http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, 2014; 19(8): 652–659 ! 2014 Informa UK Ltd. DOI: 10.3109/1354750X.2014.968210

RESEARCH ARTICLE

Functional polymorphisms of caveolin-1 variants as potential biomarkers of esophageal squamous cell carcinoma Shanshan Wang1#, Chuanzhen Zhang1, Ying Liu2, Changqing Xu1, and Ziping Chen1 Department of Gastroenterology, Qianfoshan Hospital, Shandong University, Jinan, Shandong Province, China and 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Laboratory of Genetics, Peking University School of Oncology, Beijing Cancer Hospital & Institute, Beijing, China

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1

Abstract

Keywords

Objective: To investigate the association of caveolin-1 (CAV1) genetic variants (C239A (rs1997623), G14713A (rs3807987), G21985A (rs12672038), T29107A (rs7804372)) with esophageal squamous cell carcinoma (ESCC) susceptibility. Methods: A total of 427 patients with ESCC and 427 healthy controls were genotyped using the polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) method. Results: There were significant differences between patients and controls in distributions of their genotypes and allelic frequencies in G14713A and T29107A polymorphisms. Furthermore, haplotype analysis revealed that haplotypes CAAT and CAGT were associated with high risk for ESCC, while haplotype CGGA was protective against ESCC. Stratified analysis showed the associations between the SNPs (G14713A and T29107A) and ESCC risk were noteworthy among female patients and patients who never smoke or drank alcohol. Conclusions: Genetic polymorphisms of CAV1 G14713A and T29107A might affect an individual’s susceptibility in developing ESCC, making them efficient potential genetic biomarkers for early detection of ESCC.

Caveolin-1, esophageal squamous cell carcinoma, polymerase chain reaction and restriction fragment length polymorphism, single nucleotide polymorphism

Introduction Squamous cell carcinoma (SCC) is a major concern of health problem worldwide and it is the most common histology type among esophageal cancer and head and neck cancers (Elahi et al., 2012; Macfarlane & Boyle, 1994). The rapid clinical progression in the cancers and inadequacy of current prognostic predictors in predicting metastatic indicated that it is imperative to identity efficient biomarkers, including caveolin-1 (CAV1) for early-stage diagnosis, for predicting disease prognosis and evaluating treatment efficiently (Chiu et al., 2013; Nagalakshmi et al., 2014; Yang et al., 2014). Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors, with an extremely invasive nature and a poor survival rate (Enzinger & Mayer, 2003). The patients with ESCC have a generally poor prognosis due to the relatively late stage of diagnosis and poor efficacy of treatment (Hasan et al., 2014). With the exception of environmental risk factors such as smoking tobacco, consuming alcohol and drinking hot tea, accumulating evidence suggests that genetic factors, such as single nucleotide

#Shanshan Wang is responsible for statistical design/analysis. E-mail: [email protected] Address for correspondence: Ziping Chen, Department of Gastroenterology, Qianfoshan Hospital, Shandong University, No.66 JingShi Road, Jinan, Shandong Province 250014, China. Tel: +86-053182968781. Fax: 053182967114. E-mail: [email protected]

History Received 10 July 2014 Accepted 18 September 2014 Published online 1 October 2014

polymorphisms (SNPs), may contribute to ESCC carcinogenesis (Wu et al., 2011). Caveolae (‘‘little caves’’) are non-clathrin-coated, 50–100 nm in diameter, flask-shaped invaginations of the plasma membrane (Palade & Bruns, 1968) that are implicated in many cellular processes, from the regulation of signal transduction, cholesterol homeostasis, the cell cycle and cell migration to vesicular transport (transcytosis, pinocytosis and endocytosis) (Razani et al., 2002). Essential to the formation of caveolae are members of the caveolins, including CAV1, CAV2 and CAV3 (Parton, 1996). The CAV1 human gene, located in the q31.1 region of chromosome 7, plays an important role in tumorigenesis, tumor invasion and metastasis. CAV1 is expressed in a variety of tumor cells and has a dual role in carcinogenesis and tumor suppression. Initially, the expression of CAV1 was shown to be downregulated in some human tumors, including head and neck (Nohata et al., 2011), colon (Bender et al., 2000) and breast (Sloan et al., 2004) carcinomas. However, growing evidences suggest that the activation of CAV1 may be more common in advanced or metastatic tumors, as it was shown to be typically upregulated in lung (Kato et al., 2004), prostate (Yang et al., 1999), bladder (Thomas et al., 2011) and lymphocyte (Hatanaka et al., 1998) tumors. In ESCC, CAV1 was upregulated more than two-fold at the mRNA level (Hu et al., 2001) and overexpression of CAV1 was associated with tumor spreading, lymphatic metastasis and poor prognosis after surgery (Ando et al., 2007; Guangmin et al., 2011; Kato et al., 2002).

