Literature DB >> 29511035

The association of polymorphisms in lncRNA-H19 with hepatocellular cancer risk and prognosis.

Ming-Li Yang1, Zhe Huang2, Qian Wang1, Huan-Huan Chen1, Sai-Nan Ma1, Rong Wu1, Wei-Song Cai3.   

Abstract

Hepatocellular cancer (HCC) is one of the major causes of cancer-related mortality. Genetic polymorphisms may affect the susceptibility and clinical outcomes of cancers. We aim to manifest the association of single nucleotide polymorphisms (SNPs) of lncRNA-H19 gene with the risk and prognosis of HCC. A total of 944 samples composed of 472 HCC patients and 472 matched controls were included in the risk analysis and amongst them 350 HCC samples were investigated in the prognosis analysis. KASP method was conducted for the SNP genotyping. The TT + CT genotype of rs2839698 was found to be associated with a 1.32-fold increased HCC risk (P=0.037, 95% confidence interval (CI) = 1.02-1.70). In the stratified analysis, rs2839698 (odds ratio (OR) = 1.57, P=0.007, 95% CI = 1.13-2.18) and rs3024270 (OR = 1.71, P=0.019, 95% CI = 1.09-2.68) were found to show more obvious increased HCC risk in the age ≤60 subgroup. And we found that rs2839698 showed an increased HCC risk in the ever smoking subgroup. But in the male subgroup of rs2735971, it showed a decreased HCC risk. Furthermore, haplotype analysis showed that rs2735971-rs2839698-rs3024270 G-T-C significantly increased the risk of HCC (OR = 1.23, 95% CI = 1.01-1.51, P=0.043). Multilogistic analysis revealed no significant results of the interaction effects of the SNPs and environment factors. And in our study, rs2839698 showed a significant poor prognosis in the ever smoking subgroup (hazard rate (HR) = 5.19, 95% CI = 1.12-24.07, P=0.035). lncRNA-H19 rs2839698 SNP has the potential to be predictors for HCC risk and prognosis.
© 2018 The Author(s).

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Keywords:  Single nucleotide polymorphism; hepatocellular cancer; prognosis; risk

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Year:  2018        PMID: 29511035      PMCID: PMC6123070          DOI: 10.1042/BSR20171652

Source DB:  PubMed          Journal:  Biosci Rep        ISSN: 0144-8463            Impact factor:   3.840


Introduction

Hepatocellular cancer (HCC) is the major liver malignancy that attributes toward the second foremost cause of cancer-related mortality worldwide [1]. Individual hereditary and environmental factors proved to be associated with the incidence of HCC [2]. So far, there are many single nucleotide polymorphisms (SNPs) that have been reported to be related to HCC risk in some coding and non-coding genes and have manifested great significance for the selection of individuals who would benefit from the specific diagnostic and preventative measures [3]. However, few studies investigated the role of lncRNA polymorphisms as a precaution biomarker for HCC risk and prognosis. Furthermore, many studies have reported that the gene polymorphisms could serve as the predictor of the diagnosis and prognosis of cancers [4,5], suggesting a valuable application for the diagnosis and prognosis associated with polymorphisms. In human genome, there are approximately 5–10% sequences transcribed constantly, and only approximately 1% are protein-coding sequences while a large part of others are non-coding RNAs (ncRNAs) [6]. lncRNA, larger than 200 nts, is one of the most important members of the ncRNA family and has been identified as abnormally altered in the genes and differently expressed in tumors [7,8]. The lncRNA-H19, located on chromosome 11p15.5 [9], was reported to be one of the major genes in cancer [10]. Many studies have reported that H19 as an oncogene lncRNA in multiple cancers, such as, colorectal cancer [11], gastric cancer [12], breast cancer [13], bladder cancer [14], and so on. In addition, recent researches have proved that lncRNA-H19 plays important role in cancer initiation, progression, metastasis, and indicates poor prognosis and promotes tumor growth [11,15,16]. It is well accepted that lncRNA-H19 works importantly in the incidence and prognosis of cancers and SNPs in H19 can be used as a promising biomarker for cancers risk [17]. Recently, a meta-analysis for the association of H19 polymorphisms and cancer risk had published [18], but the interaction of H19 SNPs and environmental factors as well as the association of H19 SNPs and the cancer prognosis were not analyzed further. And there is still no investigation about the H19 polymorphism associated with both HCC risk and prognosis. Whether lncRNA-H19 polymorphisms play some roles in HCC and could be promising biomarkers for the risk and prognosis of HCC, it is still not clear. In the present study, we selected three potential functional SNPs in lncRNA-H19 gene according to the candidate gene association study strategy to explore the relationship between H19 polymorphism and HCC risk and prognosis. We aimed to manifest predictive biomarkers for risk and prognosis of HCC and provide the basic for the use of H19 gene polymorphisms as precautionary biomarkers of individuals and improve the comprehension of the etiology and disease development of HCC.

Materials and methods

Patients and study design

This research project was approved by the Ethical Committee of the Shengjing Hospital of the China Medical University and written informed consent was obtained. The present study was designed as two independent but related parts including risk research and prognosis research. In the risk study, a total of 944 participants were involved, including 472 HCC patients and 472 sex and age (±5) frequency-matched controls from the Shengjing Hospital of China Medical University from 2013 to 2015. The response rate for cases and controls are up to 90% or more. For the aim to manifest the relationship between lncRNA-H19 polymorphisms and overall survival in HCC patients, we conducted the research with the data of 350 HCC patients, whose information of death and survival was available for analysis. The HCC patients had pathologically confirmed HCC. Patients (i) with distant metastasis found preoperatively, (ii) who underwent preoperative radiotherapy or chemotherapy, or (iii) with incomplete pathological data entries were excluded from the prognosis analysis. Follow-up was completed by 10 July, 2017.

