Quantitative assessment of lncRNA H19 polymorphisms and cancer risk: a meta-analysis based on 48,166 subjects

Abstract

Recently, numerous studies have been performed to detect the association between H19 polymorphisms and cancer susceptibility. However, their results were inconsistent and controversial. So, we carried out a meta-analysis aiming to define the association exactly. Eligible studies were collected using PubMed and Embase databases up to March 31, 2019. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the strength of association. Sensitivity analysis and publication bias were established to evaluate the robustness of our results. Totally, 60 studies involving 20763 cases and 27403 patients were analysed in our meta-analysis. Our results indicated that H19 rs217727 C > T polymorphism was significantly associated with increased cancer risk. In subgroup analysis, similarly increased risk could be found in gastrointestinal group for rs2839698 and protective effect was observed in rs2107425 polymorphism for cancer development in Caucasian population. Trial sequential analysis strengthened findings of our meta-analysis that cumulative evidence was adequate. In summary, our meta-analysis supported that H19 polymorphisms may be associated with cancer susceptibility.

Keywords:

• H19
• polymorphism
• cancer risk
• meta-analysis
• trial sequential analysis

Introduction

Cancer is one of the leading causes of death in economically more or less developed countries. It is estimated that, in 2019, 1.7 million newly appeared cancer cases and 0.6 million cancer deaths may occur only in the United States [1]. As the increasing incidence and mortality, more and more studies have been performed to detect risk factors of cancer [2,3]. Usage of tobacco, obesity and alcohol intake are thought to be well-known risk factors for cancer while other putative factors such as air pollution and viral infection also contribute to cancer development [4–8]. However, the underlying pathogenesis of cancer remains unclear by now.

In the past decades, many evidences indicated that genetic variations such as single-nucleotide polymorphisms (SNPs) play an important role in detecting cancer susceptibility [9,10]. H19, located at chromosome 11p15.5 and 2.3 kb in length, is a type of long non-coding RNA (lncRNA) and a paternally imprinted gene [11,12]. It has been shown to be over expressed in a variety of cancer cell lines and tumour types including breast, pancreas, bladder, stomach and so on [13–16]. Furthermore, abnormal lncRNA H19 expression may play a vital role in cancer initiation, progression and metastasis [17,18]. These clearly indicated that H19 polymorphisms could be biomarkers for evaluating cancer risk. Numerous studies have examined the association existed between H19 polymorphisms and tumours, such as breast cancer, bladder cancer, gastric cancer and oral squamous cell carcinoma, etc [19–22]. However, these studies reported inconsistent results. Therefore, we conducted a meta-analysis to examine the accurate correlation between H19 polymorphisms and cancer susceptibility.

Material and method

The present systematic review and meta-analysis was incorporated the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [23]. The PRISMA checklist including the page number for each item can be found in Additional file 1.

Literature search strategy

A systematic literature search was performed using PubMed and Embase databases to detect all relevant publications examining the relationship between H19 polymorphisms and risk of cancer published up to 31 March 2019. The search was performed using the following terms: “polymorphism/genotype/SNP/allele/variant” and “H19” and “cancer/tumor/carcinoma/neoplasm”. In addition, the references in retrieved articles were also reviewed for possible inclusion.

Inclusion and exclusion criteria

All the selected studies included in the current meta-analysis were based on the following criteria: (1) case-control studies; (2) studies published in English; (3) included available data on genotype distribution of H19 polymorphisms for pooled analysis. (4) For the duplicate studies using overlapping data, we included only the study with the larger sample size. Duplicated studies, letters, reviews and studies without usable data were excluded.

Data extraction

The relevant information was extracted by two investigators using a standardised form independently including: first author, publishing year, ethnicity, source of controls, genotyping methods, cancer types, sample size, allele and genotype frequencies, p value for Hardy-Weinberg equilibrium (HWE) in control group, the Newcastle-Ottawa Quality Assessment Scale (NOS) score. The quality of studies was evaluated using the NOS and scores greater than six were considered to be of high quality, otherwise, they were thought to be of low quality.

Statistical analysis

The strength of association between H19 polymorphisms and susceptibility of cancer was assessed by computing the crude odds ratios (ORs) with 95% confidence intervals (CIs). For the H19 rs217727 C > T polymorphism, the pooled ORs were calculated in an allelic model (T vs. C), recessive model (TT vs. CT + CC), dominant model (TT + CT vs. CC), homozygous model (TT vs. CC), heterozygous model (CT vs. CC). As for rs2839698, rs2107425 and rs3024270 polymorphisms, similar five genetic models were also assessed. The I² statistic was used to detect heterogeneity between studies. If I² was >50%, the random effect model would be used on account of significant between-study heterogeneity. Or else, the fixed-effect model would be applied. Subgroup analyses based on ethnicity, source of controls, genotyping methods, quality of studies and types of cancer were subsequently performed to obtain more specific results. Sensitivity analyses were carried out in order to evaluate the robustness of the results by omitting each study sequentially. Funnel plots and Egger’s regression test were used to test the publication bias. Statistical analyses were conducted using STATA software 12.0 (StataCorp LP, College Station, TX). p value <.05 was considered statistically significant.

Trial sequential analysis (TSA)

Although meta-analysis is thought to be suitable to evaluate the pooled effect of genetic association studies, it may lead to random and systematic errors occasionally. In order to reduce the errors, a new statistical analysis method called TSA has been introduced, which calculates the required information size (RIS) and estimates the power of results [24]. Here, we performed this TSA at a level of 5% alpha risk, 20% beta risk, 20% relative risk reduction (RRR) and a two-sided boundary type. If the Z-curve crosses the monitoring boundaries or the RIS line, robust evidence might be confirmed. Otherwise, more studies are needed.

