Correlations between Genetic Polymorphisms in Long Non-Coding RNA PRNCR1 and Gastric Cancer Risk in a Korean Population
by
, and
Jang Hee Hong
1,2,†, 
Eun-Heui Jin
3,†,
Hyojin Kang
2,
In Ae Chang
2,
Sang-Il Lee
4,* and
Jae Kyu Sung
5,* 1
Clinical Trials Center, Chungnam National University Hospital, Daejeon 35015, Korea
2
Department of Pharmacology, Chungnam National University College of Medicine, Daejeon 35015, Korea
3
Research Institute for Medical Sciences, Chungnam National University College of Medicine, Daejeon 35015, Korea
4
Department of Surgery, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon 35015, Korea
5
Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon 35015, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2019, 20(13), 3355; https://doi.org/10.3390/ijms20133355
Submission received: 29 May 2019
/
Revised: 28 June 2019
/
Accepted: 5 July 2019
/
Published: 8 July 2019
(This article belongs to the Special Issue Gastric Cancers: Molecular Pathways and Candidate Biomarkers)
Abstract
:We evaluated the association between prostate cancer non-coding RNA 1 (PRNCR1) polymorphisms and the risk of developing gastric cancer (GC) and GC subgroups in Korea. A case–control study was conducted with 437 GC patients and 357 healthy controls using a TaqMan genotyping assay. A chi-squared test, binary logistic regression, and genetic models were used to explore the association between five PRNCR1 polymorphisms and GC risk. After adjusting for gender and age, overall analyses using the recessive model indicated that the rs13252298 GG genotype was significantly associated with increased risk of intestinal-type gastric cancer (IGC). In the stratification analyses, the recessive model indicated that the rs1016343 TT genotype was significantly associated with decreased GC risk in individuals aged <60 years showing lymph node metastasis (LNM)-negative results. The rs13252298 GG genotype in the recessive model showed increased GC risk in subjects aged ≥60 years showing LNM-positive results and those aged ≥60 years in tumor stage III. In the dominant model, the rs16901946 combined genotype (AG/GG) was significantly associated with increased GC risk in subjects aged <60 years with tumor stage III. In the recessive model, the rs16901946 GG genotype was associated with decreased risk of GC and IGC in males aged ≥60 years. Thus, genetic variations in PRNCR1 may contribute to susceptibility to GC.
1. Introduction
Gastric cancer (GC) is one of the most common forms of cancer worldwide. Despite a steady decline in GC incidence and mortality rates over the past few decades, the rates are still high in Asian countries. According to a report by the Korean National Cancer Center, GC is the third most common cancer, with 25,872 new cases and 7138 deaths recorded in Korea in 2016 [1,2,3].
Approximately 80% of disease-related polymorphisms occur in non-coding regions consisting of introns and intergenic regions [4]. Genome-wide association studies have reported that a large number of polymorphisms are associated with cancer and that these cancer-related polymorphisms are associated with long non-coding RNAs (lncRNAs) [5,6,7]. LncRNAs are non-translated RNA molecules over 200 nucleotides in length. Recently, it was reported that lncRNAs are involved in tumorigenesis [8,9]. Moreover, germline variants can affect lncRNA expression, regulating cancer development and progression [10]. Indeed, several studies have demonstrated that lncRNA genetic variants are related to susceptibility of various cancers, including breast cancer [11,12], colon cancer [13], gastric cancer [14,15], lung cancer [16], and prostate cancer [10,17]. Particularly, lncRNA prostate cancer non-coding RNA 1 (PRNCR1), transcribed from a non-coding region of chromosome 8q24, is involved in the carcinogenesis of prostate cancer (PC) through activation of the androgen receptor [18], and lncRNA PRNCR1 polymorphisms have been correlated with various cancers, including GC [14,18,19,20,21,22,23].
Based on previous findings, we hypothesized that polymorphisms in the lncRNA PRNCR1 might affect genetic susceptibility to GC. Therefore, we conducted a case–control study to elucidate the association between single nucleotide polymorphisms (SNPs) in PRNCR1 and risk of developing GC in a Korean population. We further analyzed the impact of PRNCR1 polymorphisms on GC risk in combination with various characteristics and clinical features, including age, sex, tumor differentiation, histologic type, T classification, lymph node metastasis (LNM), and tumor stage.
2. Results
2.1. Patient Characteristics and Single Nucleotide Polymorphisms (SNP) Selection
The characteristics and clinical features of the 437 patients with GC and the 363 cancer-free controls are presented in Table 1. There was a significant difference in the age and gender distribution between the two groups (p < 0.001 and p < 0.001, respectively). The mean age was 65.3 ± 11.1 years for GC patients and 58.1 ± 8.9 years for the controls. The proportion of male subjects was significantly higher in the group with GC (69.6%), whereas the number of female subjects was higher in the control group (67.2%). Of the 437 patients with diagnosed GC, more than half were classified as having the intestinal type (55.8%), making it the most common type, followed by the diffuse-type and the mixed-type. The majority of the patients did not show lymph node metastasis (LNM) (60.9%) and were classified as T1 (50.6%) and tumor stage I (58.8%). We selected five PRNCR1 SNPs: rs1016343, rs13252298, rs7841060, rs16901946, and rs1456315, which have been previously associated with cancer.