Polymorphisms’ sites selected

The studied polymorphisms of lncRNA-H19 were selected by the HapMap data [19]. TagSNPs were selected by Tagger via Haploview with the following criteria: pairwise tagging of HapMap population with r2 ≥ 0.8; a minor allele frequency (MAF) ≥5%; and Chinese Han Beijing (CHB) ethnicity. And we expanded 10 kbp both upstream and downstream of H19. Then, 17 SNPs were included as candidate SNPs (Supplementary Figure S1 and Materials), and we referred a published literature [17] and took the intersection as the considering promising aiming SNPs. Ultimately, there were three SNPs covering lncRNA-H19 gene selected to proceed our study which were rs2735971 (G→A), rs2839698 (C→T), rs3024270 (G→C).

Genotyping

Genomic DNA was extracted by the method of literature [20] and was diluted to working concentrations of 20 ng.μl−1 for genotyping. The genotyping assay was performed by Gene Company using KASP (Gene Company, Shanghai, China). The information of KASP primers was summarized in the Supplementary Table S1. Five percent of the whole samples were repeatedly genotyped, the concordance rate of the repeated cases performed 100% which suggested that the genotyping results were reliable.

Statistical analysis

χ2 test was used to compare the demographic characteristics of samples and ANOVA was conducted for age variability. Multivariate logistic regression with adjustments for age and gender was proceeded to calculate the association of the selected SNPs and HCC risk. SHEsis software was used to analyze the haplotype of the selected gene [21]. The analysis of polymorphisms and clinical parameters was performed by χ2 test. Univariate and multivariate survival analysis was conducted by the log-rank test and the Cox proportional hazards model. Statistical analysis was performed by using SPSS version 18.0 software (SPSS, Chicago, IL, U.S.A.) and P-value <0.05 was considered to be significantly statistical.

Results

The association of lncRNA-H19 SNPs with HCC risk

The demographic characteristics of HCC and controls are shown in Supplementary Table S2. In Table 1, it showed all the polymorphisms genotype distributions of both cases and controls, including three lncRNA-H19 SNPs (rs2735971, rs2839698, rs3024270) which were all conformed to Hardy–Weinberg equilibrium (HWE).
Table 1

The association of lncRNA gene SNPs and risk of HCC

GeneChr. pos.SNPaLoc.GenotypeControls (%)Cases (%)PbOR (95% CI)PHWE
Recognition-related
H1911p15.5rs2735971IntronGG313 (67.31)327 (70.32)1 (Ref.)0.697
AG139 (29.89)126 (27.10)0.3360.87 (0.65–1.16)
AA13 (2.80)12 (2.58)0.8240.91 (0.41–2.04)
rs2839698IntronCC245 (53.03)215 (46.14)1 (Ref.)0.297
CT185 (40.04)211 (45.28)0.0581.30 (0.99–1.70)
TT32 (6.93)40 (8.58)0.1571.44 (0.87–2.37)
TT + CT vs CC0.0371.32 (1.02–1.70)
T vs C0.0441.23 (1.01–1.50)
rs3024270IntronGG170 (36.48)151 (32.06)1 (Ref.)0.247
CG215 (46.14)225 (47.78)0.2541.18 (0.89–1.58)
CC81 (17.38)95 (20.16)0.1411.32 (0.91–1.91)

Abbreviations: Chr. Pos., chromosomal position; CI, confidence interval; Loc., localization; OR, odds ratio; PHWE, P-value for HWE.

aThe sort order was according to the SNP location in its genes from 5′ to 3′ ends.

bP-value was calculated by adjusting age and gender. The bold text in this table means the P<0.05 and is significant.

Abbreviations: Chr. Pos., chromosomal position; CI, confidence interval; Loc., localization; OR, odds ratio; PHWE, P-value for HWE. aThe sort order was according to the SNP location in its genes from 5′ to 3′ ends. bP-value was calculated by adjusting age and gender. The bold text in this table means the P<0.05 and is significant. LncRNA-H19 rs2839698 polymorphism was calculated to be associated with an increased risk of HCC. In the dominant model, rs2839698 TT + TC genotype appeared with a 1.32-fold increased HCC risk when compared with CC genotype (P=0.037, Table 1). Subsequently, we conducted stratified analysis by the factors of gender, age, smoking, and drinking to manifest the relationships between every SNP and HCC risk. The results displayed in Table 2 indicated the potential predicting values for specific subgroup populations. When stratified by gender, rs275971 showed a decreased HCC risk tendency in AG genotype of male subgroup for odds ratio (OR) = 0.72, P=0.048. In the subgroup stratified by age, rs2839698 showed an obvious HCC risk tendency in CT genotype of age ≤60 subgroup (OR = 1.57, P=0.007) and the similar situation was showed in rs3024270 CC genotype of age ≤60 subgroup (OR = 1.71, P=0.019). When stratified by smoking factor, rs2839698 showed a more obvious HCC tendency in CT subgroup of ever smoker subgroup (OR = 1.89, P=0.041, Table 2).
Table 2

The association of lncRNA polymorphisms and hepatocellular risk stratified by host characteristics

