Single nucleotide polymorphisms in telomere length-related genes are associated with hepatocellular carcinoma risk in the Chinese Han population

Abstract

Background:

Single nucleotide polymorphisms (SNPs) in telomere-related genes are associated with a high risk of hepatocellular carcinoma (HCC). In this study, we investigated the SNPs of telomere length-related genes and their correlation with HCC risk in the Chinese Han population.

Materials and methods:

A total of 473 HCC patients and 564 healthy volunteers were recruited. Overall, 42 SNPs distributed in telomere-related genes were selected and identified. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.

Results:

We found rs6713088 (OR = 1.27, 95% CI = 1.07–1.52, p = 0.007), rs843711 (OR = 1.29, 95% CI = 1.09–1.54, p = 0.004) and rs843706 (OR = 1.30, 95% CI = 1.09–1.55, p = 0.003) in the ACYP2 gene, rs10936599 (OR = 1.21, 95% CI = 1.02–1.44, p = 0.032) in the TERC gene and rs7708392 (OR = 1.24, 95% CI = 1.00–1.52, p = 0.042) in the TNIP1 gene were associated with high HCC risk (OR > 1). In contrast, rs1682111 (OR = 0.77, 95% CI = 0.64–0.94, p = 0.008) in the ACYP2 gene, rs2320615 (OR = 0.79, 95% CI = 0.64–0.99, p = 0.038) in the NAF1 gene, rs10069690 (OR = 0.75, 95% CI = 0.59–0.96, p = 0.021) and rs2242652 (OR = 0.70, 95% CI = 0.55–0.90, p = 0.004) in the TERT gene were associated with low HCC risk (OR < 1). Based on genotype frequency distributions, rs6713088, rs843645, rs843711 and rs843706 located in the ACYP2 gene as well as rs10936599 in the TERC gene were associated with a high incidence of HCC (p < 0.05). In addition, SNPs in these genes could form a linkage imbalance haplotype. Specifically, the haploid ‘GC’ formed by rs10069690 and rs2242652 within the TERT gene increased the risk of HCC (p < 0.05).

Conclusion:

SNPs in ACYP2, TERC, TERT and other genes were correlated with HCC risk in the Chinese Han population. These data may provide new insights into early diagnosis and screening of HCC.

Keywords

case–control study gene variation hepatocellular carcinoma SNP telomere length-related genes

Introduction

Hepatocellular carcinoma (HCC) is one of the most common deadly cancer types in China. Chinese HCC cases represent greater than 50% of new liver cancer cases in the world every year.^1,2 Viral hepatitis, excessive alcohol consumption, aflatoxin and metabolic diseases are causative agents of HCC.^3–6 In addition, genomic alterations including abnormal telomere length are also important risk factors for the occurrence and development of HCC.^7,8 However, the precise pathogenic mechanism of HCC remains unclear.

Telomeres are a short special structure located at the end of chromosomes that maintain the integrity of chromosome and regulate the cell cycle.⁹ In normal cells, dysfunctional telomeres trigger damage to DNA structure or function and are also associated with cellular senescence processes.¹⁰ Chromosomes shorten with each cell division. However, some highly proliferating cells, such as germ cells and cancer cells, prevent chromosome shortening by expressing telomerase.¹¹ Many studies show that abnormal telomere length is associated with an increased risk of cancers including HCC.^12,13

The ACYP2 (acylphosphatase 2) gene coding for acylphosphatase, which hydrolyzes multiple membrane proteins, regulates the glycolysis pathway, pyruvate metabolism and cell apoptosis¹⁴ and also affects telomere length. Previous studies have reported that ACYP2 polymorphisms are associated with the shorter telomere length in the European population.¹⁵ The TERC gene (telomerase RNA component) is widely distributed in embryonic tissues, including undifferentiated neural epithelial tissues and interstitial tissues; is used as a template for telomere DNA synthesis; maintains telomere stability; and affects telomere length.^16,17 The NAF1 (nuclear assembly factor 1) gene plays a vital role in maintaining telomerase activity and function by impacting the telomerase complex.¹⁸ TERT (telomerase reverse transcriptase) is involved in maintaining telomere length and is highly expressed in tumor tissues. Myc is an important transcriptional regulator of TERT that directly controls its expression by promoter binding.^19,20 The TNIP1 (TNFAIP3 interacting protein 1) gene plays an important role in the immune system and homeostasis by regulating nuclear transcription factor κB activation and is related to telomere length.^21,22 The OBFC1 (oligonucleotide/oligosaccharide-binding fold-containing protein 1) gene protects the telomere structure from degradation, maintains telomere length and participates in DNA metabolism.^22–24 The MPHOSPH6 (m-phase phosphoprotein 6) gene, which encodes for a RNA-binding protein, participates in the synthesis of 5.8s ribosomal rRNA from a 7S ribosomal precursor, plays a role in the recruitment of ribosomal precursor and is also related to telomere length.^25,26 The ZNF208 (zinc finger protein 208) gene, which is located on chromosome 19 (19p12), regulates gene transcription by binding downstream genes and maintains telomere length.^15,27 The RTEL1 (regulator of telomere elongation helicase 1) gene coding for DNA helicase, affects the extension and stability of telomeres and protects the telomere structure during the DNA replication processes.^15,28

Mutations in telomere-related genes can lead to excessive gain or loss of function and may cause many diseases, including cancers. However, the relationship between SNPs in telomere-related genes and the incidence of HCC remains poorly understood. Therefore, we conducted a case–control study to investigate the association between SNPs in telomere length-related genes and HCC risk. These data may provide new insights and a theoretical basis for the pathogenesis, early diagnosis and treatment of HCC.

Materials and methods

Study participants

We applied a case–control study to investigate the association of telomere-related genes with the occurrence and development of HCC. In total, 473 participants with newly diagnosed HCC and 564 normal individuals with a healthy physical examination at the First Affiliated Hospital and Second Affiliated Hospital of Xi’an Jiaotong University between June 2015 and October 2017 were recruited. Blood samples were collected from all participants. Particularly, all patients with HCC were identified based on pathology, cytology, imaging examinations (magnetic resonance imaging and/or computerized tomography), and serum alpha-fetoprotein level according to the standard of diagnosis and treatment of primary liver cancer published by the Ministry of Public Health of China. None of the patients with HCC previously received either chemotherapy or radiotherapy or had any other cancers. Individuals were excluded from the study if they had hepatitis C virus, human immunodeficiency virus antibodies, autoimmune disease, active schistosomiasis, or received prior treatments such as local ablation therapy and transarterial chemoembolization. Meanwhile, 564 healthy volunteers in good mental condition were included as a control group. None of the healthy volunteers had a previous history of hepatic disease such as viral hepatitis, cirrhosis and tumor history. All of them had liver functions within the reference ranges, normal liver and biliary system ultrasound, normal clinical and laboratory examination results and negative serological findings for autoimmune and viral hepatic diseases. All patients with HCC and healthy volunteers were born and lived in the same area (Shaanxi, China). This study was approved by the Human Research Committee of the First Affiliated Hospital and the Second Affiliated Hospital of Xi’an Jiaotong University. The approval ID was 2015-172. Written informed consent was obtained, and informed consent for blood analysis was obtained from all participants prior to the study.

Questionnaire survey and sample collection

Face-to-face interviews were performed using an epidemiological questionnaire survey to gather information on the participants. The questionnaire included content on participants’ basic information (age and sex). Detailed information is provided in Table 1. Moreover, 5 ml of peripheral blood was collected from each participant using vacuum EDTA anticoagulant tubes. Blood samples were stored at –80°C.

Table 1.

General characteristics in patients with HCC and healthy volunteers (‘normal’).

Characteristics	HCC(n = 473)	Percentage(%)	Normal(n = 564)	Percentage(%)	p value
Age (years)					0.010^*
⩾50	330	69.8	406	72.0
<50	143	30.2	158	28.0
Sex					<0.0001^*
Male	390	82.5	339	60.1
Female	83	17.5	225	39.9

p < 0.05.

HCC, hepatocellular carcinoma.

SNP selection

After screening, 42 SNPs distributed in nine telomere length-related genes with minor allele frequencies >5% in the HapMap Chinese Han Beijing population were selected from the 1000 Genomes Project database (www.1000genomes.org), the National Center for Biotechnology Information dbSNP database (www.ncbi.nlm.nih.gov/projects/SNP) and previously published telomere length polymorphisms reported in sequencing experiments. The 42 SNPs were located in ACYP2, TERC, TERT, NAF1, TNIP1, OBFC1, MPHOSPH6, ZNF208 and RTEL1 genes. The correlation between the above SNPs and HCC susceptibility were analyzed. The specific primer SNPs were listed in Supplemental Table 1.

Genotyping

Whole genomic DNA was extracted from blood samples using a GoldMag-Mini Whole Blood Genomic DNA Purification Kit (GoldMag Co. Ltd., Xi’an, China). DNA concentration and purity were determined using NanoDrop 2000 (Gene Company Ltd., Hong Kong, China). Sample concentrations <10 ng/ul were excluded. The purity of the DNA sample was determined based on the OD260/OD280 ratio. In our experiment, the acceptable range of the sample ratio was 1.7–2.0. We used Agena MassARRAY Assay Design 3.0 Software to design a Multiplexed SNP MassEXTEND assay.²⁹ Sequenom MassARRAY RS1000 was applied for genotyping, and data were analyzed using Sequenom Typer 4.0 software.^29,30

Statistical analysis

Data analysis was performed using Microsoft Excel (Redmond, WA, USA) and SPSS 22.0 statistical package (SPSS, Chicago, IL, USA). The p values reported in this study were two sided, and p < 0.05 was considered statistically significant. The frequency of all SNPs in the control group was assessed for Hardy–Weinberg equilibrium (HWE) using Fisher’s exact tests. The age and sex distribution differences between the two groups were calculated using Chi-square tests. Categorical variable differences in characteristics between all allele frequencies of SNPs in case and control groups were also analyzed using the Chi-square test. Odds ratios (ORs) and 95% confidence intervals (CIs) of genotypes were determined using unconditional logistic regression with adjustment for age and sex. Different models (genotype, dominant, recessive, and additive model) were performed using PLINK software (www.cog-genomics.org/plink2), to characterize the potential association of each gene polymorphism with HCC risk. We also applied Haploview software (version 4.2) to perform haplotype analysis in 564 control samples. We used the parameter r² (r²⩽1) to measure the degree of linkage disequilibrium analysis between the two SNP loci. Haplotypes were divided into haplotype blocks using the parameter D′ confidence interval, |D′|⩽1.

Results

General demographic characteristics of patients

The experiments were performed using the case–control method. This study included a total of 473 patients with HCC and 564 healthy volunteers. In the HCC group, the average age was 55.83 ± 12.20 years. There were 330 people older than 50 years, and 143 people younger than 50 years in this group. The age in the heathy group was 53.92 ± 11.50 years. There were 406 people older than 50 years, and 158 people who were younger than 50 years in this group. A significant difference in age was noted between these two groups (p = 0.01). In the HCC group, 390 were male, accounting for 82.5% of cases, and 83 were female, accounting for 17.5% of cases. The control group included 339 males, accounting for 60.1% of cases, and 225 females, accounting for 39.9%. A significant difference in sex distribution was noted between the two groups (p < 0.0001). Given that the family history of tumors in the control group was limited (only eight cases with a family history of tumors in normal healthy group, while 98 cases had a family history of tumors in the HCC group), we did not include the factor of family history of tumors in the logistic regression models to avoid model bias. Detailed characteristics of the participants and the analysis of results are shown in Table 1 and Figure 1.

