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  • Research article
  • Open Access
  • Open Peer Review

Genetic associations between the miRNA polymorphisms miR-130b (rs373001), miR-200b (rs7549819), and miR-495 (rs2281611) and colorectal cancer susceptibility

BMC Cancer201919:480

https://doi.org/10.1186/s12885-019-5641-1

  • Received: 2 August 2018
  • Accepted: 26 April 2019
  • Published:
Open Peer Review reports

Abstract

Background

Recent studies have extensively investigated the role of miRNAs in colorectal cancer (CRC), and several associations have been reported. In addition, single nucleotide polymorphisms (SNPs) in promoter regions of miRNAs have been shown to affect miRNA expression. Therefore, we aimed to analyze the effect of miRNA polymorphisms on CRC susceptibility.

Methods

We conducted association studies on the relationships between the miRNA polymorphisms miR-130bT > C rs373001, miR-200bT > C rs7549819, and miR-495A > C rs2281611 and CRC with 472 CRC patients and 399 control subjects in Korea.

Results

Multivariate logistic regressions of the CRC subgroups showed that the miR-495CC genotype associated with rectal cancer (AA+AC vs. CC; adjusted odds ratio (AOR) for CC, 1.592; 95% confidence interval (CI), 1.071–2.368; P = 0.022). The gene-environment combinatorial analysis showed that the combination of miR-495A > C and low plasma folate contributed to an increased risk of rectal cancer (AA+AC vs. CC; AOR for CC, 3.829; 95% CI, 1.577–9.300; P = 0.003). In the survival analysis, miR-200bT > C associated with CRC patient mortality (TT vs TC + CC; adjusted hazard ratio for TC + CC, 0.592; 95% CI, 0.3730.940; P = 0.026).

Conclusion

In this study, we found that miR-200b and miR-495 polymorphisms are involved in CRC susceptibility and prognosis.

Background

Colorectal cancer (CRC) is the third most prevalent cancer in the world with a high mortality rate [1], and eating habits and lifestyle patterns contribute to the high incidence in developed countries [2]. However, studies on dietary habits and lifestyle patterns have failed to sufficiently explain CRC disease outbreaks. Many groups have therefore focused on identifying the genetic causes of CRC, and molecular mechanisms such as microsatellite instability (MSI), CpG island methylator phenotype (CIMP), chromosomal instability (CIN), and KRAS or BRAF mutations have been described [37]. Recent studies indicate that microRNAs are potential prognostic biomarkers of CRC [8, 9].

MicroRNAs (miRNAs, miR) are small RNAs of ~ 22 bases, which bind to 3′-untranslated regions (UTRs) of target mRNAs to post-transcriptionally regulate the corresponding genes by silencing or degrading the mRNAs [1012]. miRNAs are involved in many biochemical and metabolic pathways in many organisms, and most miRNAs exist in the noncoding regions of genes [13]. miRNA is firstly transcribed into primary miRNA (pri-miRNA) and then transformed into precursor miRNA (pre-miRNA) by the DGCR8-DROSHA complex. Pre-miRNA is transported to the cytoplasm by the RAN-GTP/exportin-5 complex, where it is processed into a mature miRNA by DICER. Mature miRNA functions in an RNA-induced silencing complex (RISC) complex that targets mRNA [14]. Previous studies have revealed associations between miRNA expression and various cancers, including leukemia [15], hepatocarcinoma [16], gastric cancer [17], bladder cancer [18], lung cancer [19], and breast cancer [20]. It has also been shown that polymorphisms in miRNA sequences regulate miRNA expression [21, 22]. Studies have confirmed associations between miRNA polymorphisms and cancer development, progression, and metastasis [2325].

We previously demonstrated that miR-146a, miR-149, miR-196a2, and miR-499 single nucleotide polymorphisms (SNPs) associate with CRC [26]. However, because additional miRNA polymorphisms may associate with CRC, we asked whether miR-130b, miR-200b, and miR-495 SNPs also associate with CRC. MiR-130b has been shown to contribute to the occurrence of CRC and is involved in the PTEN/AKT signaling pathway [27, 28]. In addition, miR-200b has been shown to affect the breast cancer survival rate [29], to be involved in the regulation of c-Myc/PRDX2 in CRC [30], and to affect the migration, invasion, and epithelial mesenchymal transition (EMT) mechanisms of lung cancer [31]. miR-495 has been reported to reduce the proliferation of cancer cells in CRC and breast cancer [32, 33] and to affect cancer metastasis [34].

As mentioned earlier, miR-130b, 200b, and 495 have been linked to CRC development and progression. We focused on three SNPs: miR-130b rs373001T > C, miR-200b rs7549819T > C, and miR-495 rs2281611A > C, all of which are regulatory regions of miRNA expression. We hypothesized that polymorphisms in these miRNAs would ultimately influence CRC susceptibility and mortality. There is no known genetic association of these SNPs with CRC. This study specifically examined whether miRNA polymorphisms are related to CRC susceptibility in Koreans.

Methods

Study population

For this case-control study, a total of 871 individuals were enrolled from June 2005 to January 2011, including 472 patients diagnosed with CRC at CHA Bundang Medical Center (Seongnam, South Korea) and 399 randomly selected non-CRC subjects who participated in a health-screening program. This case group included only CRC patients who had gone through surgery and who had confirmed to adenocarcinoma by histology. The case group included colon and rectal cancer patients (268 and 193 patients, respectively). Tumors were classified by their tumor, node and metastasis classification (TNM) stage according to the 7th of the American joint committee on cancer (AJCC) staging manual as follows: stage I, n = 52 (11.02%); stage II, n = 191 (40.47%); stage III, n = 176 (37.29%); and stage IV, n = 47 (9.96%). Hypertension (HTN) and diabetes mellitus (DM) for overall participants were classified according to the criteria of the previous study [35]. We had were provided written informed consent for all of the participants and the study protocol was approved by the Institutional Review Board of CHA Bundang Medical Center (IRB No. 2009–08-077) and followed the recommendations of the Declaration of Helsinki.

