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. 2019 Jul 12;39(7):BSR20190708. doi: 10.1042/BSR20190708

lncRNA-PCAT1 rs2632159 polymorphism could be a biomarker for colorectal cancer susceptibility

Ming-li Yang 1, Zhe Huang 2, Li-na Wu 1, Rong Wu 1, Han-xi Ding 3, Ben-gang Wang 4,
PMCID: PMC6629943  PMID: 31253700

Abstract

Background: Single-nucleotide polymorphisms (SNPs) in lncRNAs could be biomarkers for susceptibility to colorectal cancer (CRC), but the association of PCAT1 polymorphisms and CRC susceptibility is yet to be studied. Methods: Five tagSNPs covering the PCAT1 gene were detected through Kompetitive Allele-Specific PCR among 436 CRC patients and 510 controls. An expression quantitative trait locus (eQTL) bioinformatic analysis was then performed. Results: In the present study, PCAT1 rs2632159 polymorphism increased CRC risk by 1.37-fold and 2.19-fold in the dominant and recessive models, respectively (P=0.040 and 0.041). When the CRC cases were divided into colon cancer and rectal cancer, we found that this polymorphism affected colon cancer risk under the dominant model (P=0.022, OR = 1.51) and affected rectal cancer susceptibility under the recessive model (P=0.009, OR = 3.03). A more pronounced effect was observed in the male subgroup in that PCAT1 rs2632159 SNP increased rectal cancer risk by 3.97-fold (P=0.017). When PCAT1 rs2632159 was present, epistatic effects were observed with rs1902432 and rs785005 (P=0.011 and 0.008, respectively). eQTL analysis showed that rs2632159 could influence binding with the transcription factors EBF, LUN-1, and TCF12. Conclusion: PCAT1 rs2632159 SNP could be a biomarker for CRC risk. And the rs1902432 SNP might only have potential to be a biomarker for colon cancer risk.

Keywords: colorectal cancer, PCAT1, Single nucleotide polymorphism, susceptibility

Introduction

lncRNAs are noncoding RNAs longer than 200 bp without apparent protein-coding potential; they can function as oncogenes or suppressor genes in the process of carcinogenesis [1]. At lncRNA loci, genetic variation can occur, which contributes to a change of lncRNA function. Single-nucleotide polymorphisms (SNPs) are the most common pattern of genetic variation. As reported previously, SNPs have the potential to act as biomarkers for cancer susceptibility [2,3]. Some lncRNA polymorphisms were considered to be predictive of cancer risk, to be useful for early diagnosis, and to aid in the management of personalized therapy [2,4].

The expression patterns of prostate cancer-associated transcripts (PCATs) distinguish benign localized cancer and metastatic cancer samples. In 2012, Prensner et al. [5] reported the discovery of a novel prostate cancer lncRNA, PCAT-1, which alternately demonstrates either repression by PRC2 or an active role in promoting cell proliferation through transcriptional regulation of target genes. Subsequently, Ge et al. [6] identified the up-regulation of PCAT1 expression in colorectal cancer (CRC) tissues, indicating that PCAT1 functions as an oncogene in CRC. Concerning PCAT1 SNPs, only one investigation has reported their association with cancer susceptibility, which found that PCAT1 rs1902432 tagSNP was associated with prostate cancer risk [7]. However, no comprehensive analysis of the association of all tagSNPs of PCAT1 with cancer susceptibility has yet been performed, including for CRC.

In the present study, we thus performed a study to investigate the association between the tagSNPs of the PCAT1 gene and CRC susceptibility. The present study sought biomarkers predictive of CRC risk and was intended to unearth clues providing a deeper understanding of the etiology of colorectal carcinogenesis.

Materials and methods

Patients

This research project was approved by the institutional review board of First Affiliated Hospital of China Medical University. All the participants are according to the principles expressed in the Declaration of Helsinki. A total of 436 CRC patients, who had undergone a surgical operation for CRC at the Shengjing Affiliated Hospital of China Medical University, were consecutively recruited between 2016 and 2017. The presnt study is retrospective and designed as a case-control study. In all of these subjects, the pathological diagnosis was confirmed based on the AJCC classification. According to the site of occurrence, there were 229 colon cancer patients and 207 rectal cancer patients. A total of 510 controls were also recruited from a health check program at the same hospital. And the inclusion and exclusion criteria for the case and control groups were: 1) the race is consistent and are all Asian; 2) the pathological diagnosis is definite clear in case group; 3) the healthy controls have the normal serum biochemical indexes and excluded all other systemic diseases; 4) multiple tumor patients with prostate cancer or bladder cancer or cervical cancer were excluded; and 5) patients who received preoperative radiotherapy or chemotherapy were excluded.

Written informed consent was provided by each individual, and the medical records were used for the pathological diagnosis and TNM staging of the CRC cases. A questionnaire was used to collect information on the smoking, drinking, and family history of the cases. The smoking and drinking information are defined by referring the previously published study [8]: cigarette smoker was defined as who have smoked at least 100 cigarettes during one’s lifetime or had been smoking any amount for at least 1 year, which included current smokers and former smokers. Alcohol drinker was defined as who consumed one or more alcoholic drinks per week for >1 year. One drink equals to one 12-oz can or bottle of beer (5% ethanol), one 5-oz glass of wine (12.5% ethanol), or 1.5-oz measure of liquor (40% ethanol) for each subject.

Whole blood from individuals was collected and blood clots were allowed to form by incubating clot-activating tubes at room temperature for 1 h. Each clot was transferred to a 2-ml centrifuge tube and stored at −80°C until DNA extraction.

Polymorphisms selected and SNP Genotyping

The polymorphisms selected method was based on our previously published study [9]. The linkage disequilibrium (LD) of PCAT1 gene was shown in Supplementary Figure 1. Then five SNPs covered PCAT1 gene (rs1902432, rs4573233, rs710885, rs785005, rs2632159) were selected [8,10–12], other SNPs captured by the selected ones were shown in Supplementary Table S1. Genomic DNA was purified from the blood as described previously [13], with some modifications, and was diluted to working concentrations of 50 ng/l for the PCAT1 polymorphism genotyping. Polymorphisms-genotyping assay was performed using Kompetitive Allele-Specific PCR (KASP, LGC Genomics, Hoddesdon, U.K.) by Gene Company (Shanghai, China) [14,15] and 10% of the total samples were repeatedly genotyped at the same time for quality control (the concordance rate all reached 100%).

eQTLs

To perform the promising functional polymorphism of PCAT1 gene and expression quantitative trait locus (eQTL) analysis, we used the bioinformatical online software Haploreg (http://www.broadinstitute.org/mammals/haploreg/haploreg.php) [16] to mine the data.

Statistical analysis

In the present study, all analyses were conducted on the CRC risk, colon cancer risk, and rectal cancer risk. The association between lncRNA-PCAT1 gene polymorphisms and CRC risk was determined using multivariate logistic regression adjusted for age and gender. The lncRNA haplotype was analyzed using the online software SHEsis [17]. The epistatic effects of pairwise interacting factors for the polymorphism of the PCAT1 gene (rs2632159) with the strongest potential effect and other SNPs on the risk of CRC were analyzed by multivariate logistic regression adjusted for age and gender. The association between the genotype of PCAT1 SNPs and clinicopathological parameters was calculated using χ2 two-sided test.

