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Cancer Communications

Original article
Open Access

Association of microRNA polymorphisms with the risk of head and neck squamous cell carcinoma in a Chinese population: a case–control study

  • Limin Miao†1,
  • Lihua Wang†2,
  • Longbiao Zhu1,
  • Jiangbo Du2,
  • Xun Zhu2,
  • Yuming Niu3,
  • Ruixia Wang1,
  • Zhibin Hu2, 4,
  • Ning Chen1,
  • Hongbing Shen2, 4Email author and
  • Hongxia Ma2, 4Email author
†Contributed equally
Chinese Journal of Cancer201635:77

https://doi.org/10.1186/s40880-016-0136-9

©  The Author(s) 2016

Received: 28 August 2015

Accepted: 4 March 2016

Published: 11 August 2016

Abstract

Background

MicroRNA (miRNA) polymorphisms may alter miRNA-related processes, and they likely contribute to cancer susceptibility. Various studies have investigated the associations between genetic variants in several key miRNAs and the risk of human cancers; however, few studies have focused on head and neck squamous cell carcinoma (HNSCC) risk. This study aimed to evaluate the associations between several key miRNA polymorphisms and HNSCC risk in a Chinese population.

Methods

In this study, we genotyped five common single-nucleotide polymorphisms (SNPs) in several key miRNAs (miR-149 rs2292832, miR-146a rs2910164, miR-605 rs2043556, miR-608 rs4919510, and miR-196a2 rs11614913) and evaluated the associations between these SNPs and HNSCC risk according to cancer site with a case–control study including 576 cases and 1552 controls, which were matched by age and sex in a Chinese population.

Results

The results revealed that miR-605 rs2043556 [dominant model: adjusted odds ratio (OR) 0.71, 95% confidence interval (CI) 0.58–0.88; additive model: adjusted OR 0.74, 95% CI 0.62–0.89] and miR-196a2 rs11614913 (dominant model: adjusted OR 1.36, 95% CI 1.08–1.72; additive model: adjusted OR 1.28, 95% CI 1.10–1.48) were significantly associated with the risk of oral squamous cell carcinoma (OSCC). Furthermore, when these two loci were evaluated together based on the number of putative risk alleles (rs2043556 A and rs11614913 G), a significant locus-dosage effect was noted on the risk of OSCC (P trend < 0.001). However, no significant association was detected between the other three SNPs (miR-149 rs2292832, miR-146a rs2910164, and miR-608 rs4919510) and HNSCC risk.

Conclusion

Our study provided the evidence that miR-605 rs2043556 and miR-196a2 rs11614913 may have an impact on genetic susceptibility to OSCC in Chinese population.

Keywords

Head and neck cancermicroRNAPolymorphismSquamous cell carcinomaSusceptibility

Background

Head and neck cancer, predominantly head and neck squamous cell carcinoma (HNSCC), represents a common cancer worldwide and has been considered a serious and growing public health problem in many countries [1, 2]. It was estimated that 45,780 new patients would be diagnosed with cancer of the oral cavity and the pharynx, and 8650 deaths from these diseases occurred in 2015 in the United States alone [3]. Environmental carcinogens and carcinogenic viruses have been identified as the main etiologic factors for HNSCC [4]. Furthermore, genetic variants play a risk-modulating role in the etiology of HNSCC [5].

MicroRNAs (miRNAs) are 20–24 nt single-stranded RNA molecules that repress the expression of specific target genes by binding to the 3′-untranslated regions (UTRs) of messenger RNA (mRNA) [6]. A single miRNA may regulate the expression of many genes, and it has been proposed that more than one-third of all protein-coding genes are under translational control by miRNAs [7]. Numerous studies have demonstrated that aberrant expression of miRNAs is closely associated with the cell proliferation, invasion, metastasis, and prognosis of various cancers [8, 9]. Given that small variations in the expression of a specific miRNA may affect thousands of target mRNAs and result in diverse functional consequences [10], miRNAs have been considered ideal candidate genes for cancer predisposition.

Studies have demonstrated that potentially functional single nucleotide polymorphisms (SNPs) located in several key miRNAs may influence the function of mature miRNAs and then affect the process of carcinogenesis [11–13]. For example, rs2292832 in miR-149 and rs2043556 in miR-605 were associated with the modified expression level of these two miRNAs [14]. rs2910164 in miR-146a altered the mature miR-146a expression level that was involved in the regulation of cell differentiation and cancer formation [15, 16]. rs4919510 in miR-608 has been predicted by in silico algorithms to exhibit differential capacities to bind to the potential target genes of miR-608, such as the insulin receptor (IR) and tumor protein 53 (TP53) [17]. Furthermore, rs11614913 in miR-196a2 affects the expression of miR-196a, and aberrant regulation of miR-196a is involved in the development and progression of several cancers, including oral cancer [18]. To date, some population studies and meta-analyses have been performed to investigate the associations between polymorphisms of the above important miRNAs and the risk of multiple types of malignant tumors [19, 20]. However, the results were inconsistent, and few studies focused on the associations of these SNPs with HNSCC risk in Chinese population.

