Single-nucleotide polymorphisms of the IL-12 gene lead to a higher cancer risk: a meta-analysis based on 22,670 subjects

ABSTRACT

The associations between interleukin-12 (IL-12) gene polymorphisms and cancer risk have been discussed extensively, with controversial results. Therefore, we conducted the present meta-analysis to better assess the potential roles of IL-12 gene variation in cancer occurrence. Eligible articles were found via PubMed, Medline, EMBASE, Google Scholar and CNKI. Odds ratios and 95% confidence intervals were used to evaluate the associations between IL-12 gene polymorphisms and cancer risk. Thirty-one studies with 10,749 cancer patients and 11,921 healthy subjects were included in the analyses. The overall results showed that cancer risk was increased by IL-12A rs568408 (GG versus GA + AA: P = 0.004; G versus A: P = 0.005) and IL-12B rs3212227 (AA versus AC + CC: P = 0.004; CC versus AA + AC: P = 0.03; A versus C: P = 0.007) polymorphisms. Further subgroup analyses for IL-12A rs568408 and IL-12B rs3212227 revealed that the positive results could be impacted by the ethnicity of the population, cancer type and/or genotyping methods. However, we failed to detect any significant associations between the IL-12A rs2243115 polymorphism and cancer risk in either the overall or the subgroup analyses. The current study suggests that certain IL-12 gene polymorphisms serve as biological markers of cancer susceptibility.

INTRODUCTION

Cancer is a major threat to public health. According to a recent investigation, over 14.1 million new cases and 8.2 million deaths are caused annually by cancer (Siegel et al., 2016). Furthermore, by 2020, the burden of cancer is expected to rise by 50%, due to the rapidly aging population (Popat et al., 2013). To date, the exact pathogenic mechanisms of cancer remain ambiguous. Although tobacco use, heavy alcohol intake, high caloric diet and chemical dyes have been identified as risk factors for cancer (Jemal et al., 2010), the fact that only a small proportion of individuals exposed to these carcinogenic agents ultimately develops cancer suggests that genetic factors play a crucial part in cancer pathogenesis. In addition, the familial aggregation tendency of cancer has long been acknowledged (Risch, 2001), and a number of genetic variants have been found to be associated with cancer susceptibility in different populations (Mtoert et al., 2014; Jamieson et al., 2015). Nevertheless, the mechanisms of cancer pathogenesis are highly complex, and the genetic determinants involved in cancer development are not fully clarified.

Interleukin-12 (IL-12) is a pro-inflammatory cytokine that is mainly secreted by antigen-presenting cells. IL-12 targets T-helper (Th) cells and natural killer cells, and stimulates the synthesis and secretion of interferon gamma (IFN-γ), which is a well established anti-tumor factor (Croxford et al., 2014). Additionally, lower serum IL-12 levels have been observed in patients with various types of cancer (Green et al., 2012; Stanilov et al., 2012; Tao et al., 2012; Wang et al., 2013; Fang et al., 2015), suggesting that IL-12 functions as a potent tumor-suppressive factor. Biologically active IL-12 consists of two functional subunits, p35 and p40, which are encoded by the IL-12A and IL-12B genes, respectively (Croxford et al., 2014). Since the tumor-suppressive effect of IL-12 is well documented, functional polymorphisms of the IL-12A and IL-12B genes are thought to be good genetic candidates for cancer susceptibility.

Recently, extensive studies have explored the potential associations of IL-12A and IL-12B genetic variants with cancer risk. Among these, 3’UTR A > C (1188A > C; rs3212227) in the IL-12B gene and 3’UTR G > A (277 G > A; rs568408) and 5’UTR T > G (564 T > G; rs2243115) in the IL-12A gene are the most intensively studied sites. It is well established that the 3’UTR and 5’UTR are important to mRNA stability, which may contribute to gene expression (Wu and Brewer, 2012). Thus, although these variants are located in untranslated regions, some studies note that they are able to impact IL-12 levels. Morahan et al. (2001) reported that IL-12B rs3212227 was responsible for altered levels of IL-12B mRNA expression in cell lines, and, compared to the AA genotype cell line, decreased IL-12B levels were observed in the CC line. Other scientists have subsequently found similar phenomena in different diseases, including cancer, suggesting that serum IL-12 levels could be modulated by this SNP (Seegers et al., 2002; Windsor et al., 2004; Yilmaz et al., 2005; Stanilova et al., 2007; Wang et al., 2013). For IL12A rs568408 and rs2243115, studies on their potential association with IL-12 expression are still at early stage, and no evidence was detected that they affect IL-12 production (Tao et al., 2012; Wang et al., 2013). It is notable that most studies have simply discussed whether the distributions of IL-12 SNPs in cancer patients are statistically different from that in healthy individuals, and the exact underlying mechanisms between these three SNPs and cancer remain unclear. However, the results of relevant studies were inconsistent and inconclusive. Thus, we conducted the present meta-analysis to better assess the relationship between the IL-12 gene polymorphisms and the risk of cancer.

