ACPY2 gene polymorphisms on cancer risk: a systematic review and meta-analysis

Abstract

To provide a comprehensive account of the association of ACYP2 gene polymorphisms with susceptibility to cancer. A literature search for eligible candidate gene studies published before April 20, 2022 was conducted in the PubMed, Medline and Web of Science. The following combinations of main keywords were used: (ACYP2 OR acylphosphatase 2) AND (polymorphism OR mutation OR variation OR SNP OR genotype) AND (cancer OR tumor OR neoplasm OR malignancy OR carcinoma OR adenocarcinoma). Potential sources of heterogeneity were sought out via subgroup and sensitivity analysis. Publication bias were also estimated. Overall, a total of 10 articles with 5,230 cases and 5,086 controls for thirteen polymorphisms of ACYP2 gene were enrolled. We found that ACYP2 rs11125529, rs11896604, rs12615793, rs17045754, rs6713088, rs843645, rs843706, rs843711 and rs843752 were correlated with an increased risk of cancer. However, we found that ACYP2 rs12621038 might have less susceptibility to cancer. While for other polymorphisms, the results showed no significant association with cancer risk. ACYP2 rs11125529, rs11896604, rs12615793, rs17045754, rs6713088, rs843645, rs843706, rs843711 and rs843752 are associated with cancer risk. ACYP2 rs12621038 polymorphism is inversely associated with cancer risk.

Keywords:

• Acylphosphatase 2
• ACYP2
• cancer
• SNP
• meta-analysis

Introduction

Telomeres are short repetitive hexamer (TTAGGGs) sequences located at the end of chromosomes.^[1] Chromosomes shorten with each cell division. However, cancer cells could prevent chromosome shortening by expressing telomerase.^[2] Many studies have proven that abnormal telomere length is associated with the development of many diseases, including cancers.^[3–5] The ACYP2 (Acylphosphatase 2) gene, located on chromosome 2p16.2, encodes cytosolic acylphosphatase enzyme which hydrolyzes a variety of membrane proteins, regulates the glycolysis pathway, pyruvate metabolism and cell apoptosis.^[6,7] ACYP2 has been identified as an important telomere length related gene.^[8] Many studies have demonstrated that ACYP2 polymorphisms are associated with telomere length, which could be associated with various cancers.^[9–11]

Recently, many studies have demonstrated the association between ACYP2 polymorphisms and risks of various cancers. However, these results are inconsistent, which might be due to the heterogeneity within cancer types, ethnicities, source of control, Hardy-Weinberg equilibrium (HWE), small sample sizes, data source and so on. Therefore, it is necessary to perform a comprehensive meta-analysis to evaluate the effects of the genetic variation of ACYP2 gene on cancer risk.

Materials and methods

Literature search

We conducted a systematic literature search on PubMed, Medline and Web of Science to retrieve all eligible publications on the association between ACYP2 polymorphisms and the risk of cancer (up to April 20, 2022) with the following keywords: (ACYP2 OR acylphosphatase 2) AND (polymorphism OR mutation OR variation OR SNP OR genotype) AND (cancer OR tumor OR neoplasm OR malignancy OR carcinoma OR adenocarcinoma). The language of enrolled studies was restricted to English. After carefully screening, thirteen polymorphisms were left for further investigation.

Inclusion criteria and exclusion criteria

Articles enrolled in our meta-analysis satisfied the following inclusion criteria: (1) case-control studies that evaluated the association between ACYP2 polymorphisms and cancer risk; (2) publications focusing on population genetic polymorphisms; (3) articles with sufficient genotype data to assess ORs and the corresponding 95%CIs; (4) the control subjects satisfied Hardy-Weinberg equilibrium (HWE). The major exclusion criteria were: (1) case-only studies, case reports, or reviews; (2) studies without raw data for the ACYP2 genotype; (3) combined with other influencing factors.

Data extraction

Two investigators (XC and ZY) independently extracted the data. All the case-control studies satisfied the inclusion criteria and consensus for any controversy was achieved. The data from the eligible articles comprise the first author’s name, year of publication, ethnicity, source of control, cancer type and numbers of cases and controls in ACYP2 genotypes. Ethnicity was categorized as “Asian,” “Mix.”

