Associations between polymorphisms of the ACYP2 gene and Liver cancer risk: A case-control study and meta-analysis.

BACKGROUND: ACYP2 gene may be involved in the process of telomere shortening which may be involved in the liver cancer. So, this research was to examine whether the ACYP2 gene polymorphism has impact on the risk of liver cancer in Chinese population.
METHODS: Two hundred and fifty cirrhosis patients and 248 liver cancer patients were selected. Unconditional logistic regression was to calculate the odds ratio (OR) and 95% confidence intervals (CIs). Analyze the relationship between ACYP2 gene polymorphism and tumor using meta-analysis. Analyze the expression of ACYP2 gene in liver cancer and its influence on the prognosis of liver cancer by databases (Ualcan, GTEX and Kaplan-Meier plotter).
RESULTS: In the allele model, ACYP2 rs843720 was protection against the occurrence of cirrhosis developed into liver cancer (OR = 0.76, 95% CI: 0.58-0.99, p = 0.04). Rs1682111 and rs843720 play a protective role in the additive model (rs1682111: OR = 0.69, 95% CI: 0.52-0.93, p = 0.01; rs843720: OR = 0.73, 95% CI: 0.54-0.98, p = 0.04).While rs843645 G allele increased the risk of cirrhosis developed into liver cancer under the additive model (OR = 1.42, 95% CI: 1.02-2.00, p = 0.04).The haplotype analysis detected that "ATATCGCC" decreased the risk of cirrhosis developed into liver cancer (OR = 0.69, 95% CI: 0.51-0.92, 95% CI: p = 0.013); however, "TGAGCGTC" increased the risk of cirrhosis developed into liver cancer (OR = 1.48, 95% CI: 1.04-2.10, p = 0.027). Meta-analysis shown that ACYP2 rs1682111 was associated with the risk of cancer (OR = 0.90, 95% CI: 0.78-1.05, p = 0.02). ACYP2 gene high expression was found to be associated with better OS for all liver patients.
CONCLUSION: Based on this research, we surmised that ACYP2 gene may be involved in the occurrence of liver cancer in Chinese populations.

INTRODUCTION

Primary liver cancer is a kind of cancer that occurs to the liver parenchymal cells or intrahepatic bile duct epithelial cells, which has a high degree of malignancy and short survival (Ferlay et al., 2014). The occurrence of liver cancer is the result of environmental, genetic and living habits, and other factors, is a multi‐stage and multi‐step process, the pathogenesis is complex, there is interaction between the various factors, including hepatitis B virus infection, smoking, drinking, and exposure to environmental carcinogens is a risk factor (Gao et al., 2012). With the increase in the risk of smoking, the incidence of liver cancer will increase. Alcohol consumption is another factor that cannot be ignored in the occurrence of liver cancer. To some extent, excessive drinking can lead to alcoholic fatty liver cirrhosis and thus increase the incidence of liver cancer. In addition, cirrhosis has been recognized that the most important clinical risk factor for the development of liver cancer (Nordenstedt, White, & El‐Serag, 2010).

Human telomeres complexes are composed of repetitive nucleotide sequences (TTAGGG) and nucleoproteins, which protect chromosome ends of end‐to‐end fusions and degradation to maintain genomic integrity (Shay & Wright, 2011). A number of studies show that from the normal liver tissue, chronic hepatitis, cirrhosis of the liver tissue, large regenerative nodule, and dysplastic nodules to liver cancer tissues, the telomere length shorten, prompt telomere shortening may be involved in the occurrence of liver cancer (Isokawa et al., 1999; Plentz et al., 2004). Codd et al. found that ACYP2 rs11125529 was associated with mean LTL (p < 5 × 10−8) (Codd et al., 2013). Therefore, we speculate that ACYP2 gene affects the development of liver cancer by affecting telomere length. Therefore, the mutation of ACYP2 may be involved in the occurrence of tumorigenesis (Calamai et al., 2005). So far, discusses on the relationship between the ACYP2 gene and liver cancer is still less. So, the purpose of this research was to examine whether the ACYP2 gene polymorphism has impact on the risk of liver cancer in Chinese population. In this research, we selected 13 loci in ACYP2 gene to research the influence on the risk of cirrhosis developed into liver cancer, which rs11125529 in ACYP2 gene was reported to be associated with telomere length, other sites were selected according to the minimum allele frequency more than 0.05 in the HapMap Chinese Han Beijing population.

MATERIALS AND METHODS

Ethics statement

This study strictly followed the principles of the Declaration of Helsinki of the World Medical Association and was approved by the Ethics Committee of Haikou people's Hospital and Peking University Shenzhen Hospital. We confirmed that all research was performed in accordance with relevant regulations. Informed consent forms were signed by all participants. We confirmed that informed consent was obtained from all participants and their legal guardians.