MATERIALS AND METHODS

Literature search

To retrieve all relevant articles, we searched the electronic databases PubMed, Medline, EMBASE, Google Scholar and China National Knowledge Infrastructure (CNKI) without a time limit using the following terms: Interleukin-12, IL-12, Interleukin 12, IL 12, polymorphism, variant, genotype, allele, cancer, tumor, carcinoma, neoplasm and malignancy. Moreover, the reference lists of all retrieved articles were reviewed manually for other potentially eligible articles.

Inclusion criteria and exclusion criteria

Literature matching all the following criteria was included in the present meta-analysis: (1) published articles; (2) a case-control study of associations between IL-12 gene variants and cancer susceptibility in unrelated cancer patients and control subjects; (3) identification of both genotypic and allelic distributions of the IL-12 gene polymorphisms; and (4) availability of full text in English or Chinese. Abstracts, case reports, case series, pedigree studies, reviews and experts’ opinions were intentionally excluded. Additionally, if the same patients were enrolled in multiple studies, only the most recent and complete study was included. It should be particularly noted that, since there is no consensus on how to handle studies with a control group that is not in Hardy-Weinberg equilibrium (HWE), in this meta-analysis we did not exclude studies deviating from HWE as long as they were eligible according to the inclusion criteria and were not of poor quality (Zintzaras and Lau, 2008).

Data extraction

Two of the authors (Shi and Jia) extracted the following information from every included study: name of first author, year of publication, country of origin, ethnicity of the study population, type of cancer, number of cases and controls, genotypic and allelic frequencies in the cases and controls, and P values of HWE in the control group.

Quality assessment of included studies

The Newcastle-Ottawa quality assessment scale (NOS) was used to evaluate the quality of each included study (Stang, 2010). As a classical rating tool of observational studies, NOS assesses studies from the following three aspects: selection, comparability and exposure. The score range of NOS is from 0 to 9, and studies with a score greater than 7 are assumed to be high-quality. The quality assessment was conducted independently by two of the authors (Shi and Jia), and any discordance between the investigators was resolved by discussion with a third author (Xie) until an agreement was achieved.

Statistical analysis

Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the associations between the IL-12 gene variants and cancer susceptibility in dominant, recessive and allelic models. In addition, a raw probability value (P value) of 0.05 or less was considered statistically significant, and was further subjected to Bonferroni correction to account for multiple statistical tests. The significance threshold was set at 0.017 (0.05/3) for a single SNP because three statistical models were performed for each SNP. The chi-square test was used to explore HWE. Q test and I² statistic were applied to assess the heterogeneity between studies. If the P value of the Q test was less than 0.1 or I² was greater than 50%, the between-study heterogeneity was considered statistically significant and the random-effect model (REM) was employed for the analyses. Otherwise, the analyses were carried out with the fixed-effect model (FEM). Furthermore, subgroup analyses by cancer type, genotype method and ethnicity were conducted to trace the source of the heterogeneity. Sensitivity analyses were performed to examine the stability of the results. Publication bias was tested by funnel plots. All data were analyzed with ReviewManager Version 5.3.3 (The Cochrane Collaboration, Software Update, Oxford, U.K.).

RESULTS

Included studies

The literature search generated 1,212 results, and 1,143 citations were excluded after reading the titles and abstracts. Among the remaining 69 articles, 31 studies, containing 10,749 cancer patients and 11,921 control subjects, were finally included in the present meta-analysis (Howell et al., 2003; Amirzargar et al., 2005; García-González et al., 2007; Hou et al., 2007; Lee et al., 2007; Crusius et al., 2008; Han et al., 2008; Chen et al., 2009; Miteva et al., 2009; Tamandani et al., 2009; Wei et al., 2009; Wu et al., 2009; Zhao et al., 2009; Ben Chaaben et al., 2011; Liu et al., 2011; Welsh et al., 2011; Yang, et al., 2011; do Carmo Vasconcelos de Carvalho et al., 2012; Huang et al., 2012; Kaarvatn et al., 2012; Roszak et al., 2012; Sima et al., 2012; Tao et al., 2012; Jaiswal et al., 2013; Sun et al., 2013; Wang et al., 2013; Saxena et al., 2014; Jafarzadeh et al., 2015; Sun et al., 2015; Winchester et al., 2015; Yin et al., 2015) (Fig. 1). Of these studies, 29/31 concerned the IL-12B rs3212227 polymorphism, 7/31 concerned the IL-12A rs2243115 polymorphism, and 8/31 concerned the IL-12A rs568408 polymorphism. All articles were published between 2003 and 2015. Fourteen studies were performed in Caucasians, 15 in Asians, and two in populations with mixed ethnicities. Twenty-nine articles were published in English, and the other two were in Chinese. A violation of HWE was found in three studies, which were nevertheless included in further analyses because of their high quality. The characteristics of the included studies are summarized in Table 1 (IL-12A rs568408 variant), Table 2 (IL-12A rs2243115 variant) and Table 3 (IL-12B rs3212227 variant).