Results

The association of lncRNA-PCAT1 SNPs with CRC susceptibility

In the present study, the PCAT1 rs2632159 polymorphism was found to increase CRC risk by 1.37-fold and 2.19-fold in the dominant and recessive models, respectively (P=0.040 and 0.041). When the CRC cases were divided into colon cancer and rectal cancer, we found that PCAT1 rs2632159 was associated with colon cancer risk under the dominant model (P=0.022, OR = 1.51) and with rectal cancer susceptibility under the recessive model (P=0.009, OR = 3.03). In addition, PCAT1 rs1902432 polymorphism decreased colon cancer risk to 0.59-fold (P=0.030, Table 1).

Table 1. The association of PCAT1 polymorphisms and CRC risk.

SNP* Genotype PHWE Controls (%) CRC CRC vs CON Colon cancer vs CON Rectal cancer vs CON
All (%) Colon cancer (%) Rectal cancer (%) P value OR (95% CI) P value OR (95% CI) P value OR (95% CI)
rs1902432 TT 0.557 170 (34.07) 160 (36.87) 90 (39.47) 70 (33.98) 1 (Ref) 1 (Ref)
TC 237 (47.49) 208 (47.92) 111 (48.68) 97 (47.09) 0.830 0.97 (0.71–1.31) 0.638 0.92 (0.64–1.31) 0.874 1.03 (0.71–1.51)
CC 92 (18.44) 66 (15.21) 27 (11.85) 39 (18.93) 0.163 0.74 (0.49–1.13) 0.030 0.57 (0.33–0.94) 0.916 0.97 (0.59–1.60)
CC+TC vs TT 0.519 0.91 (0.6–1.22) 0.252 0.82 (0.58–1.15) 0.893 1.03 (0.72–1.47)
CC vs TT+TC 0.158 0.76 (0.53–1.11) 0.030 0.59 (0.36–0.95) 0.897 0.97 (0.63–1.51)
C vs T 0.229 0.89 (0.73–1.08) 0.051 0.79 (0.62–1.00) 0.970 1.01 (0.79–1.28)
rs4573233 GG 0.688 331 (66.73) 274 (63.57) 141 (61.84) 133 (65.52) 1 (Ref) 1 (Ref)
GA 150 (30.24) 138 (32.02) 77 (33.77) 61 (30.05) 0.615 1.08 (0.80–1.47) 0.394 1.17 (0.82–1.67) 0.947 0.99 (0.68–1.44)
AA 15 (3.03) 19 (4.41) 10 (4.39) 9 (4.93) 0.273 1.54 (0.71–3.32) 0.333 1.55 (0.64–3.76) 0.365 1.53 (0.61–3.80)
AA+GA vs GG 0.442 1.12 (0.84–1.50) 0.297 1.20 (0.85–1.69) 0.850 1.04 (0.72–1.49)
AA vs GG+GA 0.294 1.50 (0.71–3.18) 0.400 1.46 (0.61–3.48) 0.344 1.54 (0.63–3.78)
A vs G 0.312 1.14 (0.89–1.46) 0.243 1.19 (0.89–1.60) 0.631 1.08 (0.79–1.47)
rs710885 TT 0.122 446 (88.32) 370 (85.65) 195 (85.53) 175 (85.78) 1 (Ref) 1 (Ref)
TG 55 (10.89) 57 (13.19) 32 (14.04) 25 (12.25) 0.090 1.45 (0.94–2.21) 0.100 1.51 (0.92–2.48) 0.250 1.37 (0.80–2.32)
GG 4 (0.79) 5 (1.16) 1 (0.43) 4 (1.97) 0.506 1.62 (0.39–6.76) 0.673 0.62 (0.06–5.88) 0.176 2.84 (0.63–12.93)
GG+TG vs TT 0.072 1.46 (0.97–2.21) 0.133 1.45 (0.89–2.35) 0.132 1.48 (0.89–2.45)
GG vs TT+TG 0.543 1.56 (0.37–6.54) 0.647 0.59 (0.06–5.65) 0.193 2.74 (0.60–12.51)
G vs T 0.065 1.44 (0.98–2.11) 0.192 1.35 (0.86–2.14) 0.071 1.53 (0.96–2.44)
rs785005 GG 0.629 221 (44.92) 199 (45.96) 97 (42.73) 102 (49.51) 1 (Ref) 1 (Ref)
GA 221 (44.92) 190 (43.88) 102 (44.94) 88 (42.72) 0.579 0.92 (0.68–1.24) 0.982 1.00 (0.70–1.43) 0.334 0.84 (0.58–1.20)
AA 50 (10.16) 44 (10.16) 28 (12.33) 16 (7.77) 0.763 0.93 (0.57–1.52) 0.509 1.21 (0.69–2.11) 0.198 0.65 (0.34–1.25)
AA+GA vs GG 0.570 0.92 (0.70–1.22) 0.808 1.04 (0.75–1.46) 0.216 0.80 (0.57–1.14)
AA vs GG+GA 0.896 0.97 (0.61–1.54) 0.474 1.21 (0.71–2.04) 0.292 0.72 (0.39–1.33)
A vs G 0.627 0.95 (0.77–1.17) 0.607 1.07 (0.83–1.37) 0.159 0.83 (0.64–1.08)
rs2632159 TT 0.991 357 (70.55) 281 (65.20) 142 (62.83) 139 (67.81) 1 (Ref) 1 (Ref)
TC 136 (26.88) 130 (30.16) 77 (34.07) 53 (25.85) 0.129 1.27 (0.93–1.73) 0.033 1.48 (1.03–2.12) 0.789 1.06 (0.71–1.56)
CC 13 (2.57) 20 (4.64) 7 (3.10) 13 (6.34) 0.026 2.38 (1.11–5.09) 0.304 1.67 (0.63–4.41) 0.008 3.12 (1.34–7.23)
CC+TC vs TT 0.040 1.37 (1.01–1.84) 0.022 1.51 (1.06–2.14) 0.286 1.22 (0.85–1.77)
CC vs TT+TC 0.041 2.19 (1.03–4.64) 0.445 1.46 (0.56–3.81) 0.009 3.03 (1.32–6.94)
C vs T 0.013 1.39 (1.07–1.79) 0.023 1.42 (1.05–1.92) 0.061 1.35 (0.99–1.85)

Terms in bold represent significant results (P<0.05). PHWE = P value for Hardy–Weinberg Equilibrium.Abbreviations: CRC, colorectal cancer; NA, not available. *, the sort order was according to the SNP location in its genes from 5′ starting to 3′ ends. , P value was calculated by adjusted age and gender.

Subgroup analysis

In the male subgroup, PCAT1 rs2632159 SNP also increased rectal cancer risk by 3.97-fold in the male subgroup (P=0.017, Table 2), while PCAT1 rs710885 polymorphism increased CRC risk by 2.09-fold and colon cancer risk by 2.60-fold (P = 0.033, P = 0.013, Table 2).

Table 2. Association of lnRNA-PCAT1 polymorphisms with the CRC susceptibility stratified by host characteristics.