Results

Characteristics of studies

As shown in Figure 1, 99 relevant publications with 4 SNPs on H19 were retrieved in this research. First, 43 duplicated articles and 19 obviously irrelevant articles were removed by reviewing title and abstract. Then, we excluded totally 11 articles including 7 letters, reference abstracts and reviews and 4 articles which were not focussing on cancer susceptibility. Finally, a total of 26 eligible articles (60 studies) published from 2008 to 2019 were recruited in this meta-analysis, including 19 studies on rs217727 C > T polymorphism [19–22,25–39], 13 studies on rs2839698 G > A polymorphism [19–21,26,27,30,31,33,34,36,38,40,41], 21 studies on rs2107425 polymorphism [27,28,41–46] and 7 studies on rs3024270 polymorphism [20,33,34,36,38–40]. The detailed characteristics and genotype distributions of included studies are summarised in Table 1.

Figure 1. Flow diagram of the study selection process.

Table 1. Characteristics of case-control studies on H19 polymorphisms and cancer risk included in the meta-analysis.

					Genotype distribution
First author	Year	Ethnicity	Case	Control	Case			Control			Genotyping method	Source of control	Age and sex matched	Type of cancer	P for HWE^a	NOS
rs217727 C > T					TT	CT	CC	TT	CT	CC
Safari, M. R.	2019	Asian	111	130	2	30	79	12	54	64	4P-ARMS-PCR	Hospital	Matched	Breast cancer	0.452	6
Hu C	2019	Asian	393	812	43	164	186	86	342	382	TaqMan	Hospital	Matched	Neuroblastoma	0.000	7
Abdollahzadeh, S	2018	Asian	150	100	5	29	116	0	14	86	RFLP	Hospital	Matched	Breast cancer	0.288	7
Yuan ZY	2018	Asian	444	984	51	194	186	73	423	488	MassARRAY	Population	Matched	OSCC	0.151	8
Yin ZH	2018	Asian	556	395	88	264	204	58	172	165	Illumina	Hospital	Matched	Lung cancer	0.232	7
Li Z	2018	Asian	200	200	9	140	51	26	90	84	TaqMan	Hospital	Matched	Bladder cancer	0.806	7
Li LL	2018	Asian	555	618	95	250	210	67	305	246	TaqMan	Hospital	Matched	Lung cancer	0.054	7
Cui P	2018	Asian	1492	1677	185	692	611	217	773	685	TaqMan	Population	Matched	Breast cancer	0.963	8
Shekoufeh, H	2017	Asian	230	240	27	132	71	2	113	125	RFLP	Population	Matched	Breast cancer	0.800	8
Lin YX	2017	Asian	1005	1020	131	471	403	105	450	465	Plex SNP scan	Hospital	Matched	Breast cancer	0.302	7
Hu PH	2017	Asian	416	416	83	200	133	92	196	128	TaqMan	NA	Matched	Pancreatic cancer	0.121	7
He TD	2017	Asian	193	383	12	102	79	23	165	195	TaqMan	Hospital	Matched	Osteosarcoma	0.342	7
Guo QY	2017	Asian	362	741	80	181	101	137	348	255	BeadChip	Population	Matched	OSCC	0.052	8
Xia ZJ	2016	Asian	464	467	148	156	160	116	212	139	RFLP	Population	Matched	Breast cancer	0.959	8
Li SW	2016	Asian	1147	1203	153	514	480	177	570	456	TaqMan	Hospital	Matched	Colorectal cancer	0.765	7
Jin TB	2016	Asian	247	285	26	103	117	16	99	169	MassARRAY	Hospital	Matched	Cervical cancer	0.074	7
Hua QH	2016	Asian	1046	1394	148	467	431	156	665	573	TaqMan	Hospital	Matched	Bladder cancer	0.296	7
Yang C	2015	Asian	500	500	88	252	160	63	244	193	TaqMan	Hospital	Matched	Gastric cancer	0.900	7
Verhaegh, G. W.	2008	Caucasian	177	204	4	59	114	9	80	115	RFLP	Population	Matched	Bladder cancer	0.470	8
rs2839698 G > A					AA	AG	GG	AA	AG	GG
Safari, M. R.	2019	Asian	111	130	39	57	15	22	55	53	4P-ARMS-PCR	hospital	Matched	Breast cancer	0.241	6
Hu C	2019	Asian	393	812	39	175	179	72	373	365	TaqMan	hospital	Matched	Neuroblastoma	0.090	7
Yang ML	2018	Asian	472	472	40	211	215	32	185	245	KASP	hospital	Matched	Hepatocellular cancer	0.714	7
Cui P	2018	Asian	1492	1677	122	568	801	129	673	875	TaqMan	population	Matched	Breast cancer	0.979	8
Shekoufeh, H	2017	Asian	230	240	166	64	0	222	18	0	RFLP	population	Matched	Breast cancer	0.546	8
Lin YX	2017	Asian	1005	1020	113	440	452	104	432	484	Plex SNP scan	hospital	Matched	Breast cancer	0.102	7