2.2. Associations Between PRNCR1 SNPs and GC risk
The genotype frequencies of rs1016343, rs13252298, rs7841060, and rs16901946 were in Hardy–Weinberg equilibrium (HWE) for both the GC group (p = 0.772, p = 0.968, p = 0.668, and p = 0.821, respectively) and the control group (p = 0.591, p = 0.143, p = 0.610, and p = 0.978, respectively) (Table 2). However, rs1456315 frequencies were not in HWE for either GC or controls (p < 0.05). The rs1456315 was, therefore, excluded from the genotype analysis because of Hardy-Weinberg disequilibrium. LD coefficients (|D’|) were estimated among the four SNPs, and an absolute LD (|D’| = 1 and r2) was not found for any pair-wise combination among four SNPs using Haploview 4.0 software. To determine whether rs1016343, rs13252298, rs7841060, and rs16901946 were associated with a higher risk of GC or GC subgroups, we compared the genotypic frequencies between the GC group and the control group. After adjusting for age and gender, the recessive model indicated that the rs13252298 GG genotype was associated with an increased risk of intestinal-type gastric cancer (IGC) (OR = 1.92, 95% CI = 1.01–3.63, p = 0.045), as compared to the rs13252298 AA/AG genotypes; the remaining SNPs showed no significant associations (Table 2).
2.3. Stratified Analysis for Four PRNCR1 SNPs
To further estimate the possible correlation between the four SNPs and GC risk in GC subgroups, we performed stratified analyses based on various patient characteristics, including age, sex, LNM, T classification, and tumor stage. As shown in Table 3, after adjusting for age and gender, our recessive model demonstrated that the rs1016343 TT genotype was significantly associated with a decreased risk of GC in subjects aged <60 years showing LNM-negative results (odds ratio, OR = 0.29, 95% confidence interval, CI = 0.09–0.94, p = 0.038), when compared to the rs1016343 CC/CT genotypes. Moreover, according to the recessive model, the rs13252298 GG genotype was associated with an increased risk of GC for those aged ≥60 years showing LNM-positive results (OR = 2.80, 95% CI = 1.15–6.82, p = 0.024) and those aged ≥60 years in tumor stage III (OR = 3.39, 95% CI = 1.35–8.52, p = 0.009), as compared to the rs13252298 AA/AG genotypes. Furthermore, the dominant model showed that the rs16901946 combined genotype (AG/GG) had a significant association with increased risk of GC in subjects aged <60 years in tumor stage III (OR = 2.38, 95% CI = 1.15–4.94, p = 0.020). The recessive model also showed that the rs16901946 GG genotype was associated with a decreased risk of GC (OR = 0.43, 95% CI = 0.19–0.98, p = 0.046) and IGC (OR = 0.38, 95% CI = 0.16–0.89, p = 0.026) in male subjects aged ≥60 years when compared to the rs16901946 AA/AG genotype. However, the association between polymorphisms in PRNCR1 and tumor differentiation was not observed.
3. Discussion
To date, most studies, including GWAS and a meta-analysis, have reported on the association between genetic variations in PRNCR1 and PC [18,19,20,21]. However, little is known about the associations between PRNCR1 polymorphisms and GC. Furthermore, results thus far have been inconsistent, and the genotyping methods have also varied [14,23]. In this case–control study, we investigated the association between lncRNA PRNCR1 polymorphisms and GC risk in a Korean population using a reliable TaqMan genotyping assay. We found that the rs13252298 GG genotype was associated with 1.92-times increased risk of IGC. Li et al. previously reported that there was an association between the rs13252298 AG genotype and GC in the Chinese population; in contrast, He et al. reported a lack of association between the rs13252298 polymorphism and GC in the Chinese population [14,23]. Interestingly, our age-stratified analysis found that the rs1016343 TT genotype was associated with 0.92-times reduced risk of GC in subjects aged <60 years showing LNM-negative results; the rs13252298 GG genotype was associated with 2.80- and 3.39-times increased risk of GC in subjects aged ≥60 years showing LNM-positive results and aged ≥60 years in tumor stage III, respectively. The rs16901946 AG/GG genotype was associated with 2.38-times increased risk of GC in subjects <60 years in tumor stage III. However, He et al. described an association between the rs16901946 genotype and increased risk of GC in younger and tumor stage I+II subjects. Interestingly, in our study the rs1016343 TT genotype was associated with 0.29-times decreased risk of GC in those aged <60 years showing LNM-negative results. Li et al. demonstrated an association between the rs1456315 GG genotype and a decreased risk of GC. However, we could not analyze an association because of the Hardy–Weinberg disequilibrium in either the GC group or the control group (p < 0.05).
There are several limitations to our study. First, the sample size was too small to have statistical power for our stratified analyses. Second, although Helicobacter pylori is an independent risk factor [24,25], we did not investigate its relevance with regard to the PRNCR1 polymorphisms in GC risk because of ethical considerations. Third, we failed to explore whether there is an association between the genetic factors and smoking, drinking, and diet related to GC risk owing to lack of these data from the GC and control groups. Fourth, our study was performed in a Korean population. Thus, we should include other ethnic groups and a larger sample size to confirm our results. Future studies will require that we assess the influence of these factors on GC.
In conclusion, our study suggests that the PRNCR1 rs13252298 and rs16901946 polymorphisms are associated with increased GC risk and may exacerbate the development of GC. Moreover, the PRNCR1 rs1016343 polymorphism may contribute to a decreased risk of GC in those aged <60 years showing LNM-negative results. Further studies are needed to validate our results in a larger population as well as in different ethnic groups.
4. Materials and Methods
4.1. Ethics Statement
The present study was conducted in accordance with the Declaration of Helsinki and was reviewed and approved by the Ethics Committee of the institutional review board of Chungnam National University Hospital on 23 July 2017. Informed consent was provided by all subjects when they were enrolled.
4.2. Study Subjects
In total, 437 GC patients and 357 healthy controls were enrolled in this study. The blood samples used in this study were provided by the Chungnam National Hospital Biobank, a member of the National Biobank of Korea, which is supported and audited by the Ministry of Health and Welfare of Korea. All individuals enrolled in this study provided their written informed consent for blood collection and use. GC patients were recruited from the outpatient clinic at the Chungnam National University Hospital, and classified according to the Lauren’s classification [26]. The subjects for the control group were randomly selected among healthy volunteers visiting the Chungnam National University Hospital medical center for their annual physical examinations; only individuals who had no history of cancer were included.