Statistical analysis

The risk between the ACYP2 polymorphisms and cancer was evaluated using summary ORs and the corresponding 95% CIs in allelic (B versus A), dominant (BA + BB versus AA), and recessive (BB versus BA + AA) models (A: wild allele; B: mutated allele). The Cochrane’s Q-statistic test was used to assess the heterogeneity between studies, and the inconsistency was quantified with the I² statistic. The substantial heterogeneity was considered significant when I² > 50% or P_Q ≤ 0.1, then, a random effects model was used; otherwise, the fixed effects model was applied. Subgroup meta-analysis were performed by ethnicity and the source of control. We also conducted sensitivity analysis to assess stability of the results by omitting one study each time to exclude studies. HWE was estimated by the asymptotic test, and deviation was considered when P < 0.05. The potential publication bias of the eligible studies was evaluated by Begg’s and Egger’s regression test quantitatively. Trial sequential analysis (TSA) was performed to minimize random errors and strengthen the robustness of our conclusions.^[12] The data was analyzed using the Stata 14.0 software (version 14.0; State Corporation, College Station, Texas, USA). A two-tailed P < 0.05 was considered statistically significant.

In-silico analysis using GTEx website

For the purpose of investigating the influence of polymorphisms on ACYP2, we obtained the association between polymorphism and ACYP2 expression level using the GTEx cohort.^[13]

Results

Main characteristics of the enrolled studies

The study selection processes were presented in Figure 1. A total of 10 case-control studies with 5,230 cases and 5,086 controls for thirteen polymorphisms of ACYP2 gene met the inclusion criteria.^[14–23] Nine studies of them were performed in Asians, one study was performed in Mix ethnicity. Controls of 6 studies were population-based controls, and 4 studies were hospital-based controls. All studies were in compliance with HWE. Table 1 showed the characteristics of all the eligible studies and genotype frequency distributions of the thirteen ACYP2 polymorphisms included in our meta-analysis. Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the enrolled studies, as shown in Table 2.

Figure 1. Flow chart of studies selection process for ACYP2 gene polymorphisms.

Table 1. Characteristics of eligible case-control studies included in the meta-analysis.