SNP Variable Genotype CON CRC CRC vs CON Colon cancer vs CON Rectal cancer vs CON
All Colon cancer Rectal cancer P* value OR (95% CI) P* value OR (95% CI) P* value OR (95% CI)
rs1902432 Gender
  Male TT 77 90 44 46 1 (Ref) 1 (Ref) 1 (Ref)
TC 106 113 56 57 0.733 0.93 (0.59–1.45) 0.813 0.94 (0.55–1.59) 0.733 0.91 (0.54–1.54)
CC 45 47 19 28 0.558 0.85 (0.48–1.49) 0.311 0.70 (0.35–1.40) 0.961 0.98 (0.52–1.87)
  Female TT 93 70 46 24 1 (Ref) 1 (Ref) 1 (Ref)
TC 131 95 55 40 0.973 0.99 (0.65–1.51) 0.608 0.88 (0.54–1.43) 0.531 1.20 (0.68–2.14)
CC 47 19 8 11 0.109 0.59 (0.31–1.13) 0.028 0.38 (0.16–0.90) 0.908 0.95 (0.43–2.14)
Age
  ≦60 TT 120 80 40 40 1 (Ref) 1 (Ref) 1 (Ref)
TC 173 106 59 47 0.679 0.92 (0.64–1.34) 0.925 1.02 (0.64–1.63) 0.436 0.83 (0.51–1.34)
CC 75 32 14 18 0.073 0.63 (0.38–1.04) 0.088 0.56 (0.28–1.09) 0.279 0.71 (0.38–1.33)
  >60 TT 50 80 50 30 1 (Ref) 1 (Ref) 1 (Ref)
TC 64 102 52 50 0.830 0.95 (0.58–1.54) 0.399 0.79 (0.46–1.36) 0.495 1.24 (0.67–2.26)
CC 17 34 13 21 0.867 1.06 (0.52–2.15) 0.388 0.69 (0.30–1.60) 0.206 1.70 (0.75–3.84)
rs4573233 Gender
  Male GG 152 160 77 83 1 (Ref) 1 (Ref) 1 (Ref)
GA 66 79 37 42 0.815 1.06 (0.67–1.65) 0.933 1.02 (0.60–1.75) 0.759 1.08 (0.65–1.82)
AA 5 11 5 6 0.090 2.90 (0.85–9.93) 0.162 2.69 (0.67–10.73) 0.102 3.14 (0.80–12.33)
  Female GG 10 8 64 50 1 (Ref) 1 (Ref) 1 (Ref)
GA 84 59 40 19 0.659 1.10 (0.72–1.67) 0.248 1.33 (0.82–2.16) 0.485 0.81 (0.45–1.47)
AA 179 114 5 3 0.890 1.07 (0.39–2.96) 0.818 1.15 (0.35–3.72) 0.961 0.97 (0.25–3.75)
Age
  ≦60 GG 254 142 75 67 1 (Ref) 1 (Ref) 1 (Ref)
GA 104 67 35 32 0.450 1.15 (0.80–1.67) 0.579 1.14 (0.72–1.81) 0.525 1.17 (0.72–1.89)
AA 10 7 3 4 0.659 1.25 (0.47–3.35) 0.985 1.01 (0.27–3.78) 0.495 1.51 (0.46–4.98)
  >60 GG 77 132 66 66 1 (Ref) 1 (Ref) 1 (Ref)
GA 46 71 42 29 0.749 0.92 (0.57–1.50) 0.792 1.08 (0.63–1.85) 0.343 0.75 (0.41–1.36)
AA 5 12 7 5 0.352 1.71 (0.55–5.28) 0.315 1.87 (0.55–6.32) 0.561 1.50 (0.39–5.81)
rs710885 Gender
  Male TT 210 217 102 115 1 (Ref) 1 (Ref) 1 (Ref)
TG 19 31 18 13 0.033 2.09 (1.06–4.11) 0.013 2.60 (1.22–5.55) 0.222 1.65 (0.74–3.68)
GG 1 3 0 3 0.506 2.20 (0.22–22.60) NA NA 0.233 4.12 (0.40–42.27)
  Female TT 236 153 93 60 1 (Ref) 1 (Ref) 1 (Ref)
TG 36 26 14 12 0.657 1.14 (0.65–1.99) 0.985 1.01 (0.51–.99) 0.438 1.33 (0.65–2.74)
GG 3 2 1 1 0.862 1.18 (0.18–7.62) 0.986 0.98 (0.10–10.10) 0.741 1.48 (0.15–15.10)
Age
  ≦60 TT 327 181 95 86 1 (Ref) 1 (Ref) 1 (Ref)
TG 40 33 18 15 0.106 1.51 (0.92–2.47) 0.152 1.55 (0.85–2.84) 0.254 1.45 (0.77–2.76)
GG 4 1 0 1 0.491 0.46 (0.05–4.17) NA NA 0.997 1.01 (0.11–9.20)
  >60 TT 119 189 100 89 1 (Ref) 1 (Ref) 1 (Ref)
TG 15 24 14 10 0.677 1.16 (0.57–2.37) 0.607 1.23 (0.56–2.71) 0.878 1.07 (0.44–2.60)
GG 0 4 1 3 NA NA NA NA NA NA
rs785005 Gender
  Male GG 99 121 53 68 1 (Ref) 1 (Ref) 1 (Ref)
GA 96 107 54 53 0.528 0.87 (0.56–1.35) 0.997 1.00 (0.60–1.68) 0.298 0.77 (0.46–1.27)
AA 28 24 13 11 0.395 0.73 (0.35–1.51) 0.824 0.91 (0.39–2.12) 0.240 0.60 (0.26–1.41)
  Female GG 122 78 44 34 1 (Ref) 1 (Ref) 1 (Ref)
GA 125 83 48 35 0.936 0.98 (0.65–1.48) 0.989 1.00 (0.61–1.64) 0.876 0.96 (0.56–1.65)
AA 22 20 15 5 0.458 1.30 (0.65–2.59) 0.157 1.73 (0.81–3.70) 0.576 0.74 (0.26–2.14)
Age
  ≦60 GG 169 96 48 48 1 (Ref) 1 (Ref) 1 (Ref)
GA 160 98 51 47 0.655 1.09 (0.76–1.55) 0.607 1.13 (0.72–1.76) 0.855 1.04 (0.66–1.65)
AA 52 103 14 10 0.709 1.12 (0.63–1.97) 0.413 1.34 (0.67–2.68) 0.794 0.90 (0.42–1.95)
  >60 GG 52 103 49 54 1 (Ref) 1 (Ref) 1 (Ref)
GA 61 92 51 41 0.320 0.78 (0.48–1.27) 0.706 0.90 (0.52–1.56) 0.160 0.66 (0.37–1.18)
AA 12 20 14 6 0.880 094 (0.40–2.20) 0.552 1.32 (0.53–3.27) 0.259 0.53 (0.17–1.61)
rs2632159 Gender
  Male TT 169 166 75 91 1 (Ref)
TC 56 71 41 30 0.396 1.22 (0.77–1.94) 0.100 1.56 (0.92–2.65) 0.829 0.94 (0.54–1.64)
CC 6 12 2 10 0.081 2.64 (0.89–7.82) 0.978 0.98 (0.18–5.23) 0.017 3.97 (1.28–12.25)
  Female TT 188 115 67 48 1 (Ref)
TC 80 59 36 23 0.269 1.27 (0.83–1.93) 0.250 1.34 (0.82–2.20) 0.580 1.17 (0.67–2.07)
CC 7 8 5 3 0.164 2.14 (0.73–6.23) 0.167 2.35 (0.70–7.92) 0.385 1.86 (0.46–7.61)
Age
  ≦60 TT 262 140 71 69 1 (Ref) 1 (Ref) 1 (Ref)
TC 97 65 37 28 0.185 1.73 (0.77–3.90) 0.139 1.42 (0.89–2.25) 0.679 1.11 (0.68–1.83)
CC 13 12 5 7 0.218 1.27 (0.87–1.84) 0.522 1.42 (0.49–4.11) 0.140 2.06 (0.79–5.39)
  >60 TT 95 141 71 70 1 (Ref) 1 (Ref) 1 (Ref)
TC 39 65 40 25 NA NA 0.240 1.39 (0.80–2.40) 0.706 0.89 (0.48–1.65)
CC 0 8 2 6 0.563 1.16 (0.71–1.89) NA NA NA NA

Terms in bold represent significant results (P<0.05).