Figure 1.

Detailed characteristics and analysis of the participants are shown.

Relationships between SNPs and HCC

Among all gene loci, the HWE value of rs11859599 (MPHOSPH6) is lower than 0.05 (HWE = 0.0281), which is not consistent with the Hardy–Weinberg law of equilibrium. Thus, this gene SNP was excluded. Among the detected SNP loci, based on the alleles distribution, we found that rs6713088 (OR = 1.27, 95% CI = 1.07–1.52, p = 0.007), rs843711 (OR = 1.29, 95% CI = 1.09–1.54, p = 0.004), and rs843706 (OR = 1.30, 95% CI = 1.09–1.55, p = 0.003) of the ACYP2 gene; rs10936599 (OR = 1.21, 95% CI = 1.02–1.44, p = 0.032) of the TERC gene; and rs7708392 (OR = 1.24, 95% CI = 1.00–1.52, p = 0.042) of the TNIP1 gene were associated with an increased risk of HCC (OR > 1) [Figure 2(a)]. Rs1682111 (OR = 0.77, 95% CI = 0.64–0.94, p = 0.008) of the ACYP2 gene, rs2320615 (OR = 0.79, 95% CI = 0.64–0.99, P = 0.038) of the NAF1 gene, and rs10069690 (OR = 0.75, 95% CI = 0.59–0.96, p = 0.021) and rs2242652 (OR = 0.70, 95% CI = 0.55–0.90, p = 0.004) of the TERT gene were associated with a reduced risk of HCC (OR < 1) [Figure 2(b)]. Specific data are presented in Table 2.

Figure 2.

Analysis of the relationships between SNPs and HCC.

Table 2.

Frequency distributions of alleles and the information of SNPs in HCC and healthy volunteers (‘normal’).

SNP	Gene	Chromosome	Function	Allele (A/B)	Allele frequency		HWE p value	OR (95% CI)	p
SNP	Gene	Chromosome	Function	Allele (A/B)	HCC	Normal	HWE p value	OR (95% CI)	p
rs6713088	ACYP2	2	Intron	G	0.452	0.393	0.379	1.27 (1.07–1.52)	0.007^*
				C	0.548	0.607
rs12621038	ACYP2	2	Intron	T	0.445	0.440	0.608	1.02 (0.86–1.22)	0.813
				C	0.555	0.560
rs1682111	ACYP2	2	Intron	A	0.275	0.329	0.775	0.77 (0.64–0.94)	0.008^*
				T	0.725	0.671
rs843752	ACYP2	2	Intron	G	0.296	0.266	0.518	1.16 (0.95–1.40)	0.141
				T	0.704	0.734
rs10439478	ACYP2	2	Intron	C	0.427	0.402	0.382	1.11 (0.93–1.32)	0.258
				A	0.573	0.598
rs17045754	ACYP2	2	Intron	C	0.197	0.167	0.761	1.22 (0.98–1.53)	0.077
				G	0.803	0.833
rs843720	ACYP2	2	Intron	G	0.303	0.342	0.779	0.84 (0.69–1.01)	0.057
				T	0.697	0.658
rs843645	ACYP2	2	Downstream	G	0.282	0.252	0.263	1.17 (0.96–1.42)	0.116
				T	0.718	0.748
rs11125529	ACYP2	2	Downstream	A	0.185	0.164	0.644	1.16 (0.92–1.46)	0.201
				C	0.815	0.836
rs12615793	ACYP2	2	Downstream	A	0.201	0.178	0.315	1.16 (0.93–1.45)	0.181
				G	0.799	0.822
rs843711	ACYP2	2	Downstream	T	0.501	0.437	1.000	1.29 (1.09–1.54)	0.004^*
				C	0.499	0.563
rs11896604	ACYP2	2	Downstream	G	0.214	0.185	0.675	1.20 (0.97–1.49)	0.098
				C	0.786	0.815
rs843706	ACYP2	2	3' UTR	A	0.504	0.439	1.000	1.30 (1.09–1.55)	0.003^*
				C	0.496	0.561
rs35073794	TERC	3	Downstream	A	0.010	0.006	1.000	1.54 (0.57–4.15)	0.389
				G	0.090	0.994
rs10936599	TERC	3	Promoter	C	0.484	0.437	0.123	1.21 (1.02–1.44)	0.032^*
				T	0.516	0.563
rs2320615	NAF1	4	Intron	A	0.180	0.216	1.000	0.79 (0.64–0.99)	0.038^*
				G	0.820	0.784
rs10069690	TERT	5	Intron	T	0.135	0.171	0.655	0.75 (0.59–0.96)	0.021^*
				C	0.865	0.829
rs2242652	TERT	5	Intron	A	0.133	0.179	0.391	0.70 (0.55–0.90)	0.004^*
				G	0.867	0.821
rs2853677	TERT	5	Intron	G	0.370	0.369	0.717	1.00 (0.84–1.20)	0.966
				A	0.630	0.631
rs2853676	TERT	5	Intron	T	0.132	0.159	0.874	0.81 (0.63–1.04)	0.092
				C	0.868	0.841
rs3792792	TNIP1	5	Intron	C	0.063	0.051	1.000	1.25 (0.86–1.81)	0.240
				T	0.937	0.949
rs7708392	TNIP1	5	Intron	G	0.247	0.209	0.444	1.24 (1.00–1.52)	0.042^*
				C	0.753	0.791
rs10036748	TNIP1	5	Intron	C	0.247	0.211	0.527	1.23 (1.00–1.51)	0.053
				T	0.753	0.789
rs9325507	OBFC1	10	Intron	T	0.316	0.337	0.073	0.91 (0.75–1.09)	0.306
				C	0.684	0.663
rs3814220	OBFC1	10	Intron	G	0.317	0.338	0.090	0.91 (0.76–1.09)	0.317
				A	0.683	0.662
rs12765878	OBFC1	10	Intron	C	0.314	0.338	0.090	0.90 (0.75–1.08)	0.250
				T	0.686	0.662
rs11191865	OBFC1	10	Intron	A	0.315	0.338	0.090	0.90 (0.75–1.08)	0.271
				G	0.685	0.662
rs9420907	OBFC1	10	Intron	C	0.011	0.010	1.000	1.08 (0.46–2.56)	0.859
				A	0.989	0.990
rs1056675	MPHOSPH6	16	3' UTR	C	0.421	0.397	0.725	1.11 (0.93–1.32)	0.260
				T	0.579	0.603
rs1056654	MPHOSPH6	16	3' UTR	A	0.317	0.341	0.851	0.90 (0.75–1.08)	0.249
				G	0.683	0.659
rs3751862	MPHOSPH6	16	3' UTR	C	0.059	0.058	1.000	1.03 (0.71–1.48)	0.887
				A	0.941	0.942
rs11859599	MPHOSPH6	16	Intron	C	0.201	0.207	0.028^*	0.97 (0.78–1.20)	0.766
				G	0.799	0.793
rs2967361	MPHOSPH6	16	Intron	T	0.234	0.224	0.068	1.05 (0.86–1.30)	0.611
				G	0.766	0.776
rs2188972	ZNF208	19	3' UTR	A	0.511	0.491	0.501	1.08 (0.91–1.28)	0.378
				G	0.489	0.509
rs2188971	ZNF208	19	3' UTR	T	0.304	0.290	0.473	1.07 (0.89–1.30)	0.472
				C	0.696	0.710
rs8103163	ZNF208	19	Intron	A	0.305	0.290	0.474	1.07 (0.89–1.30)	0.464
				C	0.695	0.710
rs7248488	ZNF208	19	Intron	A	0.304	0.291	0.414	1.07 (0.88–1.29)	0.498
				C	0.696	0.709
rs8105767	ZNF208	19	Intron	G	0.304	0.298	0.481	1.03 (0.85–1.24)	0.774
				A	0.696	0.702
rs6089953	RTEL1	20	Intron	G	0.292	0.288	0.473	1.02 (0.84–1.23)	0.841
				A	0.708	0.712
rs6010621	RTEL1	20	Intron	G	0.263	0.274	0.833	0.95 (0.78–1.15)	0.600
				T	0.737	0.726
rs4809324	RTEL1	20	Intron	C	0.133	0.116	0.838	1.16 (0.89–1.51)	0.261
				T	0.867	0.884
rs2297441	RTEL1	20	Intron	A	0.326	0.322	0.700	1.02 (0.85–1.22)	0.855
				G	0.674	0.678

CI, confidence interval; HCC, hepatocellular carcinoma; HWE, Hardy–Weinberg equilibrium; OR, odds ratio; SNP, single nucleotide polymorphism.

p < 0.05.

Relationships between different genotypes and HCC

Next, the relationships between different genotypes and HCC were analyzed. We found that the rs6713088 genotype in the ACYP2 gene was significantly associated with the high risk of HCC in both the additive model (OR = 1.23, 95% CI: 1.02–1.48, p = 0.028) and dominant model (OR = 1.32, 95% CI = 1.01–1.74, p = 0.043). Furthermore, other loci remarkably associated with high risk of HCC included rs843645 (codominant model: OR = 1.40, 95% CI = 1.07–1.82 for G/T, OR = 0.96, 95% CI = 0.57–1.60 for G/G, p = 0.038; dominant model: OR = 1.32, 95% CI = 1.02–1.70, p = 0.033), rs843711 (additive model: OR = 1.26, 95% CI: 1.06–1.51, p = 0.010; codominant model: OR = 1.13, 95% CI = 0.84–1.52 for T/C, OR = 1.62, 95% CI = 1.13–2.31 for T/T, p = 0.023; recessive model: OR = 1.50, 95% CI = 1.11–2.03, p = 0.009), and rs843706 (additive model: OR = 1.26, 95% CI: 1.06–1.51, p = 0.010; codominant model: OR = 1.14, 95% CI = 0.84–1.53 for A/C, OR = 1.62, 95% CI = 1.13–2.31 for A/A, p = 0.024; recessive model: OR = 1.49, 95% CI = 1.10–2.02, p = 0.009) in the ACYP2 gene as well as rs10936599 (additive model: OR = 1.20, 95% CI: 1.01–1.43, p = 0.038) in the TERC gene. Meanwhile, we also identified three loci significantly associated with a low risk of HCC, including rs1682111 in the ACYP2 gene (codominant model: OR = 0.69, 95% CI = 0.53–0.91 for A/T, OR = 0.62, 95% CI = 0.39–0.98 for A/A, p = 0.011; dominant model: OR = 0.68, 95% CI = 0.53–0.88, p = 0.003), rs2242652 in the TERT gene (additive model: OR = 0.72, 95% CI: 0.56–0.92, p = 0.009; codominant model: OR = 0.76, 95% CI = 0.57–1.02 for A/G, OR = 0.41, 95% CI = 0.17–0.95 for A/A, p = 0.029; dominant model: OR = 0.72, 95% CI = 0.54–0.95, p = 0.022), and rs10069690 in the TERT gene (additive model: OR = 0.77, 95% CI = 0.60–0.98, p = 0.038). All data reported above are presented in Table 3.