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DOI: 10.3109/1354750X.2014.968210

On the other hand, down-regulation of stromal CAV1 expression in ESCC had high malignant potential, and it could be an effective prognostic marker for ESCC patients (Jia et al., 2014). In addition, in a study by Meltzer et al., CAV1 promoter hypermethylation is frequent in esophageal carcinomas and is connected with early neoplastic progression in Barrett’s esophagus (Jin et al., 2014). Similarly, SainzJaspeado et al. (2010) showed that CAV1 upregulation induced tumor cell invasion, migration and metastasis of Ewing’s sarcoma. Taken together, these results indicate that CAV1 expression may inhibit tumor growth during the early stages of cancer progression and promote tumor invasion and metastasis during the later stages (Shatz & Liscovitch, 2008). Therefore, the tumor suppressing or tumor promoting activity of CAV1 appears to be dependent on tumor developmental stage, tumor type and tumor cellular context (Goetz et al., 2008; Quest et al., 2008). To date, many studies have explored the association of functional polymorphisms of CAV1 with the susceptibility to oral (Bau et al., 2011), breast (Liu et al., 2011), colorectal (Yang et al., 2010), gastric (Zhang et al., 2014) and hepatocellular (Hsu et al., 2013) cancers. For ESCC, several previous studies have reported clinical impact of CAV1 expression in esophageal carcinoma by immunohistochemical or cDNA expression array analysis, indicating a statistically significant increased expression of CAV1 mRNA and protein levels (Hu et al., 2001; Kato et al., 2002). Moreover, it has been suggested that overexpression of CAV1 contributed to the prognosis and progression of ESCC (Ando et al., 2007). On the basis of the biological and pathological significance of CAV1, we hypothesized that functional genetic variation in the CAV1 gene may be involved in ESCC development. However, to our knowledge, there were no studies that investigated the potential association between CAV1 polymorphisms and risk of ESCC. Thus, we conducted a matched case-control study to evaluate the association of CAV1 C239A, G14713A, G21985A and T29107A genotype frequencies with ESCC susceptibility.

Materials and methods Study subjects From February 2005 to July 2011, we recruited 427 ESCC patients at Anyang Tumor Hospital (Henan Province, PR China). All ESCC patients were diagnosed according to histopathology. Biopsy slides were read by two independent pathologists at Anyang Tumor Hospital, with disagreement being resolved by the senior pathologists. Patients with incomplete clinical information, any previous cancers or autoimmune diseases, chemotherapy or radiotherapy were excluded from the study. Demographic data and personal information, including age, sex, tobacco smoking and alcohol consumption history were obtained from patient medical records. A total of 427 normal controls from rural Anyang were matched by sex and age (±1 year) to study cases. They were randomly selected from about 1500 participants in a population-based esophageal cancer cohort study carried out among a representative sample of Anyang residents over the same time period. Criteria for participant eligibility were as