SNP	First author	Year	Ethnicity	Source of Control	Cancer Type	Case			Control			HWE
						AA	AB	BB	AA	AB	BB
rs11125529	Antwi et al.	2020	Mix	PB	PDAC	1080	338	35	1083	338	34	Y
	Chen et al.	2017	Asian	PB	HCC	310	149	13	392	159	13	Y
	Duan et al.	2019	Asian	HB	GIC	728	376	50	327	158	10	Y
	Fang et al.	2017	Asian	HB	EC	234	131	15	327	158	10	Y
	Li et al.	2017	Asian	HB	GC	186	96	20	204	90	6	Y
	Liu et al.	2017	Asian	HB	CRC	160	76	11	204	90	6	Y
	Wang et al.	2019	Asian	PB	RCC	183	95	14	327	158	10	Y
	Zhao et al.	2019	Asian	PB	LSCC	91	64	13	126	50	4	Y
rs11896604	Chen et al.	2016	Asian	PB	LC	331	209	14	386	191	25	Y
	Chen et al.	2017	Asian	PB	HCC	288	164	19	376	167	21	Y
	Duan et al.	2019	Asian	HB	GIC	696	397	61	317	164	14	Y
	Fang et al.	2017	Asian	HB	EC	226	137	17	317	164	14	Y
	Li et al.	2017	Asian	HB	GC	183	98	21	203	88	9	Y
	Liu et al.	2017	Asian	HB	CRC	156	77	14	203	88	9	Y
	Wang et al.	2019	Asian	PB	RCC	178	100	14	317	164	14	Y
	Zhao et al.	2019	Asian	PB	LSCC	86	74	10	125	49	6	Y
rs17045754	Chen et al.	2017	Asian	PB	HCC	302	156	15	390	160	14	Y
	Fang et al.	2017	Asian	HB	EC	233	127	16	325	157	13	Y
	Li et al.	2017	Asian	HB	GC	186	98	18	204	89	7	Y
	Liu et al.	2017	Asian	HB	CRC	164	71	12	204	89	7	Y
	Wang et al.	2019	Asian	PB	RCC	183	98	11	325	157	13	Y
	Zhang et al.	2016	Asian	PB	BC	135	43	5	121	68	6	Y
rs843711	Chen et al.	2017	Asian	PB	HCC	126	218	127	178	278	107	Y
	Duan et al.	2019	Asian	HB	GIC	301	566	286	139	263	93	Y
	Li et al.	2017	Asian	HB	GC	74	155	73	98	155	47	Y
	Liu et al.	2017	Asian	HB	CRC	67	115	64	98	155	47	Y
	Wang et al.	2019	Asian	PB	RCC	69	148	75	139	263	93	Y
	Zhao et al.	2019	Asian	PB	LSCC	42	86	44	57	92	31	Y
rs6713088	Chen et al.	2017	Asian	PB	HCC	138	242	93	202	279	82	Y
	Li et al.	2017	Asian	HB	GC	97	144	61	114	146	40	Y
	Liu et al.	2017	Asian	HB	CRC	80	121	45	114	146	40	Y
	Wang et al.	2019	Asian	PB	RCC	91	145	55	178	240	76	Y
	Zhang et al.	2016	Asian	PB	BC	63	90	30	64	94	36	Y
rs12615793	Chen et al.	2017	Asian	PB	HCC	297	160	15	377	173	14	Y
	Li et al.	2017	Asian	HB	GC	179	101	22	196	97	7	Y
	Liu et al.	2017	Asian	HB	CRC	156	79	12	196	97	7	Y
	Zhao et al.	2019	Asian	PB	LSCC	89	69	11	120	55	5	Y
rs843752	Chen et al.	2017	Asian	PB	HCC	232	201	39	306	214	43	Y
	Liu et al.	2017	Asian	HB	CRC	124	103	19	170	111	19	Y
	Wang et al.	2019	Asian	PB	RCC	146	120	26	262	200	33	Y
	Zhang et al.	2016	Asian	PB	BC	67	91	25	92	89	14	Y
rs843645	Chen et al.	2017	Asian	PB	HCC	235	206	30	321	202	41	Y
	Liu et al.	2017	Asian	HB	CRC	126	100	20	176	106	18	Y
	Wang et al.	2019	Asian	PB	RCC	145	122	24	270	192	33	Y
rs843706	Chen et al.	2017	Asian	PB	HCC	124	219	128	177	277	108	Y
	Li et al.	2017	Asian	HB	GC	72	156	68	96	154	48	Y
	Liu et al.	2017	Asian	HB	CRC	61	121	59	96	154	48	Y
rs12621038	Chen et al.	2017	Asian	PB	HCC	139	245	87	180	271	112	Y
	Liu et al.	2017	Asian	HB	CRC	86	118	42	94	141	64	Y
	Wang et al.	2019	Asian	PB	RCC	95	144	52	146	242	105	Y
	Zhang et al.	2016	Asian	PB	BC	67	92	24	53	101	41	Y
rs1682111	Chen et al.	2016	Asian	PB	LC	244	235	75	287	259	57	Y
	Chen et al.	2017	Asian	PB	HCC	251	181	39	252	253	59	Y
	Liu et al.	2017	Asian	HB	CRC	117	106	23	131	138	31	Y
	Wang et al.	2019	Asian	PB	RCC	141	123	28	239	212	44	Y
	Zhang et al.	2016	Asian	PB	BC	81	86	16	108	74	13	Y
	Zhao et al.	2019	Asian	PB	LSCC	91	66	11	69	91	20	Y
rs10439478	Chen et al.	2017	Asian	PB	HCC	154	233	85	206	261	96	Y
	Liu et al.	2017	Asian	HB	CRC	83	126	38	104	136	59	Y
	Wang et al.	2019	Asian	PB	RCC	107	139	46	160	241	92	Y
	Zhang et al.	2016	Asian	PB	BC	67	91	25	56	105	33	Y
	Zhao et al.	2019	Asian	PB	LSCC	41	86	40	69	84	26	Y
rs843720	Chen et al.	2016	Asian	PB	LC	217	252	84	256	280	67	Y
	Chen et al.	2017	Asian	PB	HCC	224	210	38	242	258	64	Y
	Liu et al.	2017	Asian	HB	CRC	100	117	30	120	145	35	Y
	Zhang et al.	2016	Asian	PB	BC	90	78	15	93	85	17	Y

Abbreviations: SNP, single nucleic polymorphism; HWE, Hardy-Weinberg equilibrium; PB, population based; HB, hospital based; Y, yes; GIC, gastrointestinal cancer; HCC, hepatocellular carcinoma; CRC, colorectal cancer; EC, esophageal carcinoma; GC, gastric cancer; PDAC, pancreatic ductal adenocarcinoma; RCC, renal cell cancer; BC, breast cancer; LSCC, laryngeal squamous cell carcinoma; LC, lung cancer

Table 2. Methodological quality of the enrolled studies according to the Newcastle-Ottawa scale.