He TD	2017	Asian	193	383	12	98	83	30	175	178	Taqman	hospital	Matched	Osteosarcoma	0.146	7
Guo QY	2017	Asian	362	741	58	171	133	120	377	244	BeadChip	population	Matched	OSCC	0.202	8
Li SW	2016	Asian	1147	1203	102	462	583	75	462	666	Taqman	hospital	Matched	Colorectal cancer	0.666	7
Hua QH	2016	Asian	1049	1397	79	418	552	103	565	729	Taqman	hospital	Matched	Bladder cancer	0.651	7
Gong WJ	2016	Asian	496	206	39	220	237	27	80	99	MassARRAY	hospital	NA	Lung cancer	0.098	6
Yang C	2015	Asian	500	500	55	195	250	38	178	284	Taqman	hospital	Matched	Gastric cancer	0.175	7
Verhaegh, G. W.	2008	Caucasian	177	204	49	74	54	43	109	52	RFLP	population	Matched	Bladder cancer	0.313	8
rs3024270 G > C					CC	CG	GG	CC	CG	GG
Hu C	2019	Asian	393	812	99	203	91	213	424	173	TaqMan	Hospital	Matched	Neuroblastoma	0.159	7
Yang ML	2018	Asian	472	472	95	225	151	81	215	170	KASP	Hospital	Matched	Hepatocellular cancer	0.361	7
Li Z	2018	Asian	200	200	16	101	83	22	97	81	TaqMan	Hospital	Matched	Bladder cancer	0.377	7
He TD	2017	Asian	193	383	17	91	85	31	179	173	Taqman	Hospital	Matched	Osteosarcoma	0.101	7
Guo QY	2017	Asian	362	741	75	183	104	145	350	245	BeadChip	Population	Matched	OSCC	0.321	8
Li SW	2016	Asian	1147	1203	385	527	235	420	582	201	Taqman	Hospital	Matched	Colorectal cancer	0.979	7
Hua QH	2016	Asian	1047	1395	174	527	346	260	688	447	Taqman	Hospital	Matched	Bladder cancer	0.869	7
rs2107425 C > T					TT	CT	CC	TT	CT	CC
Yin ZH	2018	Asian	556	395	129	266	161	70	185	140	Illumina	Hospital	Matched	Lung cancer	0.513	7
Gong WJ	2016	Asian	479	203	63	235	181	28	96	79	MassARRAY	Hospital	Matched	Lung cancer	0.892	6
Butt, S.	2012	Caucasian	728	1448	68	250	361	145	573	637	MassArray	Population	Matched	Breast cancer	0.342	8
Barnholtz-Sloan, J. S.-1	2010	African	742	658	186	390	161	149	339	170	Illumina GoldenGate assay	Population	Matched	Breast cancer	0.420	8
Barnholtz-Sloan, J. S.-2	2010	Caucasian	1230	1118	105	516	604	119	478	521	Illumina GoldenGate assay	Population	Matched	Breast cancer	0.549	8
Song, H.-1	2009	Caucasian	NA	NA	54	246	270	61	279	322	Sequenom	Population	NA	Ovarian cancer	0.960	6
Song, H.-2	2009	Caucasian	NA	NA	67	208	254	79	293	342	Taqman	Population	NA	Ovarian cancer	0.178	6
Song, H.-3	2009	Caucasian	NA	NA	18	68	61	33	168	143	Taqman	Population	NA	Ovarian cancer	0.103	6
Song, H.-4	2009	Caucasian	NA	NA	5	24	41	13	63	81	Taqman	Population	NA	Ovarian cancer	0.879	6
Song, H.-5	2009	Caucasian	NA	NA	32	125	126	61	271	301	Taqman	Population	NA	Ovarian cancer	1.000	6
Song, H.-6	2009	Caucasian	NA	NA	47	157	229	117	518	555	Taqman	Population	NA	Ovarian cancer	0.809	6
Song, H.-7	2009	Caucasian	NA	NA	27	139	146	43	173	207	Taqman	Hospital	NA	Ovarian cancer	0.443	5
Song, H.-8	2009	Caucasian	NA	NA	60	252	293	76	303	335	Taqman	Population	NA	Ovarian cancer	0.545	6
Song, H.-9	2009	Caucasian	NA	NA	40	106	100	70	281	247	Taqman	Population	NA	Ovarian cancer	0.46	6
Song, H.-10	2009	Caucasian	NA	NA	77	361	496	124	535	564	Taqman	Population	NA	Ovarian cancer	0.862	6
Song, H.-11	2009	Caucasian	NA	NA	30	105	147	32	160	223	Taqman	Population	NA	Ovarian cancer	0.659	6
Song, H.-12	2009	Caucasian	NA	NA	26	118	114	48	221	270	Taqman	Population	NA	Ovarian cancer	0.773	6
Song, H.-13	2009	Caucasian	NA	NA	45	171	198	54	224	284	Taqman	Population	NA	Ovarian cancer	0.314	6
Quaye, L.	2009	Caucasian	1460	2463	149	544	767	247	1098	1118	Taqman	Population	NA	Ovarian cancer	0.345	8
Bhatti, P.	2008	Caucasian	859	1083	432		392	571		502	Sequenom	Population	Matched	Breast cancer	NA	7
Verhaegh, G. W.	2008	Caucasian	177	204	20	65	92	19	96	89	RFLP	Population	Matched	Bladder cancer	0.340	8

HWE: Hardy-Weinberg Equilibrium; NA: not available; 4 P-ARMS-PCR: tetra primer-amplification refractory mutation system-PCR; RFLP: Restriction Fragment Length Polymorphism; NOS: Newcastle-Ottawa Quality Assessment Scale.

^aHWE in control.