Thus, we performed a case-control study on associations of five common SNPs in key miRNAs (rs2292832 in miR-149, rs2910164 in miR-146a, rs2043556 in miR-605, rs4919510 in miR-608, and rs11614913 in miR-196a2) with HNSCC risk in China.

Methods

Study subjects

This study is a hospital-based case–control study. All newly diagnosed HNSCC patients historically confirmed by two pathologists were consecutively recruited from Jiangsu Stomatological Hospital and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China between January 2009 and May 2013. Exclusion criteria included secondary HNSCC or metastasized cancer from other organs. None of the patients received neoadjuvant chemotherapy or radiotherapy before surgery. Cancer-free controls matched to the cases by age (±5 years) and sex were randomly selected from a cohort of more than 30,000 participants in a community-based screening program for non-infectious diseases in the Jiangsu Province, China. All participants were genetically unrelated and of the ethnic Han Chinese population. Each participant was scheduled for a face-to-face interview to answer a structured questionnaire that elicited information on demographic characteristics and environmental exposure history, such as age, sex, smoking status, and drinking status. Written informed consent was obtained from each participant, and the study was approved by the Institutional Review Boards of all relevant institutes.

SNP selection and genotyping

Based on previous reports about miRNA polymorphisms and cancer risk [14–18], we chose five most investigated and potentially functional SNPs (rs2292832 in miR-149, rs2910164 in miR-146a, rs2043556 in miR-605, rs4919510 in miR-608, and rs11614913 in miR-196a2) for genotyping. Venous blood was collected from all subjects and centrifuged at a speed of 4000 round/min for 10 min. The centrifuged blood was stored at −40 °C for use. Genomic DNA was isolated from leukocyte pellets of venous blood by proteinase K digestion, and this process was followed by phenol chloroform extraction. All DNA samples were assessed for quality and quantity using Nanodrop (Thermo Scientific, Waltham, MA, USA) and DNA electrophoresis (agarose gel imaging system, agarose gel electronic balance, and electronic tank supplied by Oxoid company, Basingstoke, England; micropipette, microwave oven, and electrophoresis apparatus supplied by Gilson company, Madison, WI, USA) before genotyping. SNPs were genotyped by using Illumina Infinium1 Human Exome BeadChip (Illumina Inc., San Diego, CA, USA), and genotype calling was performed using the GenTrain version 1.0 clustering algorithm in GenomeStudio V2011.1 (Illumina). The overall call rate was 99.77%–99.91% for all SNPs.

Statistical analysis

The Hardy–Weinberg equilibrium was tested by a goodness-of-fit χ2 test to compare the observed genotype frequencies with the expected ones among the control subjects. Distributions of selected demographic variables, risk factors, and frequencies of variant genotypes between the cases and controls were evaluated by using the Pearson’s Chi squared test (uncorrected). The associations of variant genotypes with HNSCC risk were estimated by computing odds ratios (ORs) and 95% confidence intervals (CIs) from both univariate and multivariate logistic regression analyses according to cancer site. The heterogeneity between subgroups was assessed with the Chi square-based Q test. All statistical analyses were performed with Statistical Analysis System software (v.9.1 SAS Institute, Cary, NC, USA). P < 0.05 was considered as the level of statistical significance.

Additionally, we used another data-mining tool, the non-parametric multifactor dimensionality reduction (MDR) software (version 2.0 beta 8.4, Norris-Cotton Cancer Center, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA) to identify the potential locus–locus and gene-environment interactions with trichotomies genotypes, age (dichotomized into ≥60 years and <60 years), sex, smoking status, and drinking status. The fitness of the MDR model was assessed by estimating the testing accuracy and the cross-validation consistency (CVC). Models that were true positive would have estimating the testing accuracy of >0.5. The best model with the highest CVC and the highest testing accuracy was selected [21].

Results

Selected characteristics of studied subjects

A total of 576 HNSCC patients and 1552 cancer-free controls were included in the study. Distributions of physiological characteristics in the case and control groups are presented in Table 1. No significant difference in the distributions of age, sex, and smoking status were noted between the case and control groups. Expectedly, more drinkers were found in the case group than in the control group (44.3 vs. 32.8%, P < 0.001). Further, logistic regression suggested that drinking status was associated with an increased HNSCC risk (β = 0.493, OR 1.64, 95% CI 1.35–1.99, P < 0.001). Although the proportion of smokers was a bit higher in the case group (45.3%) than in the control group (42.6%), the association between smoking and HNSCC risk was not significant (β = 0.111, OR 1.12, 95% CI 0.92–1.35, P = 0.260). In the 576 cases, 462 (80.2%) had oral squamous cell carcinoma (OSCC), and 114 (19.8%) had HNSCC at other sites [9 (1.6%) had oropharyngeal tumor, 102 (17.7%) had laryngeal tumor, 1 had nasal sinus cancer, 1 had parotid carcinoma, and 1 had salivary gland carcinoma].
Table 1

Selected characteristics of head and neck squamous cell carcinoma (HNSCC) patients and cancer-free controls

Variable

Patients [cases (%)]

Controls [cases (%)]

P a

Total

576

1552

 

Age (years)