First author	Adequacy definition	Representativeness of the cases	Control selection	Control definition	Comparability cases/controls	Exposure ascertainment	Same method ascertainment	Non-response rate
Antwi 2020	*	*	*	*	**	*	*	*
Chen 2016	*	*	*	*	**	*	*	*
Chen 2017	*	*	*	*	**	*	*	*
Duan 2019	*	*	NA	*	**	*	*	*
Fang 2017	*	*	NA	*	**	*	*	*
Li 2017	*	*	NA	*	**	*	*	*
Liu 2017	*	*	NA	*	**	*	*	*
Wang 2019	*	*	*	*	**	*	*	*
Zhang 2016	*	*	*	*	**	*	*	*
Zhao 2019	*	*	*	*	**	*	*	*

Note: A study can be awarded a maximum of one star for each numbered item within the Selection and Exposure categories. A maximum of two stars can be given for Comparability. NA, not applicable.

Quantitative synthesis

rs11125529

The pooled results based on 8 included studies (including 4,468 cases and 4,284 controls) indicated that rs11125529 was significantly related to an increased risk of cancer in dominant model (AB + BB versus AA: OR = 1.12, 95%CI = 1.02–1.23, P = 0.018) and recessive model (BB versus AA + AB: OR = 1.75, 95%CI = 1.34–2.27, P < 0.001).^{[14,16–21,23]} Then, in the stratification analysis by ethnicity, we observed that rs11125529 polymorphism was significantly related to an increased cancer risk for Asians in dominant model (BB + AB versus AA: OR = 1.17, 95%CI = 1.05–1.31, P = 0.005) and recessive model (BB versus AA + AB: OR = 2.18, 95%CI = 1.58–2.99, P < 0.001). (Figure 2, Tables 3 and 4)

Figure 2. Forest plot of ACYP2 rs11125529 polymorphism and cancer risk in recessive model stratified by ethnicity.

Table 3. Meta-analysis of ACYP2 polymorphisms.

SNP	n	Allele contrast		Dominant model		Recessive model
		p, OR(99% CI)	p (Q test), I^2	p, OR(99% CI)	p (Q test), I^2	p, OR(99% CI)	p (Q test), I^2
rs11125529	8	0.197, 1.10(0.95, 1.28)	0.002, 68.5%	0.018, 1.12(1.02, 1.23)	0.822, 0.0%	<0.001, 1.75(1.34, 2.27)	0.198, 28.8%
rs11896604	8	0.119, 1.12(0.97, 1.30)	0.005, 65.4%	<0.001, 1.20(1.09, 1.33)	0.960, 0.0%	0.004, 1.44(1.12, 1.85)	0.148, 35.1%
rs17045754	6	<0.001, 1.23(1.10, 1.38)	0.787, 0.0%	0.003, 1.21(1.07, 1.38)	0.926, 0.0%	0.007, 1.61(1.14, 2.27)	0.647, 0.0%
rs843711	6	<0.001, 1.22(1.12, 1.32)	0.012, 65.7%	0.001, 1.25(1.10, 1.42)	0.839, 0.0%	<0.001, 1.53(1.33, 1.77)	0.799, 0.0%
rs6713088	5	<0.001, 1.21(1.10, 1.33)	0.404, 0.3%	0.003, 1.25(1.08, 1.44)	0.703, 0.0%	0.001, 1.35(1.13, 1.62)	0.428, 0.0%
rs12615793	4	0.762, 1.05(0.77, 1.43)	0.003, 78.7%	0.064, 1.17(0.99, 1.37)	0.818, 0.0%	0.002, 2.01(1.30, 3.11)	0.450, 0.0%
rs843752	4	0.739, 1.04(0.83, 1.30)	0.017, 70.5%	0.014, 1.21(1.04, 1.41)	0.941, 0.0%	0.105, 1.26(0.95, 1.66)	0.835, 0.0%
rs843645	3	0.007, 1.20(1.05, 1.36)	0.844, 0.0%	0.002, 1.29(1.10, 1.52)	0.853, 0.0%	0.572, 1.10(0.80, 1.51)	0.446, 0.0%
rs843706	3	<0.001, 1.33(1.18, 1.50)	0.949, 0.0%	0.001, 1.37(1.13, 1.65)	0.822, 0.0%	<0.001, 1.59(1.29, 1.96)	0.953, 0.0%
rs12621038	4	0.047, 0.90(0.80, 1.00)	0.162, 41.6%	0.283, 0.91(0.78, 1.08)	0.174, 39.7%	0.023, 0.80(0.66, 0.97)	0.521, 0.0%
rs1682111	6	0.936, 0.99(0.80, 1.22)	<0.001, 79.0%	0.448, 0.95(0.84, 1.08)	0.079, 49.4%	0.776, 1.03(0.84, 1.26)	0.114, 43.7%
rs10439478	5	0.544, 0.94(0.77, 1.15)	0.005, 73.1%	0.399, 1.07(0.92, 1.24)	0.262, 23.8%	0.378, 0.92(0.76, 1.11)	0.400, 1.2%
rs843720	4	0.954, 1.00(0.85, 1.19)	0.072, 57.1%	0.993, 1.00(0.87, 1.15)	0.288, 20.3%	0.951, 0.99(0.68, 1.44)	0.058, 59.9%