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follows: (1) permanent residents in one of the targeted villages of rural Anyang; (2) age between 25 and 80 years; (3) no self-reported history of cancer, previous cardiocerebral vascular diagnoses or psychological disorders; (4) no selfreported history of infection with hepatitis B virus (HBV), hepatitis C virus (HCV), or human immunodeficiency virus (HIV) (and no evidence of these infections based on blood screening); (5) voluntary participation in this study and agreement to complete all phases of the examination. Each participant was required to sign an informed consent and complete a questionnaire which information was similar to that obtained for ESCC cases in one-on-one interviews. After the interview, 5-mL samples of venous blood were collected from each subject. We defined regular cigarette smoking as a history of at least 1 cigarette per day for 12 months or 18 packs for 1 year, and regular alcohol consumption was defined as drinking Chinese liquor at least twice per week for 12 months (other kinds of regular drinker such as beer and red wine is very rare in local area). Genotyping Genomic DNA was extracted from 5 ml EDTA-treated, anticoagulated venous blood using the Qiagen DNA Isolation Kit (Qiagen, Dusseldorf, Germany). Genotyping for the SNPs of CAV1 (C239A, G14713A, G21985A and T29107A) was performed using the polymerase chain reaction and restriction fragment length polymorphism (PCRRFLP) method. Information on primers sequences, sizes of PCR products, restriction enzymes, enzyme digestion temperatures and restriction products are shown in Table 1. PCR was performed with 20 mL of reaction mixture containing 50– 100 ng genomic DNA, 0.4 mL dNTPs (10 mmol/L, Promega, USA), 0.8 ml each primer (10 mmol/L, SinoGenoMax Co., Ltd.), 0.5 U Hotstar Taq DNA polymerase (5 U/mL, Qiagen, Dusseldorf, Germany) and 2 mL 10 PCR buffer. The PCR reaction mixture was initially denatured at 94  C for 15 min, followed by 36 cycles of 30 s at 94  C, 30 s at 60  C, 60 s at 72  C and a final extension of 10 min at 72  C. Suitable primers for each polymorphism were used to amplify the corresponding PCR products, and restriction products were digested by the appropriate restriction enzymes. Restriction DNA products were separated by 2% agarose gel electrophoresis and visualized by ultraviolet light. In addition, a random 10% of samples were selected for confirmation by Sanger sequencing. Statistical analysis All statistical analyses were conducted using Stata software (version 11.2, StataCorp, College Station, TX). To ensure that subjects were selected from a representative population, CAV1 SNP genotype frequencies were tested for the Hardy– Weinberg equilibrium using the chi-square goodness-of-fit test (Nam, 1997). Age distribution was compared between ESCC patients and the controls using the Mann–Whitney U test, and differences for other demographic variables were compared using the McNemar test. PHASE software (version 2.1, University of Chicago, Chicago, IL) based on a Bayesian statistical method was used to construct haplotypes and estimated haplotype frequencies of the four SNPs (Stephens

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Table 1. PCR and RFLP procedures and expected products of four polymorphisms in CAV1 gene. Reference SNP ID

Primers (forward and reverse) 0

0

PCR products

Restriction enzymes 

C239A (rs1997623)

F:5 -GTGTCCGCTTCTGCTATCTG-3 R:50 -GCCAAGATGCAGAAGGAGTT-30

485 bp

AvrII (37 C)

G14713A (rs3807987)

F:50 -CCTTCCAGTAAGCAAGCTGT-30 R:50 -CCTCTCAATCTTGCCATAGT-30

268 bp

BfAI (37  C)

G21985A (rs12672038)

F:50 -CCTTCCAGTAAGCAAGCTGT-30 R:50 -CCTCTCAATCTTGCCATAGT-30

294 bp

HAeIII (37  C)

T29107A (rs7804372)

F:50 -GCCTGAATTGCAATCCTGTG-30 R:50 -ACGGTGTGAACACGGACATT-30

336 bp

Sau3AI (37  C)

Restriction products CC:485 bp AA:315 bp,170bp AC:485 bp,315bp,170bp AA:268 bp GG:202 bp,66bp AG:268 bp,202bp,66bp AA:251 bp,43bp GG:153 bp,98bp,43bp AG:251 bp,153bp,98bp,43bp AA:336 bp TT:172 bp,164bp AT:336 bp,172bp,164bp

Table 2. Distribution of demographic characteristics in ESCC patients and controls. Patients (n ¼ 427)

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Characteristics n Age(year) mean ± SD Age group(year) 560 60 Gender Male Female Tobacco smoking status Ever Never Alcohol consumption status Ever Never

%

Controls (n ¼ 427) n

60.57 ± 7.80

%

p 0.8934a

60.60 ± 7.90

186 241

43.56 56.44

186 241

43.56 56.44



245 182

57.38 42.62

245 182

57.38 42.62



191 236

44.73 55.27

170 257

39.81 60.19

0.154b

118 309

27.63 72.37

96 331

22.48 77.52

0.101b

a

Mann–Whitney U test. McNemar test.

b

& Donnelly, 2003; Stephens et al., 2001). The associations of ESCC risk with genotypes, alleles and haplotypes were analyzed by calculating the odds ratio (OR) and 95% confidence interval (95% CI) using a conditional logistic regression model for crude ORs and adjusted ORs when adjusting for age, sex, tobacco smoking and alcohol consumption. A conditional logistic regression analysis was performed with ESCC patients and controls as the dependent variables and age, sex, tobacco smoking and alcohol consumption as independent variables. Linear regression models were used to examine whether explanatory variables in multivariate models were highly collinear. Considering the potential false positive rate incurred by multiple comparisons of SNPs, we used the Bonferroni correction method to adjust the p value (Bonferroni significance threshold p ¼ 0.05 divided by the number of polymorphisms under study (n ¼ 4): p ¼ 0.0125).