 <60

265 (46.0)

719 (46.3)

0.895

 ≥60

311 (54.0)

833 (53.7)

 

Gender

  

0.750

 Female

214 (37.2)

565 (36.4)

 

 Male

362 (62.8)

987 (63.6)

 

Smoking status

  

0.260

 No

315 (54.7)

891 (57.4)

 

 Yes

261 (45.3)

661 (42.6)

 

Drinking status

  

<0.001

 No

321 (55.7)

1043 (67.2)

 

 Yes

255 (44.3)

509 (32.8)

 

Italic value indicate significance of p value (p < 0.05)

aTwo-sided Chi squared test

Primary information of selected SNPs

The position, minor allele frequencies (MAFs), and other primary information of five selected SNPs are presented in Table 2. The Hardy–Weinberg equilibrium was not severely violated judging from the goodness-of-fit χ2 test (all P > 0.05). Among the five loci, the genotype distributions of two SNPs were significantly different between the case and control groups (P = 0.004 for miR-605 rs2043556 and P = 0.019 for miR-196a2 rs11614913).
Table 2

Primary information and minor allele frequencies (MAFs) of selected single-nuclide polymorphisms (SNPs)

Gene

Chromosome

SNP

Base change

Call rates (%)

HWE

MAF in controls

P a

P b

Has-miR-149

2q37.3

rs2292832

A>G

99.77

0.092

0.322

0.349

0.436

Pre-miR-146a

5q34

rs2910164

G>C

99.81

0.468

0.429

0.558

0.558

Has-miR-605

10q21.1

rs2043556

A>G

99.77

0.753

0.281

0.004

0.020

Has-miR-608

10q25.1

rs4919510

G>C

99.85

0.835

0.425

0.245

0.408

Pre-miR-196a

12q13.13

rs11614913

A>G

99.91

0.796

0.432

0.019

0.048

Italic value indicate significance of p value (p < 0.05)

HWE Hardy–Weinberg equilibrium, MAF minor allele frequency

aTwo-sided Chi squared test for the comparison of the allele frequency between HNSCC patients and cancer-free controls

b P values adjusted by false discovery rate (FDR) method

Associations between selected SNPs and HNSCC risk

Logistic regression analyses revealed that variant genotypes of miR-605 rs2043556 significantly decreased the risk of OSCC (AG vs. AA: adjusted OR 0.74, 95% CI 0.59–0.93; GG vs. AA: adjusted OR 0.56, 95% CI 0.35–0.89; dominant model: adjusted OR 0.71, 95% CI 0.58–0.88; recessive model: adjusted OR 0.63, 95% CI 0.40–1.00; additive model: adjusted OR 0.74, 95% CI 0.62–0.89), whereas variant genotypes of rs11614913 in miR-196a2 significantly increased the risk of OSCC (GG vs. AA: adjusted OR 1.64, 95% CI 1.22–2.21; dominant model: adjusted OR 1.36, 95% CI 1.08–1.72; recessive model: adjusted OR 1.42, 95% CI 1.11–1.83; additive model: adjusted OR 1.28, 95% CI 1.10–1.48) (Table 3). After false discovery rate (FDR) adjustment, the above associations remained significant for rs2043556 in miR-605 (AG vs. AA: P = 0.045; GG vs AA: P = 0.038; dominant model: P = 0.010; additive model: P = 0.005) and rs11614913 in miR-196a2 (GG vs. AA: P = 0.005; dominant model: P = 0.025; recessive model: P = 0.030; additive model: P = 0.003). We also performed logistic regression analysis conditioning on all selected miRNAs and SNPs, and the results indicated that the effects of rs2043556 in miR-605 and rs11614913 in miR-196a2 on OSCC risk were independent (P = 0.001 for both miR-605 rs2043556 and miR-196a2 rs11614913 in additive model).
Table 3

Logistic regression analysis for associations between selected SNPs and HNSCC risk

SNP

Genotypea

Controls [number (%)]

Oral cancer patients [number (%)]

Adjusted OR (95% CI)b

P b

P c

Non-oral cancer patients [number (%)]

Adjusted OR (95% CI)b

P b

miR-605 rs2043556

AA

798 (51.6)

278 (60.3)

1.00

55 (48.2)

1.00

 

AG

631 (40.8)

160 (34.7)

0.74 (0.59–0.93)

0.009

0.045

52 (45.6)

1.19 (0.80–1.78)

0.396

GG

119 (7.7)

23 (5.0)

0.56 (0.35–0.89)

0.015

0.038

7 (6.1)

0.85 (0.38–1.94)

0.708

Dominant model

NA

NA

0.71 (0.58–0.88)

0.002

0.010

NA

1.14 (0.77–1.67)

0.518

Recessive model

NA

NA

0.63 (0.40–1.00)

0.050

0.125

NA

0.79 (0.36–1.75)

0.561

Additive model

NA

NA

0.74 (0.62–0.89)

0.001

0.005

NA

1.04 (0.77–1.42)

0.787

miR-196a2 rs11614913

AA

503 (32.5)

122 (26.4)

1.00

40 (35.1)

1.00

 