Genotyping

DNA was extracted from white blood cells using a “G-DEX™IIb For Blood kit” (iNtRON Biotechnology, South Korea). Genotyping of miR-130b rs373001T > C, miR-200b rs7549819T > C and miR-495 rs2281611A > C were performed by same protocol as in our previous study [36], and detailed PCR conditions were presented in Additional file 1: Table S1. We randomly repeated 10–15% of miR-130b rs373001T > C, miR-200b rs7549819T > C and miR-495 rs2281611A > C polymorphism genotyping results and confirmed the results with DNA sequencing [36]. The concordance between the experiment and randomly repeat was 100%.

Statistical analysis

To compare clinical characteristics between study groups, we used the χ2 test and the two-tail t-test or Mann-Whitney test. The adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for association with miRNAs polymorphisms in CRC risk were calculated by multivariate logistic regression adjusted for age, sex, HTN, and DM. The software program used for statistical analysis in this study were “GraphPad Prism 4.0” (GraphPad Software Inc., San Diego, CA, USA), “HAPSTAT 3.0” (University of North Carolina, Chapel Hill, NC, USA), and “Medcalc v.18.2.1” (Medcalc Software, Mariakerke, Belgium) and and the cut-off of statistically significant was considered was P values < 0.05. The false discovery rate (FDR) was calculated when performing multiple comparisons to estimate the overall experimental error rate resulting from false positives. Independent prognostic markers were investigated using the Cox proportional-hazards regression for mortality analysis, and the results were adjusted for age, sex, TNM stage, and chemotherapy. Hazard ratios (HRs) are shown with 95% CIs.

Results

Study subject characteristics

The 472 CRC cases included 212 males and 260 females with an overall mean age of 61.99 ± 12.32 years. There were no significant differences in the age and sex of the CRC patients and the controls (P = 0.290 and 0.774, respectively). The baseline characteristics of patients with colon and rectal cancers, which are subgroups of CRC, showed no statistical differences when compared to the control group (Table 1).
Table 1

Baseline characteristics between controls and CRC patients

Characteristic

Controls

(n = 399)

CRC Patients

(n = 472)

P

Colon cancer

(n = 268)

P

Rectal cancer

(n = 193)

P

Age (years, mean ± SD)

61.15 ± 10.93

61.99 ± 12.32

0.129

61.44 ± 12.88

0.464

62.28 ± 11.54

0.153

Male (%)

173 (43.4)

212 (44.9)

0.645

118 (44.0)

0.915

88 (45.6)

0.750

Hypertension (%)

155 (38.8)

281 (59.5)

< 0.0001

157 (58.6)

0.003

117 (60.6)

0.003

HDL-C (mg/dL, mean ± SD)

45.91 ± 13.48

42.18 ± 13.05

0.001

42.82 ± 13.00

0.013

41.27 ± 13.07

0.001

LDL-C (mg/dL, mean ± SD)

115.87 ± 40.28

101.31 ± 28.62

0.003

98.55 ± 28.01

0.002

104.32 ± 29.54

0.142

Diabetes mellitus (%)

52 (13.0)

156 (33.1)

< 0.0001

92 (34.3)

< 0.0001

64 (33.2)

< 0.0001

Smoking (%)

138 (34.6)

92 (19.5)

< 0.0001

55 (20.5)

0.003

35 (18.1)

0.002

Folate (nmol/L, mean ± SD)

8.64 ± 6.13

7.94 ± 7.13

< 0.0001

8.12 ± 7.36

0.001

7.70 ± 6.86

0.000

Triglyceride (mg/dL, mean ± SD)

146.79 ± 89.33

129.00 ± 86.30

0.0003

126.93 ± 84.48

0.001

132.48 ± 90.86

0.015

Homocysteine (μmol/L, mean ± SD)

9.96 ± 4.27

10.68 ± 7.83

0.671

10.47 ± 8.21

0.572

10.88 ± 7.32

0.215

Total cholesterol (mg/dL, mean ± SD)

192.00 ± 37.32

178.76 ± 40.56

0.0001

178.73 ± 38.88

0.001

176.69 ± 42.89

0.002

Tumor size (%)

  < 5 cm

 

208 (44.1)

 

106 (39.6)

 

93 (48.2)

 

  ≥ 5 cm

264 (55.9)

162 (60.4)

100 (51.8)

TNM stage (%)

 I

 

52 (11.2)

 

26 (9.7)

 

26 (13.5)

 

 II

191 (41.0)

118 (44.2)

70 (36.3)

 III

176 (37.8)

94 (35.2)

81 (42.0)

 IV

47 (10.1)

29 (10.9)

16 (8.3)

 N.A.

6

1

0

MSI (%)

61 (15.6)

49 (22.0)

12 (7.3)

 MSI-high (%)

46 (11.8)

38 (17.0)

8 (4.9)

 MSI-low (%)

15 (3.8)

11 (4.9)

4 (2.4)

 N.A.