4.3. DNA Isolation and Genotyping
Genomic DNA was isolated from the peripheral blood using the QIAamp DNA Blood Mini Kit (Qiagen GmbH, Hilden, Germany), according to the manufacturer’s instructions. Five SNPs (rs1016343, rs13252298, rs7841060, rs16901946, and rs1456315) in PRNCR1 were selected based on previous reports [14,19,20,23,24,25] and genotyped using the Applied Biosystems TaqMan SNP Genotyping Assay with the StepOnePlus Real-time PCR System (Applied Biosystems, Foster City, CA, USA).
4.4. Statistical Analysis
Hardy–Weinberg equilibrium (HWE) for each SNP in the control groups was evaluated using the chi-squared test. A pair-wise comparison of biellelic loci was employed for the analyses of linkage disequilibrium (LD) using Haploview software version 4.0 (the Broad Institute, Cambridge, MA, USA). Differences in age and gender between the GC and control groups were calculated using the two-sided Pearson chi-squared test and the Mann–Whitney U-test. Two genetic models (dominant and recessive models) were used to analyze the associations. A binary logistic regression was used to estimate the GC risk according to odds ratios (ORs) and 95% confidence intervals (CIs). The association analysis was adjusted by age and sex, which were included in the model as covariates. All statistical analyses were performed using the SPSS (SPSS Inc., Chicago, IL, USA), version 20.0 for Windows. p <0.05 was considered statistically significant.
Author Contributions
Study conception and design, J.H.H., E.-H.J.; Methodology, H.K.; Analysis and interpretation of data, J.H.H., E.-H.J., H.K., I.A.C., S.-I.L., J.K.S.; Writing-original draft, J.H.H., E.-H.J.; Writing—review and editing, J.H.H., E.-H.J., S.-I.L., J.K.S.
Funding
This work was supported by research fund of Chungnam National University, by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NRF-2015R1C1A2A01052150 and NRF-2016R1D1A3B03936067), and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B04033515 and NRF-2018R1D1A1B07050285).
Acknowledgments
This work was supported by research fund of Chungnam National University, by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NRF-2015R1C1A2A01052150 and NRF-2016R1D1A3B03936067), and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B04033515 and NRF-2018R1D1A1B07050285).
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
| GC | gastric cancer |
| LncRNAs | long non-coding RNAs |
| PRNCR1 | prostate cancer non-coding RNA 1 |
| PC | prostate cancer |
| SNPs | single nucleotide polymorphisms |
| LNM | lymph node metastasis |
| IGC | intestinal-type gastric cancer |
| HWE | Hardy–Weinberg equilibrium |
| OR | odds ratio |
| CI | confidence interval |
References
- Bertuccio, P.; Chatenoud, L.; Levi, F.; Praud, D.; Ferlay, J.; Negri, E.; Malvezzi, M.; La Vecchia, C. Recent patterns in gastric cancer: A global overview. Int. J. Cancer 2009, 125, 666–673. [Google Scholar] [CrossRef] [PubMed]
- Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics. CA Cancer J. Clin. 2011, 61, 69–90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jung, K.W.; Won, Y.J.; Kong, H.J.; Oh, C.M.; Lee, D.H.; Lee, J.S. Prediction of cancer incidence and mortality in Korea, 2018. Cancer Res. Treat. 2018, 50, 317–323. [Google Scholar] [CrossRef] [PubMed]
- Freedman, M.L.; Monteiro, A.N.; Gayther, S.A.; Coetzee, G.A.; Risch, A.; Plass, C.; Casey, G.; De Biasi, M.; Carlson, C.; Duggan, D.; et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat. Genet. 2011, 43, 513–518. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Harrow, J.; Frankish, A.; Gonzalez, J.M.; Tapanari, E.; Diekhans, M.; Kokocinski, F.; Aken, B.L.; Barrell, D.; Zadissa, A.; Searle, S.; et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 2012, 22, 1760–1774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, G.; Qiu, C.; Zhang, Q.; Liu, B.; Cui, Q. Genome-wide analysis of human SNP at long intergenic noncoding RNAs. Hum. Mutat. 2013, 34, 338–344. [Google Scholar] [CrossRef]
- Cheetham, S.W.; Gruh, L.F.; Mattick, J.S.; Dinger, M.E. Long noncoding RNAs and the genetics of cancer. Br. J. Cancer 2013, 108, 2419–2425. [Google Scholar] [CrossRef] [Green Version]
- Li, L.; Feng, T.; Lian, Y.; Zhang, G.; Garen, A.; Song, X. Role of human noncoding RNAs in the control of tumorigenesis. Proc. Natl. Acad. Sci. USA 2009, 106, 12956–12961. [Google Scholar] [CrossRef] [Green Version]
- Huarte, M. The emerging role of lncRNAs in cancer. Nat. Med. 2015, 21, 1253–1261. [Google Scholar] [CrossRef]