Quantitative analysis

Meta-analysis for H19 rs217727 C > T polymorphism

A total of 19 studies with 9688 patients and 11,769 controls were qualified for including in this meta-analysis of association between H19 rs217727 C > T polymorphism and cancer risk. The pooled analysis indicated that rs217727 polymorphism was related to increased cancer risk in allelic, dominant and homozygous model (T vs. C: OR = 1.12, 95%CI = 1.02–1.23, p = .02; TC + TT vs. CC: OR = 1.15, 95%CI = 1.01–1.30, p = .04; TT vs. CC: OR = 1.24, 95%CI = 1.02–1.49, p = .03) (Figure 2, Table 2). In the further subgroup analysis by ethnicity, we detected a significant association of rs217727 with cancer susceptibility in Asian population (Table 2). Moreover, the stratified analysis by type of cancer showed that rs217727 was associated with oral squamous cell carcinoma in all five genetic models as well as lung cancer in allelic and homozygous models (Table 2). As for the sensitivity analysis, no change of the results could be observed when eliminating each study sequentially in allelic model, but not dominant or homozygous model (Figure 3). Visual inspection of funnel plots indicated no publication bias which was further confirmed by Egger’s test (T vs. C: p_egger = 0.35; TT vs. TC + CC: p_egger = 0.99; TC + TT vs. CC: p_egger = 0.22; TT vs. CC: p_egger= 0.60; TC vs. CC: p_egger = 0.25, Figure 4). In TSA, the cumulative Z-curve crossed the monitoring line in four models and futility boundary in recessive model which indicated that cumulative evidence was adequate and our results were reliable (T vs. C: RIS = 7524; TT vs. TC + CC: RIS = 22,304; TT + CT vs. CC: RIS = 3125; TT vs. CC: RIS = 11,573; CT vs. CC: RIS = 3488, Figure 5).

Figure 2. Calculated OR and 95% CIs for the associations between H19 rs217727 polymorphism and cancer risk in overall populations. A for the allele contrast model; B for the dominant model; C for the homozygous model.

Figure 3. Sensitivity analysis via deletion of each individual study reflects the relative influence of each individual dataset on the pooled ORs of H19 rs217727 polymorphism. A for the allele contrast model; B for the dominant model; C for the homozygous model.

Figure 4. Funnel plot analysis to detect publication bias of H19 rs217727 polymorphism. A for the allele contrast model; B for the dominant model; C for the homozygous model.

Figure 5. Trial sequential analyses for H19 rs217727 polymorphism and cancer risk. A for the allele contrast model; B for the dominant model; C for the homozygous model.

Table 2. Summary ORs and 95% CIs of H19 rs217727 polymorphism and cancer risk.

Locus	N^a	Allele (T vs. C)		Recessive (TT vs. TC + CC)		Dominant (TC + TT vs. CC)		Homozygote (TT vs. CC)		Heterozygote (TC vs. CC)
		OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)
Total	19	1.12 (1.02 − 1.23) 0.016	78.6	1.18 (1.00 − 1.40) 0.057	70.3	1.15 (1.01 − 1.30) 0.037	78.3	1.24 (1.02 − 1.49) 0.029	72.1	1.11 (0.98 − 1.26) 0.115	76.0
Source of controls
Population	6	1.17 (0.96 − 1.43) 0.123	85.7	1.36 (0.96 − 1.64) 0.088	78.6	1.16 (0.88 − 1.51) 0.293	84.6	1.42 (0.92 − 2.18) 0.111	82.5	1.07 (0.82 − 1.40) 0.598	82.2
Hospital	12	1.12 (0.99 − 1.25) 0.064	76.2	1.14 (0.92 − 1.42) 0.228	68.0	1.16 (0.99 − 1.37) 0.070	77.9	1.22 (0.97 − 1.54) 0.086	67.2	1.14 (0.97 − 1.35) 0.120	76.3
NA	1	0.93 (0.77 − 1.13) 0.490	NA	0.89 (0.63 − 1.23) 0.444	NA	0.95 (0.71 − 1.27) 0.709	NA	0.87 (0.59 − 1.27) 0.470	NA	0.98 (0.72 − 1.34) 0.910	NA
Method
TaqMan	9	1.06 (0.97 − 1.15) 0.191	58.8	1.06 (0.86 − 1.30) 0.592	70.8	1.10 (0.96 − 1.25) 0.190	68.8	1.10 (0.90 − 1.34) 0.360	62.6	1.09 (0.94 − 1.27) 0.259	72.5
Others	10	1.17 (0.99 − 1.40) 0.071	82.7	1.38 (1.04 − 1.82) 0.025	65.7	1.17 (0.93 − 1.48) 0.175	82.0	1.45 (1.03 − 2.06) 0.035	74.1	1.11 (0.89 − 1.38) 0.373	78.6
Type of cancer
Breast	6	1.13 (0.87 − 1.46) 0.351	89.5	1.33 (0.85 − 2.08) 0.211	80.8	1.09 (0.79 − 1.52) 0.594	88.1	1.33 (0.79 − 2.25) 0.284	83.8	1.02 (0.74 − 1.40) 0.908	85.5
Bladder	3	1.01 (0.82 − 1.25) 0.923	56.8	0.63 (0.22 − 1.80) 0.390	85.1	1.13 (0.68 − 1.88) 0.629	85.9	0.80 (0.40 − 1.62) 0.538	64.0	1.20 (0.64 − 2.23) 0.574	90.1
OSCC	2	1.26 (1.11 − 1.42) 0.000	0	1.40 (1.10 − 1.78) 0.006	28.1	1.32 (1.11 − 1.58) 0.002	0	1.62 (1.24 − 2.12) 0.000	0	1.25 (1.04 − 1.50) 0.020	0
Lung	2	1.17 (1.03 − 1.33) 0.014	0	1.37 (0.89 − 2.11) 0.155	67.6	1.15 (0.97 − 1.37) 0.117	0	1.44 (1.11 − 1.88) 0.007	19.4	1.08 (0.89 − 1.30) 0.447	44.4
Gastrointestinal	2	1.06 (0.75 − 1.50) 0.747	90.5	1.13 (0.69 − 1.86) 0.630	82.1	1.05 (0.68 − 1.64) 0.822	88.0	1.16 (0.57 − 2.34) 0.682	89.3	1.02 (0.71 − 1.47) 0.927	80.4
Others	4	1.14 (0.93 − 1.41) 0.217	71.7	1.04 (0.83 − 1.30) 0.721	37.3	1.20 (0.93 − 1.57) 0.168	67.0	1.19 (0.81 − 1.76) 0.379	55.4	1.19 (0.94 − 1.52) 0.152	56.2
Ethnicity
Asian	18	1.14 (1.04 − 1.25) 0.006	78.5	1.20 (1.01 − 1.42) 0.039	71.0	1.17 (1.03 − 1.33) 0.018	78.5	1.26 (1.04 − 1.52) 0.017	72.6	1.13 (0.99 − 1.29) 0.071	76.4
Caucasian	1	0.74 (0.52 − 1.05) 0.089	NA	0.50 (0.15 − 1.66) 0.257	NA	0.71 (0.47 − 1.08) 0.111	NA	0.45 (0.13 − 1.50) 0.192	NA	0.74 (0.49 − 1.14) 0.172	NA