*using Logistic Regression adjusted by the two factors of gender and age. Abbreviations: CON, controls; CRC, colorectal cancer. NA, not available.

Haplotype analysis

In the haplotype analysis, no haplotype of the PCAT1 gene had any association with CRC risk (haplotype for rs1902432-rs4573233-rs710885-rs785005-rs2632159 SNPs, P>0.05). However, when cases were divided into colon cancer and rectal cancer, the PCAT1 gene C-G-T-G-T haplotype decreased colon cancer risk to 0.75-fold (P=0.019, Table 3).

Table 3. The association of haplotype of PCAT1 gene and CRC risk.

Haplotype Control (%) CRC CRC vs CON Colon cancer vs CON Rectal cancer vs CON
All (%) Colon cancer (%) Rectal cancer (%) P value OR (95% CI) P value OR (95% CI) P value OR (95% CI)
C G T G T 395.24 (41.6) 326.83 (39.0) 154.44 (34.9) 171.99 (43.4) 0.238 0.89 (0.74–1.08) 0.019 0.75 (0.60–0.95) 0.615 1.06 (0.84–1.35)
T A T G C 85.64 (9.0) 91.75 (10.9) 50.77 (11.5) 41.79 (10.6) 0.178 1.24 (0.91–1.69) 0.145 1.31 (0.91–1.90) 0.405 1.18 (0.80–1.74)
T A T G T 88.36 (9.3) 76.25 (9.1) 41.23 (9.3) 34.21 (8.6) 0.866 0.97 (0.71–1.34) 0.979 1.01 (0.68–1.48) 0.669 0.91 (0.60–1.38)
T G G G C 56.52 (5.9) 64.70 (7.7) 31.64 (7.2) 33.00 (8.3) 0.142 1.32 (0.91–1.91) 0.384 1.22 (0.78–1.92) 0.119 1.43 (0.91–2.23)
T G T A T 308.92 (32.5) 267.57 (31.9) 155.98 (35.3) 111.85 (28.2) 0.755 0.97 (0.79–1.18) 0.294 1.14 (0.90–1.44) 0.102 0.81 (0.62–1.04)

The bold terms means the significant results (P<0.05). Using SHEsis software to analysis (http://analysis.bio-x.cn/). The haplotype for rs1902432 – rs4573233 -rs710885 – rs785005 – rs2632159 SNPs. CRC: colorectal cancer.

Epistatic effect analysis

To mine the associations of the SNPs with positive results, rs2632159, and other SNPs with the risk of CRC, the epistatic effects of rs2632159 interacting with four other SNPs were calculated. When there is rs2632159 polymorphism, the rs1902432 TT+TC variant genotype was shown to increase rectal cancer risk 2.93-fold (P=0.011, 95% CI = 1.28–6.70), and the rs785005 GG variant genotype increased colon cancer risk 2.01-fold (P=0.008, 95% CI = 1.21–3.28, Table 4).

Table 4. Epistatic effect of pairwise interacting factors for PCAT1 on the risks of CRC.

Interacted pairwise SNPs Comparison Subset CRC vs CON Colon cancer vs CON Rectal cancer vs CON
P value OR (95% CI) P value OR (95% CI) P value OR (95% CI)
rs1902432 interacted with rs2632159 rs1902432 CC vs TT+TC rs2632159 CC NA NA NA NA NA NA
rs2632159 TT+TC 0.196 0.78 (0.54–1.14) 0.025 0.57 (0.35–0.93) 0.871 1.04 (0.67–1.62)
rs2632159 CC vs TT+TC rs1902432 CC NA NA NA NA NA NA
rs1902432 TT+TC 0.065 2.02 (0.96–4.27) 0.603 1.29 (0.49–3.37) 0.011 2.93 (1.28–6.70)
rs4573233 interacted with rs2632159 rs4573233 AA+GA vs GG rs2632159 CC 0.547 0.85 (0.51–1.44) NA NA NA NA
rs2632159 TT+TC 0.788 1.06 (0.70–1.61) 0.441 1.15 (0.81–1.64) 0.820 0.96 (0.67–1.40)
rs2632159 CC vs TT+TC rs4573233 AA+GA 0.406 1.23 (0.76–1.98) 0.200 1.45 (0.82–2.57) 0.991 1.00 (0.55–1.83)
rs4573233 GG 0.063 1.55 (0.98–2.45) 0.101 1.57 (0.92–2.69) 0.141 1.52 (0.87–2.66)
rs710885 interacted with rs2632159 rs710885 GG+TG vs TT rs2632159 CC 0.266 1.34 (0.80–2.23) 0.310 0.16 (0.01–5.49) 0.195 0.11 (0.00–3.09)
rs2632159 TT+TC 0.570 0.58 (0.09–3.77) 0.164 1.45 (0.86–2.44) 0.310 1.34 (0.76–2.34)
rs2632159 CC vs TT+TC rs710885 GG+TG 0.189 3.84 (0.52–28.60) 0.516 1.95 (0.26–14.50) NA NA
rs710885 TT 0.321 1.20 (0.84–1.72) 0.109 1.40 (0.93–2.13) 0.959 0.99 (0.62–1.56)
rs785005 interacted with rs2632159 rs785005 AA+GA vs GG rs2632159 CC 0.672 0.89 (0.53–1.51) NA NA NA NA
rs2632159 TT+TC 0.643 1.09 (0.76–1.55) 0.496 1.13 (0.80–1.59) 0.425 0.87 (0.61–1.23)
rs2632159 CC vs TT+TC rs785005 AA+GA 0.390 1.22 (0.77–1.94) 0.388 1.27 (0.74–2.16) 0.590 1.17 (0.66–2.10)
rs785005 GG 0.051 1.55 (1.00–2.40) 0.008 2.01 (1.21–3.28) 0.540 1.18 (0.70–1.98)

Terms in bold represent significant results (P<0.05). All tests were adjusted by age and gender. Statistically significant associations were highlighted in bold (P<0.05). Abbreviations: CON, controls; CRC, colorectal cancer.

Association of SNPs and clinical pathological parameters

We mined the distribution frequencies of five SNPs in association with different clinical pathological parameters and found in all CRC patients that the frequency of genotypes with the rs785005 SNP variant was higher in the cases with high/moderate-differentiation adenocarcinoma (variant genotype vs heterozygote/wild-type genotype: 97.5% vs 83.0%/81.5%, Table 5). Meanwhile, in colon cancer patients, the frequency of rs4573233 SNP variant genotype was higher in poor-differentiation adenocarcinoma (variant genotype vs heterozygote/wild-type genotype: 62.5% vs 83.3%/86.2%, Table 5). Moreover, the frequency of the rs2632159 SNP variant genotype was higher in the never smokers (variant genotype vs heterozygote/wild-type genotype: 100.0% vs 61.0%/64.1%, Table 5).