Fig. 1.

Flowchart of study selection for the present meta-analysis.

Table 1. Characteristics of the included studies for IL-12A rs568408 polymorphism and cancer risk

First author, year	Country	Ethnicity	Cancer type	Genotype method	Case			Control			Pvalue HWE	NOS score
					n	Genotypes GG/GA/AA	Alleles G/A (%)	n	Genotypes GG/GA/AA	Alleles G/A (%)
Chen 2009	China	Asian	Cervical cancer	PCR-RFLP	404	253/145/6	80.6/19.4	404	285/112/7	84.4/15.6	0.286	8
Hou 2007	Poland	Caucasian	Gastric cancer	TaqMan	302	211/83/8	83.6/16.4	428	306/112/10	84.6/15.4	0.948	8
Lee 2007	China	Asian	Lung cancer	TaqMan	118	101/17/0	92.8/7.2	113	81/31/1	85.4/14.6	0.288	7
Liu 2011	China	Asian	Hepatocellular carcinoma	PCR-RFLP	802	504/277/21	80.1/19.9	861	631/220/10	86.1/13.9	0.056	7
Roszak 2012	Poland	Caucasian	Cervical cancer	PCR-RFLP	405	253/128/24	78.3/21.7	450	306/125/19	81.9/18.1	0.178	7
Sun 2015	China	Asian	Colorectal cancer	PCR-RFLP	257	172/83/2	83.1/16.9	236	181/55/0	88.3/11.7	0.043	7
Tao 2012	China	Asian	Esophageal cancer	PCR-RFLP	426	267/147/12	79.9/20.1	432	304/112/16	83.3/16.7	0.166	8
Wang 2013	China	Asian	Osteosarcoma	PCR-RFLP	106	66/37/3	79.7/20.3	210	156/47/7	85.5/14.5	0.153	7

HWE: Hardy-Weinberg equilibrium; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; NOS: Newcastle-Ottawa quality assessment scale.

The figure in bold type indicates that the distributions of IL-12A rs568408 in controls deviate from HWE.

Table 2. Characteristics of the included studies for IL-12A rs2243115 polymorphism and cancer risk

First author, year	Country	Ethnicity	Cancer type	Genotype method	Case			Control			Pvalue HWE	NOS score
					n	Genotypes TT/TG/GG	Alleles T/G (%)	n	Genotypes TT/TG/GG	Alleles T/G (%)
Chen 2009	China	Asian	Cervical cancer	PCR-RFLP	404	342/60/2	92.1/7.9	404	337/64/3	91.3/8.7	0.973	8
Liu 2011	China	Asian	Hepatocellularcarcinoma	PCR-RFLP	861	735/125/1	92.6/7.4	874	732/137/5	91.6/8.4	0.604	7
Sima 2012	China	Asian	Brain tumor	PCR-RFLP	170	140/25/5	89.7/10.3	222	198/24/0	94.6/5.4	0.394	7
Sun 2013	China	Asian	Esophageal cancer	TaqMan	368	311/56/1	92.1/7.9	370	308/58/4	91.1/8.9	0.499	7
Sun 2015	China	Asian	Colorectal cancer	PCR-RFLP	257	225/31/1	93.6/6.4	236	212/24/0	94.9/5.1	0.411	7
Wang 2013	China	Asian	Osteosarcoma	PCR-RFLP	106	89/16/1	91.5/8.5	210	175/31/4	90.7/9.3	0.073	7
Yin 2015	China	Asian	Gastric cancer	TaqMan	235	197/36/2	91.5/8.5	466	387/75/4	91.1/8.9	0.862	8

HWE: Hardy-Weinberg equilibrium; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; NOS: Newcastle-Ottawa quality assessment scale.

Table 3. Characteristics of the included studies for IL-12B rs3212227 polymorphism and cancer risk