82

45

29

P-values were calculated by Man whithney U test for continuous variables and chi-square test for categorical variables.TNM stage, TNM classification of malignant tumours; MSI, microsatellite instability; N.A. row, missing data

Genotype frequencies

The distributions of genotypes for the miRNA polymorphisms miR-130bT > C, miR-200bT > C, and miR-495A > C in CRC patients and control subjects are shown in Table 2. The genotype frequencies of CRC and control groups were in Hardy-Weinberg equilibrium (HWE). There was no statistically significant difference in the distribution of miR-130bT > C, miR-200bT > C, and miR-495A > C SNPs between the CRC and control groups. In a subgroup analysis, we observed that the miR-495CC genotype was more frequent in rectal cancer patients than in the control group (AA+AC vs. CC; AOR for CC, 1.592; 95% CI, 1.071–2.368; Table 3). However, this statistical significance was lost after correcting for multiple comparisons using the FDR method (P = 0.065). There were no statistically significant differences in the distributions of the other miRNA SNPs between the CRC subgroups and the control group. We also confirmed that these SNPs are not associated to the MSI status (Additional file 1: Table S2).
Table 2

Genotype frequencies of microRNA polymorphisms in CRC patients and control subjects

Genotypes

Controls(n = 399)

Patients(n = 472)

AOR (95% CI)

P

FDR-P

miR-130b rs373001T > C

 TT

216 (54.2)

269 (57.0)

1.000 (reference)

 

 TC

157 (39.3)

168 (35.6)

0.825 (0.610–1.115)

0.210

0.416

 CC

26 (6.5)

35 (7.4)

0.943 (0.532–1.670)

0.840

0.840

Dominant (TT vs TC + CC)

 

0.846 (0.635–1.127)

0.254

0.398

Recessive (TT + TC vs CC)

1.028 (0.590–1.792)

0.923

0.923

 HWE P

0.723

0.222

 

miR-200b rs7549819T > C

 TT

171 (42.9)

216 (45.7)

1.000 (reference)

 

 TC

176 (44.1)

200 (42.4)

0.882 (0.652–1.194)

0.416

0.416

 CC

52 (13.0)

56 (11.9)

0.758 (0.481–1.194)

0.232

0.696

Dominant (TT vs TC + CC)

 

0.850 (0.638–1.132)

0.266

0.398

Recessive (TT + TC vs CC)

0.789 (0.512–1.215)

0.281

0.422

 HWE P

0.527

0.356

 

miR-495 rs2281611A > C

 AA

103 (25.8)

125 (26.5)

1.000 (reference)

 

 AC

194 (48.6)

222 (47.0)

0.829 (0.584–1.176)

0.292

0.416

 CC

102 (25.6)

125 (26.5)

1.080 (0.734–1.590)

0.696

0.840

Dominant (AA vs AC + CC)

 

0.919 (0.666–1.268)

0.608

0.608

Recessive (AA+AC vs CC)

1.208 (0.897–1.626)

0.214

0.422

 HWE P

0.582

0.197

 

AOR, adjusted odds ratio (adjusted for age, gender, hypertension, diabetes mellitus); CI, confidence interval; FDR, false discovery ratio; HWE, Hardy-Weinberg equilibrium

Table 3

Genotype frequencies of microRNA polymorphisms in CRC subgroups and control subjects

Genotypes

Controls(n = 399)

Colon(n = 268)

AOR (95% CI)

P

FDR-P

Rectal(n = 193)

AOR (95% CI)

P

FDR-P

miR-130b rs373001T > C

 TT

216 (54.2)

156 (58.2)

1.000 (reference)

 

109 (56.5)

1.000 (reference)

 

 TC

157 (39.3)

97 (36.2)

0.830 (0.585–1.177)

0.295

0.443

68 (35.2)

0.812 (0.549–1.201)

0.298

0.446

 CC

26 (6.5)

15 (5.6)

0.671 (0.327–1.377)

0.276

0.717

16 (8.3)

1.061 (0.520–2.164)

0.871

0.871

Dominant (TT vs TC + CC)

 

0.805 (0.575–1.126)

0.205

0.371

 

0.858 (0.593–1.241)

0.415

0.908

Recessive (TT + TC vs CC)

0.740 (0.369–1.486)

0.398

0.909

1.166 (0.583–2.331)

0.664

0.664

miR-200b rs7549819T > C

 TT

171 (42.9)

126 (47.0)

1.000 (reference)

 

83 (43.0)

1.000 (reference)

 

 TC

176 (44.1)

109 (40.7)

0.826 (0.580–1.177)

0.290

0.443

88 (45.6)

1.091 (0.740–1.608)

0.660

0.660

 CC

52 (13.0)

33 (12.3)

0.835 (0.493–1.412)

0.500

0.717

22 (11.4)

0.817 (0.449–1.488)

0.509

0.764

Dominant (TT vs TC + CC)

 

0.822 (0.589–1.146)

0.248

0.371

 

1.022 (0.706–1.480)

0.908

0.908

Recessive (TT + TC vs CC)

0.876 (0.531–1.447)

0.606

0.909

0.775 (0.441–1.362)

0.375

0.563

miR-495 rs2281611A > C

 AA

103 (25.8)

72 (26.9)

1.000 (reference)

 

51 (26.4)

1.000 (reference)

 

 AC

194 (48.6)

135 (50.4)

0.881 (0.587–1.321)

0.540

0.540

78 (40.4)

0.744 (0.470–1.176)

0.205

0.446

 CC

102 (25.6)

61 (22.8)

0.919 (0.583–1.450)

0.717

0.717

64 (33.2)

1.319 (0.810–2.147)

0.265

0.764

Dominant (AA vs AC + CC)

 

0.900 (0.618–1.310)

0.581

0.581

 

0.940 (0.621–1.421)

0.768

0.908

Recessive (AA+AC vs CC)

0.991 (0.675–1.453)

0.961

0.961

1.592 (1.071–2.368)

0.022

0.065

CRC, colorectal cancer; AOR, adjusted odds ratio (adjusted for age, gender, hypertension, diabetes mellitus); CI, confidence interval; FDR, false discovery ratio; HWE, Hardy-Weinberg equilibrium

Combinatorial effects of miRNA polymorphisms and environmental factors

Because CRC has been shown to be influenced by various environmental factors, we performed a stratified analysis of age, sex, HTN, DM, and test levels of peripheral blood factors (homocysteine, folate, TG, HDL) to determine whether there was an association between miRNA polymorphisms and CRC risk (Additional file 1: Table S3). We did not find any associations between miRNA polymorphisms and CRC risk in the high-risk groups for each variable.