- Guo, H.; Ahmed, M.; Zhang, F.; Yao, C.Q.; Li, S.; Liang, Y.; Hua, J.; Soares, F.; Sun, Y.; Langstein, J.; et al. Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer. Nat. Genet. 2016, 48, 1142–1150. [Google Scholar] [CrossRef]
- Xia, Z.; Yan, R.; Duan, F.; Song, C.; Wang, P.; Wang, K. Genetic Polymorphisms in Long Noncoding RNA H19 Are Associated With Susceptibility to Breast Cancer in Chinese Population. Medicine (Baltimore) 2016, 95, e2771. [Google Scholar] [CrossRef] [PubMed]
- Xu, T.; Hu, X.X.; Liu, X.X.; Wang, H.J.; Lin, K.; Pan, Y.Q.; Sun, H.L.; Peng, H.X.; Chen, X.X.; Wang, S.K.; et al. Association between SNPs in Long Non-coding RNAs and the Risk of Female Breast Cancer in a Chinese Population. J. Cancer 2017, 8, 1162–1169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, Y.; Jing, F.; Ding, Y.; He, Q.; Zhong, Y.; Fan, C. Long noncoding RNA CCAT1 polymorphisms are associated with the risk of colorectal cancer. Cancer Genet. 2018, 222–223, 13–19. [Google Scholar] [CrossRef] [PubMed]
- He, B.S.; Sun, H.L.; Xu, T.; Pan, Y.Q.; Lin, K.; Gao, T.Y.; Zhang, Z.Y.; Wang, S.K. Association of Genetic Polymorphisms in the LncRNAs with Gastric Cancer Risk in a Chinese Population. J. Cancer 2017, 8, 531–536. [Google Scholar] [CrossRef] [PubMed]
- Du, M.; Wang, W.; Jin, H.; Wang, Q.; Ge, Y.; Lu, J.; Ma, G.; Chu, H.; Tong, N.; Zhu, H.; et al. The association analysis of lncRNA HOTAIR genetic variants and gastric cancer risk in a Chinese population. Oncotarget 2015, 6, 31255–31262. [Google Scholar] [CrossRef] [PubMed]
- Yuan, H.; Liu, H.; Liu, Z.; Owzar, K.; Han, Y.; Su, L.; Wei, Y.; Hung, R.J.; McLaughlin, J.; Brhane, Y.; et al. A Novel Genetic Variant in Long Non-coding RNA Gene NEXN-AS1 is Associated with Risk of Lung Cancer. Sci. Rep. 2016, 6, 34234. [Google Scholar] [CrossRef] [PubMed]
- Jin, G.; Sun, J.; Isaacs, S.D.; Wiley, K.E.; Kim, S.T.; Chu, L.W.; Zhang, Z.; Zhao, H.; Zheng, S.L.; Isaacs, W.B.; et al. Human polymorphisms at long non-coding RNAs (lncRNAs) and association with prostate cancer risk. Carcinogenesis 2011, 32, 1655–1659. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chung, S.; Nakagawa, H.; Uemura, M.; Piao, L.; Ashikawa, K.; Hosono, N.; Takata, R.; Akamatsu, S.; Kawaguchi, T.; Morizono, T.; et al. Association of a novel long non-coding RNA in 8q24 with prostate cancer susceptibility. Cancer Sci. 2011, 102, 245–252. [Google Scholar] [CrossRef]
- Salinas, C.A.; Kwon, E.; Carlson, C.S.; Koopmeiners, J.S.; Feng, Z.; Karyadi, D.M.; Ostrander, E.A.; Stanford, J.L. Multiple independent genetic variants in the 8q24 region are associated with prostate cancer risk. Cancer Epidemiol. Biomarkers Prev. 2008, 17, 1203–1213. [Google Scholar] [CrossRef]
- Zheng, S.L.; Hsing, A.W.; Sun, J.; Chu, L.W.; Yu, K.; Li, G.; Gao, Z.; Kim, S.T.; Isaacs, W.B.; Shen, M.C.; et al. Association of 17 prostate cancer susceptibility loci with prostate cancer risk in Chinese men. Prostate 2010, 70, 425–432. [Google Scholar] [CrossRef]
- Hui, J.; Xu, Y.; Yang, K.; Liu, M.; Wei, D.; Wei, D.; Zhang, Y.; Shi, X.H.; Yang, F.; Wang, N.; et al. Study of genetic variants of 8q21 and 8q24 associated with prostate cancer in Jing-Jin residents in northern China. Clin Lab. 2014, 60, 645–652. [Google Scholar] [CrossRef] [PubMed]
- Li, L.; Sun, R.; Liang, Y.; Pan, X.; Li, Z.; Bai, P.; Zeng, X.; Zhang, D.; Zhang, L.; Gao, L. Association between polymorphisms in long non-coding RNA PRNCR1in 8q24 and risk of colorectal cancer. J. Exp. Clin. Cancer Res. 2013, 32, 104. [Google Scholar] [CrossRef] [PubMed]
- Li, L.; Jia, F.; Bai, P.; Liang, Y.; Sun, R.; Yuan, F.; Zhang, L.; Gao, L. Association between polymorphisms in long non-coding RNA PRNCR1in 8q24 and risk of gastric cancer. Tumour. Biol. 2016, 37, 299–303. [Google Scholar] [CrossRef] [PubMed]
- Solcia, E.; Fiocca, R.; Luinetti, O.; Villani, L.; Padovan, L.; Calistri, D.; Ranzani, G.N.; Chiaravalli, A.; Capella, C. Intestinal and diffuse gastric cancers arise in a different background of Helicobacter pylori gastritis through different gene involvement. Am. J. Surg Pathol. 1996, 20 (Suppl. 1), S8–S22. [Google Scholar] [CrossRef]
- Parkin, D.M. The global health burden of infection-associated cancers in the year 2002. Int. J. Cancer 2006, 118, 3030–3044. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lauren, P. The two histological main types of gastric carcinoma: Diffuse and so-called intestinal-type carcinoma. an attempt at a histo-clinical classification. Acta Pathol. Microbiol. Scand. 1965, 64, 31–49. [Google Scholar] [CrossRef]
Table 1.