Table 5. The association between the genotype of PCAT1 SNPs and clinicopathological parameters*.

Parameter n Genotype for CRC P value Genotype for Colon cancer P value Genotype for Rectal cancer P value
Wild-type (%) Heterozygote (%) Variant type (%) Wild-type (%) Heterozygote (%) Variant type (%) Wild-type (%) Heterozygote (%) Variant type (%)
rs1902432
Smoking 0.068 0.130 0.392
  Ever smoker 173 (39.86) 64 (40.0) 76 (36.5) 33 (50.0) 30 (33.3) 37 (33.3) 13 (48.1) 34 (48.6) 39 (40.2) 20 (51.3)
  Never smoker 261 (60.14) 96 (60.0) 132 (63.5) 33 (50.0) 60 (66.7) 74 (66.7) 14 (51.9) 36 (51.4) 58 (59.8) 19 (48.7)
Drinking 0.765 0.439 0.763
  Drinker 48 (11.06) 20 (12.5) 20 (9.6) 8 (12.1) 9 (10.0) 11 (9.9) 4 (14.8) 11 (15.7) 9 (9.3) 4 (10.3)
  Nondrinker 386 (88.94) 140 (87.5) 188 (90.4) 58 (87.9) 81 (90.0) 100 (90.1) 23 (85.2) 59 (84.3) 88 (90.7) 35 (89.7)
Family history 0.207 0.772 0.097
  Yes 25 (5.76) 8 (5.0) 11 (5.3) 6 (9.1) 6 (6.7) 4 (3.6) 1 (3.7) 2 (2.9) 7 (7.2) 5 (12.8)
  No 409 (94.24) 152 (95.0) 197 (94.7) 60 (90.9) 84 (93.3) 107 (96.4) 26 (96.3) 68 (97.1) 90 (92.8) 34 (87.2)
WHO classification 0.603 0.052 0.664
  High-moderate differentiation 322 (83.64) 123 (86.6) 149 (82.3) 50 (80.6) 71 (87.7) 81 (83.5) 20 (76.9) 52 (85.2) 68 (81.0) 30 (83.3)
  Poor differentiation 21 (5.45) 6 (4.2) 10 (5.5) 5 (8.1) 2 (2.5) 5 (5.2) 4 (15.4) 4 (6.6) 5 (6.0) 1 (2.8)
  Mucinous adenocarcinoma 42 (10.91) 13 (9.2) 22 (12.2) 7 (11.3) 8 (9.9) 11 (11.3) 2 (7.7) 5 (8.2) 11 (13.1) 5 (13.9)
TNM stage 0.506 0.185 0.690
  I–II 174 (40.65) 64 (40.0) 86 (42.4) 24 (36.9) 35 (38.9) 53 (49.1) 8 (30.8) 29 (41.4) 33 (34.7) 16 (41.0)
  III–IV 254 (59.35) 96 (60.0) 117 (57.8) 41 (63.1) 55 (61.1) 55 (50.9) 18 (69.2) 41 (58.6) 62 (65.3) 23 (59.0)
rs4573233
Smoking 0.858 0.730 0.548
  Ever smoker 173 (40.14) 108 (39.4) 57 (41.3) 8 (42.1) 51 (36.2) 26 (33.8) 3 (30.0) 57 (42.9) 31 (50.8) 5 (55.6)
  Never smoker 258 (59.86) 166 (60.6) 81 (58.7) 11 (57.9) 90 (63.8) 51 (66.2) 7 (70.0) 76 (57.1) 30 (49.2) 4 (44.4)
Drinking 0.931 0.267 0.323
  Drinker 48 (11.14) 29 (10.6) 17 (12.3) 2 (10.5) 16 (11.3) 8 (10.4) 0 13 (9.8) 9 (14.8) 2 (22.2)
  Nondrinker 383 (88.86) 245 (89.4) 121 (87.7) 17 (89.5) 125 (88.7) 69 (89.6) 10 (100.0) 120 (90.2) 52 (85.2) 7 (77.8)
Family history 0.918 0.435 0.404
  Yes 25 (5.80) 21 (7.7) 3 (2.2) 1 (5.3) 9 (6.4) 1 (1.3) 1 (10.0) 12 (9.0) 2 (3.3) 0
  No 406 (94.20) 253 (92.3) 135 (97.8) 18 (94.7) 132 (93.6) 76 (98.7) 9 (90.0) 121 (91.0) 59 (96.7) 9 (100.0)
WHO classification 0.055 0.040 0.435
  High-moderate differentiation 320 (83.77) 214 (85.3) 94 (81.7) 12 (75.0) 112 (86.2) 55 (83.3) 5 (62.5) 102 (84.3) 39 (79.6) 7 (87.5)
  Poor differentiation 21 (5.50) 11 (4.4) 7 (6.1) 3 (18.8) 6 (4.6) 3 (4.5) 2 (25.0) 5 (4.1) 4 (8.2) 1 (12.5)
  Mucinous adenocarcinoma 41 (10.73) 26 (10.4) 14 (12.2) 1 (6.3) 12 (9.2) 8 (12.1) 1 (12.5) 14 (11.6) 6 (12.2) 0
TNM stage 0.560 0.852 0.291
  I–II 174 (40.94) 109 (40.2) 56 (41.5) 9 (47.4) 57 (41.3) 35 (46.1) 4 (40.0) 52 (39.1) 21 (35.6) 5 (55.6)
  III–IV 251 (59.06) 162 (59.8) 79 (58.5) 10 (52.6) 81 (58.7) 41 (53.9) 6 (60.0) 81 (60.9) 38 (64.4) 4 (44.4)
rs710885
Smoking 0.352 0.457 0.404
  Ever smoker 174 (40.28) 148 (40.0) 25 (43.9) 1 (20.0) 68 (34.9) 13 (40.6) 0 80 (45.7) 12 (48.0) 1 (25.0)
  Never smoker 258 (59.72) 222 (60.0) 32 (56.1) 4 (80.0) 127 (65.1) 19 (59.4) 1 (100.0) 95 (54.3) 13 (52.0) 3 (75.0)
Drinking 0.510 0.731 0.381
  Drinker 47 (10.88) 41 (11.1) 5 (8.8) 1 (20.0) 21 (10.8) 3 (9.4) 0 20 (11.4) 2 (8.0) 1 (25.0)
  Nondrinker 385 (89.12) 329 (88.9) 52 (91.2) 4 (80.0) 174 (89.2) 29 (90.6) 1 (100.0) 155 (88.6) 23 (92.0) 3 (75.0)
Family history 0.156 0.830 0.147
  Yes 24 (5.56) 21 (5.7) 2 (3.5) 1 (20.0) 9 (4.6) 1 (3.1) 0 12 (6.9) 1 (4.0) 1 (25.0)
  No 408 (94.44) 349 (94.3) 55 (96.5) 4 (80.0) 186 (95.4) 31 (96.9) 1 (100.0) 163 (93.1) 24 (96.0) 3 (75.0)
WHO classification 0.682 0.914 0.736
  High-moderate differentiation 322 (84.07) 272 (83.4) 46 (86.8) 4 (100.0) 147 (85.0) 25 (83.3) 1 (100.0) 125 (81.7) 21 (91.3) 3 (100.0)
  Poor differentiation 20 (5.22) 18 (5.5) 2 (3.8) 0 10 (5.8) 1 (3.3) 0 8 (5.2) 1 (4.3) 0
  Mucinous adenocarcinoma 41 (10.71) 36 (11.0) 5 (9.4) 0 16 (9.2) 4 (13.3) 0 20 (13.1) 1 (4.3) 0
TNM stage 0.961 0.381 0.637
  I–II 175 (41.08) 147 (40.4) 26 (45.6) 2 (40.0) 83 (43.5) 14 (43.8) 0 64 (37.0) 12 (48.0) 2 (50.0)
  III–IV 251 (58.92) 217 (59.6) 31 (54.4) 3 (60.0) 108 (56.5) 18 (56.3) 1 (100.0) 109 (63.0) 13 (52.0) 2 (50.0)
rs785005
Smoking 0.121 0.208 0.497
  Ever smoker 175 (40.42) 84 (42.2) 78 (41.1) 13 (29.5) 35 (36.1) 39 (38.2) 7 (25.0) 49 (48.0) 39 (44.3) 6 (37.5)
  Never smoker 258 (59.58) 115 (57.8) 112 (58.9) 31 (70.5) 62 (63.9) 63 (61.8) 21 (75.0) 53 (52.0) 49 (55.7) 10 (62.5)
Drinking 0.657 0.979 0.483
  Drinker 385 (88.91) 21 (10.6) 23 (12.1) 4 (9.1) 9 (9.3) 12 (11.8) 3 (10.7) 12 (11.8) 11 (12.5) 1 (6.3)
  Nondrinker 48 (11.09) 178 (89.4) 167 (87.9) 40 (90.9) 88 (90.7) 90 (88.2) 25 (89.3) 90 (88.2) 77 (87.5) 15 (93.7)
Family history 0.320 0.122 0.928
  Yes 25 (5.77) 12 (6.0) 9 (4.7) 4 (9.1) 3 (3.1) 5 (4.9) 3 (10.7) 9 (8.8) 4 (4.5) 1 (6.3)
  No 408 (94.23) 187 (94.0) 181 (95.3) 40 (90.9) 94 (94.0) 97 (95.1) 25 (89.3) 93 (91.2) 84 (95.5) 15 (93.8)
WHO classification 0.045 0.068 0.527
  High-moderate differentiation 322 (83.85) 141 (81.5) 142 (83.0) 39 (97.5) 67 (78.8) 79 (85.9) 26 (100.0) 74 (84.1) 63 (79.7) 13 (92.9)
  Poor differentiation 20 (5.21) 13 (7.5) 7 (4.1) 0 9 (10.6) 1 (1.1) 0 4 (4.5) 6 (7.6) 0
  Mucinous adenocarcinoma 42 (10.94) 19 (11.0) 22 (12.9) 1 (2.5) 9 (10.6) 12 (13.0) 0 10 (11.4) 10 (12.7) 1 (7.1)
TNM stage 0.992 0.631 0.636
  I–II 175 (40.98) 82 (42.3) 75 (39.7) 18 (40.9) 41 (43.6) 45 (44.6) 11 (39.3) 41 (41.0) 30 (34.1) 7 (43.8)
  III–IV 252 (59.02) 112 (57.7) 114 (60.3) 26 (59.1) 53 (56.4) 56 (55.4) 17 (60.7) 59 (59.0) 58 (65.9) 9 (56.3)
rs2632159
Smoking 0.616 0.045 0.526
  Ever smoker 174 (40.37) 115 (40.9) 52 (40.0) 7 (35.0) 51 (35.9) 30 (39.0) 0 64 (46.0) 22 (41.5) 7 (53.8)
  Never smoker 257 (59.63) 166 (59.1) 78 (60.0) 13 (65.0) 91 (64.1) 47 (61.0) 7 (100.0) 75 (54.0) 31 (58.5) 6 (46.2)
Drinking 0.869 0.354 0.670
  Drinker 48 (11.14) 31 (11.0) 15 (11.5) 2 (10.0) 16 (11.3) 8 (10.4) 0 15 (10.8) 7 (13.2) 2 (15.4)
  Nondrinker 383 (88.86) 250 (89.0) 115 (88.5) 18 (90.0) 126 (88.7) 69 (89.6) 7 (100.0) 124 (89.2) 46 (86.8) 11 (84.6)
Family history 0.543 0.899
  Yes 25 (5.80) 19 (6.8) 5 (3.8) 1 (5.0) 0.875 7 (4.9) 4 (5.2) 0 12 (8.6) 1 (1.9) 1 (7.7)
  No 406 (94.20) 262 (93.2) 125 (96.2) 19 (95.0) 135 (95.1) 73 (94.8) 7 (100.0) 127 (91.4) 52 (98.1) 12 (92.3)
WHO classification 0.991 0.158 0.422
  High-moderate differentiation 319 (83.51) 215 (84.3) 90 (81.8) 14 (82.4) 111 (86.0) 55 (82.1) 4 (66.7) 104 (82.5) 35 (81.4) 10 (90.9)
  Poor differentiation 21 (5.50) 15 (5.9) 5 (4.5) 1 (5.9) 8 (6.2) 3 (4.5) 0 7 (5.6) 2 (4.7) 1 (9.1)
  Mucinous adenocarcinoma 42 (10.99) 25 (9.8) 15 (13.6) 2 (11.8) 10 (7.8) 9 (13.4) 2 (33.3) 15 (11.9) 6 (14.0) 0
TNM stage 0.183 0.436 0.237
  I–II 173 (40.71) 106 (38.0) 56 (44.4) 11 (55.0) 56 (40.0) 35 (46.7) 4 (57.1) 50 (36.0) 21 (41.2) 7 (53.8)
  III–IV 252 (59.29) 173 (62.0) 70 (55.6) 9 (45.0) 84 (60.0) 40 (53.3) 3 (42.9) 89 (64.0) 30 (58.8) 6 (46.2)