We then conducted a gene-environment analysis to assess the combined effects of miR-130bT > C, miR-200bT > C, or miR-495A > C polymorphisms and clinical factors on CRC and CRC subgroup susceptibility. The combination of miR-495A > C and low plasma folate level contributed to an increased risk for CRC (AA+AC vs. CC; AOR, 3.119; 95% CI, 1.432–6.791; Additional file 1: Table S4). In addition, the miR-495CC genotype exhibited an increased risk in rectal cancer patients with HTN (AOR, 3.404; 95% CI, 1.902–6.092, P < 0.001), DM (AOR, 3.758; 95% CI, 1.685–8.383; P = 0.001), and in rectal cancer patients with low plasma folate levels (AOR, 3.829; 95% CI, 1.577–9.300; P = 0.003 Table 4 and Fig. 1).
Table 4

Combinatorial effects of miRNA polymorphisms and environmental factors on rectal cancer risk

Characteristics

miR-130bTT

miR-130bTC + CC

miR-200bTT

miR-200bTC + CC

miR-495AA + AC

miR-495CC

AOR (95% CI)

AOR (95% CI)

AOR (95% CI)

AOR (95% CI)

AOR (95% CI)

AOR (95% CI)

Age

  < 63 years

1.000 (reference)

1.222 (0.706–2.115)

1.000 (reference)

1.032 (0.599–1.780)

1.000 (reference)

1.563 (0.874–2.793)

  ≥ 63 years

1.097 (0.672–1.790)

0.696 (0.412–1.176)

0.826 (0.475–1.436)

0.854 (0.515–1.415)

1.107 (0.706–1.736)

1.784 (1.038–3.064)

Gender

 Male

1.000 (reference)

0.799 (0.469–1.362)

1.000 (reference)

1.214 (0.707–2.085)

1.000 (reference)

1.772 (0.975–3.221)

 Female

1.087 (0.664–1.782)

0.966 (0.570–1.636)

1.454 (0.835–2.531)

1.303 (0.753–2.255)

0.885 (0.565–1.387)

1.273 (0.730–2.222)

Hypertension

 No

1.000 (reference)

0.924 (0.531–1.610)

1.000 (reference)

0.799 (0.457–1.399)

1.000 (reference)

1.906 (1.050–3.461)

 Yes

2.539 (1.535–4.200)

1.854 (1.076–3.196)

1.921 (1.101–3.350)

2.171 (1.279–3.683)

2.362 (1.496–3.727)

3.404 (1.902–6.092)

Diabetes mellitus

 No

1.000 (reference)

0.832 (0.544–1.274)

1.000 (reference)

0.872 (0.571–1.332)

1.000 (reference)

1.686 (1.077–2.642)

 Yes

2.535 (1.382–4.651)

2.545 (1.385–4.676)

1.946 (0.998–3.793)

3.261 (1.798–5.913)

3.088 (1.851–5.152)

3.758 (1.685–8.383)

Homocysteine (μmol/L)

  < 13.3

1.000 (reference)

0.795 (0.531–1.191)

1.000 (reference)

1.191 (0.794–1.787)

1.000 (reference)

1.641 (1.069–2.518)

  ≥ 13.3

0.936 (0.451–1.943)

1.199 (0.579–2.484)

1.938 (0.904–4.152)

0.820 (0.394–1.708)

1.211 (0.653–2.248)

1.619 (0.612–4.282)

Folate (nmol/L)

  > 3.7

1.000 (reference)

0.853 (0.571–1.272)

1.000 (reference)

0.977 (0.654–1.458)

1.000 (reference)

1.478 (0.956–2.286)

  ≤ 3.7

2.427 (1.152–5.114)

2.193 (0.948–5.076)

1.645 (0.685–3.953)

2.512 (1.228–5.138)

2.069 (1.016–4.216)

3.829 (1.577–9.300)

Triglyceride (mg/dL)

  < 150

1.000 (reference)

0.843 (0.545–1.303)

1.000 (reference)

0.902 (0.584–1.394)

1.000 (reference)

0.934 (0.567–1.538)

  ≥ 150

0.524 (0.300–0.914)

0.426 (0.227–0.799)

0.373 (0.187–0.745)

0.609 (0.352–1.055)

0.457 (0.196–1.066)

0.359 (0.179–0.718)

HDL-C (mg/dL)

  ≥ 40

1.000 (reference)

0.839 (0.542–1.299)

1.000 (reference)

0.965 (0.624–1.492)

1.000 (reference)

1.119 (0.683–1.836)

  < 40

2.706 (1.500–4.882)

2.259 (1.162–4.394)

2.575 (1.312–5.053)

2.624 (1.442–4.775)

4.639 (1.799–11.961)

2.137 (1.141–4.001)

Upper and lower 15% cut-off values of homocysteine and folate were 13.3 μmol/L and 3.7 ng/mL, respectively

AOR, adjusted odds ratio (adjusted for age, gender, hypertension, diabetes mellitus); CI, confidence interval

Fig. 1
Fig. 1

Combinatorial effect of miR-495C > A and folic acid on rectal cancer. Each row represents low or high plasma folate levels. Folate was divided into two concentration groups by eliminating the lower 15%. The columns represent the miR-495AA + AC and the miR-495CC genotypes. The y-axis represents the odds ratio for each group based on the reference group

Associations of miRNA SNPs with CRC survival

Associations between miRNA polymorphisms and CRC survival are shown in Table 5. Multivariate Cox proportional analysis showed that the miR-200bTC and TC + CC genotypes associated with survival in CRC patients (adjusted HR = 0.522; 95% CI, 0.307–0.888; P = 0.017 and adjusted HR = 0.522; 95% CI, 0.307–0.888; P = 0.017, respectively; Fig. 2).
Table 5

Multivariate survival analysis of polymorphisms in CRC patients

Genotype

CRC(n = 472)