First author, year	Country	Ethnicity	Cancer type	Genotype method	Case			Control			Pvalue HWE	NOS score
					n	Genotypes AA/AC/CC	Alleles A/C (%)	n	Genotypes AA/AC/CC	Alleles A/C (%)
Amirzargar 2005	Iran	Caucasian	CML	PCR-SSP	30	17/11/2	75.0/25.0	40	24/13/3	76.3/23.7	0.516	7
Ben Chaaben 2011	Tunisia	Caucasian	NPC	TaqMan	247	74/124/49	55.1/44.9	284	135/118/31	68.3/31.7	0.497	7
Chen 2009	China	Asian	Cervical cancer	PCR-RFLP	404	127/199/78	56.1/43.9	404	150/185/69	60.0/40.0	0.357	8
Crusius 2008	Netherlands	Caucasian	Gastric cancer	TaqMan	230	139/74/17	76.5/23.5	1060	677/343/40	80.0/20.0	0.672	8
do Carmo 2012	Brazil	Mixed	Cervical cancer	PCR–RFLP	9	7/1/1	83.3/16.7	76	31/37/8	65.1/34.9	0.531	7
García-González 2007	Spain	Caucasian	Gastric cancer	TaqMan	404	247/133/24	77.6/22.4	404	256/130/18	79.5/20.5	0.773	8
Han 2008	Korea	Asian	Cervical cancer	DNA sequencing	150	32/87/31	50.3/49.7	179	52/88/39	53.6/46.4	0.877	7
Howell 2003	UK	Caucasian	Skin cancer	ARMS-PCR	145	95/42/8	80.0/20.0	229	139/77/13	77.5/22.5	0.591	7
Huang 2012	China	Asian	Colorectal cancer	PCR-RFLP	410	93/219/98	49.4/50.6	450	124/230/96	53.1/46.9	0.578	8
Jafarzadeh 2015	Iran	Caucasian	Breast cancer	PCR-RFLP	100	58/32/10	74.0/26.0	100	59/33/8	76.0/24.0	0.280	8
Jaiswal 2013	India	Caucasian	Bladder cancer	PCR-RFLP	200	111/74/15	74.0/26.0	200	87/94/19	67.0/33.0	0.374	8
Kaarvatn 2012	Croatia	Caucasian	Breast cancer	TaqMan	191	126/59/6	81.4/18.6	194	104/73/17	72.4/27.6	0.419	7
Liu 2011	China	Asian	HCC	PCR-RFLP	831	249/422/160	55.4/44.6	844	272/414/158	56.8/43.2	0.983	7
Miteva 2009	Bulgaria	Caucasian	Colorectal cancer	PCR-RFLP	85	50/31/4	77.1/22.9	134	80/45/9	76.5/23.5	0.443	8
Roszak 2012	Poland	Caucasian	Cervical cancer	PCR-RFLP	405	212/174/19	73.8/26.2	450	289/151/10	81.0/19.0	0.056	7
Saxena 2014	India	Caucasian	HCC	PCR-RFLP	59	19/31/9	58.5/41.5	148	63/71/14	66.6/33.4	0.345	7
Sima 2012	China	Asian	Brain tumor	PCR-RFLP	170	66/86/18	64.1/35.9	222	71/116/35	58.1/41.9	0.275	7
Sun 2013	China	Asian	Esophageal cancer	TaqMan	368	116/176/76	55.4/44.6	370	112/179/79	54.5/45.5	0.635	7
Sun 2015	China	Asian	Colorectal cancer	PCR-RFLP	257	69/140/48	54.1/45.9	236	71/124/41	56.4/43.6	0.295	7
Tamandani 2009	India	Caucasian	Cervical cancer	PCR-RFLP	200	63/134/3	65.0/35.0	200	88/104/8	70.0/30.0	0.001	8
Tao 2012	China	Asian	Esophageal cancer	PCR-RFLP	426	109/216/101	50.9/49.1	432	148/213/71	58.9/41.1	0.701	8
Wang 2013	China	Asian	Osteosarcoma	PCR-RFLP	106	27/50/29	49.1/50.9	210	78/101/31	61.2/38.8	0.855	7
Wei 2009	China	Asian	NPC	PCR-RFLP	302	58/165/79	46.5/53.5	310	98/152/60	56.1/43.9	0.938	8
Welsh 2011	UK	Caucasian	Skin cancer	TaqMan	1483	971/446/66	80.5/19.5	763	500/230/33	80.6/19.4	0.320	8
Winchester 2015	USA	Mixed	Prostate cancer	MassArray	866	535/274/57	77.6/22.4	830	568/227/35	82.1/17.9	0.046	8
Wu 2009	China	Asian	Gastric cancer	PCR-RFLP	1035	347/508/180	58.1/41.9	1073	333/554/186	56.8/43.2	0.086	7
Yang 2011	China	Asian	HCC	TaqMan	772	220/397/155	54.2/45.8	852	236/417/199	52.2/47.8	0.574	8
Yin 2015	China	Asian	Gastric cancer	TaqMan	234	60/132/42	53.8/46.2	466	152/221/93	56.3/43.7	0.436	8
Zhao 2009	China	Asian	Brain tumor	PCR-RFLP	210	38/115/57	45.5/54.5	220	70/106/44	55.9/44.1	0.736	8

HWE: Hardy-Weinberg equilibrium; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; CML: chronic myelogenous leukemia; NPL: nasopharyngealcarcinoma; HCC: hepatocellularcarcinoma; ARMS-PCR: amplification refractory mutation system-polymerase chain reaction.