Results Distribution of demographics of the study subjects Table 2 shows the distribution of demographic parameters between ESCC patients and controls. The mean age of patients and controls were 60.57 ± 7.80 years and 60.60 ± 7.90 years, respectively. No significant differences were observed for age, tobacco smoking or alcohol consumption between patients and controls (p ¼ 0.893, p ¼ 0.154 and

p ¼ 0.101, respectively), suggesting that age and sex matching was adequate. Gene polymorphisms and ESCC risk Distributions of the genotypes and alleles frequencies of the four polymorphisms among patients and controls are shown in Table 3. Genotype distributions of the four polymorphisms were in line with the Hardy–Weinberg equilibrium for the controls at a significance level of 5% (p ¼ 0.693 for C239A; p ¼ 0.056 for G14713A; p ¼ 0.064 for G21985A; p ¼ 0.281 for T29107A). In Table 3, there are four conditional logistic regression models, each of which contain a genotype as well as the variables age, sex, tobacco smoking and alcohol consumption. All variance inflation factors in linear regression models were below 1.23, within the acceptable range. AA and AG genotypes of CAV1 G14713A were significantly more frequent in patients than in controls (AA, adjusted OR ¼ 3.199, 95% CI ¼ 1.542–6.635, p ¼ 0.00007; AG, adjusted OR ¼ 1.921, 95% CI ¼ 1.292–2.855, p ¼ 0.00004). Individuals with the A allele in CAV1 G14713A had about a two-fold increased risk of ESCC (adjusted OR ¼ 2.067, 95% CI ¼ 1.481–2.885, p50.00001). The carriers with AA genotype and the A allele of CAV1 T29107A appeared to have a reduced risk of developing ESCC (AA, adjusted OR ¼ 0.403, 95%CI ¼ 0.173–0.857, p ¼ 0.00700; A allele, adjusted OR ¼ 0.601, 95% CI ¼ 0.421–0.857, p ¼ 0.00034). As for

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Table 3. Conditional logistic regression analysis of associations between CAV1 polymorphisms and risk of ESCC. Patients (n ¼ 427)

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Genotype

n

C239A (rs1997623) CC 406 AC 21 AA 0 C Allele 833 A Allele 21 G14713A (rs3807987) GG 212 AG 168 AA 47 G Allele 592 A Allele 262 G21985A (rs12672038) GG 246 AG 139 AA 42 G Allele 631 A Allele 223 T29107A (rs7804372) TT 259 AT 143 AA 25 T Allele 661 A Allele 193

Controls (n ¼ 427) Adjusted ORa (95%CI)

p

pa

%

n

%

Crude OR (95%CI)

95.08 4.92 0 97.54 2.46

411 16 0 838 16

96.25 3.75 0 98.13 1.87

1.000 1.313 (0.573–3.007) – 1.000 1.313 (0.573–3.007)

– 0.41252 – – 0.41252

1.000 1.377 (0.597–3.177) – 1.000 1.375 (0.597–3.168)

– 0.33972 – – 0.34047

49.65 39.34 11.01 69.32 30.68

281 123 23 685 169

65.81 28.80 5.39 80.21 19.79

1.000 1.945 (1.318–2.870) 3.300 (1.598–6.816) 1.000 2.107 (1.513–2.934)

– 0.00002 0.00004 – 50.00001

1.000 1.921 (1.292–2.855) 3.199 (1.542–6.635) 1.000 2.067 (1.481–2.885)

– 0.00004 0.00007 – 50.00001

57.61 32.55 9.84 73.89 26.11

247 146 34 640 214

57.85 34.19 7.96 74.94 25.06

1.000 0.940 (0.614–1.439) 1.269 (0.659–2.445) 1.000 1.083 (0.777–1.509)