Death(n = 85)

Adjusted HRa(95% CI)

P

miR-130b rs373001T > C

 TT

269 (57.0)

47 (55.3)

1.000 (reference)

 

 TC

168 (35.6)

29 (34.1)

0.810 (0.491–1.338)

0.411

 CC

35 (7.4)

9 (10.6)

1.345 (0.632–2.864)

0.442

Dominant (TT vs TC + CC)

 

0.910 (0.575–1.438)

0.685

Recessive (TT + TC vs CC)

1.435 (0.688–2.990)

0.336

miR-200b rs7549819T > C

 TT

216 (45.7)

48 (56.5)

1.000 (reference)

 

 TC

200 (42.4)

26 (30.6)

0.522 (0.307–0.888)

0.017

 CC

56 (11.9)

11 (12.9)

0.781 (0.393–1.555)

0.482

Dominant (TT vs TC + CC)

 

0.592 (0.373–0.940)

0.026

Recessive (TT + TC vs CC)

0.994 (0.509–1.944)

0.987

miR-495 rs2281611A > C

 AA

125 (26.5)

23 (27.1)

1.000 (reference)

 

 AC

222 (47.0)

37 (43.5)

1.077 (0.618–1.879)

0.794

 CC

125 (26.5)

25 (29.4)

1.167 (0.628–2.170)

0.625

Dominant (AA vs AC + CC)

 

1.126 (0.672–1.886)

0.652

Recessive (AA+AC vs CC)

1.147 (0.691–1.903)

0.595

aHR estimates with 95% CI and P-values from the Cox-proportional hazard model on overall survival. HR, hazard ratio (adjusted for age, gender, chemotherapy, TNM stage); CI, confidence interval

Fig. 2
Fig. 2

Survival curves depicting the relationship between the miR-200bT > C polymorphism and CRC patients. Cox proportional-hazards regression model of CRC patient survival. Patients carrying the miR-200b (A) TC and (B) TC + CC genotypes had a reduced risk of death when compared with the TT genotype (P = 0.017 and P = 0.026, respectively)

Discussion

In this study, we investigated whether the miRNA polymorphisms miR-130bT > C rs373001, miR-200bT > C rs7549819, and miR-495A > C rs2281611 associate with susceptibility for CRC or a CRC subgroup in Korean subjects. These three SNPs are regulatory SNPs located in the promoter regions of the miRNA genes. SNPs in the promoter regions of miRNAs have been shown to affect the expression of mature miRNAs that regulate target genes [24, 25].

miR-495 has been shown to play a tumor suppressor role in many cancers, including gastric cancer [37], non-small cell lung cancer [38], glioma [39], and CRC [40]. In particular, miR-495 has been shown to regulate expression of genes involved in cellular processes, including mTOR, Akt, and PRL-3 [37, 41, 42]. Our data suggest that the miR-495CC genotype associates with an increased risk for rectal cancer when compared with the other genotypes. Therefore, we assume that substitution of the C allele with the rs2281611 A allele in the promoter region of the miR-495 gene leads to a reduction in miRNA expression, which then affects CRC susceptibility. In the combinatorial gene-environment analysis, the miR-495CC genotype combined with folate exhibited a significantly increased risk of CRC. Folic acid is an essential factor involved in one-carbon metabolism, including DNA synthesis, repair, and methylation [4345]. When the folate level is insufficient, DNA is abnormally replicated during cell division [46], DNA is degraded, and mutagenesis increases [43]. In addition, uracil misincorporation and double-strand breaks have been observed in tumor cells cultured in low folate conditions [43, 47]. Low folate levels have also been associated with breast cancer [48], CRC [49], and gastric cancer [50]. Thus, the effects of the miR-495CC genotype and low folate concentration appear to be synergistic.

In the survival analysis, the miR-200bTC and TC + CC genotypes associated with the survival rate of patients who had undergone CRC resection. The miR-200 family has been shown to inhibit EMT, which shares many similarities with cancer progression [51], and to associate with poor prognoses, including metastasis, invasion, and chemoresistance in gastric cancer [52], bladder cancer [53], and CRC [54]. The miR-200 family has also been implicated in CRC survival [55]. Abnormal miR-200b expression moderates the poor prognosis and progression of CRC, and these factors may affect patient survival rate.

There are several limitations to our study. The first is that expression differences in mature miRNAs due to SNPs in the regulatory regions of miRNA genes have not been confirmed at the molecular and functional levels. Therefore, we are inferring that expression of the altered miR-495 relates directly to CRC risk by targeting the tumor suppressor gene. The second limitation is that the sample size may be insufficient to draw any conclusions from the stratified analysis. Future studies should include more than 1000 ethnically homogeneous people. Lastly, this study only included Koreans who visited CHA Bundang Medical Center. Although our findings provide the first evidence that miRNA polymorphisms could be potential biomarkers of CRC prevention and prognosis, significant results should be identified in independent populations to confirm the validity of these results.

Conclusion

In conclusion, we investigated the relationship between CRC susceptibility and the miRNA polymorphisms miR-130b rs373001, miR-200b rs7549819, and miR-495 rs2281611. We found that miR-200b and miR-495 associated with CRC susceptibility and survival of CRC patients, respectively. Although there have been many studies that have described the relationships between miR-200b and miR-495 and CRC susceptibility, no associations between the miR-200b and miR-495 polymorphisms and CRC have been reported. Thus, our results provide evidence that miR-200b and miR-495 polymorphisms may be potential biomarkers for CRC diagnosis and prevention.