Study population

From March 2015 to November, 2016, a total of 498 patients were selected, including 250 patients with cirrhosis as control group, and 248 patients with liver cancer as case group. The samples of liver cancer were developed from cirrhosis. The diagnostic criteria of patients based on the American Association for the Study of Liver Disease. Liver cirrhosis was diagnosed either by histopathologically diagnosis, ultrasound, computer tomography (CT), or magnetic resonance imaging (MRI). The liver cancer were diagnosed based on pathological identification combined with at least one positive liver image on computed tomography, magnetic resonance imaging, or ultrasonography, occasionally combined with increased serum AFP levels (>400 ng/ml). Patients who had organ transplantation were excluded from this study.

SNP Genotyping

In this research, we selected 13 loci in ACYP2 gene to research the influence on the risk of cirrhosis developed into liver cancer, which rs11125529 in ACYP2 gene was reported to be associated with telomere length (Codd et al., 2013), other sites were selected according to the minimum allele frequency more than 0.05 in the HapMap Chinese Han Beijing population. DNA extraction was performed using the GoldMag‐Mini Whole Blood Genomic DNA Purification Kit (GoldMag Co. Ltd. Xi'an City, China), and the DNA concentration was measured using Nanodrop 2000(Gene Company Limited); the primers, which used in genotyping, were designed by the Agena MassARRAY Assay Design 3.0 Software (Gabriel, Ziaugra, & Tabbaa, 2009). Agena MassARRAY RS1000 was used for genotyping, and the related data were managed using Agena Typer 4.0 Software (Gabriel et al., 2009; Thomas et al., 2007).

Statistical analysis

Data analysis was performed using Microsoft Excel (Redmond, WA) and SPSS 19.0 statistical package (SPSS, Chicago, IL). All p values were two‐sided, and p < 0.05 was indicated statistical significance. Bonferroni's adjustment for multiple tests was applied to the level of significance for allele model, which was set at P less than 0.05/(13*1). Bonferroni's adjustment for multiple tests was applied to the level of significance for three genetic models analysis, which was set at p less than 0.05/(13*3). Each SNP frequency in the control subjects was assessed for departure from Hardy–Weinberg Equilibrium (HWE) using an exact test. We calculated genotype frequencies of cases and controls using a χ2 test (Adamec, 1964). Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using unconditional logistic regression with adjustment for age and sex (Bland & Altman, 2000). We used the power and sample size (PS) Calculation software (http://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize) to calculate the power of the significant difference (Dupont & Plummer, 1998).

Three genetic models (dominant, recessive, and additive model) were performed using PLINK software (http://pngu.mgh.harvard.edu/purcell/plink/), to characterize the potential association of each polymorphism with the risk of cirrhosis developed into liver cancer. We used Haploview software package (version 4.2) to do haplotype analysis used 250 control samples. First, we make linkage disequilibrium analysis. Using the parameter D' and r 2 to measure the degree of linkage disequilibrium between the two SNPs loci. Using D' confidence interval method divided haplotype block haplotype block. |D'| ≤ 1, the more close to 1, the higher the level of linkage disequilibrium between sites; R 2 ≤ 1, the more close to 1, the higher the level of linkage disequilibrium between the loci. The odds ratios (ORs) and 95% confidence intervals (CIs) of haplotype were determined using unconditional logistic regression with adjustment for age and sex (Barrett, Fry, Maller, & Daly, 2005; Hawley & Kidd, 1995).

Meta‐analysis

We conducted searches of PubMed, EMBASE, and the China National Knowledge Infrastructure (CNKI) using the following search terms: “ACYP2”, “rs843720”, “rs1682111”, “rs843645”. The search was completed in July of 2018. The articles selected for the subsequent meta‐analysis were all published in English in the primary literature, focused on humans, and free of obvious overlap with the subjects of other studies. For rs843720, rs1682111, and rs843645, we found six articles, six articles, and four articles, respectively (Table S1). All statistical analyses were performed using the STATA software (version 11.0; Stata Corporation, College Station, Texas). Two‐sided p values less than 0.05 were considered statistically significant. The OR and 95% CI in each case–control study were employed to assess the strength of the associations between ACYP2 polymorphisms and cancer risk. The OR and 95% CI in each comparison were assessed in the allele model. The I 2 statistic was then used to quantitatively estimate heterogeneity, with I 2 less than 25%, between 25% and 75%, and greater than 75% representing low, moderate, and high degrees of inconsistency, respectively (Higgins & Thompson, 2002; Higgins, Thompson, Deeks, & Altman, 2003). The significance of the combined OR was determined using a Z test (p < 0.05 was considered statistically significant).