AG

755 (48.7)

228 (49.4)

1.25 (0.98–1.61)

0.075

0.188

56 (49.1)

0.93 (0.61–1.43)

0.736

GG

292 (18.8)

112 (24.2)

1.64 (1.22–2.21)

0.001

0.005

18 (15.8)

0.76 (0.43–1.37)

0.366

Dominant model

NA

NA

1.36 (1.08–1.72)

0.010

0.025

NA

0.88 (0.59–1.33)

0.547

Recessive model

NA

NA

1.42 (1.11–1.83)

0.006

0.030

NA

0.80 (0.47–1.35)

0.402

Additive model

NA

NA

1.28 (1.10–1.48)

0.001

0.003

NA

0.88 (0.67–1.17)

0.386

miR-149 rs2292832

AA

726

226

1.00

57

1.00

 

AG

647

193

0.96 (0.77–1.19)

0.696

0.696

38

0.76 (0.49–1.17)

0.206

GG

175

42

0.76 (0.52–1.10)

0.141

0.235

19

1.37 (0.79–2.39)

0.268

Dominant model

NA

NA

0.91 (0.74–1.13)

0.399

0.499

NA

0.89 (0.60–1.31)

0.556

Recessive model

NA

NA

0.77 (0.54–1.10)

0.156

0.260

NA

1.55 (0.91–2.62)

0.107

Additive model

NA

NA

0.90 (0.77–1.06)

0.198

0.248

NA

1.05 (0.79–1.39)

0.735

miR-146a rs2910164

GG

497

154

1.00

40

  

GC

773

228

0.95 (0.75–1.21)

0.685

0.861

53

0.82 (0.53–1.27)

0.376

CC

278

80

0.93 (0.68–1.27)

0.656

0.656

21

0.90 (0.51–1.57)

0.702

Dominant model

NA

NA

0.95 (0.76–1.18)

0.633

0.633

NA

0.84 (0.56–1.27)

0.407

Recessive model

NA

NA

0.96 (0.73–1.27)

0.771

0.771

NA

1.01 (0.61–1.66)

0.975

Additive model

NA

NA

0.96 (0.83–1.12)

0.629

0.629

NA

0.93 (0.70–1.23)

0.589

miR-608 rs4919510

AA

509

137

1.00

40

  

AG

762

232

1.14 (0.90–1.45)

0.283

0.472

53

0.85 (0.55–1.31)

0.464

GG

278

93

1.23 (0.91–1.67)

0.179

0.224

21

0.97 (0.56–1.70)

0.927

Dominant model

NA

NA

1.17 (0.93–1.46)

0.187

0.312

NA

0.88 (0.59–1.32)

0.546

 

Recessive model

NA

NA

1.14 (0.87–1.48)

0.345

0.431

NA

1.07 (0.65–1.77)

0.787

 

Additive model

NA

NA

1.11 (0.96–1.29)

0.160

0.267

NA

0.96 (0.73–1.28)

0.794

Italic value indicate significance of p value (p < 0.05)

NA not available

a miR-605 rs2043556 was genotyped in 575 cases and 1548 controls; miR-196a2 was genotyped in 576 cases and 1550 controls; miR-149 rs2292832 was genotyped in 575 cases and 1548 controls; miR-146a rs2910164 was genotyped in 576 cases and 1548 controls; and miR-608 rs4919510 was genotyped in 576 cases and 1549 controls

bAdjusted by age, sex, smoking status, and drinking status

c P values of multiple comparisons for false discovery rate using the FDR method (n = 5, refer to the number of SNPs)

Combined effects of the two significant SNPs on OSCC risk

When these two loci were evaluated together by the number (0–4) of putative risk alleles (miR-605 rs2043556 A, A and miR-196a2 rs11614913 G, G), a significant locus-dosage effect was detected on HNSCC risk between the groups with 0–2 risk alleles and 3–4 risk alleles (P trend < 0.001). Compared with the group with 0–1 risk allele, the groups with 3 and 4 risk alleles had significantly increased risk of OSCC with adjusted ORs of 1.51 (95% CI 1.10–2.09) and 2.23 (95% CI 1.51–3.29) (Table 4). Compared with the risk in the groups with 0–2 risk alleles, the increase in OSCC risk remained significant for the group with 3–4 risk alleles (adjusted OR 1.48, 95% CI 1.20–1.83). Logistic regression analyses identified no association between the other three SNPs and OSCC risk (data not shown).
Table 4

Combined effects of miR-605 rs2043556 and miR-196a2 rs11614913 on oral squamous cell carcinoma (OSCC) risk

Number of risk allelesa

Patients [number (%)]

Controls [number (%)]

Adjust OR (95% CI)b

P b

0–1

66 (14.3)

303 (19.6)

1.00

 

2

153 (33.2)

575 (37.2)

1.20 (0.87–1.66)

0.262

3

168 (36.4)

517 (33.4)

1.51 (1.10–2.09)

0.011

4

74 (16.1)

151 (9.8)

2.23 (1.51–3.29)

<0.001

Trend

NA

NA

1.21 (1.10–1.32)

<0.001

Binary classification

   