NA: not available; OSCC: oral squamous cell carcinoma.

^a Numbers of comparisons.

Meta-analysis for H19 rs2839698 G > a polymorphism

Thirteen eligible studies with 7627 patients and 8985 controls reported the association between H19 rs2839698 G > A polymorphism and cancer susceptibility. As shown in Figure 6, the pooled results did not support a correlation between them (A vs. G: OR = 1.05, 95%CI = 0.93–1.19, p = .43; AA vs. AG + GG: OR = 1.04, 95%CI = 0.82–1.32, p = .74; AG + AA vs. GG: OR = 1.10, 95%CI = 0.97–1.24, p = .14; AA vs. GG: OR = 1.20, 95%CI = 0.97–1.48, p = .09; AG vs. GG: OR = 1.07, 95%CI = 0.95–1.21, p = .25, Table 3). However, in the subgroup analysis, we detected that rs2839698 G > A polymorphism was significantly related to the gastrointestinal cancer risk under all five genetic models. In addition, some significant associations were observed in subgroup analyses by genotyping method and source of controls (Table 3). Sensitivity analysis demonstrated that dropping any one of the studies, no obvious alteration of pooled OR or its 95%CI could be observed (Supplementary Figure S1). Potential publication bias was evaluated with funnel plot and Egger’s test in the current study and no obvious publication bias could be found (A vs. G: p_egger=0.78; AA vs. AG + GG: p_egger= 0.50; AG + AA vs. GG: p_egger= 0.20; AA vs. GG: p_egger= 0.41; AG vs. GG: p_egger= 0.22, Supplementary Figure S2). In TSA, the results indicated that enough studies were applied in this meta-analysis to come to a credible result (Supplementary Figure S3).

Figure 6. Calculated OR and 95% CIs for the associations between H19 rs2839698 polymorphism and cancer risk in overall populations. A for the allele contrast model; B for the recessive model; C for the dominant model; D for the homozygous model; E for the heterozygous model.

Table 3. Summary ORs and 95% CIs of H19 rs2839698 polymorphism and cancer risk.

Locus	N^a	Allele (A vs. G)		Recessive (AA vs. AG + GG)		Dominant (AG + AA vs. GG)		Homozygote (AA vs. GG)		Heterozygote (AG vs. GG)
		OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)
Total	13	1.05 (0.93 − 1.19) 0.428	82.3	1.04 (0.82 − 1.32) 0.742	78.8	1.10 (0.97 − 1.24) 0.139	69.2	1.20 (0.97 − 1.48) 0.092	67.7	1.07 (0.95 − 1.21) 0.254	62.5
Source of controls
Population	4	0.77 (0.56 − 1.06) 0.114	88.1	0.77 (0.42 − 1.45) 0.422	90.5	0.91 (0.81 − 1.02) 0.111	0	1.00 (0.81 − 1.22) 0.972	0	0.88 (0.78 − 1.00) 0.050	0
Hospital	9	1.15 (1.02 − 1.30) 0.023	74.3	1.18 (0.95 − 1.47) 0.143	60.5	1.18 (1.03 − 1.37) 0.020	68.1	1.30 (0.98 − 1.72) 0.073	73.6	1.15 (1.02 − 1.31) 0.026	54.7
Method
Taqman	6	1.07 (0.98 − 1.18) 0.149	54.1	1.17 (1.01 − 1.34) 0.032	15.2	1.05 (0.97 − 1.13) 0.211	46.7	1.18 (1.02 − 1.37) 0.026	35.2	1.03 (0.95 − 1.11) 0.541	28.1
Others	7	1.00 (0.76 − 1.31) 0.972	89.4	0.94 (0.59 − 1.51) 0.798	87.6	1.17 (0.88 − 1.56) 0.267	80.7	1.27 (0.82 − 1.97) 0.293	81.0	1.13 (0.86 − 1.49) 0.375	77.1
Type of cancer
Breast	4	0.96 (0.63 − 1.45) 0.839	94.3	0.91 (0.45 − 1.83) 0.789	92.7	1.40 (0.91 − 2.15) 0.131	90.7	1.72 (0.88 − 3.34) 0.111	89.3	1.28 (0.86 − 1.90) 0.220	87.3
Gastrointestinal	2	1.23 (1.10 − 1.37) 0.000	0	1.48 (1.15 − 1.90) 0.002	0	1.23 (1.08 − 1.41) 0.003	0	1.58 (1.22 − 2.05) 0.001	0	1.17 (1.02 − 1.35) 0.030	0
Bladder	2	1.00 (0.89 − 1.12) 0.807	0	1.13 (0.88 − 1.46) 0.346	28.0	0.96 (0.82 − 1.11) 0.570	0	1.03 (0.79 − 1.36) 0.819	0	0.85 (0.59 − 1.24) 0.405	58.0
Others	5	1.02 (0.93 − 1.11) 0.749	30.6	0.96 (0.79 − 1.18) 0.716	35.2	1.04 (0.92 − 1.18) 0.513	35.6	0.96 (0.78 − 1.19) 0.728	31.9	1.06 (0.93 − 1.20) 0.421	37.6
Ethnicity
Asian	12	1.05 (0.92 − 1.20) 0.447	83.7	1.01 (0.79 − 1.31) 0.914	80.0	1.12 (0.98 − 1.27) 0.086	70.4	1.21 (0.97 − 1.52) 0.099	70.6	1.09 (0.97 − 1.23) 0.136	61.0
Caucasian	1	1.03 (0.78 − 1.37) 0.827	NA	1.43 (0.90 − 2.30) 0.134	NA	0.78 (0.501.22) 0.276	NA	1.10 (0.63 − 1.92) 0.745	NA	0.65 (0.40 − 1.06) 0.084	NA