Terms in bold represent significant results (P<0.05). The P value was calculated using the recessive model.*, using χ2 two-side test.

eQTL analysis

In the HaploReg database, the promising SNP rs2632159 was found to potentially change the site of binding with EBF, LUN-1, and TCF12, which are transcription factors [18] (Figure 1).

Figure 1. The bioinformatical analysis for rs2632159 SNP.

Figure 1

In the HaploReg database, the promising SNP rs2632159 was found possibly to change the combination site with the following proteins with the certain polypeptide.

Discussion

To date, no study has reported on the association of PCAT1 polymorphisms and CRC risk. We thus screened all tagSNPs for the PCAT1 gene and investigated whether these SNPs could be biomarkers for CRC risk. We reported for the first time that PCAT1 rs2632159 polymorphism could increase the risk of CRC. We then performed an eQTL analysis and found that rs2632159 polymorphism could alter the site of binding with three different transcription factors, which may explain why rs2632159 polymorphism increased CRC risk. Our study should be helpful in the search for promising biomarkers for the prediction of CRC at an early stage.

These five tagSNPs represented all of the SNPs covering the PCAT1 gene. In terms of the locations, rs1902432 and rs4573233 polymorphisms are intronic and in the region 2 kb upstream of the gene, respectively; rs710885 and rs785005 are intronic; and rs2632159 is 145 bp downstream of the gene. Thus far, no study has reported the association of PCAT1 polymorphisms and CRC risk. Only the PCAT1 rs1902432 SNP was reported to be significantly associated with other cancer, namely, increasing the risk of prostate cancer [7]. In our study, we found that the rs1902432 SNP was associated with colon cancer risk, but not with disease risk in all CRC patients. Thus, rs1902432 SNP may only have potential to be a biomarker for colon cancer risk.