<0.001

 0–2

219 (47.5)

878 (56.8)

1.00

 3–4

242 (52.5)

668 (43.2)

1.48 (1.20–1.83)

Italic value indicate significance of p value (p < 0.05)

aThe miR-605 rs2043556 A and miR-196a2 rs11614913 G allele were assumed as risk alleles based on the main effect of the individual locus and were genotyped in the 461 OSCC cases and 1546 controls

bAdjusted by age, sex, smoking status, and drinking status

Stratification analysis for association between variant genotypes and OSCC risk

We further conducted a stratification analysis by age, sex, smoking status, drinking status, and tumor site on the associations between rs2043556 in miR-605 and rs11614913 in miR-196a2 and OSCC risk. As presented in Table 5, the association of decreased OSCC risk with miR-605 rs2043556 was more notable in males, whereas the association of increased risk with miR-196a2 rs11614913 was more pronounced in females, non-smokers, and non-drinkers than in their counterparts. The combined effect of rs2043556 in miR-605 and rs11614913 in miR-196a2 on OSCC risk was stronger in patients of ≥60 years old than in those of <60 years old.
Table 5

Stratification analysis for association between variant genotypes and OSCC risk

Variable

miR-605 rs2043556 genotype (GG/AG/AA)a

Adjusted OR (95% CI)b

P b

miR-196a2 rs11614913 genotype (GG/AG/AA)a

Adjusted OR (95% CI)b

P b

Combined effect (0-2/3-4 risk alleles)c

Adjusted OR (95% CI)b

P b

Cancer patients (number)

Controls (number)

Cancer patients (number)

Controls (number)

Cancer patients (number)

Controls (number)

Age (years)

  

 <60

10/75/125

55/296/366

0.76 (0.59–1.00)

0.042

56/98/57

135/352/230

1.33 (1.07–1.66)

0.011

102/105

398/317

1.32 (0.97–1.81)

0.081

 ≥60

13/85/153

64/335/432

0.73 (0.58–0.93)

0.011

56/130/65

157/403/273

1.24 (1.01–1.52)

0.038

117/137

480/351

1.62 (1.22–2.16)

0.001

Sex

  

 Female

12/68/124

41/227/296

0.78 (0.60–1.02)

0.068

59/99/46

93/275/197

1.64 (1.30–2.07)

<0.001

97/107

331/233

1.54 (1.11–2.12)

0.010

 Male

11/92/154

78/404/502

0.72 (0.57–0.91)

0.005

53/129/76

199/480/306

1.08 (0.89–1.32)

0.434

122/135

547/435

1.47 (1.11–1.94)

0.008

Smoking

  

 Never

15/99/160

70/363/456

0.79 (0.63–0.99)

0.037

74/129/72

172/430/288

1.32 (1.09–1.60)

0.004

135/139

503/385

1.36 (1.03–1.79)

0.028

 Ever

8/61/118

49/268/342

0.66 (0.50–0.89)

0.006

38/99/50

120/325/215

1.25 (0.97–1.59)

0.081

84/103

375/283

1.72 (1.22–2.42)

0.002

Drinking

  

 Never

14/97/161

78/427/534

0.76 (0.60–0.95)

0.016

72/134/67

202/505/335

1.38 (1.14–1.67)

0.001

130/142

582/456

1.46 (1.11–1.92)

0.006

 Ever

9/63/117

41/204/264

0.70 (0.53–0.93)

0.014

40/94/55

90/250/168

1.18 (0.93–1.51)

0.175

89/100

296/212

1.58 (1.12–2.22)

0.009

Italic value indicate significance of p value (p < 0.05)

aThese data are presented as the numbers of cases with genotypes GG, AG, or AA

bAdjusted by age, sex, smoking status, and drinking status

cThese data are presented as the numbers of cases with 0–2 or 3–4 risk alleles

MDR analysis for OSCC risk predication

In addition, the MDR method was used to assess potential locus–locus and gene-environment interactions with five SNPs and age, sex, smoking status, and drinking status. As shown in Table 6, age was the strongest factor for predicting HNSCC risk with the highest CVC (100%) and testing accuracy (55.70%). We also observed that the four-factor model, which included age, miR-146a rs2910164, miR-608 rs4919510, and miR-196a2 rs11614913, was the most accurate model with a testing accuracy of 54.91% and a perfect CVC of 10. However, the two-factor and three-factor models had decreased CVCs, suggesting the models were not very accurate.
Table 6

Multifactor dimensionality reduction (MDR) analysis for OSCC risk predication

Best model

Training bal. acc.

Testing bal. acc.

P a

CVC

One-factor (age)

0.6063

0.5570

0.1602

10/10

Two-factor (age and miR-605 rs2043556)

0.6575

0.5590

0.1511

5/10

Three-factor (age, miR-146a rs2910164, and miR-196a2 rs11614913)

0.7276

0.5314

0.4463

6/10

Four-factor (age, miR-146a rs2910164, miR-608 rs4919510, and miR-196a2 rs11614913)

0.8221

0.5491

0.2411

10/10

Training bal. acc. training balanced accuracy, Testing bal. acc. testing balanced accuracy, CVC cross-validation consistency

a P values for testing balanced accuracy

Discussion

In this case–control study, we examined associations between five common SNPs in miRNAs (miR-149 rs2292832, miR-146a rs2910164, miR-605 rs2043556, miR-608 rs4919510, and miR-196a2 rs11614913) and HNSCC risk. The results revealed that rs2043556 in miR-605 and rs11614913 in miR-196a2 were significantly associated with OSCC risk in a Chinese population. However, no notable association was detected between other selected SNPs and HNSCC risk.