The expression and Kaplan–Meier plotter

We used the UALCAN database to analysis the expression of ACYP2 gene in HILC and normal tissues (Chandrashekar et al., 2017), and analysis of the effects of gene polymorphism on gene expression via the GTEX database (https://gtexportal.org/). And the patient samples were divided into two cohorts according to the median expression of the ACYP2 gene (high vs. low expression), using Kaplan–Meier plotter and Log rank test to analysis the prognostic significance of mRNA expression of ACYP2 gene in liver cancer (http://kmplot.com/analysis/).

RESULTS

Table 1 shows the basic information of our selected samples, including sample size and age. A total of 498 patients with cirrhosis were selected, which consisted of 248 patients (192 males and 56 females) with cirrhosis developed into liver cancer and 250 cirrhosis patients (124 males and 60 females) without liver cancer. The mean age of liver cancer patients and cirrhosis patients without liver cancer is 54.47 ± 12.05 years and 51.10 ± 10.80 years, respectively. The mean age was statistically different between the case group and the control group, p = 0.003.

Table 1

The sample information of liver cancer and cirrhosis

	Liver	Cirrhosis	p
Gender	248	250	0.020
Female	56	60
Male	192	124
Mean Age, year	54.47 ± 12.05	51.10 ± 10.80	0.003

p < 0.05 indicates statistical significance.

Table 2 shows the basic information of our selected 13 SNPs, consists of chromosomal, HWE p value, minor allele frequency of all the SNPs. Exact test shown that all the loci were meet the HWE (p > 0.05). In the allele model, ACYP2 rs843720 decreased the risk of cirrhosis developed into liver cancer (OR = 0.76, 95% CI: 0.58–0.99, p = 0.04). Finally, after multiple testing corrections, no polymorphisms were found to influence the risk of liver cancer.

Table 2

Basic information of ACYP2 gene polymorphism and their associations with cirrhosis developed into liver cancer

SNP	Chromosome	Position	Band	Alleles A/B	Gene	Role	HWE‐ p	Allele model
								OR(95% CI)	p
rs6713088	2	54345469	2p16.2	G/C	ACYP2	Intron	0.62	1.25 (0.97–1.61)	0.09
rs12621038	2	54391113	2p16.2	T/C	ACYP2	Intron	0.41	1.21 (0.94–1.56)	0.13
rs1682111	2	54427979	2p16.2	A/T	ACYP2	Intron	0.6	0.78 (0.59–1.01)	0.06
rs843752	2	54446587	2p16.2	G/T	ACYP2	Intron	0.88	1.18 (0.89–1.57)	0.26
rs10439478	2	54459450	2p16.2	C/A	ACYP2	Intron	0.24	1.05 (0.81–1.35)	0.72
rs843645	2	54474664	2p16.2	G/T	ACYP2	Downstream	0.75	1.21 (0.91–1.62)	0.19
rs11125529	2	54475866	2p16.2	A/C	ACYP2	Downstream	0.41	1 (0.73–1.38)	0.99
rs12615793	2	54475914	2p16.2	A/G	ACYP2	Downstream	0.25	1.06 (0.78–1.44)	0.71
rs843711	2	54479117	2p16.2	T/C	ACYP2	Downstream	0.29	1.06 (0.83–1.37)	0.62
rs11896604	2	54479199	2p16.2	G/C	ACYP2	Downstream	1	0.9 (0.67–1.23)	0.52
rs843706	2	54480369	2p16.2	A/C	ACYP2	3' UTR	0.34	1.06 (0.82–1.36)	0.66
rs17045754	2	54496757	2p16.2	C/G	ACYP2	Intron	0.55	1.07 (0.78–1.46)	0.69
rs843720	2	54510660	2p16.2	G/T	ACYP2	Intron	0.45	0.76 (0.58–0.99)	0.04

p < 0.004 means that p < 0.05/13 indicates statistical significance for multiple comparisons.

Abbreviations: ORs: odds ratios; CI: confidence interval; p < 0.05 indicates statistical significance.