Abbreviations

AJCC: 

American joint committee on cancer

AOR: 

Adjusted odds ratio

CI: 

Confidence interval

CIMP: 

CpG island methylator phenotype

CIN: 

Chromosomal instability

CRC: 

Colorectal cancer

DM: 

Diabetes mellitus

EMT: 

Epithelial mesenchymal transition

FDR: 

False discovery rate

HR: 

Hazard ratio

HTN: 

Hypertension

HWE: 

Hardy-weinberg equilibrium

miRNA: 

microRNA

MSI: 

Microsatellite instability

pre-miRNA: 

precursor miRNA

pri-miRNA: 

primary miRNA

RISC: 

RNA-induced silencing complex

SNP: 

Single nucleotide polymorphism

TNM: 

Tumor, node and metastasis classification

UTR: 

Untranslated region

Declarations

Acknowledgements

Not applicable.

Funding

This study was supported by a National Research Foundation of Korea (NRF) Grant (2018R1D1A1B07047604), funded by the Korean Government and was supported by a grant of the Korea Healthcare technology R&D project, Ministry for Health, Welfare & Family Affairs (HI15C1972010015 and HI18C19990200). The funding bodies were not involved in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The data supporting the conclusions of this article are available from the authors on request.

Authors’ contributions

Conceived and designed the experiments: JWK and NKK. Performed the experiments: EGK, JOK, HSP, CSR, JO, and HHJ. Analyzed the data and statistical analyses: EGK, JOK, HSP, CSR. Contributed reagents/material/analysis tools: JOK, HHJ, JWK, and NKK. Wrote the main manuscript text: EGK. Reference collection and data management: JWK and NKK. All authors reviewed the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All of the study subjects were ethnic Koreans and provided written informed consent. The study protocol was approved by the Institutional Review Board of CHA Bundang Medical Center (IRB No. 2009–08-077) and followed the recommendations of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors have no conflicts of interest to declare.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biomedical Science, College of Life Science, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam, 13488, South Korea
(2)
Department of Internal Medicine, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam, 13496, South Korea
(3)
Department of Surgery, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam, 13496, South Korea