The major finding in the present study was that the PCAT1 rs2632159 SNP increased CRC susceptibility, and functioned as a risk factor for colon cancer under the dominant model and for rectal cancer under the recessive model. This SNP was thus reported to be related to disease risk for the first time. The explanation for this phenomenon should be considered from two aspects: first, the etiology of colon cancer and rectal cancer is different. The etiology of colon cancer is often related to genetic factors. Its precancerous disease may be familial polyposis, and its patients often have microsatellite instability, for example, MLH1, MSH2, MSH6 and PMS2 deficiency. While the etiology of rectal cancer is often related to the unhealthy dietary habit, and patients often have poor defecation habits, resulting in a large number of toxins accumulated in the rectum, resulting in repeated irritation of rectal mucosa by inflammation, thus resulting the occurrence of colon cancer. Second, the basis of genetics varies from the dominant model and recessive model. The dominant model is a single variation for the allele of one polymorphism, while the recessive model needs to achieve double variation for two alleles of one polymorphism. This is similar to the ‘two-hit hypothesis’ [19] in genetics.

PCAT1 rs2632159 SNP was also found to have a more pronounced effect in the male subgroup. This SNP was also shown to have epistatic effects with other polymorphisms. Epistasis is a phenomenon in which the effects of complex interactions are more important than the independent main effects of any one susceptibility gene [20–22]. When rs2632159 polymorphism is present, the rs1902432 TT+TC variant genotype was shown to increase the risk of rectal cancer, and the rs785005 GG variant genotype increased the risk of colon cancer; this suggests the potential for pairwise effects of SNP–SNP interaction in the PCAT1 gene. Finally, eQTL analysis using an online bioinformatic tool was performed for this promising rs2632159 polymorphism, and EBF, LUN-1, and TCF12 were found to probably combine with the rs2632159 T>C variant. Then, this rs2632159 T>C variant would cause a gain or loss function for these transcription factors. PCAT1 is a lncRNA that functions as an oncogene [23–28]. We thus speculate that these three transcription factors could stimulate PCAT1 transcription and increase the level of oncogene functional PCAT1 expression, causing individuals carrying the variant genotype to express more oncogenic PCAT1 and therefore increase the risk of CRC.

Gene research may be related to race and this will limit some application of the SNP for the different ethnicity. For example, our research found that PCAT1 rs2632159 SNP was shown to increase CRC susceptibility in a Chinese population, and the rs1902432 SNP might only have potential to be a biomarker for colon cancer risk in the Chinese population. While the PCAT1 rs1902432 SNP was also reported to be significantly associated with the risk of prostate cancer in Chinese population [7]. In Figure 2, we listed the Hapmap data for the PCAT1 rs2632159 and rs1902432 SNPs, which showed rs2632159 varied from Europeans to Asians to Africans (the T allele percentage, 30% in Europeans, 80% in Asians, 10% in Africans), so was the rs1902432 SNP. Thus, the application of PCAT1 rs2632159 and rs1902432 SNPs for cancer risk might be considered from race to race.

Figure 2. The genotype information from Hapmap database.

Figure 2

The genotype for PCAT1 rs2632159 and rs1902432 SNPs from the Hapmap database (https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?do_not_redirect&rs=rs2632263; https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?do_not_redirect&rs=rs1902432.)

It was reported that the combined haplotype analyses for multiple SNPs are more sensitive and powerful than single-locus SNP analysis alone [29]. The single-locus SNP rs4573233, rs710885 and rs785005 SNPs alone had no significant association with colon risk, but when these three SNPs composed a combination with others (rs1902432 and rs2632159), we found that the PCAT1 C G T G T haplotype of rs1902432 – rs4573233 -rs710885 – rs785005 – rs2632159 SNPs significantly decreased the colon risk (OR = 0.75), indicating that the haplotype might be more sensitive than these three single SNP its own, and could also have potential for the prediction of colon cancer risk.

There are some limitations in the present study. First, the sample size could be increased to obtain more reliable results, but the total sample size of nearly 1000 is reasonable. Second, the prognostic data could have been studied to achieve a more comprehensive analysis, but the survival rate of CRC was high, which limited the predictive role of the SNP for CRC prognosis. Third, whether the PCAT1 rs2632159 associated with other gastrointestinal cancers were not studied in this research, which would determine the application and specificity of the PCAT1 polymorphisms.

In summary, in the present study, the PCAT1 rs2632159 SNP was shown to increase CRC susceptibility, and it was shown to increase the risk of colon cancer under the dominant model and of rectal cancer under the recessive model. In the subgroup analysis, the PCAT1 rs2632159 SNP also increased the risk of rectal cancer in males. And the rs1902432 SNP might only have potential to be a biomarker for colon cancer risk. These findings should help in the search for biomarkers predictive of CRC susceptibility and provide more clues to obtain a deeper understanding of the etiology of colorectal carcinogenesis.

Supporting information

Supplementary Figure S1.

bsr20190708_Supp1.pdf (5.1MB, pdf)

Supplementary Table S1. The captured SNPs covered by the selected tagSNPs.

bsr20190708_Supp1.pdf (5.1MB, pdf)

Supplementary Table S2. The baseline of the subjects.

bsr20190708_Supp1.pdf (5.1MB, pdf)

Abbreviations

CI

confidence interval

CON

controls

CRC

colorectal cancer

eQTL

quantitative trait locus

LD

linkage disequilibrium

OR

odds ratio

PCAT

prostate cancer-associated transcript

SNP

single nucleotide polymorphism

Funding

This work is supported partly by the Natural Science Foundation of Liaoning Province in China [grant number 20170541001]. We thank Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contribution

Ben-gang Wang conceived and designed the present study. Ming-li Yang, Zhe Huang, Li-na Wu preformed the experiment. Han-xi Ding was responsible for the data analysis and performed data interpretation. Ming-li Yang wrote the paper. Rong Wu and Ben-gang Wang revised the manuscript.