Once activated, the tumor suppressor p53 selectively modulates the expression of target genes involved in cell cycle arrest, apoptosis, and DNA repair [22]. A recent study indicated that miR-605 was a new component in the p53 gene network [23]. This network is transcriptionally activated by p53 and post-transcriptionally repressed by murine double minute 2 (Mdm2), which inhibits the function of p53. Thus, a positive feedback loop is created that aids in the rapid accumulation of p53 to facilitate its function in response to stress [23]. Id Said et al. [24] reported that high expression of miR-605 could result in a significant reduction in cell viability, clonogenicity, and cell migration in TP53-mutant cell types and that rs2043556-variant G allele could significantly result in a decreased expression of miR-605. Several studies have investigated the associations between miR-605 rs2043556 and cancer risk, and a recent meta-analysis concluded that miR-605 rs2043556 was associated with a significant overall risk of human cancer [25]. In this study, we first examined the effect of miR-605 rs2043556 on the risk of HNSCC and identified a significant linkage between this SNP and the decreased risk of OSCC in a Chinese population. Thus, we hypothesize that miR-605 rs2043556 may affect the expression of miR-605 and the risk of OSCC, which may provide a visual cue regarding the role of this SNP in the development of OSCC.

Rs11614913, which is located at miR-196a2, impacts the expression of miR-196a2 and is involved in the carcinogenesis of different types of cancer [17, 26, 27]. For example, Tian et al. [28] reported that miR-196a2 rs11614913 was associated with the increased risk of non-small cell lung cancer and poor patient survival, and Hu et al. [29] reported its association with the increased risk of breast cancer. It was also reported that miR-196a2 rs11614913 influenced mature miR-196a expression (but not the pre-miR-196a2 level) and affected the binding ability of miR-196a-3p to its targets [27]. Additionally, Hoffman et al. [30] demonstrated that mature miR-196a2 level was increased 9.3-fold in breast cancer cells transfected with pre-miR-196a2-C (rs11614913), but the levels were only increased 4.4-fold in cells transfected with pre-miR-196a2-T. Such associations were then further supported by studies on other types of cancers. A recent meta-analysis revealed that miR-196a2 rs11614913 was associated with cancer risk, especially risks of lung, colorectal, and breast cancers among Asian populations [31]. Specially, a few studies have investigated the association of rs11614913 in miR-196a2 with HNSCC risk in Caucasian populations, but the results were inconclusive. Liu et al. [32] found no association between miR-196a2 rs11614913 and risk of HNSCC, whereas Christensen et al. [33] reported that the miR-196a2 rs11614913 CC genotype was related with an increased HNSCC risk. Another study identified a significant association between rs11614913 and miR-196a2 expression levels in tumor tissues from OSCC patients, but no association of miR-196a2 rs11614913 with OSCC risk was noted [17]. In this study, we demonstrated that the miR-196a2 rs11614913 G allele was significantly associated with an increased OSCC risk, which is consistent with the study by Christensen et al. [33]. The difference between our study and the other two studies [32, 33] may due to different ethnic backgrounds and different composition of cases. The MAF in our controls was 0.432, whereas it was either 0.420 [32] or not obtained [33] in the literature. Furthermore, the proportion of oral cancer was much higher in our study (80.2%) than that in the other two studies (29.4% and 55.6%, respectively). Larger studies with different ethnic backgrounds and functional investigation are needed to validate these findings.

Studies on associations between the other three SNPs (rs2292832 in miR-149, rs2910164 in miR-146a, and rs4919510 in miR-608) and cancer risk were inconsistent [34–38]. A recent meta-analysis of 12 studies, including 5937 cases and 6081 controls, revealed that miR-149 rs2292832 was not associated with cancer risk [39]. Additionally, only two studies investigated the effect of miR-149 rs2292832 on HNSCC risk, and neither produced significant results [32, 40]. A meta-analysis of 66 case–control studies reported that miR-146a rs2910164 was a risk factor for HNSCC, which included four studies from a Caucasian population and one study from a Chinese population [41]. However, the results from the Chinese population indicated that miR-146a rs2910164 was not significantly associated with oral cancer risk [40]. To date, two studies have focused on the associations of miR-608 rs4919510 and cancer risk: one on colorectal cancer [38] and another on breast cancer [37], and their results were inconsistent. In our study, the results demonstrated that none of these three SNPs (rs2292832 in miR-149, rs2910164 in miR-146a, and rs4919510 in miR-608) contributed to the risk of HNSCC in a Chinese population. Given heterogeneous genetic backgrounds in different populations, these findings must be validated in further larger studies.