Abbreviations: n, number; PB, population based; HB, hospital based; OR, odds ratio; CI, confidence interval; NA, not applicable.

Table 4. Meta-analysis of rs11125529.

Variables	n	Allele contrast		Dominant model		Recessive model
		p, OR(99% CI)	p (Q test), I^2	p, OR(99% CI)	p (Q test), I^2	p, OR(99% CI)	p (Q test), I^2
Total	8	0.197, 1.10(0.95, 1.28)	0.002, 68.5%	0.018, 1.12(1.02, 1.23)	0.822, 0.0%	<0.001, 1.75(1.34, 2.27)	0.198, 28.8%
Ethnicity
Mix	1	0.923, 1.01(0.87, 1.17)	NA	0.949, 1.01(0.85, 1.19)	NA	0.898, 1.03(0.64, 1.66)	NA
Asian	7	0.215, 1.12(0.94, 1.34)	0.003, 69.9%	0.005, 1.17(1.05, 1.31)	0.968, 0.0%	<0.001, 2.18(1.58, 2.99)	0.734, 0.0%
Source of control
PB	4	0.778, 0.96(0.74, 1.26)	0.001, 80.5%	0.311, 1.07(0.94, 1.20)	0.651, 0.0%	0.067, 1.38(0.98, 1.94)	0.175, 39.5%
HB	4	0.001, 1.24(1.10, 1.40)	0.777, 0.0%	0.015, 1.19(1.03, 1.37)	0.898, 0.0%	<0.001, 2.38(1.57, 3.61)	0.830, 0.0%

Abbreviations: n, number; PB, population based; HB, hospital based; OR, odds ratio; CI, confidence interval; NA, not applicable.

rs11896604

The pooled results based on 8 included studies (including 3,570 cases and 3,431 controls) indicated that rs11896604 was significantly related to an increased risk of cancer in dominant model (AB + BB versus AA: OR = 1.20, 95%CI = 1.09–1.33, P < 0.001) and recessive model (BB versus AA + AB: OR = 1.44, 95%CI = 1.12–1.85, P = 0.004).^[15–21,23] Then, in the stratification analysis by source of control, population-based control group was significantly related to an increased risk of cancer in dominant model (BB + AB versus AA: OR = 1.18, 95%CI = 1.02–1.36, P = 0.023). In addition, hospital-based control group was significantly related to an increased risk of cancer in allelic contrast (B versus A: OR = 1.25, 95%CI = 1.11–1.40, P < 0.001), dominant model (BB + AB versus AA: OR = 1.22, 95%CI = 1.06–1.40, P = 0.005) and recessive model (BB versus AA + AB: OR = 1.93, 95%CI = 1.35–2.77, P < 0.001). (Figure 3, Tables 3 and 5)

Figure 3. Forest plot of ACYP2 rs11896604 polymorphism and cancer risk in dominant model stratified by source of control.

Table 5. Meta-analysis of rs11896604.