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67(1):7–30.View ArticleGoogle Scholar
  2. Center MM, Jemal A, Smith RA, Ward E. Worldwide variations in colorectal cancer. CA Cancer J Clin. 2009;59(6):366–78.View ArticleGoogle Scholar
  3. Jass JR. Colorectal cancer: a multipathway disease. Crit Rev Oncog. 2006;12(3–4):273–87.View ArticleGoogle Scholar
  4. Sideris M, Papagrigoriadis S. Molecular biomarkers and classification models in the evaluation of the prognosis of colorectal cancer. Anticancer Res. 2014;34(5):2061–8.PubMedGoogle Scholar
  5. Timmermann B, Kerick M, Roehr C, Fischer A, Isau M, Boerno ST, Wunderlich A, Barmeyer C, Seemann P, Koenig J, et al. Somatic mutation profiles of MSI and MSS colorectal cancer identified by whole exome next generation sequencing and bioinformatics analysis. PLoS One. 2010;5(12):e15661.View ArticleGoogle Scholar
  6. Guo F, Gong H, Zhao H, Chen J, Zhang Y, Zhang L, Shi X, Zhang A, Jin H, Zhang J, et al. Mutation status and prognostic values of KRAS, NRAS, BRAF and PIK3CA in 353 Chinese colorectal cancer patients. Sci Rep. 2018;8(1):6076.View ArticleGoogle Scholar
  7. Rokni P, Shariatpanahi AM, Sakhinia E, Kerachian MA. BMP3 promoter hypermethylation in plasma-derived cell-free DNA in colorectal cancer patients. Genes Genom. 2018;40(4):423–8.View ArticleGoogle Scholar
  8. Oh J, Kim JW, Lee BE, Jang MJ, Chong SY, Park PW, Hwang SG, Oh D, Kim NK. Polymorphisms of the pri-miR-34b/c promoter and TP53 codon 72 are associated with risk of colorectal cancer. Oncol Rep. 2014;31(2):995–1002.View ArticleGoogle Scholar
  9. Molina-Pinelo S, Carnero A, Rivera F, Estevez-Garcia P, Bozada JM, Limon ML, Benavent M, Gomez J, Pastor MD, Chaves M, et al. MiR-107 and miR-99a-3p predict chemotherapy response in patients with advanced colorectal cancer. BMC Cancer. 2014;14:656.View ArticleGoogle Scholar
  10. Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007;27(1):91–105.View ArticleGoogle Scholar
  11. Krol J, Loedige I, Filipowicz W. The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010;11(9):597–610.View ArticleGoogle Scholar
  12. Hammond SM. An overview of microRNAs. Adv Drug Deliv Rev. 2015;87:3–14.View ArticleGoogle Scholar
  13. Mohr AM, Mott JL. Overview of microRNA biology. Semin Liver Dis. 2015;35(1):3–11.View ArticleGoogle Scholar
  14. Connerty P, Ahadi A, Hutvagner G. RNA binding proteins in the miRNA pathway. Int J Mol Sci. 2015;17(1):E31.Google Scholar
  15. Volinia S, Galasso M, Costinean S, Tagliavini L, Gamberoni G, Drusco A, Marchesini J, Mascellani N, Sana ME, Abu Jarour R, et al. Reprogramming of miRNA networks in cancer and leukemia. Genome Res. 2010;20(5):589–99.View ArticleGoogle Scholar
  16. Wen Y, Han J, Chen J, Dong J, Xia Y, Liu J, Jiang Y, Dai J, Lu J, Jin G, et al. Plasma miRNAs as early biomarkers for detecting hepatocellular carcinoma. Int J Cancer. 2015;137(7):1679–90.View ArticleGoogle Scholar
  17. Mirzaei H, Khataminfar S, Mohammadparast S, Sales SS, Maftouh M, Mohammadi M, Simonian M, Parizadeh SM, Hassanian SM, Avan A. Circulating microRNAs as Potential Diagnostic Biomarkers and Therapeutic Targets in Gastric Cancer: Current Status and Future Perspectives. Curr Med Chem. 2016;23(36):4135–50.View ArticleGoogle Scholar
  18. Tolle A, Ratert N, Jung K. miRNA panels as biomarkers for bladder cancer. Biomark Med. 2014;8(5):733–46.View ArticleGoogle Scholar
  19. Zhang Y, Yang Q, Wang S. MicroRNAs: a new key in lung cancer. Cancer Chemother Pharmacol. 2014;74(6):1105–11.View ArticleGoogle Scholar
  20. Matamala N, Vargas MT, Gonzalez-Campora R, Minambres R, Arias JI, Menendez P, Andres-Leon E, Gomez-Lopez G, Yanowsky K, Calvete-Candenas J, et al. Tumor microRNA expression profiling identifies circulating microRNAs for early breast cancer detection. Clin Chem. 2015;61(8):1098–106.View ArticleGoogle Scholar
  21. Qi P, Wang L, Zhou B, Yao WJ, Xu S, Zhou Y, Xie ZB. Associations of miRNA polymorphisms and expression levels with breast cancer risk in the Chinese population. Genet Mol Res. 2015;14(2):6289–96.View ArticleGoogle Scholar
  22. Xu Q, Dong Q, He C, Liu W, Sun L, Liu J, Xing C, Li X, Wang B, Yuan Y. A new polymorphism biomarker rs629367 associated with increased risk and poor survival of gastric cancer in chinese by up-regulated miRNA-let-7a expression. PLoS One. 2014;9(4):e95249.View ArticleGoogle Scholar
  23. Lv H, Pei J, Liu H, Wang H, Liu J. A polymorphism site in the premiR34a coding region reduces miR34a expression and promotes osteosarcoma cell proliferation and migration. Mol Med Rep. 2014;10(6):2912–6.View ArticleGoogle Scholar
  24. Li L, Pan X, Li Z, Bai P, Jin H, Wang T, Song C, Zhang L, Gao L. Association between polymorphisms in the promoter region of miR-143/145 and risk of colorectal cancer. Hum Immunol. 2013;74(8):993–7.View ArticleGoogle Scholar
  25. Zhang S, Qian J, Cao Q, Li P, Wang M, Wang J, Ju X, Meng X, Lu Q, Shao P, et al. A potentially functional polymorphism in the promoter region of miR-34b/c is associated with renal cell cancer risk in a Chinese population. Mutagenesis. 2014;29(2):149–54.View ArticleGoogle Scholar
  26. Min KT, Kim JW, Jeon YJ, Jang MJ, Chong SY, Oh D, Kim NK. Association of the miR-146aC>G, 149C>T, 196a2C>T, and 499A>G polymorphisms with colorectal cancer in the Korean population. Mol Carcinog. 2012;51(Suppl 1):E65–73.View ArticleGoogle Scholar
  27. Zhang HD, Jiang LH, Sun DW, Li J, Ji ZL. The role of miR-130a in cancer. Breast Cancer. 2017;24(4):521–7.View ArticleGoogle Scholar
  28. Colangelo T, Fucci A, Votino C, Sabatino L, Pancione M, Laudanna C, Binaschi M, Bigioni M, Maggi CA, Parente D, et al. MicroRNA-130b promotes tumor development and is associated with poor prognosis in colorectal cancer. Neoplasia. 2013;15(9):1086–99.View ArticleGoogle Scholar
  29. Li X, Roslan S, Johnstone CN, Wright JA, Bracken CP, Anderson M, Bert AG, Selth LA, Anderson RL, Goodall GJ, et al. MiR-200 can repress breast cancer metastasis through ZEB1-independent but moesin-dependent pathways. Oncogene. 2014;33(31):4077–88.View ArticleGoogle Scholar