– 0.71465 0.36374 – 0.55038

1.000 0.940 (0.612–1.444) 1.117 (0.567–2.203) 1.000 1.019 (0.727–1.429)

– 0.72012 0.68352 – 0.88912

60.66 33.49 5.85 77.40 22.60

221 166 40 608 246

51.76 38.87 9.37 91.19 28.81

1.000 0.692 (0.460–1.040) 0.422 (0.184–0.968) 1.000 0.607 (0.428–0.862)

– 0.02392 0.00948 – 0.00037

1.000 0.687 (0.453–1.044) 0.403 (0.173–0.935) 1.000 0.601 (0.421–0.857)

– 0.02497 0.00700 – 0.00034

Bold value represents p50.0125 that are considered statistically significant. a Adjusted Odds ratios (OR), 95% confidence intervals (CI) and p value derived from conditional logistic models including all listed variables as well as age, sex, tobacco smoking and alcohol consumption status. Table 4. Distribution of haplotype frequencies in patients and controls. SNP at positions Haplotypes a b c d e f g h i

Patients

Controls a

C239A

G14713A

G21985A

T29107A

n

%

n

%a

C C C C C C C C A

A A A A G G G G G

A A G G A A G G G

A T A T A T A T T

109 32 13 107 59 35 8 468 21

12.80 3.78 1.55 12.55 6.92 4.13 0.96 54.86 2.44

90 17 17 44 77 40 63 491 13

10.59 2.01 1.99 5.15 8.96 4.62 7.33 57.48 1.58

a

Calculated by PHASE 2.1 software.

the other polymorphisms, no significant differences were observed in genotypic or allelic distributions between the two groups (p40.0125).

0.00001), whereas the presence of haplotype CGGA appeared to decrease the risk (adjusted OR ¼ 0.176, 95% CI ¼ 0.071– 0.437, p50.00001) (Table 5).

Haplotypes frequencies and ESCC risk

Stratification analyses of CAV1 polymorphisms (G14713A and T29107A) and the risk of ESCC

Nine haplotypes were built by the PHASE software, and their distribution in patients and controls are shown in Table 4. The most common haplotype in both groups was CGGT, and the frequency of haplotype CAGT was higher in patients than in controls (12.55% versus 5.15%). In Table 5, there are nine conditional logistic regression models, each containing a haplotype as well as the variables age, sex, tobacco smoking and alcohol consumption. The presence of haplotypes CAAT and CAGT appeared to increase the risk of ESCC (CAAT, adjusted OR ¼ 3.968, 95% CI ¼ 1.251–12.579, p ¼ 0.00285; CAGT, adjusted OR ¼ 3.345, 95% CI ¼ 1.882–5.944, p5

To evaluate the effects of CAV1 G14713A and T29107A genotypes on the risk of ESCC, the ESCC patients and controls were stratified based on different age, sex, tobacco smoking and alcohol consumption (Table 6). A significant increased risk of ESCC associated with the CAV1 G14713A polymorphism were evident among male patients (AG + AA versus GG, adjusted OR ¼ 1.984, 95% CI ¼ 1.105–3.563, p ¼ 0.00344), female patients (AG + AA versus GG, adjusted OR ¼ 3.792, 95% CI ¼ 1.623–8.862, p ¼ 0.00009), those in the younger and older age bracket (AG + AA versus GG,

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Table 5. Association between CAV1 haplotypes and ESCC risk. Patients (n ¼ 427)

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Haplotype a ¼ CAAA /b /a + a/a b ¼ CAAT / /b + b/b c ¼ CAGA / /c + c/c d ¼ CAGT / /d + d/d e ¼ CGAA / /e + e/e f ¼ CGAT / /f + f/f g ¼ CGGA / /g + g/g h ¼ CGGT / /h + h/h i ¼ AGGT / /e + e/e

Controls (n ¼ 427) p

pa

n

%

n

%

Crude OR (95% CI)

Adjusted OR* (95%CI)

312 115

73.07 26.93

329 98

77.05 22.95

1.000 1.266 (0.833–1.922)

– 0.15898

1.000 1.227 (0.803–1.877)

– 0.22799

401 26

93.91 6.09

418 9

97.89 2.11

1.000 3.833 (1.220–12.046)

– 0.00338

1.000 3.968 (1.251–12.579)

– 0.00285

416 11

97.42 2.58

408 19

95.55 4.45

1.000 0.579 (0.225–1.491)