VariablesGenotypeHCC compared with CONPOR (95% CI)
H19 rs2735971
Gender
MaleGG276/2481 (Ref.)
AG93/1170.0480.72 (0.52–1.00)
AA9/90.7910.88 (0.34–2.26)
FemaleGG51/651 (Ref.)
AG33/220.0581.92 (0.98–3.78)
AA3/40.8040.82 (0.17–4.05)
Age
≤60GG205/2301 (Ref.)
AG87/1020.8110.96 (0.68–1.35)
AA8/70.5371.35 (0.48–3.81)
>60GG122/831 (Ref.)
AG39/370.1600.68 (0.40–1.17)
AA4/60.2160.44 (0.12–1.62)
Smoking
Ever smokerGG56/801 (Ref.)
AG19/430.1050.58 (0.30–1.12)
AA3/40.9521.05 (0.23–4.89)
Never smokedGG152/1281 (Ref.)
AG62/620.3570.81 (0.53–1.26)
AA7/60.8861.09 (0.33–3.63)
Alcohol drinking
DrinkerGG32/571 (Ref.)
AG11/330.1710.57 (0.25–1.28)
AA3/10.1575.39 (0.52–55.55)
Non-drinkerGG175/1511 (Ref.)
AG70/710.4130.84 (0.56–1.27)
AA7/90.4850.68 (0.24–1.98)
H19 rs2839698
Gender
MaleCC176/1951 (Ref.)
CT168/1520.1911.22 (0.91–1.65)
TT33/240.1471.52 (0.86–2.68)
FemaleCC39/501 (Ref.)
CT43/330.0921.74 (0.91–3.30)
TT7/80.7291.23 (0.39–3.91)
Age
≤60CC135/1851 (Ref.)
CT143/1250.0071.57 (1.13–2.18)
TT25/250.2761.40 (0.78–2.55)
>60CC80/601 (Ref.)
CT68/600.5280.86 (0.53–1.39)
TT15/70.3201.63 (0.62–4.27)
Smoking
Ever smokerCC29/651 (Ref.)
CT40/500.0411.89 (1.03–3.48)
TT9/100.1552.08(0.76–5.68)
Never smokedCC112/1051 (Ref.)
CT88/780.9171.02 (0.67–1.55)
TT22/120.1211.88 (0.85–4.16)
Alcohol drinking
DrinkerCC19/451 (Ref.)
CT22/400.4021.39 (0.65–2.97)
TT5/40.0883.68 (0.82–16.46)
Non-drinkerCC121/1241 (Ref.)
CT106/880.3601.20 (0.81–1.76)
TT26/180.1661.61 (0.82–3.15)
H19 rs3024270
Gender
MaleGG129/1361 (Ref.)
CG181/1730.5731.10 (0.80–1.51)
CC71/640.4521.17 (0.77–1.78)
FemaleGG22/341 (Ref.)
CG44/420.0921.86 (0.90–3.82)
CC24/170.0512.39 (1.00–5.73)
Age
≤60GG93/1281 (Ref.)
CG145/1570.1531.40 (0.88–2.21)
CC67/560.0191.71 (1.09–2.68)
>60GG58/421 (Ref.)
CG80/580.1511.29 (0.91–1.84)
CC28/250.4380.76 (0.39–1.51)
Smoking
Ever smokerGG17/441 (Ref.)
CG44/570.0551.96 (0.99–3.89)
CC18/240.1221.93 (0.84–4.42)
Never smokedGG74/691 (Ref.)
CG100/940.8020.94 (0.60–1.48)
CC50/360.3101.34 (0.76–2.34)
Alcohol drinking
DrinkerGG13/261 (Ref.)
CG23/480.9210.96 (0.41–2.22)
CC10/160.5531.37 (0.48–3.93)
Non-drinkerGG77/871 (Ref.)
CG121/1020.1861.33 (0.87–2.01)
CC58/440.0991.53 (0.92–2.55)

Abbreviation: CI, confidence interval. The bold text means the significant results.

Abbreviation: CI, confidence interval. The bold text means the significant results. We further analyzed the relationship between lncRNA-H19 SNPs haplotype and HCC risk and found that rs2735971-rs2839698- rs3024270 G-T-C significantly increased the risk of HCC (OR = 1.23, confidence interval (CI) = 1.01–1.51, P=0.043) (Table 3).
Table 3

The association of haplotype of lncRNA gene and HCC risk

HaplotypeControl (%)Case (%)POR (95% CI)
H19a
 ACC*100.87 (0.110)110,67 (0.122)0.4610.90 (0.67–1.20)
 ACG*47.08 (0.051)49.23 (0.054)0.8120.95 (0.63–1.44)
 GCG*460.01 (0.501)485.37 (0.537)0.2070.89 (0.74–1.07)
 GTC*282.04 (0.307)242.50 (0.268)0.0431.23 (1.01–1.51)

Haplotype for a, H19 rs2735971-rs2839698-rs3024270. The bold text means the significant results.

Haplotype for a, H19 rs2735971-rs2839698-rs3024270. The bold text means the significant results.