We used unconditional logistic regression adjusted for age and sex to calculate the odds ratio (OR) and 95% confidence intervals (CIs) in three genetic models, including dominant model, recessive model, and additive model. We found that ACYP2 rs1682111 decreased the risk of cirrhosis developed into liver cancer in the additive model (OR = 0.69, 95% CI: 0.52–0.93, p = 0.01, power = 0.479) and dominant model (OR = 0.60, 95% CI: 0.41–0.90, p = 0.01), respectively. Additive model and recessive model indicated that ACYP2 rs843720 decreased the risk of cirrhosis developed into liver cancer (power = 0.381; additive model: OR = 0.73, 95% CI: 0.54–0.98, p = 0.04; recessive model: OR = 0.53, 95% CI: 0.28–0.97, p = 0.04). Besides that, additive model revealed that ACYP2 rs843645 G allele increased the risk of cirrhosis developed into liver cancer (OR = 1.42, 95% CI: 1.02–2.00, p = 0.04, power = 0.400; Table 3). Finally, after multiple testing corrections, no polymorphisms were found to influence the risk of liver cancer.

Table 3

The relationship between ACYP2 gene polymorphism and cirrhosis developed into liver cancer adjusted by age and gender

SNP	Alleles A/B	Gene(s)	Additive model		Dominant model		Recessive model		Power
			OR(95% CI)	p	OR(95% CI)	p	OR(95% CI)	p
rs1682111	A/T	ACYP2	0.69 (0.52–0.93)	0.01	0.6 (0.41–0.9)	0.01	0.66 (0.36–1.22)	0.18	0.479
rs843645	G/T	ACYP2	1.42 (1.02–2)	0.04	1.5 (1.01–2.23)	0.05	1.67 (0.66–4.24)	0.28	0.400
rs843720	G/T	ACYP2	0.73 (0.54–0.98)	0.04	0.74 (0.49–1.1)	0.13	0.53 (0.28–0.97)	0.04	0.381

p < 0.05 indicates statistical significance.

p < 0.006 means that p < 0.05/(13*3) indicates statistical significance for multiple comparisons.

Abbreviations: ORs: odds ratios; CI: confidence interval.

Using the parameters D', r 2 to measure the degree of linkage disequilibrium between SNP loci, the D' confidence interval method was used to divide the haplotype block. Figure 1 shown that rs1682111, rs843752, rs10439478, rs843645, rs11125529, rs12615793, rs843711, rs11896604 exist strong linkage. Further haplotype analysis detected that “ATATCGCC” decreased the risk of cirrhosis developed into liver cancer (OR = 0.69, 95% CI: 0.51–0.92, p = 0.013; Table 4). While the haplotype “TGAGCGTC” increased the risk of cirrhosis developed into liver cancer (OR = 1.48, 95% CI: 1.04–2.10, p = 0.027; Table 4).

Figure 1

Haplotype block map for all the SNPs of the ACYP2 gene

Table 4

The haplotype frequencies of ACYP2 gene polymorphisms and cirrhosis developed into liver cancer risk

Gene	SNPs	Haplotype	Crude analysis
			OR(95% CI)	p
ACYP2 rs1682111|rs843752|rs10439478|rs843645|rs11125529|rs12615793|rs843711|rs11896604		ATATCGCC	0.69 (0.51–0.92)	0.013
		TTCTCGCC	1.10 (0.77–1.57)	0.609
		TTCTAATG	1.02 (0.71–1.46)	0.93
		TTCTCACC	1.32 (0.46–3.77)	0.61
		TGAGCGTC	1.48 (1.04–2.10)	0.027
		TTCTCGTG	0.55 (0.15–2.03)	0.37

p < 0.05 indicates statistical significance.

Abbreviations: ORs: odds ratios; CI: confidence interval.

In the meta‐analysis, we found a significant association between ACYP2 rs1682111 polymorphisms decreased the risk of cancer in the allele model (OR = 0.90, 95% CI = 0.78–1.05, p = 0.02). The forest plots for rs1682111 in the allele model are shown in Figure 2. No correlation between rs843720, rs843645, and tumors was found (Figure S1 and S2).

Figure 2

Forest plot of cancer risk associated with the ACYP2 rs1682111 polymorphism (A vs. T)

By UALCAN database, we found that there were differences in the expression of ACYP2 gene in different tumor stages. The ACYP2 gene low expression in LIHC grade 3, when compared with the normal tissue (Figure 3), and rs1682111 mutation affects the expression of ACYP2 gene in the Colon‐Sigmoid tissue (p = 0.000025) (Figure 4). The expression of the TT genotype was significantly lower than that of the AA genotype. Survival curve revealed that patients with high expression have higher survival rates than patients with low expression. ACYP2 gene high expression was found to be associated with better OS for all liver patients (HR, 0.54; 95% CI, 0.38–0.77; p = 0.00049) (Figure 5).