Characteristics and clinical features of the gastric cancer group and the control group.
| Variables | Gastric Cancers | Controls | p |
|---|---|---|---|
| N (%) | N (%) | ||
| Age (years) (mean ± SD) | 437 (65.3 ± 11.1) | 357 (58.1 ± 8.9) | <0.001 * |
| <60 | 185 (42.3) | 186 (52.1) | 0.007 † |
| ≥60 | 252 (57.7) | 171 (47.9) | |
| Gender (%) | |||
| Male | 304 (69.6) | 117 (32.8) | <0.001 † |
| Female | 133 (30.4) | 240 (67.2) | |
| Tumor differentiation | |||
| Differentiated | 208 (47.6) | ||
| Undifferentiated | 190 (43.5) | ||
| Missing | 39 (8.9) | ||
| Histological type (%) | |||
| Intestinal | 244 (55.8) | ||
| Diffuse | 140 (32.1) | ||
| Mixed | 53 (12.1) | ||
| T classification (%) | |||
| T1 | 221 (50.6) | ||
| T2 | 59 (13.5) | ||
| T3 | 16 (3.6) | ||
| T4 | 141 (32.3) | ||
| Lymph node metastasis (%) | |||
| Negative | 266 (60.9) | ||
| Positive | 171 (39.1) | ||
| Tumor stage (%) | |||
| I (A+B) | 257 (58.8) | ||
| II (A+B) | 50 (11.5) | ||
| III (A+B+C) | 130 (29.7) |
SD, standard deviation. * Mann–Whitney U-test. † Two-sided Pearson’s chi-squared test.
Table 2.
Genotype and allelic frequencies of PRNCR1 polymorphisms in subjects and their association with GC risk.
Table 2.
Genotype and allelic frequencies of PRNCR1 polymorphisms in subjects and their association with GC risk.
| Genotype | CON | GC vs. CON | IGC vs. CON | DGC vs. CON | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| N (%) | N (%) | AOR (95% CI) a | p | N (%) | AOR (95% CI) a | p | N (%) | AOR (95% CI) a | p | |
| rs1016343 | ||||||||||
| Codominant | ||||||||||
| CC | 171 (47.9) | 209 (47.8) | 1 | 119 (48.5) | 1 | 62 (44.6) | 1 | 0.661 | ||
| CT | 158 (44.3) | 191 (43.7) | 0.94 (0.69–1.29) | 0.709 | 106 (43.3) | 0.94 (0.64–1.38) | 0.756 | 66 (47.5) | 1.10 (0.72–1.68) | 0.892 |
| TT | 28 (7.8) | 37 (8.5) | 0.88 (0.49–1.56) | 0.654 | 20 (8.2) | 0.84 (0.42–1.67) | 0.612 | 11 (7.9) | 0.95 (0.44–2.06) | |
| Dominant | ||||||||||
| CC | 171 (47.9) | 209 (47.8) | 1 | 119 (48.6) | 1 | 62 (44.6) | 1 | 0.727 | ||
| CT + TT | 186 (52.1) | 228 (52.2) | 0.93 (0.69–1.26) | 0.645 | 126 (51.4) | 0.92 (0.64–1.33) | 0.672 | 77 (55.4) | 1.08 (0.72–1.61) | |
| Recessive | ||||||||||
| CC + CT | 329 (92.2) | 406 (91.5) | 1 | 225 (91.8) | 1 | 128 (92.1) | 1 | 0.790 | ||
| TT | 28 (8.7) | 37 (8.5) | 0.90 (0.52–1.57) | 0.718 | 20 (8.2) | 0.86 (0.44–1.68) | 0.659 | 11 (7.9) | 0.90 (0.43–1.91) | |
| HWE | 0.591 | 0.772 | ||||||||
| rs13252298 | ||||||||||
| Codominant | ||||||||||
| AA | 158 (44.3) | 214 (49.0) | 1 | 122 (49.6) | 1 | 67 (48.6) | 1 | 0.450 | ||
| AG | 171 (47.9) | 182 (41.6) | 0.90 (0.66–1.24) | 0.518 | 97 (39.4) | 0.87 (0.59–1.29) | 0.497 | 61 (44.2) | 0.85 (0.56–1.30) | 0.854 |
| GG | 28 (7.8) | 41 (9.4) | 1.36 (0.77–2.40) | 0.285 | 27 (11.0) | 1.80 (0.93–3.49) | 0.084 | 10 (7.2) | 0.93 (0.42–2.06) | |
| Dominant | ||||||||||
| AA | 158 (44.3) | 214 (49.0) | 1 | 122 (49.6) | 1 | 67 (48.6) | 1 | 0.469 | ||
| AG + GG | 199 (55.7) | 223 (51.0) | 0.96 (0.71–1.31) | 0.805 | 124 (50.4) | 0.99 (0.69–1.43) | 0.954 | 71 (51.4) | 0.86 (0.57–1.30) | |
| Recessive | ||||||||||
| AA+AG | 329 (92.2) | 396 (90.6) | 1 | 219 (89.0) | 1 | 128 (92.8) | 1 | 0.982 | ||
| GG | 28 (7.8) | 41 (9.4) | 1.43 (0.83–2.47) | 0.193 | 27 (11.0) | 1.92 (1.01–3.63) | 0.045 | 10 (7.2) | 1.01 (0.47–2.18) | |
| HWE | 0.143 | 0.968 | ||||||||
| rs7841060 | ||||||||||
| Codominant | ||||||||||
| TT | 169 (47.4) | 204 (46.7) | 1 | 116 (47.5) | 1 | 61 (43.5) | 1 | 0.556 | ||