LncRNA-H19 SNP–environment interaction with HCC risk

Data mining was conducted to analysis the possible association between interaction model for lncRNA-H19 polymorphisms and environmental factors in HCC risk (Table 4) and found that there were no significant results.
Table 4

The interaction effect of H19 SNPs and environmental factors

GeneLocationSNPPinteraction with smokingPinteraction with drinking
H1911p15.5rs27359710.5350.775
rs28396980.1900.755
rs30242700.8220.973

The association of lncRNA-H19 SNPs with HCC prognosis

The association of HCC patient clinical features and univariate analysis of overall survival was shown in Supplementary Table S3. We also analyzed the relationship of each lncRNA-H19 SNPs and the overall survival of HCC, there existed no significant association between the SNPs and the survival of HCC either in the univariate or multivariate survival analysis (Table 5). In the stratified analysis, we found that the rs2839698 showed a significant poor prognosis in the ever smoking subgroup (hazard rate (HR) = 5.19, CI = 1.12–24.07, P=0.035, Table 6).
Table 5

Univariate and multivariate cox proportional hazard analysis for H19 SNPs on HCC prognosis

VariablesAll HCC, n (%)Deaths, nMSTa (M)Univariate P-valueHazard ratio (95% CI)Multivariatec P-valueHazard ratio (95% CI)
n=286n=156
H19rs2735971GG193 (67.48)117 (75)47.0001 (Ref.)1 (Ref.)
AG85 (29.72)37 (23.72)52.0000.7691.06 (0.72–1.57)0.7520.94 (0.64–1.39)
AA8 (2.80)2 (1.28)33.0000.7561.12 (0.55–2.26)0.7630.80 (0.20–3.30)
n=344n=136
rs2839698CC182 (52.9)69 (50.7)56.0001 (Ref.)1 (Ref.)
CT144 (41.9)61 (44.9)48.0000.7760.95 (0.67–1.34)0.7161.07 (0.75–1.51)
TT18 (5.2)6 (4.4)78.4b0.5011.16 (0.76–1.76)0.4990.75 (0.32–1.73)
n=349n=137
rs3024270GG59 (16.9)21 (15.3)90.0001 (Ref.)1 (Ref.)
CG159 (45.6)67 (48.9)48.0000.7160.91 (0.56–1.49)0.8871.03 (0.71–1.49)
CC131 (37.5)49 (35.8)56.0000.8310.97 (0.75–1.26)0.8080.94 (0.56–1.58)

a, MST, median survival time (months).

b, Mean survival time was provided when MST could not be calculated.

c, Multivariate survival analysis was carried out by adding the age and gender variable to the clinicopathological parameters with P<0.05.

Table 6

Univariate proportional hazard analysis stratified by host characteristics for the association of H19 polymorphisms and HCC

GeneSNPStratifiedStratified factorsGenotypeHCC (n (%))Deaths (n)MSTa(M)P-valueHazard ratio (95% CI)
H19rs2735971
GenderMaleGG160 (69.57)6747.01 (Ref.)
AG64 (27.83)2852.00.6690.91 (0.58–1.41)
AA6 (2.60)233.00.7781.23 (0.30–5.04)
FemaleGG33 (58.93)1338.01 (Ref.)
AG21(37.50)932.00.9181.05 (0.45–2.45)
AA2 (3.57)0NANANA
Age≤60GG109 (63.01)4347.01 (Ref.)
AG58 (33.53)2848.00.5991.14 (0.71–1.83)
AA6 (3.46)125.8b0.8440.82 (0.11–6.03)
>60GG84 (74.34)3745.01 (Ref.)
AG27 (23.89)956.00.2760.67 (0.32–1.38)
AA2 (1.77)133.00.8470.82 (0.11–6.02)