Table 3.

Distribution of different SNP genotypes and risk analysis of HCC.

Gene	SNP	Model	Genotype	HCC	Control	Crude analysis		Adjustment analysis
				n (%)	n (%)	OR (95% CI)	p	OR (95% CI)	p
ACYP2	rs6713088	Codominant	C/C	138 (29.2%)	202 (35.9%)	1		1
			G/C	242 (51.2%)	279 (49.6%)	1.27 (0.96–1.67)	0.023^*	1.27 (0.95–1.69)	0.087
			G/G	93 (19.7%)	82 (14.6%)	1.66 (1.15–2.40)		1.49 (1.02–2.18)
		Dominant	C/C	138 (29.2%)	202 (35.9%)	1		1
			G/C+G/G	335 (70.9%)	361 (64.2%)	1.36 (1.05–1.77)	0.022^*	1.32 (1.01–1.74)	0.043^*
		Recessive	C/C+G/C	380 (80.4%)	481 (85.5%)	1		1
			G/G	93 (19.7%)	82 (14.6%)	1.44 (1.04–9.1.99)	0.030^*	1.29 (0.92–1.81)	0.138
		Log-additive	–	–	–	1.29 (1.08–1.54)	0.006^*	1.23 (1.02–1.48)	0.028^*
ACYP2	rs12621038	Codominant	C/C	139 (29.5%)	180 (31.9%)	1		1
			T/C	245 (52.0%)	271 (48.1%)	1.17 (0.88–1.55)	0.462	1.28 (0.96–1.71)	0.228
			T/T	87 (18.5%)	112 (19.9%)	1.01 (0.70–1.44)		1.08 (0.75–1.56)
		Dominant	C/C	139 (29.5%)	180 (31.9%)	1		1
			T/C+T/T	332 (70.5%)	383 (68.0%)	1.12 (0.86–1.46)	0.394	1.22 (0.93–1.61)	0.158
		Recessive	C/C+T/C	384 (81.5%)	451 (80.0%)	1		1
			T/T	87 (18.5%)	112 (19.9%)	0.91 (0.67–1.25)	0.564	0.93 (0.67–1.28)	0.646
		Log-additive	–	–	–	1.02 (0.86–1.21)	0.812	1.06 (0.89–1.28)	0.499
ACYP2	rs1682111	Codominant	T/T	251 (53.3%)	252 (44.7%)	1		1
			A/T	181 (38.4%)	253 (44.9%)	0.72 (0.55–0.93)	0.021^*	0.69 (0.53–0.91)	0.011^*
			A/A	39 (8.3%)	59 (10.5%)	0.66 (0.43–1.03)		0.62 (0.39–0.98)
		Dominant	T/T	251 (53.3%)	252 (44.7%)	1		1
			A/T+A/A	220 (46.7%)	312 (55.4%)	0.71 (0.55–0.91)	0.006^*	0.68 (0.53–0.88)	0.003^*
		Recessive	T/T+A/T	432 (91.7%)	505 (89.6%)	1		1
			A/A	39 (8.3%)	59 (10.5%)	1.33 (0.92–1.90)	0.130	0.73 (0.47–1.14)	0.168
		Log-additive	–	–	–	0.77 (0.51–1.18)	0.234	1.06 (0.81–1.39)	0.670
ACYP2	rs843752	Codominant	T/T	232 (49.2%)	306 (54.4%)	1		1
			G/T	201 (42.6%)	214 (38.0%)	1.24 (0.96–1.60)	0.247	1.23 (0.94–1.61)	0.309
			G/G	39 (8.3%)	43 (7.6%)	1.20 (0.75–1.91)		1.13 (0.70–1.83)
		Dominant	T/T	232 (49.2%)	306 (54.4%)	1		1
			T/G+G/G	240 (50.9%)	257 (45.6%)	1.32 (0.99–1.76)	0.062	1.18 (0.80–1.72)	0.400
		Recessive	T/T+T/G	433 (91.8%)	520 (92.4%)	1		1
			G/G	39 (8.3%)	43 (7.6%)	1.09 (0.69–1.71)	0.711	1.03 (0.65–1.65)	0.886
		Log-additive	–	–	–	1.16 (0.95–1.40)	0.143	1.13 (0.93–1.38)	0.218
ACYP2	rs10439478	Codominant	A/A	154 (32.6%)	206 (36.6%)	1		1
			C/A	233 (49.4%)	261 (46.4%)	1.19 (0.91–1.57)	0.411	1.20 (0.91–1.60)	0.286
			C/C	85 (18.0%)	96 (17.1%)	1.18 (0.83–1.70)		1.31 (0.90–1.90)
		Dominant	A/A	154 (32.6%)	206 (36.6%)	1		1
			C/A+C/C	318 (67.4%)	357 (63.5%)	1.19 (0.92–1.54)	0.183	1.23 (0.94–1.61)	0.130
		Recessive	A/A+C/A	387 (82.0%)	467 (83.0%)	1		1
			C/C	85 (18.0%)	96 (17.1%)	1.07 (0.77–1.47)	0.687	1.17 (0.84–1.64)	0.351
		Log-additive	–	–	–	1.11 (0.93–1.32)	0.262	1.15 (0.96–1.38)	0.125
ACYP2	rs843645	Codominant	T/T	235 (49.9%)	321 (56.9%)	1		1
			G/T	206 (43.7%)	202 (35.8%)	1.39 (1.08–1.80)	0.035^*	1.40 (1.07–1.82)	0.038^*
			G/G	30 (6.4%)	41 (7.3%)	1.00 (0.61–1.65)		0.96 (0.57–1.60)
		Dominant	T/T	235 (49.9%)	321 (56.9%)	1		1
			G/T+G/G	236 (50.1%)	243 (43.1%)	1.33 (1.04–1.70)	0.024^*	1.32 (1.02–1.70)	0.033^*
		Recessive	T/T+G/T	441 (93.6%)	523 (92.7%)	1		1
			G/G	30 (6.4%)	41 (7.3%)	0.87 (0.53–1.41)	0.569	0.83 (0.50–1.37)	0.471
		Log-additive	–	–	–	1.17 (0.96–1.43)	0.115	1.16 (0.94–1.42)	0.158
ACYP2	rs11125529	Codominant	C/C	310 (65.7%)	392 (9.5%)	1		1
			A/C	149 (31.6%)	159 (28.2%)	1.19 (0.91–1.55)	0.418	1.20 (0.91–1.58)	0.434
			A/A	13 (2.8%)	13 (2.3%)	1.27 (0.58–2.77)		1.16 (0.52–2.60)
		Dominant	C/C	310 (65.7%)	392 (69.5%)	1		1
			A/C+A/A	162 (34.4%)	172 (30.5%)	1.19 (0.92–1.55)	0.190	1.20 (0.91–1.57)	0.197
		Recessive	C/C+A/C	459 (97.3%)	551 (97.7%)	1		1
			A/A	13 (2.8%)	13 (2.3%)	1.20 (0.55–2.62)	0.646	1.10 (0.49–2.45)	0.818
		Log-additive	–	–	–	1.17 (0.93–1.47)	0.193	1.16 (0.91–1.48)	0.226
ACYP2	rs12615793	Codominant	G/G	297 (62.9%)	377 (66.8%)	1		1
			A/G	160 (33.9%)	173 (30.7%)	1.17 (0.90–1.53)	0.391	1.18 (0.90–1.55)	0.477
			A/A	15 (3.2%)	14 (2.5%)	1.36 (0.65–2.86)		1.21 (0.56–2.60)
		Dominant	G/G	297 (62.9%)	377 (66.8%)	1		1
			A/G+A/A	175 (37.1%)	187 (33.2%)	1.19 (0.92–1.54)	0.188	1.18 (0.90–1.54)	0.224
		Recessive	G/G+A/G	457 (96.8%)	550 (97.5%)	1		1
			A/A	15 (3.2%)	14 (2.5%)	1.29 (0.62–2.70)	0.500	1.15 (0.53–2.45)	0.728
		Log-additive	–	–	–	1.17 (0.93–1.47)	0.171	1.15 (0.91–1.46)	0.238
ACYP2	rs843711	Codominant	C/C	126 (26.8%)	178 (31.6%)	1		1
			T/C	218 (46.3%)	278 (49.4%)	1.11 (0.83–1.48)	0.008^*	1.13 (0.84–1.52)	0.023^*
			T/T	127 (30.0%)	107 (19.0%)	1.68 (1.19–2.37)		1.62 (1.13–2.31)
		Dominant	C/C	126 (26.8%)	178 (31.6%)	1		1
			T/C+T/T	345 (76.3%)	385 (68.4%)	1.27 (0.97–1.66)	0.088	1.27 (0.96–1.68)	0.095
		Recessive	C/C+T/C	344 (73.1%)	456 (81.0%)	1		1
			T/T	127 (30.0%)	107 (19.0%)	1.57 (1.17–2.11)	0.002^*	1.50 (1.11–2.03)	0.009^*
		Log-additive	–	–	–	1.28 (1.08–1.52)	0.004^*	1.26 (1.06–1.51)	0.01^*
ACYP2	rs11896604	Codominant	C/C	288 (61.2%)	376 (66.7%)	1		1
			G/C	164 (34.8%)	167 (29.6%)	1.28 (0.98–1.67)	0.178	1.32 (1.00–1.73)	0.146
			G/G	19 (4.0%)	21 (3.7%)	1.18 (0.62–2.24)		1.08 (0.56–2.08)
		Dominant	C/C	288 (61.2%)	376 (66.7%)	1		1
			G/C+G/G	183 (38.8%)	188 (33.3%)	1.27 (0.98–1.64)	0.065	1.29 (0.99–1.68)	0.061
		Recessive	C/C+G/C	452 (96.0%)	543 (96.3%)	1		1
			G/G	19 (4.0%)	21 (3.7%)	1.09 (0.58–2.05)	0.796	0.98 (0.51–1.89)	0.960
		Log-additive	–	–	–	1.20 (0.97–1.49)	0.097	1.20 (0.96–1.50)	0.115
ACYP2	rs843706	Codominant	C/C	124 (26.3%)	177 (31.5%)	1		1
			A/C	219 (46.5%)	277 (49.3%)	1.13 (0.84–1.51)	0.007^*	1.14 (0.84–1.53)	0.024^*
			A/A	128 (27.2%)	108 (19.2%)	1.69 (1.20–2.39)		1.62 (1.13–2.31)
		Dominant	C/C	124 (56.3%)	177 (31.5%)	1		1
			A/C+A/A	347 (73.7%)	385 (68.5%)	1.29 (0.98–1.69)	0.069	1.28 (0.96–1.69)	0.090
		Recessive	C/C+A/C	343 (72.8%)	454 (80.8%)	1		1
			A/A	128 (27.2%)	108 (19.2%)	1.57 (1.17–2.10)	0.003^*	1.49 (1.10–2.02)	0.009^*
		Log-additive	–	–	–	1.29 (1.09–1.53)	0.004^*	1.26 (1.06–1.51)	0.01^*
ACYP2	rs17045754	Codominant	G/G	302 (63.8%)	390 (69.1%)	1		1
			G/C	156 (33.0%)	160 (28.3%)	1.26 (0.96–1.64)	0.392	1.27 (0.97–1.67)	0.076
			C/C	15 (3.2%)	14 (2.5%)	1.38 (0.66–2.91)		1.37 (0.63–2.97)
		Dominant	G/G	302 (63.8%)	390 (69.1%)	1		1
			G/C+C/C	171 (36.2%)	174 (30.8%)	1.27 (0.98–1.64)	0.071	1.28 (0.88–1.92)	0.190
		Recessive	G/G+G/C	458 (96.8%)	550 (97.4%)	1		1
			C/C	15 (3.2%)	14 (2.5%)	1.38 (0.92–2.07)	0.120	1.27 (0.59–2.74)	0.536
		Log-additive	–	–	–	1.29 (0.61–2.69)	0.504	1.24 (0.97–1.57)	0.080
ACYP2	rs843720	Codominant	T/T	224 (47.5%)	242 (42.9%)	1		1
			G/T	210 (44.4%)	258 (45.7%)	0.88 (0.68–1.34)	0.130	0.85 (0.65–1.11)	0.134
			G/G	38 (8.1%)	64 (11.3%)	0.64 (0.41–1.00)		0.64 (0.41–1.01)
		Dominant	T/T	224 (47.5%)	242 (42.9%)	1		1
			G/T+G/G	248 (52.5%)	322 (57.0%)	0.83 (0.65–1.06)	0.143	0.81 (0.63–1.05)	0.109
		Recessive	T/T+G/T	434 (91.9%)	500 (88.6%)	1		1
			G/G	38 (8.1%)	64 (11.3%)	1.38 (0.92–2.07)	0.120	0.70 (0.45–1.08)	0.103
		Log-additive	–	–	–	0.68 (0.45–1.04)	0.077	0.82 (0.68–1.00)	0.049^*
TERC	rs35073794	Codominant	G/G	463 (98.1%)	557 (98.7%)	1		1
			A/G	9 (1.9%)	7 (1.3%)
			A/A	0 (0%)	0 (0%)
		Dominant	G/G	463 (98.1%)	557 (98.7%)	1		1
			A/G+A/A	9 (1.9%)	7 (1.3%)	1.55 (0.57–4.19)	0.390	1.53 (0.55–4.26)	0.419
		Recessive	G/G+A/G	472 (100.0%)	564 (100.0%)	1		1