Quality assessment of included studies

The average NOS score of the included studies was 7.53 (range, 7 to 8), indicating that all enrolled articles were of high quality. Potential biases in the eligible studies mainly originated from unmatched baseline characteristics of the cases and controls, such as age and gender.

IL-12A rs568408 polymorphism and cancer susceptibilit

Eight studies containing 2,820 cancer patients and 3,134 control subjects were included to assess the association between the IL-12A rs568408 variant and cancer risk. For GG versus GA + AA (dominant model) and G versus A (allelic model), analyses were conducted with REMs because of obvious between-study heterogeneity. For AA versus GG + GA (recessive model), the heterogeneity between studies was mild, and the FEM was therefore used for the analysis. A significant association between the IL-12A rs568408 polymorphism and cancer susceptibility was detected in GG versus GA + AA (P = 0.004, OR = 0.75, 95%CI 0.62–0.91) and G versus A (P = 0.005, OR = 0.79, 95%CI 0.67–0.93) (Fig. 2). Further subgroup analyses were conducted by ethnicity, cancer type and genotype method. By subdividing the studies according to different ethnicities, we found that six studies were conducted in Asians and two in Caucasians. The between-study heterogeneity remained significant in the Asian subgroup, but was no longer notable in the Caucasian subgroup. Similar positive results were observed in the Asian population (GG versus GA + AA: P = 0.01, OR = 0.73, 95%CI 0.57–0.93; G versus A: P = 0.02, OR = 0.78, 95%CI 0.63–0.97) while no significant associations were found in the Caucasian population. Subgroup analysis by cancer type could only be performed for cervical cancer with two studies that were homogeneous, and the results were consistent with the overall results (GG versus GA + AA: P = 0.004, OR = 0.74, 95%CI 0.61–0.91; G versus A: P = 0.006, OR = 0.78, 95%CI 0.66–0.93). By subdividing the studies according to genotype method, we observed that polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used in six studies, and TaqMan was used in two. The heterogeneity between the studies was trivial in the PCR-RFLP group, and FEM-effects meta-analysis pooled significant results in both GG versus GA + AA (P < 0.00001, OR = 0.67, 95%CI 0.59–0.76) and G versus A (P < 0.00001, OR = 0.72, 95%CI 0.65–0.80), which were consistent with the overall results. In the TaqMan group, REMs were carried out in the analyses because the between-study heterogeneity remained obvious. However, no significant association was detected in this subgroup (Table 4).

Fig. 2.

Forest plots on the association between IL-12A rs568408 polymorphism and cancer risk. (A). Forest plot of GG versus GA + AA for IL12A rs568408 polymorphism and cancer risk. (B). Forest plot of AA versus GG + GA for IL12A rs568408 polymorphism and cancer risk. (C). Forest plot of G versus A for IL12A rs568408 polymorphism and cancer risk.