SmokingEver smokerGG17 (62.96)697.1b1 (Ref.)
AG8 (29.63)1117.2b0.2640.30 (0.04–2.49)
AA2 (7.41)0NANANA
Never smokedGG99 (66.44)3581.3b1 (Ref.)
AG46 (30.87)2032.00.7021.11 (0.64–1.93)
AA4 (2.69)133.00.6650.65 (0.09–4.72)
Alcohol drinkingDrinkerGG9 (60.00)2NA1 (Ref.)
AG4 (26.67)0NANANA
AA2 (13.33)0NANANA
Non-drinkerGG107 (66.46)3984.5b1 (Ref.)
AG50 (31.06)2148.00.9190.97 (0.57–1.66)
AA4 (2.48)133.00.6480.63 (0.09–4.59)
HBVPositiveGG86 (65.15)3188.1b1 (Ref.)
AG40 (30.30)1390.00.3310.73 (0.38–1.39)
AA6 (4.55)164.1b0.5520.55 (0.07–4.02)
NegativeGG19 (63.33)727.01 (Ref.)
AG11 (36.67)425.7b0.8031.17 (0.34–4.09)
AA00NANANA
H19rs2839698
GenderMaleCC157 (56.07)6156.01 (Ref.)
CT110 (39.29)5047.00.6921.08 (0.74–1.57)
TT13 (4.64)297.5b0.0870.29 (0.07–1.20)
FemaleCC25 (39.06)855.01 (Ref.)
CT34 (53.13)1151.00.9091.05 (0.42–2.63)
TT5 (7.81)45.00.0703.14 (0.91–10.85)
Age≤60CC111 (51.63)4256.01 (Ref.)
CT94 (43.72)4348.00.6141.12 (0.73–1.71)
TT10 (4.65)291.4b0.2330.42 (0.10–1.75)
>60CC71 (55.04)2756.01 (Ref.)
CT50 (38.76)1879.4b0.8380.94 (0.52–1.71)
TT8 (6.20)427.00.6811.25 (0.44–3.58)
SmokingEver smokerCC16 (50.00)2127.1b1 (Ref.)
CT16 (50.00)945.00.0355.19 (1.12–24.07)
TT00NANANA
Never smokedCC97 (57.06)3169.01 (Ref.)
CT59 (34.71)2347.00.6241.15 (0.67–1.97)
TT14 (8.23)575.1b0.7920.88 (0.34–2.28)
Alcohol drinkingDrinkerCC12 (63.16)170.2b1 (Ref.)
CT7 (36.84)445.00.1105.99 (0.67–53.96)
TT00NANANA
Non-drinkerCC101 (55.19)3269.01 (Ref.)
CT68 (37.16)2848.00.3391.28 (0.77–2.13)
TT14 (7.65)575.1b0.9270.96 (0.37–2.47)
HBVPositiveCC70 (52.24)1994.3b1 (Ref.)
CT55 (41.04)2452.00.1251.60 (0.88–2.93)
TT9 (6.72)378.7b0.9240.94 (0.28–3.20)
NegativeCC17 (56.67)642.2b1 (Ref.)
CT11 (36.67)358.3b0.8820.90 (0.22–3.65)
TT2 (6.66)22.00.2292.74 (0.53–14.18)
H19rs3024270
GenderMaleGG114 (40.14)4556.01 (Ref.)
CG131 (46.13)5748.00.9460.99 (0.67–1.46)
CC39 (13.73)1290.00.2330.68 (0.36–1.28)
FemaleGG17 (26.15)455.01 (Ref.)
CG28 (43.08)1051.00.5321.45 (0.45–4.65)
CC20 (30.77)927.00.0862.83 (0.87–9.28)
Age≤60GG79 (36.41)2886.7b1 (Ref.)
CG99 (45.62)4448.00.9261.02 (0.64–1.65)
CC39 (17.97)1590.00.6111.18 (0.63-2.21)
>60GG52 (39.39)2155.01 (Ref.)
CG60 (45.46)2376.8b0.9460.98 (0.54–1.77)
CC20 (15.15656.6b0.3700.66 (0.27–1.64)
SmokingEver smokerGG7 (21.21)0NA1 (Ref.)
CG21 (63.64)10NANANA
CC5 (15.15)1NANANA
Never smokedGG65 (37.79)2086.3b1 (Ref.)
CG71 (41.28)2747.00.7101.12 (0.63–1.99)
CC36 (20.93)1390.00.9781.01 (0.50–2.04)
Alcohol drinkingDrinkerGG7 (36.84)0NA1 (Ref.)
CG10 (52.63)5NA0.298NA
CC2 (10.53)0NANANA
Non-drinkerGG65 (34.95)2091.3b1 (Ref.)
CG82 (44.09)3269.00.5181.20 (0.69–2.11)
CC39 (20.96)1490.00.9391.03 (0.52–2.04)
HBVPositiveGG45 (32.61)1395.0b1 (Ref.)
CG63 (45.65)2569.00.3981.34 (0.68–2.62)
CC30 (21.74)990.00.9510.97 (0.42–2.28)
NegativeGG12 (40.00)444.7b1 (Ref.)
CG13 (43.33)451.5b0.7861.22 (0.29–5.05)
CC5 (16.67)327.00.3582.03 (0.45–9.20)