			A/A	0 (0%)	0 (0%)				0.120
		Log-additive	–	–	–	1.55 (0.57–4.19)	0.390	1.53 (0.55–4.26)	0.419
TERC	rs10936599	Codominant	T/T	134 (28.3%)	188 (33.3%)	1		1
			C/T	220 (46.5%)	259 (45.9%)	1.19 (0.90–1.59)	0.117	1.20 (0.89–1.61)	0.115
			C/C	119 (25.2%)	117 (20.7%)	1.43 (1.02–2.00)		1.45 (1.02–2.05)
		Dominant	T/T	134 (28.3%)	188 (33.3%)	1		1
			C/T+C/C	339 (71.7%)	376 (66.6%)	1.27 (0.97–1.65)	0.083	1.28 (0.97–1.68)	0.081
		Recessive	T/T+C/T	354 (74.8%)	447 (79.2%)	1		1
			C/C	119 (25.2%)	117 (20.7%)	1.28 (0.96–1.72)	0.092	1.30 (0.96–1.75)	0.091
		Log-additive	–	–	–	1.19 (1.01–1.41)	0.038^*	1.20 (1.01–1.43)	0.038^*
NAF1	rs2320615	Codominant	G/G	315 (66.6%)	346 (61.3%)	1		1
			A/G	146 (30.9%)	192 (34.0%)	0.84 (0.64–1.09)	0.089	0.84 (0.64–1.10)	0.160
			A/A	12 (2.5%)	26 (4.6%)	0.51 (0.25–1.02)		0.56 (0.27–1.15)
		Dominant	G/G	315 (66.6%)	346 (61.3%)	1		1
			A/G+A/A	158 (33.4%)	218 (38.6%)	0.80 (0.62–1.03)	0.080	0.81 (0.62–1.05)	0.112
		Recessive	G/G+A/G	461 (97.5%)	538 (95.3%)	1		1
			A/A	12 (2.5%)	26 (4.6%)	0.54 (0.27–1.08)	0.081	0.59 (0.29–1.21)	0.150
		Log-additive	–	–	–	0.79 (0.63–0.99)	0.036^*	0.81 (0.64–1.01)	0.064
TERT	rs10069690	Codominant	C/C	353 (74.8%)	386 (68.9%)	1		1
			T/C	111 (23.5%)	156 (27.9%)	0.78 (0.59–1.03)	0.069	0.80 (0.60–1.07)	0.103
			T/T	8 (1.7%)	18 (3.2%)	0.49 (0.21–1.13)		0.48 (0.20–1.15)
		Dominant	C/C	353 (74.8%)	386 (68.9%)	1		1
			T/C+T/T	119 (25.2%)	174 (31.1%)	0.75 (0.57–0.98)	0.038^*	0.77 (0.58–1.02)	0.067
		Recessive	C/C+T/C	464 (98.3%)	542 (96.8%)	1		1
			T/T	8 (1.7%)	18 (3.2%)	0.52 (0.22–1.21)	0.127	0.51 (0.21–1.21)	0.126
		Log-additive	–	–	–	0.75 (0.59–0.96)	0.022^*	0.77 (0.60–0.98)	0.038^*
TERT	rs2242652	Codominant	G/G	355 (75.1%)	383 (67.9%)	1		1
			A/G	110 (23.3%)	160 (28.4%)	0.74 (0.56–0.98)	0.018^*	0.76 (0.57–1.02)	0.029^*
			A/A	8 (1.6%)	21 (3.7%)	0.41 (0.18–0.94)		0.41 (0.17–0.95)
		Dominant	G/G	355 (75.1%)	383 (67.9%)	1		1
			A/G+A/A	118 (24.9%)	181 (32.1%)	0.70 (0.54–0.92)	0.012^*	0.72 (0.54–0.95)	0.022^*
		Recessive	G/G+A/G	465 (98.4%)	543 (96.3%)	1		1
			A/A	8 (1.6%)	21 (3.7%)	0.44 (0.20–1.01)	0.054	0.44 (0.19–1.01)	0.054
		Log-additive	–	–	–	0.71 (0.56–0.91)	0.005^*	0.72 (0.56–0.92)	0.009^*
TERT	rs2853677	Codominant	A/A	183 (38.7%)	227 (40.2%)	1		1
			G/A	229 (48.5%)	258 (45.7%)	1.10 (0.85–1.43)	0.643	1.03 (0.78–1.36)	0.679
			G/G	60 (12.7%)	79 (14.1%)	0.94 0.64–1.39)		0.87 (0.58–1.29)
		Dominant	A/A	183 (38.7%)	227 (40.2%)	1		1
			G/A+G/G	289 (61.2%)	337 (59.8%)	1.06 (0.83–1.37)	0.628	0.99 (0.77–1.29)	0.951
		Recessive	A/A+G/A	412 (87.2%)	485 (85.9%)	1		1
			G/G	60 (12.7%)	79 (14.1%)	0.89 (0.62–1.28)	0.543	0.85 (0.59–1.24)	0.394
		Log-additive	–	–	–	1.00 (0.84–1.20)	0.966	0.96 (0.79–1.15)	0.636
TERT	rs2853676	Codominant	C/C	356 (75.4%)	398 (70.6%)	1		1
			C/T	107 (22.7%)	153 (27.1%)	0.78 (0.59–1.04)	0.217	0.80 (0.59–1.07)	0.134
			T/T	9 (1.9%)	13 (2.3%)	0.77 (0.33–1.83)		0.68 (0.28–1.65)
		Dominant	C/C	356 (75.4%)	398 (70.6%)	1		1
			C/T+T/T	116 (24.6%)	166 (29.4%)	0.78 (0.59–1.03)	0.081	0.79 (0.59–1.05)	0.103
		Recessive	C/C+C/T	463 (98.1%)	551 (97.7%)	1		1
			T/T	9 (1.9%)	13 (2.3%)	0.82 (0.35–1.95)	0.658	0.72 (0.30–1.74)	0.470
		Log-additive	–	–	–	0.81 (0.63–1.04)	0.093	0.81 (0.62–1.04)	0.097
TNIP1	rs3792792	Codominant	T/T	414 (87.5%)	507 (89.9%)	1		1
			C/T	58 (12.3%)	56 (9.9%)	1.27 (0.86–1.87)	0.485	1.34 (0.90–2.02)	0.351
			C/C	1 (0.2%)	1 (0.2%)	1.23 (0.08–19.64)		1.45 (0.08–25.43)
		Dominant	T/T	414 (87.5%)	507 (89.9%)	1		1
			C/T+C/C	59 (12.5%)	57 (10.1%)	1.27 (0.86–1.87)	0.229	1.35 (0.90–2.01)	0.148
		Recessive	T/T+C/T	472 (99.8%)	563 (99.8%)	1		1
			C/C	1 (0.2%)	1 (0.2%)	1.19 (0.07–19.12)	0.901	1.40 (0.08–24.55)	0.817
		Log-additive	–	–	–	1.26 (0.86–1.83)	0.235	1.33 (0.90–1.97)	0.150
TNIP1	rs7708392	Codominant	C/C	266 (56.4%)	349 (61.9%)	1		1
			G/C	179 (37.9%)	194 (34.4%)	1.21 (0.94–1.57)	0.112	1.19 (0.91–1.55)	0.275
			G/G	27 (5.7%)	21 (3.7%)	1.69 (0.93–3.05)		1.45 (0.79–2.68)
		Dominant	C/C	266 (56.4%)	349 (61.9%)	1		1
			G/C+G/G	206 (43.6%)	215 (38.1%)	1.26 (0.98–1.61)	0.072	1.21 (0.94–1.57)	0.139
		Recessive	C/C+G/C	445 (94.3%)	543 (96.3%)	1		1
			G/G	27 (5.7%)	21 (3.7%)	1.57 (0.88–2.81)	0.131	1.36 (0.74–2.49)	0.317
		Log-additive	–	–	–	1.25 (1.01–1.54)	0.039^*	1.20 (0.96–1.48)	0.108
TNIP1	rs10036748	Codominant	T/T	266 (56.4%)	348 (61.7%)	1		1
			C/T	179 (37.9%)	194 (34.4%)	1.21 (0.93–1.56)	0.142	1.19 (0.91–1.55)	0.301
			C/C	27 (5.7%)	22 (3.9%)	1.61 (0.89–2.88)		1.42 (0.77–2.59)
		Dominant	T/T	266 (56.4%)	348 (61.7%)	1		1
			C/T+C/C	206 (43.6%)	216 (38.3%)	1.25 (0.97–1.60)	0.081	1.21 (0.93–1.56)	0.148
		Recessive	T/T+C/T	445 (94.3%)	542 (96.1%)	1		1
			C/C	27 (5.7%)	22 (3.9%)	1.49 (0.84–2.66)	0.172	1.33 (0.73–2.41)	0.354
		Log-additive	–	–	–	1.23 (1.00–1.52)	0.050	1.19 (0.96–1.47)	0.121
OBFC1	rs9325507	Codominant	C/C	216 (45.8%)	238 (42.2%)	1		1
			T/C	214 (45.3%)	272 (48.2%)	0.87 (0.67–1.12)	0.515	0.87 (0.66–1.13)	0.515
			T/T	42 (8.9%)	54 (9.6%)	0.86 (0.55–1.34)		0.84 (0.53–1.33)
		Dominant	C/C	216 (45.8%)	238 (42.2%)	1		1
			T/C+T/T	256 (54.2%)	326 (57.8%)	0.87 (0.68–1.11)	0.250	0.86 (0.67–1.11)	0.252
		Recessive	C/C+T/C	430 (91.1%)	510 (90.4%)	1		1
			T/T	42 (8.9%)	54 (9.6%)	0.92 (0.60–1.41)	0.709	0.91 (0.59–1.40)	0.661
		Log-additive	–	–	–	0.90 (0.75–1.09)	0.290	0.90 (0.74–1.09)	0.278
OBFC1	rs3814220	Codominant	A/A	216 (50.0%)	238 (42.2%)	1		1
			G/A	210 (44.7%)	271 (48.0%)	0.85 (0.66–1.11)	0.475	0.86 (0.65–1.12)	0.487
			G/G	44 (9.3%)	55 (9.8%)	0.88 (0.57–1.37)		0.85 (0.54–1.34)
		Dominant	A/A	216 (50.0%)	238 (42.2%)	1		1
			G/A+G/G	254 (54.0%)	326 (57.8%)	0.86 (0.67–1.10)	0.225	0.86 (0.66–1.10)	0.230
		Recessive	A/A+G/A	426 (94.7%)	509 (90.2%)	1		1
			G/G	44 (9.3%)	55 (9.8%)	0.96 (0.63–1.45)	0.832	0.92 (0.60–1.42)	0.722
		Log-additive	–	–	–	0.91 (0.75–1.10)	0.304	0.90 (0.74–1.09)	0.279
OBFC1	rs12765878	Codominant	T/T	218 (46.1%)	238 (42.2%)	1		1
			C/T	213 (45.0%)	271 (48.0%)	0.86 (0.66–1.11)	0.450	0.86 (0.66–1.12)	0.453
			C/C	42 (8.9%)	55 (9.8%)	0.83 (0.54–1.30)		0.81 (0.52–1.28)
		Dominant	T/T	218 (46.1%)	238 (42.2%)	1		1
			C/T+C/C	255 (53.9%)	326 (57.8%)	0.85 (0.67–1.09)	0.209	0.85 (0.66–1.10)	0.217
		Recessive	T/T+C/T	431 (91.1%)	509 (90.2%)	1		1
			C/C	42 (8.9%)	55 (9.8%)	0.90 (0.59–1.38)	0.631	0.88 (0.57–1.36)	0.561
		Log-additive	–	–	–	0.89 (0.74–1.08)	0.235	0.89 (0.73–1.08)	0.224
OBFC1	rs11191865	Codominant	G/G	217 (45.9%)	238 (42.2%)	1		1
			A/G	214 (45.2%)	271 (48.0%)	0.86 (0.66–1.11)	0.450	0.87 (0.67–1.13)	0.493
			A/A	42 (8.9%)	55 (9.8%)	0.83 (0.54–1.30)		0.82 (0.52–1.29)
		Dominant	G/G	217 (45.9%)	238 (42.2%)	1		1
			A/G+A/A	256 (54.1%)	326 (57.8%)	0.85 (0.67–1.09)	0.209	0.86 (0.67–1.11)	0.246
		Recessive	G/G+A/G	431 (91.1%)	509 (90.2%)	1		1
			A/A	42 (8.9%)	55 (9.8%)	0.90 (0.59–1.38)	0.631	0.88 (0.57–1.36)	0.561
		Log-additive	–	–	–	0.89 (0.74–1.08)	0.235	0.89 (0.73–1.08)	0.246
OBFC1	rs9420907	Codominant	A/A	463 (97.9%)	551 (98.0%)	1		1
			C/A	10 (21.1%)	11 (20.0%)
			C/C	0 (0%)	0 (0%)	/	/	/	/
		Dominant	A/A	463 (97.9%)	551 (98.0%)	1		1
			C/A+C/C	10 (21.1%)	11 (20.0%)	1.08 (0.46–2.57)	0.859	0.90 (0.37–2.17)	0.808
		Recessive	A/A+C/A	473 (100.0%)	562 (100.0%)	1		1
			C/C	0 (0%)	0 (0%)
		Log-additive	–	–	–	1.08 (0.46–2.57)	0.859	0.90 (0.37–2.17)	0.808