Several potential limitations of the present study warrant consideration. First, a relatively small sample size may limit the statistical power of our study, especially in the stratification analysis. We made multiple testing adjustments using the FDR method, and the results indicate that the associations between SNPs and OSCC risk remained significant. However, the effect of miR-605 rs2043556 on HNSCC risk was borderline significant after the FDR correction. Thus, our results must be confirmed in further studies. Second, our study is a hospital-based, case–control study, and inherent selection bias cannot be completely excluded. Third, the functional significance of rs2043556 in miR-605 and rs11614913 in miR-196a2 for the development of HNSCC remains largely unknown.

In summary, we identified that miR-605 rs2043556 and miR-196a2 rs11614913 were associated with OSCC risk in a Chinese population. Further replication studies with diverse ethnic groups and functional characterization are warranted to validate our findings.

Notes

Declarations

Acknowledgements

This work was supported in part by Grants from the National Natural Science Foundation of China (Nos. 81473048 and 81302361), Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine), Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20133234120013), China Postdoctoral Science Foundation (No. 2013M540457), and Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1301018A).

Competing interests

The authors declare that they have no competing interests.

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)
Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, P. R. China
(2)
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
(3)
Department of Stomatology and Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, P. R. China
(4)
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, P. R. China

References

  1. Marcu LG, Yeoh E. A review of risk factors and genetic alterations in head and neck carcinogenesis and implications for current and future approaches to treatment. J Cancer Res Clin Oncol. 2009;135:1303–14.View ArticlePubMedGoogle Scholar
  2. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108.View ArticlePubMedGoogle Scholar
  3. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65:5–29.View ArticlePubMedGoogle Scholar
  4. Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA Cancer J Clin. 2010;60:277–300.View ArticlePubMedGoogle Scholar
  5. da Silva SD, Ferlito A, Takes RP, Brakenhoff RH, Valentin MD, Woolgar JA, et al. Advances and applications of oral cancer basic research. Oral Oncol. 2011;47:783–91.View ArticlePubMedGoogle Scholar
  6. Chen PY, Meister G. microRNA-guided posttranscriptional gene regulation. Biol Chem. 2005;386:1205–18.View ArticlePubMedGoogle Scholar
  7. Carthew RW. Gene regulation by microRNAs. Curr Opin Genet Dev. 2006;16:203–8.View ArticlePubMedGoogle Scholar
  8. Wu C, Li M, Hu C, Duan H. Prognostic role of microRNA polymorphisms in patients with advanced esophageal squamous cell carcinoma receiving platinum-based chemotherapy. Cancer Chemother Pharmacol. 2014;73:335–41.View ArticlePubMedGoogle Scholar
  9. Yu H, Jiang L, Sun C, Guo L, Lin M, Huang J, et al. Decreased circulating miR-375: a potential biomarker for patients with non-small-cell lung cancer. Gene. 2013;24:01419–24.Google Scholar
  10. Paranjape T, Slack FJ, Weidhaas JB. microRNAs: tools for cancer diagnostics. Gut. 2009;58:1546–54.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Loktionov A. Common gene polymorphisms, cancer progression and prognosis. Cancer Lett. 2004;208:1–33.View ArticlePubMedGoogle Scholar
  12. Duan R, Pak C, Jin P. Single nucleotide polymorphism associated with mature miR-125a alters the processing of pri-miRNA. Hum Mol Genet. 2007;16:1124–31.View ArticlePubMedGoogle Scholar
  13. Zeng Y, Cullen BR. Sequence requirements for micro RNA processing and function in human cells. RNA. 2003;9:112–23.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Zhang MW, Jin MJ, Yu YX, Zhang SC, Liu B, Jiang X, et al. Associations of lifestyle-related factors, hsa-miR-149 and hsa-miR-605 gene polymorphisms with gastrointestinal cancer risk. Mol Carcinog. 2012;51(Suppl 1):E21–31.PubMedGoogle Scholar
  15. Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de la Chapelle A. Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci USA. 2008;105:7269–74.View ArticlePubMedPubMed CentralGoogle Scholar
  16. He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, et al. The role of microRNA genes in papillary thyroid carcinoma. Proc Natl Acad Sci USA. 2005;102:19075–80.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Dahiya N, Sherman-Baust CA, Wang TL, Davidson B, Shih Ie M, Zhang Y, et al. MicroRNA expression and identification of putative miRNA targets in ovarian cancer. PLoS One. 2008;3:e2436.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Liu CJ, Tsai MM, Tu HF, Lui MT, Cheng HW, Lin SC. miR-196a overexpression and miR-196a2 gene polymorphism are prognostic predictors of oral carcinomas. Ann Surg Oncol. 2013;20(Suppl 3):S406–14.View ArticlePubMedGoogle Scholar
