217

ASF1B = anti-silencing function 1B.

3.3. ASF1B was closely related to cell cycle regulation

To understand the biological function of ASF1B in STAD, the top 25 genes positively associated with ASF1B, and the top 25 negatively associated genes were identified based on the Spearman test (Fig. 4A). The GO/KEGG gene enrichment analysis was performed separately and the KEGG signaling pathway was enriched in the cell cycle and DNA replication. GO functional enrichment includes molecular function, cellular component and biological process, among which biological process was enriched in the regulation of transcription involved in G1/S transition of the mitotic cell cycle, DNA-dependent DNA replication and DNA replication (Fig. 4B). ASF1B and related genes was involved in kinetochore, chromosomal region and chromosome, centromeric region was enriched in cellular component analysis (Fig. 4B). In molecular function analysis, these genes were enriched in catalytic activity, acting on DNA replication origin binding and 3′−5′ DNA helicase were enriched, as detailed in Figure 4B. Similarly, KEGG analysis based on DEGs between high and low ASF1B expression groups showed major involvement in the cell cycle and extracellular matrix (Fig. 4C). All the above results indicate that ASF1B is significantly enriched in cell cycle regulation.

Figure 4.

Functional enrichment analysis of ASF1B-related genes. (A) Heatmap showing the top 50 genes positively and negatively associated with ASF1B in stomach adenocarcinoma (STAD). (B) Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of ASF1B-associated genes in STAD. (C) KEGG pathway analysis of differentially expressed genes between high and low ASF1B expression groups. ASF1B = anti-silencing function 1B.

3.4. Correlation analysis of ASF1B and immune infiltration

Studies have shown that ASF1B can be used as a prognostic marker and a potential immunotherapeutic target for several malignancies.^[16] In the present study, we found that ASF1B may act as an oncogenic factor in STAD. Therefore, we speculate that ASF1B may promote cancer cell proliferation by modulating tumor immune responses. The correlation between ASF1B and immune infiltration was investigated, and the results showed that STAD patients with high ASF1B expression had significantly higher levels of activated DC (aDC), Neutrophils, Natural Killer (NK) CD56dim cells, T helper (Th) cells, Th1 cells, Th17 cells, Th2 cells, and Regulatory T cells infiltration, while B cells, Mast cells, NK cells, Plasmacytoid DC (pDC), T central memory (Tcm), T effector memory (Tem), and T follicular helper (TFH) infiltration were significantly lower (Fig. 5A). The results of correlation analysis showed that the expression level of ASF1B was significantly correlated with the level of aDC cells (r = 0.227, P < .001, Fig. 5B), CD56dim cells (r = 0.288, P < .001, Fig. 5E), Th2 cells (r = 0.592, P < .001, Fig. 5K), Th17 cells (r = 0.124, P = .017, Fig. 5L), TReg (r = 0.155, P = .003, Fig. 5M) were positively correlated with penetration levels. In contrast, the expression level of ASF1B was significantly negatively correlated with the level of B cells (r = −0.201, P < .001, Fig. 5C), Mast cells (r = −0.440, P < .001, Fig. 5D), NK cells (r = −0.309, P < .001, Fig. 5F), pDC (r = −0.316, P < .001, Fig. 5G), Tcm (r = −0.216, P < .001, Fig. 5H), Tem (r = −0.230, P < .001, Fig. 5I), TFH (r = −0.166, P = .001, Fig. 5J) in STADs.

Figure 5.

Correlation analysis of ASF1B expression with immune cell infiltration. (A) The Violin plot showed differences in the abundance of infiltration between the 15 immune cell subpopulations in the high and low ASF1B expression groups. (B–M) Correlation analysis of ASF1B expression with the infiltration abundance of 12 immune cell subtypes. ASF1B = anti-silencing function 1B.

3.5. Predicts immunotherapy efficacy

We explored the correlation between ASF1B expression and immunotherapy. As shown in Figure 6A–C, ASF1B expression was significantly and positively correlated with TMB score and MSI score, as well as with immune checkpoints (P < .01), including Programmed Cell Death 1 (PD1), CD274 (PDL1), CTLA4, Lymphocyte-activation Gene 3, Hepatitis A virus Cellular Receptor 2. The immune efficacy of immune checkpoint inhibitor treatment represented by CTLA-4/PD-1 inhibitors was further explored between the two groups. These results showed a significant benefit of immunotherapy in the high ASF1B expression group in the combined anti-CTLA-4/PD-1 treatment cohort (Fig. 6D, P = .014). Among patients receiving PD-L1 immunotherapy alone (Imvigor210),^[17] patients with high ASF1B expression had a better OS rate compared with that with low ASF1B expression (Fig. 6E, P = .013). Moreover, a better outcome of immunotherapy was observed in patients with high ASF1B expression than that with the low expression group (Fig. 6F, P < .001).

Figure 6.

ASF1B predicts immunotherapy efficacy. (A and B) Correlation of tumor mutational burden (TMB) and microsatellite instability (MSI) with ASF1B expression stomach adenocarcinoma (STAD). (C) Correlation of immune checkpoint and ASF1B expression in STAD. (D) The relative distribution of immunophenoscore (IPS) was compared between the high and low ASF1B expression groups. (E) Using the Imvigor210 dataset, the impact of immunotherapy on OS at ASF1B expression levels was explored. (F) The immunotherapy efficacy between the high and low ASF1B expression groups. ASF1B = anti-silencing function 1B, CR = complete response, PD = progressive disease, PR = partial response, SD = stable disease.