Figure 3

ACYP2 gene expression is down‐regulated in LIHC base on tumor grade compared with normal tissues

Figure 4

ACYP2 mRNA expression by eQTL analysis in Human Colon—Sigmoid tissue based on GTEx database (rs1682111)

Figure 5

ACYP2 high expression is associated with better survival in liver

DISCUSSION

Liver cancer is a highly inherited malignant tumor with high incidence, concealment, high malignancy, rapid progress, and poor prognosis. Through the case–control study, we found that ACYP2 rs1682111 and rs843720 were protective factors for cirrhosis developed into liver cancer, while ACYP2 rs843645 increased the risk of cirrhosis developed into liver cancer.

The study found that ACYP2 is associated with a variety of tumors or diseases, such as lung cancer (Chen et al., 2016), colorectal cancer (Liu et al., 2017), breast cancer (Zhang et al., 2016), stroke (Liang et al., 2017), high altitude pulmonary edema (He et al., 2016). Cui et al researched the influence of telomere related genes on the prognosis of glioma, found that ACYP2 rs843720 influence the overall survival rate, the overall survival rate of glioma patients with mutated genotype was lower than that of wild‐type patients (Cui et al., 2017). Chen et al. recruited 1,156 participants, assessed the relationship between ACYP2 gene polymorphism and lung cancer, and revealed that rs1682111 (A/T) and rs843720 (G/T) in ACYP2 gene promoted the risk of lung cancer (Chen et al., 2016). At the same time, for breast cancer, Zhang et al. research the influence of ACYP2 gene polymorphism on breast cancer by 183 cases and 195 controls, and found that rs1682111 risk allele A boosted the risk of BC risk, as a risk factor for breast cancer occur (Zhang et al., 2016). In the study of colorectal cancer, also found that ACYP2 gene promoted the occurrence of colorectal cancer, for colorectal cancer, rs843645 is also a risk factor (Liu et al., 2017). In spite of ACYP2 gene was found to affect the occurrence of disease in a number of items, ACYP2 gene and liver cancer genetic susceptibility is unclear. By UALCAN database (http://ualcan.path.uab.edu/index.html) (Chandrashekar et al., 2017), we found that there were differences in the expression of ACYP2 gene in different tumor stages and different populations. But by the OncoLnc database (http://www.oncolnc.org/) (Anaya, 2016), we found that patients with high expression have higher survival rates than patients with low expression, in patients with liver cancer. ACYP2 gene high expression was found to be associated with better OS for all liver patients (HR, 0.54; 95% CI, 0.38–0.77; p = 0.00049).

ACYP2 gene was related to cell differentiation and apoptosis, and apoptosis or programmed cell death participated in embryonic development, immune system regulation, tissue homeostasis, and prevention of malignant tumors. Therefore, the mutation of ACYP2 may be involved in the occurrence of tumorigenesis (Calamai et al., 2005). Codd et al. researched the effects of several genes on telomere length, identifying seven loci, including ACYP2 rs11125529, which were associated with mean LTL (p < 5 × 10−8) (Codd et al., 2013). From chronic hepatitis, cirrhosis of the liver to the trilogy is the clinical common liver cancer of liver cancer. Previous studies have found that telomere length of the progress of the chronic liver disease gradually shortened, suggesting that in the process of chronic liver disease progression, telomeres shorten involved in the progression of chronic liver disease (Miura et al., 1997). A number of studies show that from the normal liver tissue, chronic hepatitis, cirrhosis of the liver tissue, large regenerative nodule and dysplastic nodules to liver cancer tissues, the telomere length shorten, prompt telomere shortening may be involved in the occurrence of liver cancer (Isokawa et al., 1999; Plentz et al., 2004).

Etiologically, the occurrence of liver cancer related to chronic hepatitis B (HBV) infection, excessive alcohol intake, and metabolic disease. In these samples, only part of the sample has information on smoking and drinking, as a result, we did not analyze the impact of smoking, drinking alcohol on the liver cancer. In a follow‐up study, we will try to improve these samples information, explore the genes and the environment (smoking, drinking, etc.) on the interaction of liver cancer.

CONCLUSIONS

In summary, our study results provided new evidence that ACYP2 gene was associated with cirrhosis developed into liver cancer in the Chinese Han population and database study provides evidence that ACYP2 gene was down‐regulated in the liver patients, which was harmful to the survival of liver patients. In a follow‐up study, selected the liver tissues and control tissues to analysis the expression of ACYP2 gene in cancer tissues and normal tissues by real‐time PCR, and the effects of different genotypes on expression were further analyzed in cancer tissues. Further clarify the role of ACYP2 gene in the development of liver cancer.

CONFLICT OF INTEREST

The authors declare that they have no competing interests, and the manuscript is approved by all authors for publication.

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