Table 4. Subgroup analyses for IL-12B rs3212227polymorphism and cancer risk

Polymorphism	Variables	P value	OR (95%Cl)	I-square (%)	P for the heterogeneity
IL-12A rs568408	Ethnicity
	Asian (No.a: 6)
	GG versus GA + AA	0.01	0.73 (0.57–0.93)	67	0.009
	AA versus GG + GA	0.42	1.19 (0.78–1.83)	20	0.28
	G versus A	0.02	0.78 (0.63–0.97)	67	0.01
	Caucasian (No.a: 2)
	GG versus GA + AA	0.11	0.84 (0.68–1.04)	0	0.45
	AA versus GG + GA	0.27	1.33 (0.80–2.23)	0	0.69
	G versus A	0.08	0.85 (0.71–1.02)	0	0.41
	Genotype method
	PCR-RFLP (No.a: 6)
	GG versus GA + AA	0.00001	0.67 (0.59–0.76)	0	0.76
	AA versus GG + GA	0.16	1.29 (0.91–1.84)	14	0.33
	G versus A	0.00001	0.72 (0.65–0.80)	0	0.68
	TaqMan(No.a: 2)
	GG versus GA + AA	0.46	1.41 (0.57–3.49)	84	0.01
	AA versus GG + GA	0.99	1.01 (0.41–2.46)	0	0.45
	G versus A	0.46	1.37 (0.59–3.17)	84	0.01
	Cancer type
	Cervical cancer (No.a: 2)
	GG versus GA + AA	0.004	0.74 (0.61–0.91)	0	0.59
	AA versus GG + GA	0.39	1.26 (0.74–2.16)	0	0.42
	G versus A	0.006	0.78 (0.66–0.93)	0	0.83
IL-12A rs2243115	Genotype method
	PCR-RFLP (No.a: 5)
	TT versus TG + GG	0.96	1.00 (0.84–1.20)	27	0.24
	GG versus TT + TG	0.93	1.03 (0.46–2.32)	38	0.17
	T versus G	0.62	0.93 (0.70–1.24)	55	0.06
	TaqMan(No.a: 2)
	TT versus TG + GG	0.60	1.08 (0.81–1.44)	0	0.90
	GG versus TT + TG	0.37	0.55 (0.15–2.03)	0	0.33
	T versus G	0.49	1.10 (0.84–1.44)	0	0.76
IL-12B rs3212227	Ethnicity
	Asian (No.a: 14)
	AA versus AC + CC	0.005	0.81 (0.70–0.94)	67	0.0002
	CC versus AA + AC	0.16	1.10 (0.96–1.26)	49	0.02
	A versus C	0.02	0.89 (0.80–0.98)	70	0.0001
	Caucasian (No.a: 13)
	AA versus AC + CC	0.27	0.89 (0.73–1.09)	74	0.00001
	CC versus AA + AC	0.31	1.17 (0.86–1.58)	49	0.03
	A versus C	0.30	0.92 (0.78–1.08)	74	0.00001
	Genotype method
	PCR-RFLP (No.a: 17)
	AA versus AC + CC	0.01	0.81 (0.68–0.96)	72	0.00001
	CC versus AA + AC	0.02	1.19 (1.03–1.38)	36	0.07
	A versus C	0.009	0.86 (0.77–0.96)	70	0.00001
	TaqMan(No.a: 8)
	AA versus AC + CC	0.38	0.92 (0.75–1.11)	73	0.0004
	CC versus AA + AC	0.61	1.08 (0.81–1.43)	69	0.002
	A versus C	0.52	0.95 (0.81–1.12)	79	0.0001
	Cancer type
	Nasopharyngeal carcinoma (No.a: 2)
	AA versus AC + CC	0.00001	0.49 (0.38–0.64)	0	0.75
	CC versus AA + AC	0.0009	1.66 (1.23–2.24)	0	0.32
	A versus C	0.00001	0.63 (0.53–0.75)	8	0.30
	Hepatocellular carcinoma (No.a: 3)
	AA versus AC + CC	0.46	0.94 (0.79–1.11)	17	0.30
	CC versus AA + AC	0.78	0.97 (0.75–1.24)	43	0.17
	A versus C	0.74	0.97 (0.83–1.14)	54	0.11
	Cervical cancer (No.a: 5)
	AA versus AC + CC	0.006	0.69 (0.53–0.90)	48	0.10
	CC versus AA + AC	0.58	1.12 (0.76–1.65)	32	0.21
	A versus C	0.01	0.80 (0.68–0.95)	41	0.15
	Colorectal cancer (No.a: 3)
	AA versus AC + CC	0.09	0.83 (0.66–1.03)	0	0.77
	CC versus AA + AC	0.42	1.11 (0.86–1.44)	0	0.71
	A versus C	0.12	0.89 (0.77–1.03)	0	0.76
	Brain tumor (No.a: 2)
	AA versus AC + CC	0.67	0.80 (0.29–2.24)	91	0.0008
	CC versus AA + AC	0.99	1.00 (0.43–2.30)	80	0.03
	A versus C	0.80	0.92 (0.48–1.77)	91	0.0009
	Skin cancer (No.a: 2)
	AA versus AC + CC	0.73	1.03 (0.87–1.22)	0	0.38
	CC versus AA + AC	0.92	1.02 (0.69–1.50)	0	0.91
	A versus C	0.80	1.02 (0.88–1.18)	0	0.44
	Breast cancer (No.a: 2)
	AA versus AC + CC	0.33	1.31 (0.76–2.26)	59	0.12
	CC versus AA + AC	0.52	0.65 (0.18–2.42)	73	0.06
	A versus C	0.42	1.27 (0.71–2.26)	76	0.04
	Esophageal cancer (No.a: 2)
	AA versus AC + CC	0.44	0.83 (0.52–1.33)	79	0.03
	CC versus AA + AC	0.40	1.23 (0.76–2.01)	75	0.05
	A versus C	0.43	0.87 (0.61–1.24)	84	0.01
	Gastric cancer (No.a: 4)
	AA versus AC + CC	0.41	0.92 (0.76–1.12)	51	0.10
	CC versus AA + AC	0.37	1.15 (0.85–1.58)	53	0.09
	A versus C	0.33	0.94 (0.83–1.06)	34	0.21

OR: odds ratio; CI: confidence interval; NA: not applicable; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism.

^a  Number of articles.

Bold-type numbers indicate astatistically significant difference between cases and controls.

IL-12A rs2243115 polymorphism and cancer susceptibility

Estimation of the association between the IL-12A rs2243115 polymorphism and cancer risk was based on seven studies, including 2,401 cases and 2,782 controls. FEMs were applied for all analyses, because no obvious between-study heterogeneity was detected. No significant associations with cancer risk were observed for the IL-12A rs2243115 polymorphism (Fig. 3). Subgroup analyses were performed by the genotype method and were mostly under FEMs, because there was negligible heterogeneity between the studies. Nevertheless, neither the PCR-RFLP group nor the TaqMan group showed positive results in any of the comparison models (Table 4).