MPHOSPH6	rs1056675	Codominant	T/T	160 (34.1%)	202 (35.9%)	1		1
			C/T	224 (47.6%)	274 (48.8%)	1.03 (0.79–1.36)	0.427	1.02 (0.77–1.36)	0.442
			C/C	86 (18.3%)	86 (15.3%)	1.26 (0.88–1.82)		1.26 (0.87–1.84)
		Dominant	T/T	160 (34.1%)	202 (35.9%)	1		1
			C/T+C/C	310 (65.9%)	360 (64.1%)	1.09 (0.84–1.41)	0.524	1.08 (0.83–1.41)	0.566
		Recessive	T/T+C/T	384 (81.7%)	476 (84.7%)	1		1
			C/C	86 (18.3%)	86 (15.3%)	1.24 (0.89–1.72)	0.199	1.25 (0.89–1.75)	0.205
		Log-additive	–	–	–	1.11 (0.93–1.32)	0.260	1.11 (0.92–1.33)	0.283
MPHOSPH6	rs1056654	Codominant	G/G	224 (47.4%)	243 (43.2%)	1		1
			A/G	198 (41.9%)	256 (45.5%)	0.84 (0.65–1.09)	0.397	0.84 (0.65–1.11)	0.388
			A/A	51 (10.7%)	64 (11.3%)	0.86 (0.57–1.30)		0.81 (0.53–1.24)
		Dominant	G/G	224 (47.4%)	243 (43.2%)	1		1
			A/G+A/A	249 (52.6%)	320 (56.8%)	0.84 (0.66–1.08)	0.177	0.84 (0.65–1.08)	0.173
		Recessive	G/G+A/G	422 (89.3%)	499 (88.7%)	1		1
			A/A	51 (10.7%)	64 (11.3%)	0.94 (0.64–1.39)	0.765	0.88 (0.59–1.32)	0.537
		Log-additive	–	–	–	0.90 (0.75–1.08)	0.252	0.88 (0.73–1.07)	0.193
MPHOSPH6	rs3751862	Codominant	A/A	417 (88.2%)	499 (88.6%)	1		1
			C/A	56 (11.8%)	63 (11.2%)	1.06 (0.73–1.56)	0.951	1.09 (0.74–1.63)	0.906
			C/C	0 (0.0%)	1 (0.2%)	7.41E-10		5.28E-10
						(0-inf)		(0-inf)
		Dominant	A/A	417 (88.2%)	499 (88.6%)	1		1
			C/A+C/C	56 (11.8%)	64 (11.4%)	1.05 (0.72–1.53)	0.813	1.07 (0.72–1.59)	0.730
		Recessive	A/A+C/A	473 (100.0%)	562 (99.8%)	1		1
			C/C	0 (0.0%)	1 (0.2%)	7.36E-10	0.999	5.23E-10	0.999
						(0-INF)		(0-INF)
		Log-additive	–	–	–	1.03 (0.71–1.50)	0.884	1.05 (0.71–1.55)	0.816
MPHOSPH6	rs11859599	Codominant	G/G	306 (64.8%)	364 (64.5%)	1		1
			C/G	142 (30.1%)	167 (29.6%)	1.01 (0.77–1.33)	0.862	1.09 (0.82–1.44)	0.651
			C/C	24 (5.1%)	33 (5.9%)	0.87 (0.50–1.50)		0.84 (0.47–1.47)
		Dominant	G/G	306 (64.8%)	364 (64.5%)	1		1
			C/G+C/C	166 (35.2%)	200 (35.5%)	0.99 (0.76–1.28)	0.922	1.04 (0.80–1.36)	0.755
		Recessive	G/G+C/G	448 (94.9%)	531 (94.1%)	1		1
			C/C	24 (5.1%)	33 (5.9%)	0.86 (0.50–1.48)	0.590	0.81 (0.47–1.42)	0.471
		Log-additive	–	–	–	0.97 (0.79–1.19)	0.775	1.00 (0.80–1.23)	0.979
MPHOSPH6	rs2967361	Codominant	G/G	276 (58.4%)	346 (61.6%)	1		1
			T/G	173 (36.6%)	180 (32.0%)	1.21 (0.93–1.57)	0.249	1.26 (0.96–1.65)	0.151
			T/T	24 (5.0%)	36 (6.4%)	0.84 (0.49–1.43)		0.81 (0.46–1.42)
		Dominant	G/G	276 (58.4%)	346 (61.6%)	1		1
			T/G+T/T	197 (41.6%)	216 (38.4%)	1.14 (0.89–1.47)	0.293	1.18 (0.91–1.53)	0.212
		Recessive	G/G+G/T	449 (95.0%)	526 (93.6%)	1		1
			G/G	24 (5.0%)	36 (6.4%)	0.78 (0.46–1.33)	0.362	0.75 (0.43–1.30)	0.300
		Log-additive	–	–	–	1.05 (0.86–1.29)	0.617	1.07 (0.87–1.32)	0.541
ZNF208	rs2188972	Codominant	G/G	111 (23.5%)	150 (24.8%)	1		1
			A/G	241 (50.9%)	274 (48.6%)	1.19 (0.88–1.61)	0.510	1.21 (0.88–1.65)	0.486
			A/A	121 (25.6%)	140 (24.8%)	1.17 (0.83–1.65)		1.17 (0.82–1.68)
		Dominant	G/G	111 (23.5%)	150 (24.8%)	1		1
			A/G+A/A	362 (76.5%)	414 (73.4%)	1.18 (0.89–1.57)	0.248	1.20 (0.89–1.60)	0.235
		Recessive	G/G+A/G	352 (74.4%)	424 (73.4%)	1		1
			A/A	121 (25.6%)	140 (24.8%)	1.04 (0.79–1.38)	0.779	1.04 (0.77–1.39)	0.816
		Log-additive	–	–	–	1.08 (0.91–1.28)	0.380	1.08 (0.91–1.29)	0.385
ZNF208	rs2188971	Codominant	C/C	229 (48.5%)	288 (51.2%)	1		1
			T/C	199 (42.2%)	224 (39.8%)	1.12 (0.86–1.45)	0.694	1.22 (0.93–1.59)	0.326
			T/T	44 (9.3%)	51 (10.0%)	1.09 (0.70–1.68)		1.21 (0.77–1.92)
		Dominant	C/C	229 (48.5%)	288 (51.2%)	1		1
			T/C+T/T	243 (51.5%)	275 (49.8%)	1.11 (0.87–1.42)	0.398	1.22 (0.94–1.57)	0.134
		Recessive	C/C+T/C	428 (90.7%)	512 (91.0%)	1		1
			T/T	44 (9.3%)	51 (10.0%)	1.03 (0.68–1.58)	0.884	1.11 (0.72–1.72)	0.640
		Log-additive	–	–	–	1.07 (0.89–1.29)	0.476	1.15 (0.94–1.39)	0.175
ZNF208	rs8103163	Codominant	C/C	228 (48.4%)	288 (51.1%)	1		1
			A/C	199 (42.3%)	225 (39.9%)	1.12 (0.86–1.45)	0.692	1.22 (0.93–1.59)	0.317
			A/A	44 (9.3%)	51 (9.0%)	1.09 (0.70–1.69)		1.23 (0.78–1.94)
		Dominant	C/C	228 (48.4%)	288 (51.1%)	1		1
			C/C+A/C	243 (51.6%)	276 (48.9%)	1.11 (0.87–1.42)	0.395	1.22 (0.94–1.57)	0.130
		Recessive	A/C+A/A	431 (90.7%)	513 (91.0%)	1		1
			A/A	44 (9.3%)	51 (9.0%)	1.04 (0.68–1.58)	0.868	1.12 (0.72–1.74)	0.616
		Log-additive	–	–	–	1.07 (0.89–1.29)	0.468	1.15 (0.94–1.40)	0.166
ZNF208	rs7248488	Codominant	C/C	231 (48.9%)	288 (51.1%)	1		1
			A/C	196 (41.4%)	224 (39.7%)	1.09 (0.84–1.41)	0.774	1.18 (0.90–1.55)	0.385
			A/A	46 (9.7%)	52 (9.2%)	1.10 (0.72–1.70)		1.24 (0.79–1.95)
		Dominant	C/C	231 (48.9%)	288 (51.1%)	1		1
			C/C+A/C	242 (51.1%)	276 (48.9%)	1.09 (0.86–1.40)	0.475	1.19 (0.93–1.54)	0.172
		Recessive	A/C+A/A	427 (90.3%)	512 (90.8%)	1		1
			A/A	46 (9.7%)	52 (9.2%)	1.06 (0.70–1.61)	0.782	1.15 (0.75–1.77)	0.527
		Log-additive	–	–	–	1.07 (0.89–1.28)	0.504	1.14 (0.94–1.39)	0.185
ZNF208	rs8105767	Codominant	A/A	226 (47.9%)	272 (48.6%)	1		1
			G/A	205 (43.4%)	242 (43.2%)	1.02 (0.79–1.32)	0.953	0.93 (0.72–1.22)	0.861
			G/G	41 (8.7%)	46 (8.2%)	1.07 (0.68–1.70)		1.02 (0.64–1.64)
		Dominant	A/A	226 (47.9%)	272 (48.6%)	1		1
			G/A+G/G	246 (52.1%)	288 (51.4%)	1.03 (0.80–1.31)	0.825	0.95 (0.73–1.22)	0.680
		Recessive	A/A+G/A	431 (91.3%)	514 (91.8%)	1		1
			G/G	41 (8.7%)	46 (8.2%)	1.06 (0.68–1.65)	0.786	1.05 (0.67–1.66)	0.824
		Log-additive	–	–	–	1.03 (0.85–1.25)	0.771	0.98 (0.80–1.19)	0.821
RTEL1	rs6089953	Codominant	A/A	241 (50.9%)	289 (51.3%)	1		1
			G/A	188 (39.7%)	224 (39.8%)	1.01 (0.78–1.30)	0.972	1.01 (0.77–1.32)	0.812
			G/G	44 (9.3%)	50 (8.9%)	1.06 (0.68–1.64)		1.16 (0.73–1.84)
		Dominant	A/A	241 (50.9%)	289 (51.3%)	1		1
			G/A+G/G	232 (49.0%)	274 (48.7%)	1.02 (0.80–1.30)	0.903	1.04 (0.80–1.33)	0.791
		Recessive	A/A+G/A	429 (90.6%)	513 (91.1%)	1		1
			G/G	44 (9.3%)	50 (8.9%)	1.05 (0.69–1.61)	0.814	1.16 (0.74–1.80)	0.520
		Log-additive	–	–	–	1.02 (0.84–1.23)	0.844	1.05 (0.86–1.28)	0.627
RTEL1	rs6010621	Codominant	T/T	259 (55.0%)	298 (52.9%)	1		1
			G/T	176 (37.4%)	222 (39.4%)	0.91 (0.70–1.18)	0.784	0.90 (0.69–1.17)	0.637
			G/G	36 (7.6%)	43 (7.6%)	0.96 (0.60–1.55)		1.08 (0.66–1.77)
		Dominant	T/T	259 (55.0%)	298 (52.9%)	1		1
			G/T+G/G	212 (45.0%)	265 (47.0%)	0.92 (0.72–1.18)	0.508	0.92 (0.72–1.19)	0.536
		Recessive	T/T+G/T	435 (92.4%)	520 (92.3%)	1		1
			G/G	36 (7.6%)	43 (7.6%)	1.00 (0.63–1.59)	0.997	1.13 (0.70–1.83)	0.620
		Log-additive	–	–	–	0.95 (0.78–1.15)	0.604	0.97 (0.79–1.19)	0.777
RTEL1	rs4809324	Codominant	T/T	355 (75.4%)	440 (78.1%)	1		1
			C/T	107 (22.7%)	115 (20.4%)	1.15 (0.86–1.55)	0.534	1.18 (0.87–1.61)	0.324
			C/C	9 (1.9%)	8 (1.5%)	1.39 (0.53–3.65)		1.80 (0.65–4.94)
		Dominant	T/T	355 (75.4%)	440 (78.1%)	1		1
			C/T+C/C	116 (24.6%)	123 (21.9%)	1.17 (0.87–1.56)	0.291	1.19 (0.93–1.54)	0.172
		Recessive	T/T+C/T	462 (98.1%)	555 (98.5%)	1		1
			C/C	9 (1.9%)	8 (1.5%)	1.06 (0.70–1.61)	0.782	1.15 (0.75–1.77)	0.527
		Log-additive	–	–	–	1.35 (0.52–3.53)	0.539	1.14 (0.94–1.39)	0.185
RTEL1	rs2297441	Codominant	G/G	224 (47.4%)	257 (45.6%)	1		1
			A/G	190 (40.2%)	251 (44.5%)	0.87 (0.67–1.13)	0.246	0.86 (0.66–1.13)	0.194
			A/A	224 (12.4%)	56 (9.9%)	1.21 (0.80–1.82)		1.25 (0.82–1.91)
		Dominant	G/G	224 (47.4%)	257 (45.6%)	1		1
			A/G+A/A	414 (52.6%)	307 (54.4%)	0.93 (0.73–1.19)	0.565	0.93 (0.72–1.20)	0.575
		Recessive	G/G+A/G	414 (87.6%)	508 (90.1%)	1		1
			A/A	224 (12.4%)	56 (9.9%)	1.29 (0.88–1.91)	0.195	1.35 (0.90–2.01)	0.149
		Log-additive	–	–	–	1.02 (0.85–1.22)	0.857	1.03 (0.85–1.24)	0.795
(Continued)