Variables	n	Allele contrast		Dominant model		Recessive model
		p, OR(99% CI)	p (Q test), I^2	p, OR(99% CI)	p (Q test), I^2	p, OR(99% CI)	p (Q test), I^2
Total	8	0.119, 1.12(0.97, 1.30)	0.005, 65.4%	<0.001, 1.20(1.09, 1.33)	0.960, 0.0%	0.004, 1.44(1.12, 1.85)	0.148, 35.1%
Source of control
PB	4	0.891, 0.98(0.74, 1.30)	0.001, 80.7%	0.023, 1.18(1.02, 1.36)	0.721, 0.0%	0.810, 1.05(0.73, 1.50)	0.175, 39.5%
HB	4	<0.001, 1.25(1.11, 1.40)	0.825, 0.0%	0.005, 1.22(1.06, 1.40)	0.910, 0.0%	<0.001, 1.93(1.35, 2.77)	0.908, 0.0%

Abbreviations: n, number; PB, population based; HB, hospital based; OR, odds ratio; CI, confidence interval.

rs17045754

The pooled results based on 6 included studies (including 1,873 cases and 2,349 controls) indicated that rs17045754 was significantly related to an increased risk of cancer in allelic contrast (B versus A: OR = 1.23, 95%CI = 1.10–1.38, P < 0.001), dominant model (BB + AB versus AA: OR = 1.21, 95%CI = 1.07–1.38, P = 0.003) and recessive model (BB versus AA + AB: OR = 1.61, 95%CI = 1.14–2.27, P = 0.007).^[16,18–22] (Figure 4, Table 3)

Figure 4. Forest plot of ACYP2 rs17045754 polymorphism and cancer risk in recessive model.

rs843711

The pooled results based on 6 included studies (including 2,636 cases and 2,333 controls) indicated that rs843711 was significantly related to an increased risk of cancer in allelic contrast (B versus A: OR = 1.22, 95%CI = 1.12–1.32, P < 0.001), dominant model (BB + AB versus AA: OR = 1.25, 95%CI = 1.10–1.42, P = 0.001) and recessive model (BB versus AA + AB: OR = 1.53, 95%CI = 1.33–1.77, P < 0.001).^{[16,17,19–21,23]} (Figure 5, Table 3).

Figure 5. Forest plot of ACYP2 rs843711 polymorphism and cancer risk in recessive model.

rs6713088

The pooled results based on 5 included studies (including 1,495 cases and 1,851 controls) indicated that rs6713088 was significantly related to an increased risk of cancer in allelic contrast (B versus A: OR = 1.21, 95%CI = 1.10–1.33, P < 0.001), dominant model (BB + AB versus AA: OR = 1.25, 95%CI = 1.08–1.44, P = 0.003) and recessive model (BB versus AA + AB: OR = 1.35, 95%CI = 1.13–1.62, P = 0.001).^[16,19–22] (Table 3).

rs12615793

The pooled results based on 4 included studies (including 1,190 cases and 1,344 controls) indicated that rs11896604 was significantly related to an increased risk of cancer in recessive model (BB versus AA + AB: OR = 2.01, 95%CI = 1.30–3.11, P = 0.002)^{[16,19,20,23]} (Table 3).

rs843752

The pooled results based on 4 included studies (including 1,193 cases and 1,553 controls) indicated that rs843752 was significantly related to an increased risk of cancer in dominant model (BB + AB versus AA: OR = 1.21, 95%CI = 1.04–1.41, P = 0.014)^[16,20–22] (Table 3).

rs843645

The pooled results based on 3 included studies (including 1,008 cases and 1,359 controls) indicated that rs843645 was significantly related to an increased risk of cancer in allelic contrast (B versus A: OR = 1.20, 95%CI = 1.05–1.36, P = 0.007), dominant model (BB + AB versus AA: OR = 1.29, 95%CI = 1.10–1.52, P = 0.002)^[16,20,21] (Table 3).

rs843706

The pooled results based on 3 included studies (including 1,008 cases and 1,158 controls) indicated that rs843706 was significantly related to an increased risk of cancer in allelic contrast (B versus A: OR = 1.33, 95%CI = 1.18–1.50, P < 0.001), dominant model (BB + AB versus AA: OR = 1.37, 95%CI = 1.13–1.65, P = 0.001) and recessive model (BB versus AA + AB: OR = 1.59, 95%CI = 1.29–1.96, P < 0.001)^[16,19,20] (Table 3).

rs12621038

The pooled results based on 4 included studies (including 1,191 cases and 1,550 controls) indicated that rs12621038 was significantly related to a reduced risk of cancer in allelic contrast (B versus A: OR = 0.90, 95%CI = 0.80–1.00, P = 0.047) and recessive model (AB + BB versus AA: OR = 0.80, 95%CI = 0.66–0.97, P = 0.023)^[16,20–22] (Table 3).