NA, not available. The bold text means the significant results.

a, MST, median survival time (months);

b, Mean survival time was provided when MST could not be calculated.

a, MST, median survival time (months). b, Mean survival time was provided when MST could not be calculated. c, Multivariate survival analysis was carried out by adding the age and gender variable to the clinicopathological parameters with P<0.05. NA, not available. The bold text means the significant results. a, MST, median survival time (months); b, Mean survival time was provided when MST could not be calculated.

Discussion

H19 gene, with the length of 2.3 kb and located in 11p15.5, containing five exons and three introns [22], which has been well accepted that lncRNA-H19 plays an important role in the development, migration, invasion, and metastasis of cancers [23]. As a long ncRNA, H19 lacks the ORF to translate protein, however, the end product of which is RNA sequence and can also participate in RNA regulation [24]. Due to the relationship between H19 variants and cancer risk as well as prognosis is still needed to be clarified; the H19 polymorphisms have been of great interest in the recent years [17]. Many studies have been reported that H19 SNPs were related to cancers risk and prognosis, such as, rs217727 with breast cancer [13], rs2389698 with gastric cancer [25], but rs1859168 was reported to reduce the risk of pancreatic cancer [26]. However, the association between lncRNA-H19 SNPs and HCC risk and prognosis is still unreported. In order to research the role of H19 SNPs in HCC risk and prognosis, we screened three intron SNPs in H19 gene. Under the dominant model, TT + CT genotype of rs2389698 was found to be 1.32-fold increased HCC risk compared with CC wild-type; this is the first report indicating that H19 SNPs was related to HCC risk. Thus, rs2389698 may serve as a promising predictor for HCC risk. The rs2389698 was an intron SNP and it is accepted now that intron SNP also had its possibly own functions such as affecting selective splicing [27,28]. Because some intron polymorphisms had some important location and even function, it is believable that choosing intron SNP for research is also a good choice such as this rs2389698 SNP. When stratified by gender, age, smoking, and drinking factors, a more obvious OR of 1.57 and 1.71 was shown for the rs2389698 and rs3024270 in the age ≤60 subgroup, respectively. And in the ever-smoking subgroup of rs2389698, it showed 1.89-fold increased HCC risk. It was reported that the expression of H19 could be induced by cigarette smoke condensate in human respiratory epithelial cells [29]. Thus, we suppose that the mature lncRNA-H19 could be affected by cigarette smoke and when in the ever-smoking subgroup, the polymorphisms could contribute to more functions than the environmental factors. These results indicated that the promising SNPs of H19 may be better biomarkers for the certain subgroup and could bring benefit to the individualized diagnosis for HCC in the certain population. Furthermore, we performed interaction analysis for the multiple lncRNA-H19 SNPs and environmental factors including smoking and drinking. Yet there showed no interaction between the three polymorphisms and environmental factors. And in the lncRNA-H19 SNPs haplotype and HCC risk analysis, rs2735971-rs2839698-rs3024270 G-T-C were found significantly increased the risk of HCC (OR = 1.23, CI = 1.01–1.51, P=0.043). These results suggested that the G-T-C haplotype suffers more risk than other haplotype. We further performed univariate and multivariate Cox proportional hazards regression analysis of overall survival time to explore the association between lncRNA-H19 SNPs and HCC prognosis. No significant association was found between H19 SNPs and HCC overall survival. In addition, we performed subgroup analysis for HCC prognosis and found rs2839698 that showed significant poor survival condition in the ever-smoking subgroup which suggested that it may affect HCC prognosis and could be a promising biomarker for HCC prognosis. As discussed above, the expression of H19 could be induced by cigarette smoke [29]. Thus, when in the ever-smoking subgroup, the polymorphisms could contribute more functions than the environmental factors and individuals carrying the variant genotype which also had a higher incidence of cancer risk could have a poorer survival of HCC. However, there still existed several limitations in the present study. First, the sample size was relatively not large enough for the analysis which may limit the possible analysis of other subgroup analysis and interaction analysis for variant genotype. Second, we only studied the local population and did not include residents of other areas. Third, because the controls were collected from the health check program of our hospital and there was no message of the HBV for them which could not assess the influence of this factor. In future, larger sample and multicenter samples are needed for the confirmation study of our findings. In conclusion, we found an intron rs2839698 SNP of lncRNA-H19 was associated with an increased risk of HCC. And more significant findings were shown in the age ≤60 subgroup in rs2839698 and rs3024270. In the ever-smoking subgroup, rs2839698 showed an obvious increased HCC risk too. But we got an adverse result in the male subgroup of rs2735971 SNP that showed a decreased HCC risk. In addition, we found that the rs2735971-rs2839698- rs3024270 G-T-C significantly increased the risk of HCC in the analysis of haplotype and HCC risk. And the MDR analysis had no significant findings. In the prognosis analysis, the rs2839698 showed a poor prognosis in the ever-smoking subgroup. In the future, the larger scale sample experiments and analyses are needed to confirm our results. The LD figure of the H19 genetic polymorphisms
Supplementary Table S1.