4. Discussion

GC has a high incidence and poses a serious threat to human health. Surgery, radiotherapy, and chemotherapy in the advanced stages of GC have made it difficult to improve survival rates, and cancer cells are prone to develop resistance to chemotherapy drugs.^[18] The most fundamental biological feature of malignant tumors is the uncontrolled proliferation of tumor cells, and the biological basis of uncontrolled cell proliferation is the disorder of cell cycle regulation.^[19,20] Therefore, understanding the cell cycle and its associated genes in the mechanism of STAD may provide effective marker molecules for its early diagnosis. Current study suggests that ASF1 is a chaperone protein of histone H3/H4, involved in DNA replication and repair, as well as transcriptional regulation.^[7] ASF1B is an important molecule involved in cell cycle regulation, cell proliferation, and differentiation. Localized in the nucleus,ASF1 may be involved in the development of many malignancies and sever as a potential tumor target.^[21] Liu et al^[22] finds that overexpression of ASF1B promots the proliferation of cervical cancer cells and acted as a pro-oncogene. Silencing of ASF1B inhibits the Phosphatidyl-inositol 3-kinase/serine-threonine Kinase signaling pathway thereby reducing prostate cancer proliferation, promoting apoptosis and cell cycle arrest, and patients with prostate cancer with high ASF1B expression has a poor prognosis.^[9] In renal clear cell carcinoma, the high expression of ASF1B promotes cell proliferation through the upregulation of Proliferating Cell Nuclear Antigen.^[12] In the present study, ASF1B was highly expressed in most cancer tissues. Also, in STAD, higher level of ASF1B expression was found in STAD tissues compared to normal gastric tissues, both in public databases and in clinically collected tissues. Survival analysis by the Kaplan–Meier method showed that patients with low ASF1B expression had a better prognosis, suggesting that ASF1B may act as an oncogenic factor in STAD. Functional enrichment analysis of ASF1B and related genes suggests high enrichment in cell cycle, and DNA repair, consistent with previous studies.^[1]

Few studies have been reported on the immunological role of ASF1B in cancer. In this study, ASF1B expression was found to be significantly positively correlated with the infiltration levels of aDC cells, CD56dim cells, Th2 cells, Th17 cells, TReg, and negatively correlated with the infiltration levels of B cells, Mast cells, NK cells, pDC, Tcm, Tem, TFH. Previous studies have found a clear correlation between ASF1B expression and immune cell infiltration, affecting tumor progression by altering immune infiltration.^[23] It is suggested that ASF1B may promote cancer cell proliferation by altering the immune microenvironment of STAD. In addition, we found that TMB and MSI were positively correlated with ASF1B expression, with the high ASF1B expression group showing higher tumor heterogeneity, which was generally associated with impaired immunity and poorer survival.^[24–26] Further basic experiments are needed to verify exactly how ASF1B regulates the tumor immune microenvironment.

The advancement of immunotherapy offers more possibilities for comprehensive cancer treatment and improved patient prognosis and shows good perspectives. Targeting immune checkpoint and activation of anti-tumor immunity play a vital role in eradicating tumor cells.^[27] Immunotherapy offers hope to STAD patients with unresectable cancers.^[28] IPS is a superior predictor of response to anti- CTLA-4 and anti-PD-1 antibody and high IPS indicates a better response to immunotherapy.^[29] Our study should that STAD patients with high ASF1B expression had a higher PD-1 IPS. Immune checkpoint, including PD1/PD-L1 and CTLA-4, played a vital role in the tumor progression and immune escape. High expression of immune checkpoints indicated a higher likelihood of Immunotherapy benefit. The expression of common immune checkpoints, such as Programmed Cell Death 1 (PD1), CD274 (PDL1), CTLA4, Lymphocyte-activation Gene 3, and Hepatitis A virus Cellular Receptor 2, was higher in the high ASF1B expression group, suggesting higher likelihood of immune escape in STAD patients with low ASF1B expression. MSI high was a positive predictor for anti-PD-1/PD-L1 immunotherapy efficacy.^[30] TMB score was an indicator for immunotherapy efficacy and high TMB score suggested a better immunotherapy efficacy.^[31,32] The data showed that STAD patients with low ASF1B expression had a lower TMB score. These evidences suggested that STAD patients with high ASF1B expression may benefit more from immunotherapy.

Several limitations could be found in our study. All analyses are performed at the mRNA level and the role of ASF1B at the protein level is not elucidated. It would be better to explore the molecular pathways or downstream targets of ASF1B involved in cell cycle regulation and DNA repair. Further study should be focus on experimental validation of ASF1B’s role in the tumor immune microenvironment and the functional mechanisms of ASF1B in STAD.

5. Conclusion

Our results suggest that ASF1B was an independent prognostic factor for STAD as well as a potential target for immunotherapy.

Author contributions

Formal analysis: Jianwei Xing.

Investigation: Cailing Zhao, Qiushi Yin.

Methodology: Jianghao Zhou, Qiushi Yin.

Project administration: Jianwei Xing.

Supervision: Jianghao Zhou.

Validation: Jianghao Zhou, Jianwei Xing.

Writing – original draft: Cailing Zhao, Jianghao Zhou, Qiushi Yin.

Writing – review & editing: Jianwei Xing.