rs1682111

The pooled results based on 6 included studies (including 1,914 cases and 2,337 controls) indicated that no significant association between rs1682111 polymorphism and cancer risk was found^{[15,16,20–23]} (Table 3).

rs10439478

The pooled results based on 5 included studies (including 1,361 cases and 1,728 controls) indicated that no significant association between rs10439478 polymorphism and cancer risk was found^[16,20–23] (Table 3).

rs843720 rs10439478 rs1682111

The pooled results based on 4 included studies (including 1,455 cases and 1,662 controls) indicated that no significant association between rs843720 polymorphism and cancer risk was found^{[15,16,20,22]} (Table 3).

Sensitivity analysis and publication bias

Sensitivity analysis were performed to evaluate the influence of each separate case-control study. The results showed that there was no material alteration in corresponding pooled ORs for thirteen polymorphisms of ACYP2 gene (Supplementary Figure S1–S13). In addition, Begg’s test and Egger’s regression test were performed to evaluate the publication bias. Publication bias was detected for rs11125529 in recessive model, rs12621038, and rs843706 in allelic contrast. (Supplementary Table S1 and Supplementary figure S14–S26 2)

Trial sequential analysis

To evaluate random errors, we performed TSA (Figure 6). This analysis showed that the cumulative z-curve didn’t cross the trial sequential monitoring boundary and the required information size, suggesting that more evidences are needed to verify the conclusions.

Figure 6. TSA for ACYP2 rs11125529 polymorphism under the allele contrast model.

The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) checklist is reported in Supplementary Table S2.

In-silico analysis using GTEx website

Based on the results of the GTEx database, we observed that the mutant allele increased the expression of ACYP2 mRNA in rs11125529(P = 1.1*10⁻⁶), rs11896604(P = 1.1*10⁻⁶), rs17045754 (P = 1.1*10⁻⁷), rs843711 (P = 4.8*10⁻⁵), rs6713088 (P = 8.6*10⁻¹³), rs12615793 (P = 3.8*10⁻⁷), rs843706 (P = 1.8*10⁻¹⁰), rs12621038 (P = 1.4*10⁻¹⁷), rs1682111 (P = 3*10⁻⁵) and rs10439478 (P = 2.7*10⁻⁶), while the mutant allele of rs843752 (P = 7.4*10⁻⁸) and rs843645 (P = 6*10⁻⁵) resulted in a reduction in the expression of ACYP2 (Figure 7).

Figure 7. ACYP2 mRNA expression by eQTL analysis in Human tissues based on GTEx database. rs843752 and rs843645 detected in esophagus mucosa; rs11125529, rs11896604, rs17045754, rs843711, rs6713088, rs12615793, rs843706, rs12621038, rs1682111, rs10439478 and rs843720 detected in muscle-skeletal.

Discussion

ACYP2 encodes cytosolic acylphosphatase enzyme which can hydrolyze the phosphoenzyme intermediate of different membrane pumps, especially the Ca²⁺/Mg²⁺-ATPase from skeletal muscle sarcoplasmic reticulum.^[24] ACYP2 has been shown to play a key role in pyruvate metabolism, and its activation is an important factor in malignant transformation.^[25,26] Furthermore, ACYP2 has been confirmed as an important telomere length related gene.^[8] Many studies have investigated the relationship between the SNP polymorphisms of ACYP2 gene and cancer risk. However, the results are inconsistent. Therefore, we conducted this systematic review and meta-analysis to investigate the association between genetic variation of ACYP2 gene and cancer susceptibility.

In this systematic review and meta-analysis, we analyze 10 case-control studies among 10,316 participants to verify the association between the thirteen SNPs of ACYP2 gene and cancer risk. Our findings suggest that rs11125529, rs11896604, rs12615793, rs17045754, rs6713088, rs843645, rs843706, rs843711 and rs843752 are able to increase cancer risk under different genetic models. Meanwhile, rs12621038 polymorphism is associated with reduced cancer risk in different genetic models. However, rs843720, rs10439478 and rs1682111 are not found to be significantly associated with cancer risk.