CI, confidence interval; HCC, hepatocellular carcinoma; OR, odds ratio; SNP, single nucleotide polymorphism.

p < 0.05.

Relationships between haplotypes and HCC

D′ and r² were used to measure the degree of linkage disequilibrium between the two SNPs. D′ CIs were used to classify the haplotypes. Overall, eight main linkage blocks were observed across the loci [Figure 3(a–h)]. In the ACYP2 gene on chromosome 2, rs168211, rs843752, rs10439478, rs843645, rs11125529, rs12615793, rs843711 and rs11896604 constituted block 1 that was 51 kb in length. Rs843706 and rs17015754 in the ACYP2 gene also constituted block 2 that was 16 kb in length [Figure 3(a)]. In the TERC gene on chromosome 3, rs35073794 and rs10939599 constituted a block [Figure 3(b)]. In the TERT gene on chromosome 5, rs10069690 and rs2242652 constituted block 1 [Figure 3(c)]. In the TNIP1 gene, rs7708392 and rs10036748 also constituted block 1 that was 0 kb in length [Figure 3(d)]. In the OBFC1 gene on chromosome 10, rs9325507, rs3814220, rs12765878 and rs11191865 constituted block 1 that was 27 kb in length [Figure 3(e)]. In the MPHOSPH6 gene on chromosome 16, rs1056675, rs1056654, rs3751862 and rs2967361 constituted block 1 that was 21 kb in length [Figure 3(f)]. In the ZNF208 gene on chromosome 19, rs2188972, rs2188971, rs8103163 and rs7248488 constituted block 1 that was 39 kb in length [Figure 3(g)]. In the RTEL1 gene on chromosome 20, rs6089953, rs6010621 and rs4809324 constituted block 1 that was 27 kb in length [Figure 3(h)]. To further analyze the correlation between the haplotypes formed by these detected SNP loci in this experiment and the risk of HCC, we processed the data by both unadjusted analysis and unconditional logistic regression analysis after adjusting for age and sex. The data obtained were analyzed using HAPSTAT software. The results were summarized in Table 4. Taken together, haplotype analysis revealed that haplotype ‘CG’ in the TERT gene (OR = 1.37, 95% CI: 1.07–1.75, p = 0.013) increased the risk of HCC. Furthermore, the haplotype ‘ATATCGCC’ in the ACYP2 gene (OR = 0.76, 95% CI: 0.62–0.92, p = 0.006), the haplotype ‘CG’ in the TERC gene (OR = 0.78, 95% CI: 0.65–0.93, p = 0.006), and the haplotype ‘TA’ in the TERT gene (OR = 0.77, 95% CI: 0.60–0.99, p = 0.040) decreased the risk of HCC.

Figure 3.

Linkage disequilibrium between the two SNPs.

Table 4.

The correlation between the haplotype frequency and the risk of HCC.