| TG | 159 (44.5) | 195 (44.6) | 0.96 (0.70–1.31) | 0.794 | 108 (44.3) | 0.96 (0.65–1.40) | 0.814 | 68 (48.6) | 1.14 (0.75–1.73) | 0.842 |
| GG | 29 (8.1) | 38 (8.7) | 0.88 (0.50–1.55) | 0.647 | 20 (8.2) | 0.82 (0.41–1.64) | 0.578 | 11 (7.9) | 0.92 (0.43–2.00) | |
| Dominant | ||||||||||
| TT | 169 (47.3) | 204 (46.7) | 1 | 116 (47.5) | 1 | 61 (43.6) | 1 | 0.643 | ||
| TG + GG | 188 (52.7) | 233 (53.3) | 0.95 (0.70–1.28) | 0.716 | 128 (52.5) | 0.93 (0.65–1.35) | 0.711 | 79 (56.4) | 1.10 (0.73–1.65) | |
| Recessive | ||||||||||
| TT + TG | 328 (91.9) | 399 (91.3) | 1 | 224 (91.8) | 1 | 129 (92.1) | 1 | 0.705 | ||
| GG | 29 (8.1) | 38 (8.7) | 0.89 (0.52–1.54) | 0.688 | 20 (8.2) | 0.84 (0.43–1.63) | 0.609 | 11 (7.9) | 0.87 (0.41–1.82) | |
| HWE | 0.610 | 0.668 | ||||||||
| rs16901946 | ||||||||||
| Codominant | ||||||||||
| AA | 178 (49.9) | 208 (47.6) | 1 | 117 (48.0) | 1 | 68 (48.6) | 1 | 0.506 | ||
| AG | 147 (41.1) | 191 (43.7) | 1.21 (0.88–1.66) | 0.245 | 105 (43.0) | 1.26 (0.85–1.85) | 0.253 | 62 (44.3) | 1.15 (0.76–1.76) | 0.643 |
| GG | 32 (9.0) | 38 (8.7) | 0.84 (0.49–1.46) | 0.539 | 22 (9.0) | 0.78 (0.40–1.49) | 0.445 | 10 (7.1) | 0.83 (0.38–1.83) | |
| Dominant | ||||||||||
| AA | 178 (49.9) | 208 (47.6) | 1 | 117 (48.0) | 1 | 68 (48.6) | 1 | 0.658 | ||
| AG + GG | 179 (50.1) | 229 (52.4) | 1.13 (0.84–1.54) | 0.414 | 127 (52.0) | 1.15 (0.79–1.65) | 0.468 | 72 (51.4) | 1.10 (0.73–1.64) | |
| Recessive | ||||||||||
| AA + AG | 325 (91.0) | 399 (91.3) | 1 | 222 (91.0) | 1 | 130 (92.9) | 1 | 0.516 | ||
| GG | 32 (9.0) | 38 (8.7) | 0.77 (0.45–1.31) | 0.338 | 22 (9.0) | 0.70 (0.37–1.32) | 0.269 | 10 (7.1) | 0.78 (0.36–1.67) | |
| HWE | 0.978 | 0.821 | ||||||||
CON, control; GC, gastric cancer; IGC, intestinal-type gastric cancer; DGC, diffuse-type gastric cancer; AOR, adjusted odds ratio; CI, confidence interval; HWE, Hardy–Weinberg equilibrium. a Adjusted for age and gender. The significant results are in bold.
Table 3.
Stratified analysis of PRNCR1 polymorphisms in GC patients and controls by age.
| SNP | Variable | GC vs. CON | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Dominant (ht+mt/wt) | Recessive (mt/wt+ht) | ||||||||
| GC | CON | AOR (95% CI) a | p | GC | CON | AOR (95% CI) a | p | ||
| rs1016343 | Gender (M) | 76/56 | 28/17 | 0.93 (0.46–1.90) | 0.849 | 8/124 | 6/39 | 0.43 (0.14–1.32) | 0.140 |
| Age | Gender (F) | 27/28 | 68/73 | 1.00 (0.53–1.90) | 0.992 | 2/53 | 9/132 | 0.40 (0.08–2.07) | 0.276 |
| <60 | IGC | 43/44 | 96/90 | 0.72 (0.38–1.37) | 0.320 | 4/83 | 15/171 | 0.34 (0.10–1.19) | 0.090 |
| DGC | 47/30 | 96/90 | 1.43 (0.79–2.56) | 0.236 | 4/73 | 15/171 | 0.50 (0.15–1.63) | 0.248 | |
| LNM (−) | 73/50 | 96/90 | 1.28 (0.74–2.23) | 0.380 | 5/118 | 15/171 | 0.29 (0.09–0.94) | 0.038 | |
| LNM (+) | 30/34 | 96/90 | 0.71 (0.38–1.33) | 0.284 | 5/59 | 15/171 | 0.76 (0.25–2.34) | 0.634 | |
| Tumor stage I + II | 84/60 | 96/90 | 1.19 (0.70–2.02) | 0.512 | 8/136 | 15/171 | 0.45 (0.17–1.24) | 0.123 | |
| Tumor stage III | 19/24 | 96/90 | 0.61 (0.30–1.26) | 0.184 | 2/41 | 15/171 | 0.40 (0.08–1.90) | 0.248 | |
| ≥60 | Gender (M) | 36/38 | 91/85 | 1.14 (0.66–1.97) | 0.646 | 21/155 | 6/68 | 1.48 (0.56–3.88) | 0.426 |
| Gender (F) | 34/40 | 54/43 | 1.14 (0.66–1.97) | 0.144 | 6/68 | 7/90 | 1.48 (0.56–3.88) | 0.663 | |
| IGC | 83/75 | 90/81 | 1.09 (0.67–1.76) | 0.727 | 16/142 | 13/158 | 1.39 (0.59–3.26) | 0.453 | |
| DGC | 30/32 | 90/81 | 0.81 (0.45–1.48) | 0.495 | 7/55 | 13/158 | 1.40 (0.51–3.84) | 0.514 | |
| LNM (−) | 76/69 | 90/81 | 0.99 (0.62–1.59) | 0.967 | 14/131 | 13/158 | 1.28 (0.55–3.01) | 0.566 | |