NA: not available.

^a Numbers of comparisons.

Meta-analysis for H19 rs2107425 C > T polymorphism

The association between H19 rs2107425 C > T polymorphism and cancer susceptibility was investigated in 21 studies with 11,314 patients and 15,746 controls which included in this meta-analysis. No significant correlation was identified between rs2107425 C > T polymorphism and cancer risk in any genetic models (Supplementary Figure S4, Table 4). In the stratified analysis, the results of allelic and heterozygous models (C vs. T: OR = 0.94, 95%CI = 0.90–0.98, p < .01, CT vs. TT: OR = 0.91, 95%CI = 0.83–0.99, p = .03, Figure 7, Table 4) implicated protective effects on cancer risk in Caucasian-based subgroup analysis. Sensitivity analysis, which was performed by eliminating studies one by one to see whether the overall results were altered, suggested robustness of our results (Supplementary Figure S5). Besides, symmetric funnel plots revealed no evidence of publication bias and further Egger’s test showed similar results in two genetic models except for allelic, dominant and heterozygous model (C vs. T: p_egger= 0.04; CC vs. CT + TT: p_egger =0.30; CT + CC vs. TT: p_egger = 0.02; CC vs. TT: p_egger =0.12; CT vs. TT: p_egger = 0.03, Supplementary Figure S6). TSA in all five genetic models indicated that cumulative evidences were enough and our results were reliable and conclusive (Supplementary Figure S7).

Figure 7. Calculated OR and 95% CIs for the associations between H19 rs2107425 polymorphism and cancer risk in subgroup analysis. A for the allele contrast model; B for the heterozygous model.

Table 4. Summary ORs and 95% CIs of H19 rs2107425 polymorphism and cancer risk.

Locus	N^a	Allele (C vs. T)		Recessive (CC vs. CT + TT)		Dominant (CT + CC vs. TT)		Homozygote (CC vs. TT)		Heterozygote (CT vs. TT)
		OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)	OR (95%CI) P	I^2 (%)
Total	20	0.99 (0.93 − 1.06) 0.800	58.1	1.05 (0.96 − 1.13) 0.278	0	0.96 (0.89 − 1.05) 0.370	59.4	1.01 (0.92 − 1.10) 0.888	33.4	0.94 (0.86 − 1.03) 0.190	57.5
Source of controls
Population	18	0.97 (0.91 − 1.04) 0.417	55.0	1.03 (0.95 − 1.13) 0.449	0	0.94 (0.86 − 1.02) 0.127	57.1	0.98 (0.90 − 1.08) 0.692	27.1	0.91 (0.83 − 1.00) 0.053	56.0
Hospital	3	1.12 (0.99 − 1.27) 0.067	40.4	1.14 (0.90 − 1.44) 0.281	44.1	1.17 (0.99 − 1.39) 0.072	0	1.16 (0.79 − 1.72) 0.443	51.9	1.16 (0.97 − 1.39) 0.109	0
Method
Taqman	13	0.98 (0.91 − 1.06) 0.683	52.9	1.06 (0.95 − 1.17) 0.328	0	0.95 (0.85 − 1.05) 0.321	58.1	0.99 (0.89 − 1.11) 0.873	15.4	0.92 (0.83 − 1.03) 0.151	55.4
Others	7	1.00 (0.89 − 1.13) 0.961	68.1	1.03 (0.91 − 1.17) 0.600	31.8	0.99 (0.86 − 1.13) 0.855	62.0	1.05 (0.84 − 1.31) 0.692	57.7	0.98 (0.84 − 1.14) 0.766	60.4
Type of cancer
Ovarian cancer	14	0.99 (0.92 − 1.06) 0.725	50.8	1.05 (0.95 − 1.17) 0.325	0	0.96 (0.86 − 1.06) 0.370	57.3	1.00 (0.90 − 1.11) 0.933	8.9	0.93 (0.84 − 1.04) 0.190	55.3
Breast cancer	4	0.95 (0.80 − 1.13) 0.563	77.9	0.95 (0.76 − 1.20) 0.672	51.9	0.95 (0.80 − 1.12) 0.509	67.3	0.94 (0.67 − 1.31) 0.712	73.0	0.94 (0.75 − 1.19) 0.614	73.5
Lung cancer	2	1.15 (0.92 − 1.44) 0.226	56.0	1.24 (0.95 − 1.63) 0.114	43.7	1.22 (0.98 − 1.51) 0.070	21.0	1.30 (0.81 − 2.09) 0.280	56.1	1.17 (0.94 − 1.47) 0.168	0
Bladder cancer	1	0.86 (0.63 − 1.17) 0.345	NA	1.24 (0.64 − 2.41) 0.524	NA	0.72 (0.48 − 1.07) 0.104	NA	1.02 (0.51 − 2.04) 0.959	NA	0.66 (0.43 − 1.01) 0.053	NA
Ethnicity
Caucasian	18	0.94 (0.90 − 0.98) 0.002	46.2	1.01 (0.92 − 1.11) 0.802	0	0.93 (0.86 − 1.00) 0.057	50.9	0.95 (0.87 − 1.05) 0.300	11.8	0.91 (0.83 − 0.99) 0.029	51.7
Asian	2	1.15 (0.92 − 1.44) 0.226	56.0	1.24 (0.95 − 1.63) 0.114	43.7	1.22 (0.98 − 1.51) 0.070	21.0	1.30 (0.81 − 2.09) 0.280	56.1	1.17 (0.94 − 1.47) 0.168	0
African	1	1.14 (0.98 − 1.32) 0.083	NA	1.15 (0.90 − 1.48) 0.258	NA	1.25 (0.97 − 1.60) 0.081	NA	1.32 (0.97 − 1.79) 0.076	NA	1.22 (0.94 − 1.58) 0.143	NA
Quality
High quality	19	0.99 (0.93 − 1.06) 0.795	60.1	1.05 (0.97 − 1.14) 0.225	1.0	0.96 (0.88 − 1.04) 0.323	60.4	1.01 (0.93 − 1.10) 0.829	36.4	0.93 (0.85 − 1.02) 0.139	57.6