References

  • 1.Hu X., Sood A.K., Dang C.V. and Zhang L. (2018) The role of long noncoding RNAs in cancer: the dark matter matters. Curr. Opin. Genet. Dev. 48, 8–15 10.1016/j.gde.2017.10.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Lv Z., Xu Q. and Yuan Y. (2017) A systematic review and meta-analysis of the association between long non-coding RNA polymorphisms and cancer risk. Mutat. Res. 771, 1–14 10.1016/j.mrrev.2016.10.002 [DOI] [PubMed] [Google Scholar]
  • 3.Ma G., Gu D., Lv C., Chu H., Xu Z., Tong N.. et al. (2015) Genetic variant in 8q24 is associated with prognosis for gastric cancer in a Chinese population. J. Gastroenterol. Hepatol. 30, 689–695 10.1111/jgh.12801 [DOI] [PubMed] [Google Scholar]
  • 4.Gao P. and Wei G.H. (2017) Genomic Insight into the Role of lncRNA in cancer susceptibility. Int. J. Mol. Sci. 18, pii: E1239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Prensner J.R., Iyer M.K., Balbin O.A., Dhanasekaran S.M., Cao Q., Brenner J.C.. et al. (2011) Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat. Biotechnol. 29, 742–749 10.1038/nbt.1914 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Ge X., Chen Y., Liao X., Liu D., Li F., Ruan H.. et al. (2013) Overexpression of long noncoding RNA PCAT-1 is a novel biomarker of poor prognosis in patients with colorectal cancer. Med. Oncol. 30, 588. 10.1007/s12032-013-0588-6 [DOI] [PubMed] [Google Scholar]
  • 7.Yuan Q., Chu H., Ge Y., Ma G., Du M., Wang M.. et al. (2018) LncRNA PCAT1 and its genetic variant rs1902432 are associated with prostate cancer risk. J. Cancer 9, 1414–1420 10.7150/jca.23685 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Xu Q., Chen M.Y., He C.Y., Sun L.P. and Yuan Y. (2013) Promoter polymorphisms in trefoil factor 2 and trefoil factor 3 genes and susceptibility to gastric cancer and atrophic gastritis among Chinese population. Gene 529, 104–112 10.1016/j.gene.2013.07.070 [DOI] [PubMed] [Google Scholar]
  • 9.Wang B.G., Xu Q., Lv Z., Fang X.X., Ding H.X., Wen J.. et al. (2018) Association of twelve polymorphisms in three onco-lncRNA genes with hepatocellular cancer risk and prognosis: a case-control study. World J. Gastroenterol. 24, 2482–2490 10.3748/wjg.v24.i23.2482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Gong Y., He C., Duan Z., Sun L., Xu Q., Xing C.. et al. (2013) Association of two ERCC4 tagSNPs with susceptibility to atrophic gastritis and gastric cancer in Chinese. Gene 519, 335–342 10.1016/j.gene.2013.01.059 [DOI] [PubMed] [Google Scholar]
  • 11.Vineis P., Manuguerra M., Kavvoura F.K., Guarrera S., Allione A., Rosa F.. et al. (2009) A field synopsis on low-penetrance variants in DNA repair genes and cancer susceptibility. J. Natl. Cancer Inst. 101, 24–36 10.1093/jnci/djn437 [DOI] [PubMed] [Google Scholar]
  • 12.Wang B.G., Lv Z., Ding H.X., Fang X.X., Wen J., Xu Q.. et al. (2018) The association of lncRNA-HULC polymorphisms with hepatocellular cancer risk and prognosis. Gene 670, 148–154 10.1016/j.gene.2018.05.096 [DOI] [PubMed] [Google Scholar]
  • 13.Xu Q., Yuan Y., Sun L.P., Gong Y.H., Xu Y., Yu X.W.. et al. (2009) Risk of gastric cancer is associated with the MUC1 568 A/G polymorphism. Int. J. Oncol. 35, 1313–1320 [DOI] [PubMed] [Google Scholar]
  • 14.Fiorentino A., Sharp S.I. and McQuillin A. (2015) Association of rare variation in the glutamate receptor gene SLC1A2 with susceptibility to bipolar disorder and schizophrenia. Eur. J. Hum. Genet. 23, 1200–1206 10.1038/ejhg.2014.261 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Xiong D.H., Liu X.G., Guo Y.F., Tan L.J., Wang L., Sha B.Y.. et al. (2009) Genome-wide association and follow-up replication studies identified ADAMTS18 and TGFBR3 as bone mass candidate genes in different ethnic groups. Am. J. Hum. Genet. 84, 388–398 10.1016/j.ajhg.2009.01.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Westra H.J., Peters M.J., Esko T., Yaghootkar H., Schurmann C., Kettunen J.. et al. (2013) Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 10.1038/ng.2756 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Li Z., Zhang Z., He Z., Tang W., Li T., Zeng Z.. et al. (2009) A partition-ligation-combination-subdivision EM algorithm for haplotype inference with multiallelic markers: update of the SHEsis (http://analysis.bio-x.cn). Cell Res. 19, 519–523 10.1038/cr.2009.33 [DOI] [PubMed] [Google Scholar]
  • 18.Kheradpour P. and Kellis M. (2014) Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 10.1093/nar/gkt1249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Hino O., Kobayashi T. and Mourning A.G. Dr (2017) Knudson: the two-hit hypothesis, tumor suppressor genes, and the tuberous sclerosis complex. Cancer Sci. 108, 5–11 10.1111/cas.13116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Moore J.H. (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56, 73–82 10.1159/000073735 [DOI] [PubMed] [Google Scholar]
  • 21.He C., Tu H., Sun L., Xu Q., Gong Y., Jing J.. et al. (2015) SNP interactions of Helicobacter pylori-related host genes PGC, PTPN11, IL1B, and TLR4 in susceptibility to gastric carcinogenesis. Oncotarget 6, 19017–19026 10.18632/oncotarget.4231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Xu Q., Wu Y.F., Li Y., He C.Y., Sun L.P., Liu J.W.. et al. (2016) SNP-SNP interactions of three new pri-miRNAs with the target gene PGC and multidimensional analysis of H. pylori in the gastric cancer/atrophic gastritis risk in a Chinese population. Oncotarget 7, 23700–23714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Shang Z., Yu J., Sun L., Tian J., Zhu S., Zhang B.. et al. (2019) LncRNA PCAT1 activates AKT and NF-kappaB signaling in castration-resistant prostate cancer by regulating the PHLPP/FKBP51/IKKalpha complex. Nucleic Acids Res., 47, 4211–4225 10.1093/nar/gkz108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Zhang X., Zhang Y., Mao Y. and Ma X. (2018) The lncRNA PCAT1 is correlated with poor prognosis and promotes cell proliferation, invasion, migration and EMT in osteosarcoma. Onco. Targets Ther. 11, 629–638 10.2147/OTT.S152063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Liang C., Qi Z., Ge H., Liang C., Zhang Y., Wang Z.. et al. (2018) Long non-coding RNA PCAT-1 in human cancers: a meta-analysis. Clin. Chim. Acta 480, 47–55 10.1016/j.cca.2018.01.043 [DOI] [PubMed] [Google Scholar]
  • 26.Zhang D., Cao J., Zhong Q., Zeng L., Cai C., Lei L.. et al. (2017) Long noncoding RNA PCAT-1 promotes invasion and metastasis via the miR-129-5p-HMGB1 signaling pathway in hepatocellular carcinoma. Biomed. Pharmacother. 95, 1187–1193 10.1016/j.biopha.2017.09.045 [DOI] [PubMed] [Google Scholar]
  • 27.Xu W., Chang J., Du X. and Hou J. (2017) Long non-coding RNA PCAT-1 contributes to tumorigenesis by regulating FSCN1 via miR-145-5p in prostate cancer. Biomed. Pharmacother. 95, 1112–1118 10.1016/j.biopha.2017.09.019 [DOI] [PubMed] [Google Scholar]
  • 28.Zhang F., Wan M., Xu Y., Li Z., Leng K., Kang P.. et al. (2017) Long noncoding RNA PCAT1 regulates extrahepatic cholangiocarcinoma progression via the Wnt/beta-catenin-signaling pathway. Biomed. Pharmacother. 94, 55–62 10.1016/j.biopha.2017.07.025 [DOI] [PubMed] [Google Scholar]
  • 29.Yin J., Vogel U., Ma Y., Qi R. and Wang H. (2009) HapMap-based study of the DNA repair gene ERCC2 and lung cancer susceptibility in a Chinese population. Carcinogenesis 30, 1181–1185 10.1093/carcin/bgp107 [DOI] [PubMed] [Google Scholar]

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