| LNM (+) | 49/56 | 90/81 | 0.80 (0.47–1.36) | 0.409 | 13/92 | 13/158 | 1.42 (0.57–3.53) | 0.446 | |
| Tumor stage I + II | 83/83 | 90/81 | 0.92 (0.58–1.45) | 0.706 | 17/149 | 13/158 | 1.40 (0.62–3.15) | 0.418 | |
| Tumor stage III | 42/42 | 90/81 | 0.90 (0.51–1.58) | 0.703 | 10/74 | 13/158 | 1.25 (0.47–3.37) | 0.658 | |
| rs13252298 | Gender (M) | 69/63 | 27/18 | 0.75 (0.37–1.49) | 0.405 | 8/124 | 1/44 | 2.69 (0.32–22.47) | 0.360 |
| Age | Gender (F) | 33/21 | 89/53 | 0.88 (0.46–1.71) | 0.716 | 6/48 | 16/126 | 0.95 (0.34–2.63) | 0.916 |
| <60 | IGC | 45/42 | 116/71 | 0.75 (0.40–1.40) | 0.369 | 10/77 | 17/170 | 2.42 (0.83–7.00) | 0.104 |
| DGC | 44/32 | 116/71 | 0.82 (0.46–1.48) | 0.517 | 4/72 | 17/170 | 0.78 (0.24–2.54) | 0.680 | |
| LNM (−) | 67/54 | 116/71 | 0.86 (0.49–1.49) | 0.590 | 10/111 | 17/170 | 1.26 (0.47–3.35) | 0.645 | |
| LNM (+) | 35/30 | 116/71 | 0.72 (0.39–1.34) | 0.297 | 4/61 | 17/170 | 1.13 (0.34–3.75) | 0.847 | |
| Tumor stage I + II | 77/65 | 116/71 | 0.81 (0.48–1.37) | 0.434 | 12/130 | 17/170 | 1.51 (0.60–3.81) | 0.382 | |
| Tumor stage III | 25/19 | 116/71 | 0.82 (0.41–1.66) | 0.585 | 2/42 | 17/170 | 0.69 (0.15–3.28) | 0.645 | |
| ≥60 | Gender (M) | 79/95 | 32/41 | 1.04 (0.59–1.81) | 0.898 | 18/156 | 5/68 | 1.77 (0.62–5.06) | 0.289 |
| Gender (F) | 42/35 | 51/46 | 0.96 (0.50–1.87) | 0.912 | 9/68 | 6/91 | 1.83 (0.54–6.21) | 0.332 | |
| IGC | 79/80 | 83/87 | 1.07 (0.66–1.73) | 0.780 | 17/142 | 11/159 | 1.73 (0.73–4.10) | 0.210 | |
| DGC | 27/35 | 83/87 | 0.87 (0.47–1.59) | 0.646 | 6/56 | 11/159 | 2.19 (0.74–6.48) | 0.157 | |
| LNM (−) | 71/75 | 83/87 | 1.01 (0.63–1.62) | 0.967 | 12/134 | 11/159 | 1.43 (0.58–3.56) | 0.442 | |
| LNM (+) | 50/55 | 83/87 | 1.07 (0.62–1.82) | 0.817 | 15/90 | 11/159 | 2.80 (1.15–6.82) | 0.024 | |
| Tumor stage I + II | 80/86 | 83/87 | 1.01 (0.64–1.60) | 0.975 | 14/152 | 11/159 | 1.37 (0.56–3.32) | 0.493 | |
| Tumor stage III | 41/44 | 83/87 | 1.08 (0.61–1.92) | 0.782 | 13/72 | 11/159 | 3.39 (1.35–8.52) | 0.009 | |
| rs7841060 | Gender (M) | 76/52 | 28/16 | 0.93 (0.45–1.92) | 0.847 | 10/118 | 6/38 | 0.55 (0.19–1.64) | 0.286 |
| Age | Gender (F) | 27/30 | 69/73 | 0.94 (0.50–1.76) | 0.838 | 2/55 | 10/132 | 0.37 (0.07–1.88) | 0.230 |
| <60 | IGC | 43/43 | 97/89 | 0.68 (0.36–1.28) | 0.232 | 5/81 | 16/170 | 0.43 (0.13–1.37) | 0.153 |
| DGC | 47/30 | 97/89 | 1.39 (0.77–2.50) | 0.272 | 4/73 | 16/170 | 0.48 (0.15–1.57) | 0.224 | |
| LNM (−) | 71/49 | 97/89 | 1.19 (0.68–2.08) | 0.535 | 6/114 | 16/170 | 0.34 (0.12–1.03) | 0.056 | |
| LNM (+) | 32/33 | 97/89 | 0.74 (0.39–1.37) | 0.333 | 6/59 | 16/170 | 0.86 (0.30–2.47) | 0.778 | |
| Tumor stage I + II | 82/59 | 97/89 | 1.11 (0.66–1.88) | 0.695 | 9/132 | 16/170 | 0.49 (0.19–1.29) | 0.147 | |
| Tumor stage III | 21/23 | 97/89 | 0.67 (0.33–1.36) | 0.264 | 3/41 | 16/170 | 0.55 (0.14–2.11) | 0.384 | |
| ≥60 | Gender (M) | 93/83 | 36/37 | 1.14 (0.66–1.98) | 0.644 | 20/156 | 6/67 | 1.35 (0.51–3.56) | 0.544 |
| Gender (F) | 37/39 | 55/43 | 0.74 (0.38–1.44) | 0.378 | 6/70 | 7/91 | 1.31 (0.39–4.42) | 0.668 | |
| IGC | 85/73 | 91/80 | 1.15 (0.71–1.86) | 0.577 | 15/143 | 13/158 | 1.26 (0.53–3.01) | 0.602 | |
| DGC | 32/31 | 91/80 | 0.90 (0.50–1.64) | 0.737 | 7/56 | 13/158 | 1.38 (0.50–3.78) | 0.536 | |
| LNM (−) | 79/67 | 91/80 | 1.08 (0.67–1.73) | 0.753 | 14/132 | 13/158 | 1.26 (0.54–2.95) | 0.598 | |
| LNM (+) | 51/55 | 91/80 | 0.82 (0.48–1.39) | 0.450 | 12/94 | 13/158 | 1.22 (0.48–3.09) | 0.676 | |