  19. Xu W, Xu J, Liu S, Chen B, Wang X, Li Y, et al. Effects of common polymorphisms rs11614913 in miR-196a2 and rs2910164 in miR-146a on cancer susceptibility: a meta-analysis. PLoS One. 2011;6:e20471.View ArticlePubMedPubMed CentralGoogle Scholar
  20. He B, Pan Y, Cho WC, Xu Y, Gu L, Nie Z, et al. The association between four genetic variants in microRNAs (rs11614913, rs2910164, rs3746444, rs2292832) and cancer risk: evidence from published studies. PLoS One. 2012;7:e49032.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001;69:138–47.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Vousden KH. Outcomes of p53 activation–spoilt for choice. J Cell Sci. 2006;119:5015–20.View ArticlePubMedGoogle Scholar
  23. Xiao J, Lin H, Luo X, Luo X, Wang Z. miR-605 joins p53 network to form a p53:miR-605:Mdm2 positive feedback loop in response to stress. EMBO J. 2011;30:5021.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Id Said B, Malkin D. A functional variant in miR-605 modifies the age of onset in Li-Fraumeni syndrome. Cancer Genet. 2015;208:47–51.View ArticlePubMedGoogle Scholar
  25. Hu Y, Yu CY, Wang JL, Guan J, Chen HY, Fang JY. MicroRNA sequence polymorphisms and the risk of different types of cancer. Sci Rep. 2014;4:3648.PubMedGoogle Scholar
  26. Chen C, Zhang Y, Zhang L, Weakley SM, Yao Q. MicroRNA-196: critical roles and clinical applications in development and cancer. J Cell Mol Med. 2011;15:14–23.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Hu Z, Chen J, Tian T, Zhou X, Gu H, Xu L, et al. Genetic variants of miRNA sequences and non-small cell lung cancer survival. J Clin Invest. 2008;118:2600–8.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Tian T, Shu Y, Chen J, Hu Z, Xu L, Jin G, et al. A functional genetic variant in microRNA-196a2 is associated with increased susceptibility of lung cancer in Chinese. Cancer Epidemiol Biomarkers Prev. 2009;18:1183–7.View ArticlePubMedGoogle Scholar
  29. Hu Z, Liang J, Wang Z, Tian T, Zhou X, Chen J, et al. Common genetic variants in pre-microRNAs were associated with increased risk of breast cancer in Chinese women. Hum Mutat. 2009;30:79–84.View ArticlePubMedGoogle Scholar
  30. Hoffman AE, Zheng T, Yi C, Leaderer D, Weidhaas J, Slack F, et al. MicroRNA miR-196a-2 and breast cancer: a genetic and epigenetic association study and functional analysis. Cancer Res. 2009;69:5970–7.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Zhang H, Su YL, Yu H, Qian BY. Meta-analysis of the association between Mir-196a-2 polymorphism and cancer susceptibility. Cancer Biol Med. 2012;9:63–72.PubMedPubMed CentralGoogle Scholar
  32. Liu Z, Li G, Wei S, Niu J, El-Naggar AK, Sturgis EM, et al. Genetic variants in selected pre-microRNA genes and the risk of squamous cell carcinoma of the head and neck. Cancer. 2010;116:4753–60.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Christensen BC, Avissar-Whiting M, Ouellet LG, Butler RA, Nelson HH, McClean MD, et al. Mature microRNA sequence polymorphism in MIR196A2 is associated with risk and prognosis of head and neck cancer. Clin Cancer Res. 2010;16:3713–20.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Greenland S, O’Rourke K. On the bias produced by quality scores in meta-analysis, and a hierarchical view of proposed solutions. Biostatistics. 2001;2:463–71.View ArticlePubMedGoogle Scholar
  35. Linhares JJ, Azevedo M Jr, Siufi AA, de Carvalho CV, Wolgien Mdel C, Noronha EC, et al. Evaluation of single nucleotide polymorphisms in microRNAs (hsa-miR-196a2 rs11614913 C/T) from Brazilian women with breast cancer. BMC Med Genet. 2012;13:119.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Lian H, Wang L, Zhang J. Increased risk of breast cancer associated with CC genotype of Has-miR-146a Rs2910164 polymorphism in Europeans. PLoS One. 2012;7:e31615.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Huang AJ, Yu KD, Li J, Fan L, Shao ZM. Polymorphism rs4919510:C > G in mature sequence of human microRNA-608 contributes to the risk of HER2-positive breast cancer but not other subtypes. PLoS One. 2012;7:e35252.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Ryan BM, McClary AC, Valeri N, Robinson D, Paone A, Bowman ED, et al. rs4919510 in hsa-mir-608 is associated with outcome but not risk of colorectal cancer. PLoS One. 2012;7:e36306.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Xu L, Zhou X, Qiu MT, Yin R, Wu YQ, Xu L. Lack of association between hsa-miR-149 rs2292832 polymorphism and cancer risk: a meta-analysis of 12 studies. PLoS One. 2013;8:e73762.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Chu YH, Tzeng SL, Lin CW, Chien MH, Chen MK, Yang SF. Impacts of microRNA gene polymorphisms on the susceptibility of environmental factors leading to carcinogenesis in oral cancer. PLoS One. 2012;7:e39777.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Ma XP, Zhang T, Peng B, Yu L, de Jiang K. Association between microRNA polymorphisms and cancer risk based on the findings of 66 case–control studies. PLoS One. 2013;8:e79584.View ArticlePubMedPubMed CentralGoogle Scholar

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