  30. Lv Z, Wei J, You W, Wang R, Shang J, Xiong Y, Yang H, Yang X, Fu Z. Disruption of the c-Myc/miR-200b-3p/PRDX2 regulatory loop enhances tumor metastasis and chemotherapeutic resistance in colorectal cancer. J Transl Med. 2017;15(1):257.View ArticleGoogle Scholar
  31. Gibbons DL, Lin W, Creighton CJ, Rizvi ZH, Gregory PA, Goodall GJ, Thilaganathan N, Du L, Zhang Y, Pertsemlidis A, et al. Contextual extracellular cues promote tumor cell EMT and metastasis by regulating miR-200 family expression. Genes Dev. 2009;23(18):2140–51.View ArticleGoogle Scholar
  32. Yan L, Yao J, Qiu J. miRNA-495 suppresses proliferation and migration of colorectal cancer cells by targeting FAM83D. Biomed Pharmacother. 2017;96:974–81.View ArticleGoogle Scholar
  33. Chen Y, Luo D, Tian W, Li Z, Zhang X. Demethylation of miR-495 inhibits cell proliferation, migration and promotes apoptosis by targeting STAT-3 in breast cancer. Oncol Rep. 2017;37(6):3581–9.View ArticleGoogle Scholar
  34. Liu C, Jian M, Qi H, Mao WZ. MicroRNA-495 inhibits proliferation, metastasis and promotes apoptosis by targeting Twist1 in gastric cancer cells. Oncol Res. 2018;27(3):389–97.Google Scholar
  35. Jeon YJ, Kim JW, Park HM, Jang HG, Kim JO, Oh J, Chong SY, Kwon SW, Kim EJ, Oh D, et al. Interplay between 3′-UTR polymorphisms in the vascular endothelial growth factor (VEGF) gene and metabolic syndrome in determining the risk of colorectal cancer in Koreans. BMC Cancer. 2014;14:881.View ArticleGoogle Scholar
  36. Kim J, Choi GH, Ko KH, Kim JO, Oh SH, Park YS, Kim OJ, Kim NK. Association of the Single Nucleotide Polymorphisms in microRNAs 130b, 200b, and 495 with Ischemic Stroke Susceptibility and Post-Stroke Mortality. PLoS One. 2016;11(9):e0162519.View ArticleGoogle Scholar
  37. Li Z, Zhang G, Li D, Jie Z, Chen H, Xiong J, Liu Y, Cao Y, Jiang M, Le Z, et al. Methylation-associated silencing of miR-495 inhibit the migration and invasion of human gastric cancer cells by directly targeting PRL-3. Biochem Biophys Res Commun. 2015;456(1):344–50.View ArticleGoogle Scholar
  38. Song L, Li Y, Li W, Wu S, Li Z. miR-495 enhances the sensitivity of non-small cell lung cancer cells to platinum by modulation of copper-transporting P-type adenosine triphosphatase a (ATP7A). J Cell Biochem. 2014;115(7):1234–42.View ArticleGoogle Scholar
  39. Zhang B, Yuan F, Liu J, Li Y, Zhou F, Liu X, Hao Z, Li Q, Zheng Y, Wang W. Hsa-miR-495 acts as a tumor suppressor gene in glioma via the negative regulation of MYB. Mol Med Rep. 2016;14(1):977–82.View ArticleGoogle Scholar
  40. Bai Z, Wang J, Wang T, Li Y, Zhao X, Wu G, Yang Y, Deng W, Zhang Z. The MiR-495/Annexin A3/P53 Axis inhibits the invasion and EMT of colorectal Cancer cells. Cell Physiol Biochem. 2017;44(5):1882–95.View ArticleGoogle Scholar
  41. Mao Y, Li L, Liu J, Wang L, Zhou Y. MiR-495 inhibits esophageal squamous cell carcinoma progression by targeting Akt1. Oncotarget. 2016;7(32):51223–36.View ArticleGoogle Scholar
  42. Li JZ, Wang ZL, Xu WH, Li Q, Gao L, Wang ZM. MicroRNA-495 Regulates Migration and Invasion in Prostate Cancer Cells Via Targeting Akt and mTOR Signaling. Cancer Investig. 2016;34(4):181–8.View ArticleGoogle Scholar
  43. Liu JJ, Ward RL. Folate and one-carbon metabolism and its impact on aberrant DNA methylation in cancer. Adv Genet. 2010;71:79–121.View ArticleGoogle Scholar
  44. Nijhout HF, Reed MC, Ulrich CM. Mathematical models of folate-mediated one-carbon metabolism. Vitam Horm. 2008;79:45–82.View ArticleGoogle Scholar
  45. Choi SW, Mason JB. Folate status: effects on pathways of colorectal carcinogenesis. J Nutr. 2002;132(8 Suppl):2413S–8S.View ArticleGoogle Scholar
  46. Kim YI. Folate and colorectal cancer: an evidence-based critical review. Mol Nutr Food Res. 2007;51(3):267–92.View ArticleGoogle Scholar
  47. Duthie SJ, Hawdon A. DNA instability (strand breakage, uracil misincorporation, and defective repair) is increased by folic acid depletion in human lymphocytes in vitro. FASEB J. 1998;12(14):1491–7.View ArticleGoogle Scholar
  48. Ericson U, Sonestedt E, Ivarsson MI, Gullberg B, Carlson J, Olsson H, Wirfalt E. Folate intake, methylenetetrahydrofolate reductase polymorphisms, and breast cancer risk in women from the Malmo diet and Cancer cohort. Cancer Epidemiol Biomark Prev. 2009;18(4):1101–10.View ArticleGoogle Scholar
  49. Ryan BM, Weir DG. Relevance of folate metabolism in the pathogenesis of colorectal cancer. J Lab Clin Med. 2001;138(3):164–76.View ArticleGoogle Scholar
  50. Shen H, Xu Y, Zheng Y, Qian Y, Yu R, Qin Y, Wang X, Spitz MR, Wei Q. Polymorphisms of 5,10-methylenetetrahydrofolate reductase and risk of gastric cancer in a Chinese population: a case-control study. Int J Cancer. 2001;95(5):332–6.View ArticleGoogle Scholar
  51. Pichler M, Ress AL, Winter E, Stiegelbauer V, Karbiener M, Schwarzenbacher D, Scheideler M, Ivan C, Jahn SW, Kiesslich T, et al. MiR-200a regulates epithelial to mesenchymal transition-related gene expression and determines prognosis in colorectal cancer patients. Br J Cancer. 2014;110(6):1614–21.View ArticleGoogle Scholar
  52. Kurashige J, Kamohara H, Watanabe M, Hiyoshi Y, Iwatsuki M, Tanaka Y, Kinoshita K, Saito S, Baba Y, Baba H. MicroRNA-200b regulates cell proliferation, invasion, and migration by directly targeting ZEB2 in gastric carcinoma. Ann Surg Oncol. 2012;19(Suppl 3):S656–64.View ArticleGoogle Scholar
  53. Wiklund ED, Bramsen JB, Hulf T, Dyrskjot L, Ramanathan R, Hansen TB, Villadsen SB, Gao S, Ostenfeld MS, Borre M, et al. Coordinated epigenetic repression of the miR-200 family and miR-205 in invasive bladder cancer. Int J Cancer. 2011;128(6):1327–34.View ArticleGoogle Scholar
  54. Hur K, Toiyama Y, Takahashi M, Balaguer F, Nagasaka T, Koike J, Hemmi H, Koi M, Boland CR, Goel A. MicroRNA-200c modulates epithelial-to-mesenchymal transition (EMT) in human colorectal cancer metastasis. Gut. 2013;62(9):1315–26.View ArticleGoogle Scholar
  55. Diaz T, Tejero R, Moreno I, Ferrer G, Cordeiro A, Artells R, Navarro A, Hernandez R, Tapia G, Monzo M. Role of miR-200 family members in survival of colorectal cancer patients treated with fluoropyrimidines. J Surg Oncol. 2014;109(7):676–83.View ArticleGoogle Scholar

Copyright

© The Author(s). 2019

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