| Tumor stage I + II | 85/81 | 91/80 | 0.98 (0.62–1.55) | 0.922 | 16/150 | 13/158 | 1.28 (0.56–2.93) | 0.554 | |
| Tumor stage III | 45/41 | 91/80 | 0.95 (0.54–1.67) | 0.861 | 10/76 | 13/158 | 1.21 (0.45–3.25) | 0.702 | |
| rs16901946 | Gender (M) | 66/63 | 17/27 | 1.48 (0.72–3.02) | 0.282 | 14/115 | 3/41 | 1.64 (0.44–6.05) | 0.462 |
| Age | Gender (F) | 29/26 | 68/75 | 1.35 (0.71–2.57) | 0.364 | 1/54 | 9/134 | 0.34 (0.04–2.76) | 0.312 |
| <60 | IGC | 45/40 | 85/102 | 1.75 (0.92–3.32) | 0.088 | 10/75 | 12/175 | 1.38 (0.46–4.11) | 0.562 |
| DGC | 39/38 | 85/102 | 1.20 (0.68–2.14) | 0.533 | 2/75 | 12/175 | 0.35 (0.07–1.81) | 0.210 | |
| LNM (−) | 58/62 | 85/102 | 1.14 (0.66–1.98) | 0.638 | 10/110 | 12/175 | 0.95 (0.33–2.74) | 0.920 | |
| LNM (+) | 37/27 | 85/102 | 1.78 (0.96–3.32) | 0.069 | 5/59 | 12/175 | 1.09 (0.33–3.58) | 0.883 | |
| Tumor stage I+II | 68/73 | 85/102 | 1.08 (0.64–1.83) | 0.766 | 12/129 | 12/175 | 1.01 (0.37–2.76) | 0.989 | |
| Tumor stage III | 27/16 | 85/102 | 2.38 (1.15–4.94) | 0.020 | 3/40 | 12/175 | 1.03 (0.26–4.08) | 0.972 | |
| ≥60 | Gender (M) | 91/85 | 40/33 | 0.91 (0.52–1.58) | 0.734 | 15/161 | 12/61 | 0.43 (0.19–0.98) | 0.046 |
| Gender (F) | 43/34 | 54/43 | 1.14 (0.58–2.22) | 0.709 | 8/69 | 8/89 | 1.33 (0.41–4.25) | 0.635 | |
| IGC | 82/77 | 94/76 | 0.94 (0.58–1.52) | 0.797 | 12/147 | 20/150 | 0.38 (0.16–0.89) | 0.026 | |
| DGC | 47/30 | 94/76 | 1.43 (0.79–2.56) | 0.236 | 4/73 | 20/150 | 0.50 (0.15–1.63) | 0.248 | |
| LNM (−) | 80/66 | 94/76 | 1.05 (0.65–1.69) | 0.841 | 16/130 | 20/150 | 0.69 (0.32–1.47) | 0.336 | |
| LNM (+) | 54/53 | 94/76 | 0.85 (0.50–1.44) | 0.538 | 7/100 | 20/150 | 0.39 (0.15–1.06) | 0.064 | |
| Tumor stage I + II | 92/75 | 94/76 | 1.04 (0.66–1.65) | 0.871 | 18/149 | 20/150 | 0.65 (0.31–1.36) | 0.249 | |
| Tumor stage III | 42/44 | 94/76 | 0.82 (0.47–1.45) | 0.495 | 5/81 | 20/150 | 0.37 (0.12–1.12) | 0.078 | |
GC, gastric cancer; CON, control; ht, heterozygous; mt, mutant; wt, wild-type; SNP, single nucleotide polymorphism; AOR, adjusted odds ratio; CI, confidence interval; M, male; F, female; IGC, intestinal-type gastric cancer; DGC, diffuse-type gastric cancer; LNM; lymph node metastasis. a Adjusted for age and gender. The significant results are in bold.
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Hong, J.H.; Jin, E.-H.; Kang, H.; Chang, I.A.; Lee, S.-I.; Sung, J.K. Correlations between Genetic Polymorphisms in Long Non-Coding RNA PRNCR1 and Gastric Cancer Risk in a Korean Population. Int. J. Mol. Sci. 2019, 20, 3355. https://doi.org/10.3390/ijms20133355
AMA Style
Hong JH, Jin E-H, Kang H, Chang IA, Lee S-I, Sung JK. Correlations between Genetic Polymorphisms in Long Non-Coding RNA PRNCR1 and Gastric Cancer Risk in a Korean Population. International Journal of Molecular Sciences. 2019; 20(13):3355. https://doi.org/10.3390/ijms20133355
Chicago/Turabian StyleHong, Jang Hee, Eun-Heui Jin, Hyojin Kang, In Ae Chang, Sang-Il Lee, and Jae Kyu Sung. 2019. "Correlations between Genetic Polymorphisms in Long Non-Coding RNA PRNCR1 and Gastric Cancer Risk in a Korean Population" International Journal of Molecular Sciences 20, no. 13: 3355. https://doi.org/10.3390/ijms20133355
APA StyleHong, J. H., Jin, E. -H., Kang, H., Chang, I. A., Lee, S. -I., & Sung, J. K. (2019). Correlations between Genetic Polymorphisms in Long Non-Coding RNA PRNCR1 and Gastric Cancer Risk in a Korean Population. International Journal of Molecular Sciences, 20(13), 3355. https://doi.org/10.3390/ijms20133355
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