Gene	SNP	Haplotype	Frequency	Crude analysis		Adjusted analysis
				OR (95% CI)	p	OR (95% CI)	p
ACYP2	rs1682111rs843752 rs10439478 rs843645 rs11125529 rs12615793 rs843711 rs11896604	ATATCGCC	0.2754	0.76 (0.62–0.92)	0.006^*	0.76 (0.62–0.92)	0.006^*
		TTCTAATG	0.1879	1.18 (0.93–1.51)	0.176	1.18 (0.93–1.51)	0.176
		TGAGCGTC	0.2711	1.12 (0.91–1.38)	0.288	1.12 (0.91–1.38)	0.288
		TTCTCGCC	0.1922	1.02 (0.81–1.28)	0.851	1.02 (0.81–1.28)	0.851
		TTCTCGTG	0.014	1.19 (0.53–2.67)	0.677	1.19 (0.53–2.67)	0.677
		TTCTCACC	0.013	0.81 (0.38–1.72)	0.587	0.81 (0.38–1.72)	0.587
TERC	rs843706 rs17045754	AC	0.19	1.21 (0.96–1.53)	0.100	1.22 (0.96–1.55)	0.107
	rs843706 rs17045754	AG	0.3142	1.20 (1.00–1.46)	0.055	1.17 (0.96–1.43)	0.115
		CG	0.4894	0.76 (0.64–0.91)	0.002^*	0.78 (0.65–0.93)	0.006^*
TERT	rs10069690 rs2242652	TA	0.1282	0.75 (0.59–0.96)	0.020^*	0.77 (0.60–0.99)	0.040^*
	rs10069690 rs2242652	CG	0.8602	1.38 (1.08–1.75)	0.009^*	1.37 (1.07–1.75)	0.013^*
TNIP1	rs7708392 rs10036748	GC	0.2468	1.25 (1.01–1.54)	0.039^*	1.20 (0.96–1.48)	0.108
	rs7708392 rs10036748	CT	0.7532	0.81 (0.66–1.00)	0.050	0.84 (0.68–1.05)	0.121
OBFC1	rs9325507 rs3814220 rs12765878 rs11191865	TCGA	0.3142	0.90 (0.74–1.08)	0.258	0.89 (0.73–1.09)	0.254
	rs9325507 rs3814220 rs12765878 rs11191865	CATC	0.6815	1.10 (0.91–1.33)	0.338	1.11 (0.91–1.35)	0.316
MPHOSPH6	rs1056675 rs1056654 rs3751862 rs2967361	TGCT	0.0593	1.08 (0.74–1.58)	0.685	1.11 (0.75–1.64)	0.616
		TGAT	0.1695	1.03 (0.82–1.28)	0.829	1.04 (0.82–1.30)	0.770
		TAAG	0.3167	0.90 (0.75–1.09)	0.273	0.89 (0.73–1.07)	0.209
		CGAG	0.4184	1.10 (0.92–1.31)	0.294	1.10 (0.91–1.32)	0.325
		TGAG	0.0318	0.82 (0.52–1.32)	0.428	0.89 (0.55–1.46)	0.648
ZNF208	rs2188972 rs2188971 rs8103163 rs7248488	ATAA	0.303	1.07 (0.89–1.30)	0.464	1.15 (0.94–1.39)	0.175
		GCCC	0.4873	0.93 (0.78–1.10)	0.394	0.92 (0.77–1.10)	0.385
		ACCC	0.2055	1.02 (0.82–1.26)	0.887	0.94 (0.75–1.17)	0.569
RTEL1	rs6089953 rs6010621 rs4809324	GGC	0.1255	1.15 (0.88–1.50)	0.312	1.20 (0.91–1.59)	0.188
		GGT	0.1319	0.82 (0.64–1.05)	0.118	0.82 (0.63–1.06)	0.123
		GTT	0.033	1.67 (0.97–2.88)	0.064	1.79 (1.02–3.14)	0.044^*
		ATT	0.7021	0.99 (0.82–1.19)	0.917	0.96 (0.79–1.16)	0.667

CI, confidence interval; HCC, hepatocellular carcinoma; OR, odds ratio; SNP, single nucleotide polymorphism.

p < 0.05.

Discussion

Due to its high morbidity and mortality, HCC seriously threatens human health and represents a significant medical burden worldwide. China has more than half of the world’s new cases of liver cancer every year.³¹ However, the lack of effective early screening and diagnosis of liver cancer leads to ineffective treatment and poor prognosis. Thus, it is necessary to explore novel and potential useful methods and biomarkers for early diagnosis and treatment of liver cancer.

In this study, 42 candidate SNP sites were closely associated with the occurrence of liver cancer as assessed by gene screening. Briefly, the SNP sites were distributed in nine telomere length-related genes including ACYP2, TERC, NAF1, TERT, TNIP1, OBFC1, MPHOSPH6, ZNF208 and RTEL1.

1, ACYP2 gene polymorphisms

The ACYP2 gene encodes acylphosphatase and regulates different physiological behaviors such as the glycolysis pathway, pyruvate metabolism and cell apoptosis.¹⁴ It also has biological functions affecting telomere length. Previous studies reported that the ACYP2 gene was associated with leukocyte telomere length, and its polymorphisms are associated with lung disease risk in the Han Chinese population.³² The ACYP2 rs1872328 mutant is potentially related to the toxicity induced by cisplatin chemotherapy in patients with osteosarcoma and could be used to identify patients who should receive cisplatin chemotherapy.³³ Acylphosphatase encoded by the ACYP2 gene is also associated with cell differentiation, cell senescence and cell apoptosis.³⁴ It regulates intracellular Ca²⁺ homeostasis.¹⁴ Dysregulation of the ACYP2 gene leads to cell apoptosis.³⁵ Cancer cells prevented Ca²⁺ influx by altering cell membrane receptors and reducing the expression of Ca²⁺ channels,³⁶ thereby achieving resistance to long-term endoplasmic reticulum calcium deficiency and downregulating mitochondrial calcium one-way transporters and subsequently escaping apoptosis.³⁷ Thus, mutations in the ACYP2 gene may modulate apoptosis and promote tumor development. Current studies reported that ACYP2 gene polymorphisms were associated with stroke,³⁸ lung cancer,³² esophageal cancer,³⁹ breast cancer⁴⁰ and gastric cancer.⁴¹ In this study, the ‘G’ allele of rs6713088 in the ACYP2 gene, was distributed in 45.2% of patients with HCC and 39.3% of healthy individuals, revealing a statistically significant association with HCC risk (OR = 1.27, 95% CI = 1.07–1.52, p = 0.007). Based on the genotype frequency distribution, the ‘G/C+G/G’ genotype was associated with increased HCC risk (OR = 1.32, 95% CI = 1.01–1.74, p = 0.043) in the dominant model. This site also affects the susceptibility of the Chinese Han population to increased lung edema at high altitude.⁴² Another ‘A/T’ genotype of rs1682111 was associated with reduced HCC risk (OR = 0.69, 95% CI = 0.53–0.91, p = 0.011) in the Chinese Han population.

2, TERC gene polymorphisms

The TERC gene is found on chromosome 3q26, contains a sequence that is complementary to telomeres and could be used as a template for telomere repeats, and encodes telomerase RNA. This gene maintains telomere length by adding ‘TTAGGG’ repeats to telomere ends. Telomerase plays an important role in cell senescence, and its degradation in somatic cells may also lead to cancer. Montanaro et al.⁴³ reported decreased expression of keratins along with low TERC gene expression in patients with primary breast cancer, which further affects telomerase activity. Furthermore, lentivirus transfection to induce high expression of the TERC gene could eliminate telomerase damage caused by keratin reduction. Flacco et al. evaluated the correlation between genomic imbalance and clinicopathological parameters and prognosis by exploring copy number changes in the TERC gene in patients with early non-small cell lung cancer and found that the increased TERC gene copy number significantly affected histopathological changes in the lungs of patients.⁴⁴ These findings highlighted the importance of TERC gene in maintaining telomerase activity. This study found that the ‘C’ allele of rs10936599 located in the promoter region of TERC gene was associated with a statistically significant reduction in HCC risk (OR = 1.21, 95% CI = 1.02–1.44, p = 0.032). Genotype frequency distribution and additive model correction analysis confirmed that TERC gene was involved in increased susceptibility to liver cancer. This finding is potentially attributed to the fact that gene polymorphism in the promoter region changed telomerase activity by affecting TERC gene copy number and expression.

3, NAF1 gene polymorphisms

The NAF1 gene, which can be replaced by NOLA1/GAR1 in protein particles assembly, enabled the generation of mature ribosomal protein particles and affects telomerase synthesis and activity.⁴⁵ SNPs located in this gene region (4q32.2) affect telomere length and play an important role as potential susceptibility sites in telomerase activity and cancer development in colorectal cancer patients.¹⁵ In this study, the rs2320615 ‘A’ allele located in the intron region of the NAF1 gene was associated with reduced risk of HCC (OR = 0.79, 95% CI = 0.64–0.99, p = 0.038). Based on genotype frequency distribution analysis, this site was still associated with reduced susceptibility in the additive model (OR = 0.79, 95% CI = 0.63–0.99, p = 0.036).

4, TERT gene polymorphisms

The TERT gene regulates telomere extension based on its catalytic properties. TERT also interacts and combines with other proteins to modulate the formation and subcellular localization of telomerase.⁴⁵ TERT gene expression levels significantly affect telomerase activity in various cells and tissues. The TERT gene is involved in the occurrence and development of various diseases, including congenital dyskeratosis,⁴⁶ aplastic anemia,⁴⁷ bone marrow failure syndrome⁴⁸ and pulmonary fibrosis.⁴⁹ In addition, TERT gene polymorphisms are also involved in the pathogenesis of a variety of tumors. The functional repeat small satellite sequence polymorphism of TERT affects the prognosis of patients with non-small cell lung cancer.⁵⁰ The rs2242652 SNP located in the intron region of TERT gene is associated with shortened telomere length and significantly affects the risk of prostate cancer.⁵¹ In breast cancer, alleles rs2736109 ‘G’ (OR = 1.56, 95% CI = 1.22–1.99) and rs3816659 ‘T’ (OR = 1.27, 95% CI = 1.05–1.52) located in the TERT gene also increase the risk of breast cancer compared with the healthy population. The above studies suggested that TERT gene polymorphisms play an important role in the pathogenesis of cancer. In our study, TERT gene polymorphism sites rs10069690 and rs2242652 could affect the risk of HCC in the Chinese Han population. This study provided new insights into the development of HCC that may have important clinical application value in screening and early diagnosis in high-risk HCC populations.

Multiple studies showed that SNPs and gene variations could result in the occurrence and development of HCC. In the present study, a total of five loci were significantly associated with a high risk of HCC. Based on the genotype distribution, rs6713088, rs843645, rs843711 and rs843706 located in the ACYP2 gene and rs10936599 located in the TERT gene were obviously associated with a high risk of HCC. In addition, SNPs in these genes could form a linkage imbalance haplotype. Specifically, the haploid ‘GC’ formed by rs10069690 and rs2242652 within the TERT gene increased the risk of HCC. The results suggested that the SNPs in these genes could influence telomere length and may play a key role in the occurrence and progression of HCC. The results revealed that some specific gene site alterations might be associated with HCC. This study also provided more insights into the pathogenic mechanism and early detection of HCC. Of note, we attributed the significant differences among dominant, codominant and additive models to the following reasons: (1) the deviation was caused by the large proportion of heterozygotes G/T in these three genotypes; (2) the population sample size was small, causing statistical deviation; and (3) sex and age mismatch between case and control groups may also explain these findings.

We identified polymorphisms in telomere length-related genes, and SNPs in some gene loci correlated with high HCC risk. However, the functions and the precise mechanism of gene variability were not extensively investigated. We do not exactly understand how environment factors and other gene mutations alone or in combination could impact the results. Therefore, research and studies in liver cancer cell lines and animal HCC models are required to clarify the above gene functions in HCC. Further studies are needed to assess whether these gene variation will support our findings. In our study, we found that some gene loci were associated with HCC risk, but whether mutations in these loci could predict the prognosis of HCC remains unknown. We will continue to track the prognosis of these patients for further analysis in future studies. Some limitations in our study should be noted. First, the sample size of the population was relatively small. Second, all of these volunteers were recruited from Xi’an, Shaanxi province. More samples from different areas are therefore needed for analysis. Third, this study lacked complete detailed clinical information (such as smoking, drinking, and hepatitis C virus infection) in all volunteers; only age and gender were recorded. We need to collect sufficient information on the clinical characteristic of participants to obtain more data and valuable results in the future studies. Finally, telomere shortening is a common phenomenon in human cancers, including HCC; however, we did not investigate whether the presence of these SNPs influences telomere length in this cohort of patients.