Fig. 3.

Forest plots on association between IL12A rs2243115 polymorphism and cancer risk. (A). Forest plot of TT versus TG + GG for IL12A rs2243115 polymorphism and cancer risk is shown. (B). Forest plot of GG versus TT + TG for IL12A rs2243115 polymorphism and cancer risk is shown. (C). Forest plot of T versus G for IL12A rs2243115 polymorphism and cancer risk is shown.

IL-12B rs3212227 polymorphism and cancer susceptibility

For the IL-12B rs3212227 polymorphism, a total of 29 studies, including 10,329 cancer patients and 11,380 control subjects, were analyzed. Due to the significant heterogeneity, comparisons were performed with REMs in AA versus AC + CC (dominant model), CC versus AA + AC (recessive model), and A versus C (allelic model). We found a significant association between the IL-12B rs3212227 variant and cancer risk in all three models (AA versus AC + CC: P = 0.004, OR = 0.85, 95%CI 0.75–0.95; CC versus AA + AC: P = 0.03, OR = 1.15, 95%CI 1.02–1.31; A versus C: P = 0.007, OR = 0.89, 95%CI 0.82–0.97) (Fig. 4). Further stratified analyses of the IL-12B rs3212227 variant were conducted by ethnicity, cancer type and genotype method. Regarding the ethnic background, there were 14 studies of Asian ethnicity and 13 studies of Caucasian ethnicity. The heterogeneity between the studies remained obvious in both subgroups. Consistent with our overall results, we found a significant association between the IL-12B rs3212227 polymorphism and cancer risk in the Asian subgroup for AA versus AC + CC (P = 0.005, OR = 0.81, 95%CI 0.70–0.94) and A versus C (P = 0.02, OR = 0.89, 95%CI 0.80–0.98). However, no associations were observed in the Caucasian subgroup. When considering the different genotype methods, the numbers of studies using PCR-RFLP or the TaqMan method were 17 and eight, respectively. REMs were applied in both subgroups due to the significant heterogeneity. These models demonstrated that the PCR-RFLP group was responsible for the positive results (AA versus AC + CC: P = 0.01, OR = 0.81, 95%CI 0.69–0.96, CC versus AA + AC: P = 0.02, OR = 1.19, 95%CI 1.03–1.38, A versus C: P = 0.009, OR = 0.86, 95%CI 0.77–0.96), whereas the TaqMan group showed no cancer susceptibility. Moreover, we performed a subgroup analysis by cancer type for the IL-12B rs3212227. As shown in Table 4, the between-study heterogeneity was reduced in some genetic models after stratifying the available data according to cancer types. Compared to our overall findings, similar results were observed for nasopharyngeal carcinoma (AA versus AC + CC, P = 0.00001, OR = 0.49, 95%CI 0.38–0.64; CC versus AA + AC: P = 0.0009, OR = 1.66, 95%CI 1.23–2.24; A versus C: P = 0.00001, OR = 0.63, 95%CI 0.53–0.75) and cervical cancer (AA versus AC + CC: P = 0.006, OR = 0.69, 95%CI 0.53–0.90; A versus C: P = 0.01, OR = 0.80, 95%CI 0.68–0.95) (Table 4).

Fig. 4.

Forest plots on association between IL12B rs3212227 polymorphism and cancer risk. (A). Forest plot of AA versus AC + CC for IL12B rs3212227 polymorphism and cancer risk is shown. (B). Forest plot of CC versus AA + AC for IL12B rs3212227 polymorphism and cancer risk is shown. (C). Forest plot of A versus C for IL12B rs3212227 polymorphism and cancer risk is shown.

Bonferroni correction of overall results

Since multiple statistical tests were conducted for each SNP, Bonferroni corrections of overall results were further performed, and only the CC versus AA + AC comparison (recessive model) of IL-12B rs3212227 yielded the opposite outcome (raw P value = 0.03 > 0.017), suggesting that most of our results were statistically robust.

Sensitivity analyses and publication bias

Sensitivity analyses were performed by removing one individual study at a time. The overall results were not impacted by any individual study, including those that were not in HWE for all of the three analyzed SNPs. No other changes in the results were found in the subgroup analyses. Funnel plots were used to evaluate the potential publication bias. A visual inspection of the funnel plots revealed no apparent asymmetry for any of the IL-12 polymorphisms, and these results suggest that there was no significant publication bias in the present meta-analysis (Supplementary Fig. S1).