The primier information for the H19 polymorphisms

Supplementary Table S2.

The baseline of the subjects

Supplementary Table S3.

HCC patient clinical features and univariate analysis of overall survival

  29 in total

1.  Epigenomic alterations and gene expression profiles in respiratory epithelia exposed to cigarette smoke condensate.

Authors:  F Liu; J K Killian; M Yang; R L Walker; J A Hong; M Zhang; S Davis; Y Zhang; M Hussain; S Xi; M Rao; P A Meltzer; D S Schrump
Journal:  Oncogene       Date:  2010-05-03       Impact factor: 9.867

Review 2.  The alternative splicing side of cancer.

Authors:  Giuseppe Biamonti; Morena Catillo; Daniela Pignataro; Alessandra Montecucco; Claudia Ghigna
Journal:  Semin Cell Dev Biol       Date:  2014-03-19       Impact factor: 7.727

Review 3.  A systematic review and meta-analysis of the association between long non-coding RNA polymorphisms and cancer risk.

Authors:  Zhi Lv; Qian Xu; Yuan Yuan
Journal:  Mutat Res Rev Mutat Res       Date:  2016-11-05       Impact factor: 5.657

4.  Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.

Authors:  Ewan Birney; John A Stamatoyannopoulos; Anindya Dutta; Roderic Guigó; Thomas R Gingeras; Elliott H Margulies; Zhiping Weng; Michael Snyder; Emmanouil T Dermitzakis; Robert E Thurman; Michael S Kuehn; Christopher M Taylor; Shane Neph; Christoph M Koch; Saurabh Asthana; Ankit Malhotra; Ivan Adzhubei; Jason A Greenbaum; Robert M Andrews; Paul Flicek; Patrick J Boyle; Hua Cao; Nigel P Carter; Gayle K Clelland; Sean Davis; Nathan Day; Pawandeep Dhami; Shane C Dillon; Michael O Dorschner; Heike Fiegler; Paul G Giresi; Jeff Goldy; Michael Hawrylycz; Andrew Haydock; Richard Humbert; Keith D James; Brett E Johnson; Ericka M Johnson; Tristan T Frum; Elizabeth R Rosenzweig; Neerja Karnani; Kirsten Lee; Gregory C Lefebvre; Patrick A Navas; Fidencio Neri; Stephen C J Parker; Peter J Sabo; Richard Sandstrom; Anthony Shafer; David Vetrie; Molly Weaver; Sarah Wilcox; Man Yu; Francis S Collins; Job Dekker; Jason D Lieb; Thomas D Tullius; Gregory E Crawford; Shamil Sunyaev; William S Noble; Ian Dunham; France Denoeud; Alexandre Reymond; Philipp Kapranov; Joel Rozowsky; Deyou Zheng; Robert Castelo; Adam Frankish; Jennifer Harrow; Srinka Ghosh; Albin Sandelin; Ivo L Hofacker; Robert Baertsch; Damian Keefe; Sujit Dike; Jill Cheng; Heather A Hirsch; Edward A Sekinger; Julien Lagarde; Josep F Abril; Atif Shahab; Christoph Flamm; Claudia Fried; Jörg Hackermüller; Jana Hertel; Manja Lindemeyer; Kristin Missal; Andrea Tanzer; Stefan Washietl; Jan Korbel; Olof Emanuelsson; Jakob S Pedersen; Nancy Holroyd; Ruth Taylor; David Swarbreck; Nicholas Matthews; Mark C Dickson; Daryl J Thomas; Matthew T Weirauch; James Gilbert; Jorg Drenkow; Ian Bell; XiaoDong Zhao; K G Srinivasan; Wing-Kin Sung; Hong Sain Ooi; Kuo Ping Chiu; Sylvain Foissac; Tyler Alioto; Michael Brent; Lior Pachter; Michael L Tress; Alfonso Valencia; Siew Woh Choo; Chiou Yu Choo; Catherine Ucla; Caroline Manzano; Carine Wyss; Evelyn Cheung; Taane G Clark; James B Brown; Madhavan Ganesh; Sandeep Patel; Hari Tammana; Jacqueline Chrast; Charlotte N Henrichsen; Chikatoshi Kai; Jun Kawai; Ugrappa Nagalakshmi; Jiaqian Wu; Zheng Lian; Jin Lian; Peter Newburger; Xueqing Zhang; Peter Bickel; John S Mattick; Piero Carninci; Yoshihide Hayashizaki; Sherman Weissman; Tim Hubbard; Richard M Myers; Jane Rogers; Peter F Stadler; Todd M Lowe; Chia-Lin Wei; Yijun Ruan; Kevin Struhl; Mark Gerstein; Stylianos E Antonarakis; Yutao Fu; Eric D Green; Ulaş Karaöz; Adam Siepel; James Taylor; Laura A Liefer; Kris A Wetterstrand; Peter J Good; Elise A Feingold; Mark S Guyer; Gregory M Cooper; George Asimenos; Colin N Dewey; Minmei Hou; Sergey Nikolaev; Juan I Montoya-Burgos; Ari Löytynoja; Simon Whelan; Fabio Pardi; Tim Massingham; Haiyan Huang; Nancy R Zhang; Ian Holmes; James C Mullikin; Abel Ureta-Vidal; Benedict Paten; Michael Seringhaus; Deanna Church; Kate Rosenbloom; W James Kent; Eric A Stone; Serafim Batzoglou; Nick Goldman; Ross C Hardison; David Haussler; Webb Miller; Arend Sidow; Nathan D Trinklein; Zhengdong D Zhang; Leah Barrera; Rhona Stuart; David C King; Adam Ameur; Stefan Enroth; Mark C Bieda; Jonghwan Kim; Akshay A Bhinge; Nan Jiang; Jun Liu; Fei Yao; Vinsensius B Vega; Charlie W H Lee; Patrick Ng; Atif Shahab; Annie Yang; Zarmik Moqtaderi; Zhou Zhu; Xiaoqin Xu; Sharon Squazzo; Matthew J Oberley; David Inman; Michael A Singer; Todd A Richmond; Kyle J Munn; Alvaro Rada-Iglesias; Ola Wallerman; Jan Komorowski; Joanna C Fowler; Phillippe Couttet; Alexander W Bruce; Oliver M Dovey; Peter D Ellis; Cordelia F Langford; David A Nix; Ghia Euskirchen; Stephen Hartman; Alexander E Urban; Peter Kraus; Sara Van Calcar; Nate Heintzman; Tae Hoon Kim; Kun Wang; Chunxu Qu; Gary Hon; Rosa Luna; Christopher K Glass; M Geoff Rosenfeld; Shelley Force Aldred; Sara J Cooper; Anason Halees; Jane M Lin; Hennady P Shulha; Xiaoling Zhang; Mousheng Xu; Jaafar N S Haidar; Yong Yu; Yijun Ruan; Vishwanath R Iyer; Roland D Green; Claes Wadelius; Peggy J Farnham; Bing Ren; Rachel A Harte; Angie S Hinrichs; Heather Trumbower; Hiram Clawson; Jennifer Hillman-Jackson; Ann S Zweig; Kayla Smith; Archana Thakkapallayil; Galt Barber; Robert M Kuhn; Donna Karolchik; Lluis Armengol; Christine P Bird; Paul I W de Bakker; Andrew D Kern; Nuria Lopez-Bigas; Joel D Martin; Barbara E Stranger; Abigail Woodroffe; Eugene Davydov; Antigone Dimas; Eduardo Eyras; Ingileif B Hallgrímsdóttir; Julian Huppert; Michael C Zody; Gonçalo R Abecasis; Xavier Estivill; Gerard G Bouffard; Xiaobin Guan; Nancy F Hansen; Jacquelyn R Idol; Valerie V B Maduro; Baishali Maskeri; Jennifer C McDowell; Morgan Park; Pamela J Thomas; Alice C Young; Robert W Blakesley; Donna M Muzny; Erica Sodergren; David A Wheeler; Kim C Worley; Huaiyang Jiang; George M Weinstock; Richard A Gibbs; Tina Graves; Robert Fulton; Elaine R Mardis; Richard K Wilson; Michele Clamp; James Cuff; Sante Gnerre; David B Jaffe; Jean L Chang; Kerstin Lindblad-Toh; Eric S Lander; Maxim Koriabine; Mikhail Nefedov; Kazutoyo Osoegawa; Yuko Yoshinaga; Baoli Zhu; Pieter J de Jong
Journal:  Nature       Date:  2007-06-14       Impact factor: 49.962

5.  lncRNA H19 regulates epithelial-mesenchymal transition and metastasis of bladder cancer by miR-29b-3p as competing endogenous RNA.