Supplemental Material

Supplementary_Table_1 – Supplemental material for Single nucleotide polymorphisms in telomere length-related genes are associated with hepatocellular carcinoma risk in the Chinese Han population

Supplemental material, Supplementary_Table_1 for Single nucleotide polymorphisms in telomere length-related genes are associated with hepatocellular carcinoma risk in the Chinese Han population by Peng Huang, Rong Li, Lin Shen, Weizhou He, Shuo Chen, Yu Dong, Jiancang Ma, Xi Chen and Meng Xu in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

We thank all the participants. Authors Peng Huang and Rong Li contributed equally to this work and should be considered as co-first authors.

Conflict of interest statement

The authors declare that there is no conflict of interest.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: we acknowledge the generous support provided by the National Natural Science Foundation of China, NSFC (grant number 81902449) and Xi’an Jiaotong University Education Foundation, XJTUEF (grant number xjj2018141).

ORCID iD

Meng Xu

Data availability

The data used to support the findings of this study are included within the article.

Supplemental material

Supplemental material for this article is available online.

References

Kulik

El-Serag

. Epidemiology and management of hepatocellular carcinoma. Gastroenterology 2019; 156: 477–491e1.

Maluccio

Covey

. Recent progress in understanding, diagnosing, and treating hepatocellular carcinoma. CA Cancer J Clin 2012; 62: 394–399.

Arzumanyan

Reis

Feitelson

. Pathogenic mechanisms in HBV- and HCV-associated hepatocellular carcinoma. Nat Rev Cancer 2013; 13: 123–135.

Dhanasekaran

Nault

Roberts

, et al. Genomic medicine and implications for hepatocellular carcinoma prevention and therapy. Gastroenterology 2019; 156: 492–509.

Zhang

Zang

, et al. Genetic features of aflatoxin-associated hepatocellular carcinoma. Gastroenterology 2017; 153: 249–262e2.

Kanwal

Kramer

, et al. Effect of metabolic traits on the risk of cirrhosis and hepatocellular cancer in non-alcoholic fatty liver disease. Hepatology 2020; 71: 808–819.

Cai

, et al. Association of telomere length in peripheral leukocytes with chronic hepatitis B and hepatocellular carcinoma. Medicine (Baltimore) 2016; 95: e4970.

Seo

Jung

. Telomere length and reactive oxygen species levels are positively associated with a high risk of mortality and recurrence in hepatocellular carcinoma. Hepatology 2018; 67: 1378–1391.

Blackburn

Epel

Lin

. Human telomere biology: a contributory and interactive factor in aging, disease risks, and protection. Science 2015; 350: 1193–1198.

10.

Hewitt

Jurk

Marques

, et al. Telomeres are favoured targets of a persistent DNA damage response in ageing and stress-induced senescence. Nat Commun 2012; 3: 708.

11.

Palm

de Lange

. How shelterin protects mammalian telomeres. Annu Rev Genet 2008; 42: 301–34.

12.

Cheng

Huang

, et al. Genetic association of telomere length with hepatocellular carcinoma risk: a mendelian randomization analysis. Cancer Epidemiol 2017; 50: 39–45.

13.

Plentz

Caselitz

Bleck

, et al. Hepatocellular telomere shortening correlates with chromosomal instability and the development of human hepatoma. Hepatology 2004; 40: 80–86.

14.

Robinson

Huang

, et al. Common variants in ACYP2 influence susceptibility to cisplatin-induced hearing loss. Nat Genet 2015; 47: 263–266.

15.

Codd

Nelson

Albrecht

, et al. Identification of seven loci affecting mean telomere length and their association with disease. Nat Genet 2013; 45: 422–427e1–2.

16.

Ueda

Calado

Norberg

, et al. A mutation in the H/ACA box of telomerase RNA component gene (TERC) in a young patient with myelodysplasti2c syndrome. BMC Med Genet 2014; 15: 68.

17.

Codd

Mangino

van der Harst

, et al. Common variants near TERC are associated with mean telomere length. Nat Genet 2010; 42: 197–199.

18.

Stanley

Gable

Wagner

, et al. Loss-of-function mutations in the RNA biogenesis factor NAF1 predispose to pulmonary fibrosis-emphysema. Sci Transl Med 2016; 8: 351ra107.

19.

Grandori

Amacker

, et al. Direct activation of TERT transcription by c-MYC. Nat Genet 1999; 21: 220–224.

20.

Khattar

Tergaonkar

. Transcriptional regulation of telomerase reverse transcriptase (TERT) by MYC. Front Cell Dev Biol 2017; 5: 1.

21.

Gurevich

Zhang

Francis

, et al. TNIP1, a retinoic acid receptor corepressor and A20-binding inhibitor of NF-kappaB, distributes to both nuclear and cytoplasmic locations. J Histochem Cytochem 2011; 59: 1101–1112.

22.

Zhao

Zhou

, et al. Relationship between the TERT, TNIP1 and OBFC1 genetic polymorphisms and susceptibility to colorectal cancer in Chinese Han population. Oncotarget 2017; 8: 56932–56941.

23.

Wan

Qin

Songyang

, et al. OB fold-containing protein 1 (OBFC1), a human homolog of yeast Stn1, associates with TPP1 and is implicated in telomere length regulation. J Biol Chem 2009; 284: 26725–26731.

24.

Levy

Neuhausen

Hunt

, et al. Genome-wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology. Proc Natl Acad Sci U S A 2010; 107: 9293–9298.

25.

Schilders

Raijmakers

Raats

, et al. MPP6 is an exosome-associated RNA-binding protein involved in 5.8S rRNA maturation. Nucleic Acids Res 2005; 33: 6795–6804.

26.

Zhang

Wang

Wen

, et al. Association of ACYP2 and MPHOSPH6 genetic polymorphisms with the risk of hepatocellular carcinoma in chronic hepatitis B virus carriers. Oncotarget 2017; 8: 86011–86019.

27.

Chen

Zhang

, et al. Single nucleotide polymorphisms in ZNF208 are associated with increased risk for HBV in Chinese people. Oncotarget 2017; 8: 112451–112459.

28.

Jin

Qin

, et al. Genetic variants in RTEL1 influencing telomere length are associated with prostate cancer risk. J Cancer 2019; 10: 6170–6174.

29.

Gabriel

Ziaugra

Tabbaa

. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr Protoc Hum Genet 2009; Chapter 2: Unit 2 12.

30.

Thomas

Baker

Debiasi

, et al. High-throughput oncogene mutation profiling in human cancer. Nat Genet 2007; 39: 347–351.

31.

Wong

Jiang

Goggins

, et al. International incidence and mortality trends of liver cancer: a global profile. Sci Rep 2017; 7: 45846.

32.

Chen

Yang

Guo

, et al. Association of polymorphisms in the telomere-related gene ACYP2 with lung cancer risk in the Chinese Han population. Oncotarget 2016; 7: 87473–87478.

33.

Vos

Guchelaar

Gelderblom

, et al. Replication of a genetic variant in ACYP2 associated with cisplatin-induced hearing loss in patients with osteosarcoma. Pharmacogenet Genomics 2016; 26: 243–247.

34.

Degl’Innocenti

Ramazzotti

Sarchielli

, et al. Oxadiazon affects the expression and activity of aldehyde dehydrogenase and acylphosphatase in human striatal precursor cells: a possible role in neurotoxicity. Toxicology 2019; 411: 110–121.

35.

Dasgupta

Sawant

Lavhale

, et al. AECHL-1, a novel triterpenoid, targets tumor neo-vasculature and impairs the endothelial cell cytoskeleton. Angiogenesis. 2015; 18: 283–299.

36.

Bruna

Lobos

, et al. Calcium release mediated by redox-sensitive RyR2 channels has a central role in hippocampal structural plasticity and spatial memory. Antioxid Redox Signal 2018; 29: 1125–1146.

37.

Wang

Zheng

. Ca²⁺ homeostasis dysregulation in Alzheimer’s disease: a focus on plasma membrane and cell organelles. FASEB J 2019; 33: 6697–6712.

38.

Cheng

, et al. Interactions between ACYP2 genetic polymorphisms and environment factors with susceptibility to ischemic stroke in a Han Chinese population. Oncotarget 2017; 8: 97913–97919.

39.

Fang

Hui

Min

, et al. Leukocyte telomere length-related genetic variants in ACYP2 contribute to the risk of esophageal carcinoma in Chinese Han population. Oncotarget 2017; 8: 25564–25570.

40.

Zhang

Deng

, et al. Genetic variants in the acylphosphatase 2 gene and the risk of breast cancer in a Han Chinese population. Oncotarget 2016; 7: 86704–86712.

41.

Zhu

, et al. Association analysis of telomere length-related gene ACYP2 with the gastric cancer risk in the northwest Chinese Han population. Oncotarget 2017; 8: 31144–31152.

42.

Zhu

Liu

, et al. Association between genetic polymorphism of telomere-associated gene ACYP2 and the risk of HAPE among the Chinese Han population: a case–control study. Medicine (Baltimore) 2017; 96: e6504.

43.

Montanaro

Calienni

Ceccarelli

, et al. Relationship between dyskerin expression and telomerase activity in human breast cancer. Cell Oncol 2008; 30: 483–490.

44.

Flacco

Ludovini

Bianconi

, et al. MYC and human telomerase gene (TERC) copy number gain in early-stage non-small cell lung cancer. Am J Clin Oncol 2015; 38: 152–158.

45.

Egan

Collins

. An enhanced H/ACA RNP assembly mechanism for human telomerase RNA. Mol Cell Biol 2012; 32: 2428–2439.

46.

Armanios

Chen

Chang

, et al. Haploinsufficiency of telomerase reverse transcriptase leads to anticipation in autosomal dominant dyskeratosis congenita. Proc Natl Acad Sci USA 2005; 102: 15960–15964.

47.

Mehta

Krishnamohan

Gulati

, et al. Detection of mutations in TERT, the genes for telomerase reverse transcriptase, in Indian patients of aplastic anaemia: a pilot study. J Assoc Physicians India 2014; 62: 13–17.

48.

Gansner

Rosas

Ebert

. Pulmonary fibrosis, bone marrow failure, and telomerase mutation. N Engl J Med 2012; 366: 1551–1553.

49.

Tsakiri

Cronkhite

Kuan

, et al. Adult-onset pulmonary fibrosis caused by mutations in telomerase. Proc Natl Acad Sci USA 2007; 104: 7552–7557.

50.

Jin

Yoo

Cho

, et al. Dual roles of a variable number of tandem repeat polymorphism in the TERT gene in lung cancer. Cancer Sci 2011; 102: 144–149.

51.

Haiman

Chen

Blot

, et al. Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21. Nat Genet 2011; 43: 570–573.

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