NA: not available.

^a Numbers of comparisons.

Meta-analysis for H19 rs3024270 G > C polymorphism

As shown in Figure 8 and Table 5, a pooled analysis of 7 eligible studies involving 3814 patients and 5206 controls disclosed no association between H19 rs3024270 G > C polymorphism and cancer risk. Not only that, further subgroup analyses by the source of control and type of cancer also revealed similar results (Table 5). The pooled ORs exhibited no change in the following sensitivity analyses (Supplementary Figure S8) and no publication bias could be found in both funnel plots (Supplementary Figure S9) and Egger’s test (C vs. G: p_egger = 0.28; CC vs. CG + GG: p_egger = 0.69；CG + CC vs. GG：p_egger = 0.43； CC vs. GG： p_egger = 0.43； CG vs. GG：p_egger = 0.47). Although Z-curve of TSA did not cross the monitoring boundary, the number of participants in our study was larger than required information size (C vs. G: RIS = 1359; CC vs. CG + GG: RIS = 2490；CG + CC vs. GG: RIS = 668； CC vs. GG: RIS = 1655； CG vs. GG: RIS = 754, Supplementary Figure S10). To sum up, enough evidences were established in our meta-analysis.

Figure 8. Calculated OR and 95% CIs for the associations between H19 rs3024270 polymorphism and cancer risk in overall populations. A for the allele contrast model; B for the recessive model; C for the dominant model; D for the homozygous model; E for the heterozygous model.

Table 5. Summary ORs and 95% CIs of H19 rs3024270 polymorphism and cancer risk.

Locus	N^a	Allele (C vs. G)		Recessive (CC vs. CG + GG)		Dominant (CG + CC vs. GG)		Homozygote (CC vs. GG)		Heterozygote (CG vs. GG)
		OR (95%CI) P	I ^2(%)	OR (95%CI) P	I ^2(%)	OR (95%CI) P	I ^2(%)	OR (95%CI) P	I ^2(%)	OR (95%CI) P	I ^2(%)
Total	7	0.97 (0.92 − 1.04) 0.400	29.1	0.96 (0.86 − 1.06) 0.409	0	0.97 (0.89 − 1.07) 0.578	41.6	0.93 (0.82 − 1.05) 0.227	33.1	0.98 (0.89 − 1.09) 0.737	30.3
Source of controls
Hospital	6	0.96 (0.90 − 1.02) 0.182	15.2	0.95 (0.85 − 1.05) 0.305	0	0.94 (0.85 − 1.04) 0.252	30.0	0.89 (0.78 − 1.02) 0.092	22.4	0.95 (0.86 − 1.06) 0.379	16.4
Population	1	1.12 (0.94 − 1.34) 0.222	NA	1.07 (0.79 − 1.47) 0.661	NA	1.23 (0.93 − 1.62) 0.143	NA	1.22 (0.85 − 1.75) 0.283	NA	1.23 (0.92 − 1.65) 0.160	NA
Method
Taqman	5	0.93 (0.87 − 1.00) 0.044	0	0.92 (0.82 − 1.03) 0.142	0	0.90 (0.81 − 1.01) 0.070	0	0.84 (0.73 − 0.97) 0.017	0	0.92 (0.82 − 1.03) 0.163	0
Others	2	1.14 (1.00 − 1.29) 0.048	0	1.13 (0.90 − 1.42) 0.283	0	1.22 (1.01 − 1.48) 0.041	0	1.27 (0.98 − 1.64) 0.071	0	1.21 (0.98 − 1.48) 0.075	0
Type of cancer
Bladder	2	0.94 (0.84 − 1.04) 0.230	0	0.85 (0.70 − 1.04) 0.124	0	0.96 (0.82 − 1.12) 0.574	0	0.85 (0.68 − 1.06) 0.151	0	0.99 (0.84 − 1.17) 0.941	0
Others	5	0.99 (0.92 − 1.07) 0.846	47.8	1.00 (0.89 − 1.13) 0.968	0	1.01 (0.83 − 1.23) 0.935	60.8	0.96 (0.68 − 1.06) 0.622	49.1	1.00 (0.83 − 1.21) 0.987	53.3

NA: not available.

^a Numbers of comparisons.