Authors:  Mengxin Lv; Zhenyu Zhong; Mengge Huang; Qiang Tian; Rong Jiang; Junxia Chen
Journal:  Biochim Biophys Acta Mol Cell Res       Date:  2017-08-02       Impact factor: 4.739

6.  Tag SNPs in long non-coding RNA H19 contribute to susceptibility to gastric cancer in the Chinese Han population.

Authors:  Chao Yang; Ran Tang; Xiang Ma; Younan Wang; Dakui Luo; Zekuan Xu; Yi Zhu; Li Yang
Journal:  Oncotarget       Date:  2015-06-20

7.  Genetic variants in long noncoding RNA H19 contribute to the risk of breast cancer in a southeast China Han population.

Authors:  Yuxiang Lin; Fangmeng Fu; Yazhen Chen; Wei Qiu; Songping Lin; Peidong Yang; Meng Huang; Chuan Wang
Journal:  Onco Targets Ther       Date:  2017-09-07       Impact factor: 4.147

8.  Meta-analysis of association between rs1447295 polymorphism and prostate cancer susceptibility.

Authors:  Juan Zhou; Yang Yu; Anyou Zhu; Fengchao Wang; Shuxia Kang; Yunfeng Pei; Chunping Cao; Chen Ding; Duping Wang; Li Sun; Guoping Niu
Journal:  Oncotarget       Date:  2017-05-05

9.  Genetic Polymorphisms in Long Noncoding RNA H19 Are Associated With Susceptibility to Breast Cancer in Chinese Population.

Authors:  Zongjiang Xia; Rui Yan; Fujiao Duan; Chunhua Song; Peng Wang; Kaijuan Wang
Journal:  Medicine (Baltimore)       Date:  2016-02       Impact factor: 1.889

Review 10.  Comprehensive assessment and meta-analysis of the association between CTNNB1 polymorphisms and cancer risk.

Authors:  Yanke Li; Fuqiang Zhang; Dehua Yang
Journal:  Biosci Rep       Date:  2017-11-23       Impact factor: 3.840

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  17 in total

Review 1.  Long noncoding RNA H19 - a new player in the pathogenesis of liver diseases.

Authors:  Zhihong Yang; Ting Zhang; Sen Han; Praveen Kusumanchi; Nazmul Huda; Yanchao Jiang; Suthat Liangpunsakul
Journal:  Transl Res       Date:  2020-11-20       Impact factor: 10.171

Review 2.  SNPs and Somatic Mutation on Long Non-Coding RNA: New Frontier in the Cancer Studies?

Authors:  Linda Minotti; Chiara Agnoletto; Federica Baldassari; Fabio Corrà; Stefano Volinia
Journal:  High Throughput       Date:  2018-11-16

3.  Associations between H19 polymorphisms and neuroblastoma risk in Chinese children.

Authors:  Chao Hu; Tianyou Yang; Jing Pan; Jiao Zhang; Jiliang Yang; Jing He; Yan Zou
Journal:  Biosci Rep       Date:  2019-04-05       Impact factor: 3.840

4.  H19 gene polymorphisms and neuroblastoma susceptibility in Chinese children: a six-center case-control study.

Authors:  Yong Li; Zhen-Jian Zhuo; Haiyan Zhou; Jiabin Liu; Jiao Zhang; Jiwen Cheng; Haixia Zhou; Suhong Li; Ming Li; Jun He; Zhenghui Xiao; Jing He; Yaling Xiao
Journal:  J Cancer       Date:  2019-10-18       Impact factor: 4.207

Review 5.  The Good, the Bad, the Question-H19 in Hepatocellular Carcinoma.

Authors:  Lysann Tietze; Sonja M Kessler
Journal:  Cancers (Basel)       Date:  2020-05-16       Impact factor: 6.639

6.  The clinicopathological characteristic associations of long non-coding RNA gene H19 polymorphisms with uterine cervical cancer.

Authors:  Ming-Chao Huang; Ying-Hsiang Chou; Huang-Pin Shen; Soo-Cheen Ng; Yueh-Chun Lee; Yi-Hung Sun; Chun-Fang Hsu; Shun-Fa Yang; Po-Hui Wang
Journal:  J Cancer       Date:  2019-10-15       Impact factor: 4.207

Review 7.  Long Non-coding RNAs Mechanisms of Action in HIV-1 Modulation and the Identification of Novel Therapeutic Targets.

Authors:  Roslyn M Ray; Kevin V Morris
Journal:  Noncoding RNA       Date:  2020-03-13

8.  Associations of lncRNA H19 Polymorphisms at MicroRNA Binding Sites with Glioma Susceptibility and Prognosis.

Authors:  Yujiao Deng; Linghui Zhou; Jia Yao; Yu Liu; Yi Zheng; Si Yang; Ying Wu; Na Li; Peng Xu; Lijuan Lyu; Dai Zhang; Jun Lyu; Zhijun Dai
Journal:  Mol Ther Nucleic Acids       Date:  2020-02-13       Impact factor: 8.886

9.  Association between lncRNA H19 rs217727 polymorphism and the risk of cancer: an updated meta-analysis.

Authors:  Xue Wang; Jialing Zhong; Fang Chen; Kang Hu; Suhong Sun; Yuanxiu Leng; Xumei Chen; Fengjiao Gan; Yana Pan; Qing Luo
Journal:  BMC Med Genet       Date:  2019-11-21       Impact factor: 2.103

10.  Significant association between long non-coding RNA H19 polymorphisms and cancer susceptibility: A PRISMA-compliant meta-analysis and bioinformatics prediction.

Authors:  Wei Li; Xia Jiang; Xiaojing Jin; Weitao Yan; Ying Liu; Dongyun Li; Zengren Zhao
Journal:  Medicine (Baltimore)       Date:  2020-04       Impact factor: 1.817

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