DISCUSSION

IL-12 is an anti-tumor cytokine with multiple functions. First, IL-12 can stimulate the synthesis and secretion of IFN-γ, which serves as a pro-inflammatory and tumor-suppressive factor (Croxford et al., 2014). In addition, it was found that p53, which triggers apoptosis in cancer cells, was up-regulated by the secretion of IFN-γ, thus, subsequently, inhibiting tumor growth (Takaoka et al., 2003). Second, IL-12 can promote Th1 cell differentiation by stimulating the synthesis of interferon regulatory factors 1 (IRF1) and 4 (IRF4) (Lehtonen et al., 2003). Additionally, when IL-12 binds to its receptor (IL-12R), binding sites form for Tyk2 and Jak2 kinases. These kinases are able to recruit transcription factor STAT4, which accumulates at the promoter region of IL-2R. Consequently, the expression levels of IL-2R increase, leading to enhanced Th1 cell proliferation and Th1-cell related immune responses, especially cytotoxic reactions (Lee et al., 2012; Stark et al., 2013). Third, IL-12 can inhibit the formation of tumor microvessels by reducing the production of vascular endothelial growth factor (Cavallo et al., 2001). As mentioned above, the IL-12 p35 and IL-12 p40 subunits are encoded by the IL-12A and IL-12B genes, respectively (Croxford et al., 2014). Consequently, IL-12A and IL-12B genetic polymorphisms, which may affect the expression levels and normal functioning of IL-12, are thought to be implicated in the occurrence and development of various types of cancer (Yuzhalin and Kutikhin, 2012). Among these, IL-12A rs568408, IL-12B rs2243115 and IL-12B rs3212227 polymorphisms were the three most investigated sites. IL-12A rs568408 and IL-12B rs3212227 are located in the 3’-untranslated region (3’UTR). These two polymorphisms may disrupt exonic splicing and influence the production level of IL-12 (Chen et al., 2009; Liu et al., 2011). IL-12A rs2243115, however, is situated in the 5’UTR, and its functional significance has not been reported. Despite these potential molecular mechanisms, the results of relevant studies remain conflicting. Therefore, we conducted the present study in an attempt to obtain a more conclusive result.

The results of the current meta-analysis support the idea that the IL-12A rs568408 and IL-12B rs3212227 polymorphisms significantly correlate with cancer risk in the Asian ethnicity, especially when the PCR-RFLP genotype method is used. In addition, the A allele of the IL-12A rs568408 variant and the C allele of theIL-12B rs3212227 variant contribute to the increased risk of cancer. However, no associations were detected between the IL-12A 2243115 variant and cancer risk. It is worth noting that Zhou et al. (2012) performed a meta-analysis for these three IL-12 polymorphisms in 2012. Compared with that study, similar results were found for the IL-12A 2243115 and IL-12B rs3212227 polymorphisms in the present meta-analysis, whereas the results for IL-12A rs568408 were opposite. Since the sample size of our analysis was much larger than the previous one (22,670 subjects versus 13,875 subjects), our findings should be more reliable. Further subgroup analyses yielded similar positive results for the IL-12A rs568408 and IL-12B rs3212227 polymorphisms in Asians. However, no significant associations were found in Caucasians. In addition, the IL-12B rs3212227 polymorphism was found to be implicated in nasopharyngeal carcinoma and cervical cancer but not in hepatocellular carcinoma, colorectal cancer, brain tumor, skin cancer, breast cancer, esophageal cancer or gastric cancer. The between-study heterogeneity remained significant for the IL-12A rs568408 and IL-12B rs3212227 polymorphisms in different ethnicities, TaqMan subgroups and cancer subgroups, suggesting that the correlations between the IL-12 genetic polymorphisms and cancer risk may also be modulated by other unevaluated factors.

Several limitations of our study should be noted. First, the number of studies investigating the associations between the IL-12A rs568408 and rs2243115 polymorphisms and cancer risk is still limited, and the sample sizes of several of the included studies were not sufficient, which precluded us from drawing definite conclusions. Second, our results were based on unadjusted estimates, because the majority of the included studies failed to report baseline characteristics of the individuals, such as age, sex, smoking status and eating habits, and the lack of analyses adjusted for these potential confounding factors might affect the reliability of our results. Third, although funnel plots revealed no apparent publication bias, we still could not eliminate the possibility of publication bias, because only published studies were included. Fourth, all included studies were published in English or Chinese; therefore, some qualified articles in other languages may have been missed. Fifth, the relationship between a certain gene polymorphism and cancer risk could be affected by gene-gene or gene-environment interactions. It is possible that a particular polymorphism may be associated with cancer susceptibility, but, due to interactions with multiple genetic and environmental factors, the association would no longer be observed. Given these limitations, we should interpret the current results with caution.

In conclusion, the present meta-analysis of 31 studies demonstrated that the IL-12A rs568408 and IL-12B rs3212227 polymorphisms serve as promising candidate biomarkers of cancer susceptibility in the Asian population. Further studies with larger sample sizes from different populations are warranted to confirm our results. Moreover, since interleukins play a crucial role in regulating anti-tumor immune responses, future investigations are needed to explore the potential roles of other interleukin gene polymorphisms in cancer pathogenesis.

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