Discussion

As a complex disease, cancer becomes a major threat towards worldwide public health [47]. However, the pathogenesis of cancer was still unclear. Although lncRNAs do not encode proteins, they play an important role in human transcription process, especially in cancer-related aspects [48]. Hence, abnormal lncRNA expression may lead to cancer occurrence and development. Large numbers of SNPs have been detected in lncRNAs and many studies focussing on the association between SNPs and cancer susceptibility were performed. H19 is the very first eukaryote lncRNA gene which was found in 1980s, even though it was identified as mRNA at that time [48]. In 2008, Verhaegh et al. reported association between H19 polymorphisms and bladder cancer firstly [27]. Since then, increasingly studies were performed to assess the relationship between H19 polymorphisms (rs217727, rs2839698, rs2107425 and rs3024270) and cancer risk.

As for the rs217727 polymorphism, Verhaegh et al. found no correlation between rs217727 and cancer risk of bladder cancer [27]. However, this result could not be repeated in the subsequent studies by Li and Hua et al., which reported an increased cancer risk for rs217727 [20,39]. Thus, in 2016, Chu et al. performed a meta-analysis involving 5 articles with 7110 subjects that indicated no correlation between rs217727 and overall cancer risk [49]. Compared with previous meta-analysis, we led to an opposite conclusion that rs217727 polymorphism was significantly associated with increased cancer susceptibility, especially in the Asian population. The discrepancy may be due to the different sample sizes. Our updated study was the largest and latest. We added another 14 recent studies including 14,347 subjects, three times as much as previous study [19,22,25,26,28–34,37–39]. More importantly, TSA, a new method applied in our pooled analysis, evaluated the required information size of this study which was adequate to come to a reliable conclusion.

How can we explain the association of H19 rs217727 with cancer risk? Growing evidence indicated that lncRNAs could interact with miRNAs directly, and the SNPs of lncRNA might change the structure of lncRNA at the miRNA binding sites and affect their interaction and function ultimately. As for H19, it has been reported to serve critical roles in carcinogenesis. Through its mediator miR-675, H19 can promote the growth of breast cancer by downregulating Cbl-b and c-Cbl [50]. Also, Kallen et al. found that H19 acts as a molecular sponge to antagonise let-7, a well-known tumour-suppressive microRNA [51]. However, no miRNA which combined with rs217727 variant was identified using lncRNASNP database. So, changing the binding site of miRNAs might not be the precise mechanism for rs217727 to associate with cancer risk. In addition, located in the exons in all transcripts of H19, rs217727 has great potential to alter H19's structure and expression level [52]. However, in the gastric cancer tissue, the rs217727 TT or CT genotype was found not to affect H19 mRNA expression compared with the CC genotype [21]. Thus, on the basis of current information, we speculate that rs217727 polymorphism might alter H19 mRNA conformation or translational efficiency, which may finally affect the function for H19. Further functional studies are required to verify our hypothesis.

Previous meta-analysis for rs2839698 G > A polymorphism included 5 publications with 6890 subjects showed marginally association between rs2839698 and cancer risk [49]. With regard to rs2107425 C > T polymorphism, 6 articles were applied in Chu’s meta-analysis, which reflected a protective effect on cancer development [49]. However, in our study, after further adding 8 more articles (9722 subjects), we came to a definitely different conclusion that rs2839698 was not associated with cancer susceptibility [19,26,30,31,33,34,38,40]. Similarly, no correlation was identified between rs2107425 and overall cancer risk, due to the addition of two recent publications and removal of the studies using overlapping data with smaller sample sizes [28,44–46]. Moreover, subsequent subgroup analyses indicated that A allele or AA genotype of rs2839698 was a potential genetic marker for gastrointestinal cancer, and that the C allele in rs2107425 might be related to decreased cancer risk in Caucasian population. Stratified analyses indicated that the differences in genetic backgrounds and cancer location might have resulted in the discrepancies among the individual studies. In addition, for the first time, we examined the association between rs3024270 G > C polymorphism and cancer susceptibility and found no correlation between them as well as in the subgroup analysis.

Like all meta-analyses, our study is not without limitation certainly. First, we only included articles published in English which might cause a language bias. Second, we mostly included the Asian population in the study of rs217727 and rs2839698 polymorphism while Caucasian population in the study of rs2107425. Therefore, patients of different ethnicities should be enrolled which would help the results suitable for more ethnicities. Third, in the sensitivity analysis for rs217727, we detected slight changes of results when omitted each study in two genetic models with positive results. These changes could be mainly accounted for the studies by Yuan ZY, Jin TB and Shekoufeh H. These data suggest that the pooled results are less stable and these conclusions should be considered cautiously. Additionally, publication bias could be detected in the study for rs2107425 in three models except for recessive and homozygous models. Hence, these results should be interpreted with caution. Fourth, significant heterogeneity could be observed in the analyses for rs217727 and rs2839698. Therefore, we performed sensitivity analysis and subgroup analysis to look for the source of heterogeneity. Subgroup analyses based on type of cancer may explain part of source of heterogeneity. Finally, owing to the lack of data, we could not detect potential gene-gene and gene-environmental interactions.

In conclusion, our meta-analysis provides evidence that rs217727 polymorphisms of H19 might contribute to overall cancer risk, rs2107425 polymorphism may have a protective effect on cancer development in Caucasian population, and rs2839698 polymorphism is associated to increased gastrointestinal cancer susceptibility whereas rs3024270 may have no impact. More well-designed studies involving participants of different ethnicities and larger sample size are needed to confirm our findings.

Disclosure statement

The authors declare that they have no conflicts of interest to disclose.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 81872044, 81400950] and Basis Research Foundation of Institutions of Higher Education of Liaoning Province of China [Grant No. LQNK201728].