It has been reported that rs11125529 of ACYP2 was associated with reduced telomere length.^[27] However, publication bias was detected for rs11125529 in recessive model. The main source of significant heterogeneity might be ethnicity. In subgroup meta-analysis stratified by ethnicity, we found that rs11125529 was significantly associated with cancer risk in Asians in dominant model and recessive model. We only included one study of mix races. And our results indicated that rs11125529 was not associated with cancer susceptibility in various models in mix races group. So, our study indicated that rs11125529 might increase cancer susceptibility in Asian population. However, more studies are needed to confirm this result.

Many studies have identified that there is a strong linkage disequilibrium (LD) between each pair of eight SNPs (rs1682111, rs843752, rs10439478, rs843645, rs11125529, rs12615793, rs843711, and rs11896604) of ACYP2 gene.^{[17,20,21,23]} However, the results of various studies are not consistent, so it is necessary to analyze each SNP of ACYP2 gene.

For ACYP2 rs11896604, our meta-analysis showed that rs11896604 was significantly increased cancer susceptibility in dominant model and recessive model. In subgroup meta-analysis stratified by source of control, we found that rs11896604 was significantly increased cancer susceptibility in HB group in allele contrast, dominant model and recessive model. We also found that rs11896604 was significantly increased cancer susceptibility in dominant model. But no publication bias was detected. This result indicated that rs11896604 might increase cancer susceptibility. Nevertheless, the result needs to be confirmed by more large sample size studies.

In this meta-analysis, we also found that ACYP2 rs12615793, rs17045754, rs6713088, rs843645, rs843706, rs843711 and rs843752 were significantly associated with cancer risk in various genetic models. In addition, no publication bias was detected except for rs843706 in allelic contrast. Although there are few studies on these SNPs, these results still indicated a positive correlation with cancer risk. More studies are needed to verify the results.

For ACYP2 rs12621038, our meta-analysis showed that rs12621038 was significantly reduced cancer susceptibility in allele contrast and recessive model. However, publication bias was detected in allele contrast dominant model and recessive model. Due to the small sample size, there is not enough data to analyze the potential sources of heterogeneity. This result need more large sample size studies for further evaluation.

Our results showed that ACYP2 rs843720, rs10439478 and rs1682111 were not associated with cancer susceptibility in various models. More studies with larger sample sizes are needed to reevaluate the results.

In this study, we conducted a systematic and comprehensive search to obtain accurate and reliable results. Then, NOS was used to evaluate the quality of the included studies. Sensitivity analysis, Egger’s and Begg’s tests were used to better control the quality of the included studies. However, there are some limitations in this meta-analysis. First, the sample size was relatively small, which limited the reliability of the results. Second, we included studies only reported in English, which may influence the effects of the polymorphisms. Third, most studies only reported Chinese Han population. Fourth, we cannot obtain enough data to assess the relationship between ACYP2 polymorphisms and cancer types. Fifth, publication bias was detected for rs11125529, rs12621038 and rs843706, which might cause large deviations. Sixth, the previous reported linkage disequilibrium of the ACYP2 SNPs reflected inconsistent function. More large sample size case-control studies of various ethnicities are needed to investigate the functions of ACYP2 polymorphisms.

Conclusion

Our meta-analysis suggests that ACYP2 rs11125529, rs11896604, rs12615793, rs17045754, rs6713088, rs843645, rs843706, rs843711 and rs843752 are associated with the risk of cancer in Asian population. While, ACYP2 rs12621038 polymorphism is inversely associated with cancer risk for Asians. Further well-designed case-control studies of various ethnicities with larger sample size are needed to confirm these findings.

Author’s contribution

Zhenwei Han had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. Zhenwei Han and Xueliang Chang learned concept and design. Xueliang Chang and Zhan Yang conducted data analysis and interpretation. Xueliang Chang and Zhenwei Han Drafted of the manuscript. Yaxuan Wang, Xueliang Chang and Jingdong Li reviewed important academic content. Yaxuan Wang, Yanping Zhang, Zhihai Teng and Hu Wang conducted the statistical analysis.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Acknowledgments

We thank Dr. Chawnshang Chang at University of Rochester Medical Center for helping with the preparation of the manuscript.

Conflicts of interest

All authors declare that they have no conflict of interest.

Data availability

All data generated or analyzed during this study are included in this published article.

Additional information

Funding

This study was supported by The National Natural Science Foundation of China under Grant (No. 81802544), and Hebei Province Natural Science Foundation under Grant (H2020206146).