– 0.14914

1.000 0.618 (0.236–1.617)

– 0.21149

332 95

77.75 22.25

390 37

91.33 8.67

1.000 3.320 (1.878–5.870)

– 50.00001

1.000 3.345 (1.882–5.944)

– 50.00001

381 46

89.23 10.77

361 66

84.54 15.46

1.000 0.608 (0.344–1.074)

– 0.02882

1.000 0.618 (0.349–1.094)

– 0.0353

394 33

92.27 7.73

389 38

91.10 8.90

1.000 0.853 (0.454–1.604)

– 0.52917

1.000 0.822 (0.431–1.569)

– 0.44877

418 9

97.89 2.11

375 52

87.82 12.18

1.000 0.173 (0.070–0.426)

– 50.00001

1.000 0.176 (0.071–0.437)

– 50.00001

99 328

23.19 76.81

89 338

20.84 79.16

1.000 0.867 (0.568–1.323)

– 0.39843

1.000 0.880 (0.573–1.349)

– 0.45313

406 21

95.08 4.92

412 15

96.49 3.51

1.000 1.400 (0.602–3.257)

– 0.31959

1.000 1.461 (0.624–3.423)

– 0.26559

Bold value represents p50.0125 that are considered statistically significant. Conditional logistic regression model, adjusted for age, sex, tobacco smoking and alcohol consumption status. Denotes any haplotype, for example: /a – indicates a haplotype in combination with any other haplotypes.

a

b

Table 6. Stratified analysis between CAV1 G14713A and T29107A polymorphisms and ESCC risk by selected status. G14713A (case/control)

pa; Adjusted ORa (95%CI)

Variable

GG

AG + AA

GG

Sex Male

138/158

107/87

1.000

74/123

107/59

1.000

Age 560

96/133

90/53

1.000

60

116/148

125/93

1.000

Tobacco smoking Ever 93/99

98/71

1.000

119/182

117/75

1.000

Alcohol consumption Ever 55/64

63/32

1.000

152/114

1.000

Female

Never

Never

157/217

AG + AA

T29107A (case/control)

pa; Adjusted ORa (95%CI)

TT

AT + AA

TT

AT + AA

0.00344 1.984 (1.105–3.563) 0.00009 3.792 (1.623–8.862)

141/132

104/113

1.000

118/89

64/93

1.000

0.97869 1.006 (0.583–1.737) 0.00013 0.157 (0.047–0.526)

0.00013 2.437 (1.363–4.357) 0.00021 2.325 (1.318–4.101)

107/87

79/99

1.000

152/134

89/107

1.000

100/95

91/75

1.000

159/126

77/131

1.000

46/61

72/35

1.000

213/160

96/171

1.000

0.86957 1.072 (0.375–3.064) 50.00001 3.833 (1.884–7.800) 0.37194 1.750 (0.366–8.374) 0.00003 2.229 (1.385–3.588)

0.01773 0.557 (0.300–1.032) 0.15123 0.738 (0.435–1.252) 0.35538 0.725 (0.303–1.730) 0.00140 0.398 (0.194–0.818) 0.71021 1.219 (0.321–4.628) 0.00027 0.463 (0.273–0.786)

Bold value represents p50.0125 that are considered statistically significant. Adjusted for age, sex, tobacco smoking, alcohol consumption (besides stratified factors accordingly) in conditional logistic regression model.

a

adjusted OR ¼ 2.437, 95% CI ¼ 1.363–4.357, p ¼ 0.00013; AG + AA versus GG, adjusted OR ¼ 2.325, 95% CI ¼ 1.318– 4.101, p ¼ 0.00021; respectively), patients who had never smoked (AG + AA versus GG, adjusted OR ¼ 3.833, 95% CI ¼ 1.884–7.800, p50.00001) and patients who were nondrinkers (AG + AA versus GG, adjusted OR ¼ 2.229, 95%

CI ¼ 1.385–3.588, p ¼ 0.00003). In participants with the CAV1 T29107A polymorphism, a significantly decrease risk of ESCC was found in female patients (AT + AA versus TT, adjusted OR ¼ 0.157, 95% CI ¼ 0.047–0.526, p ¼ 0.00013), patients who were non-smokers (AT + AA versus TT, adjusted OR ¼ 0.398, 95% CI ¼ 0.194–0.818, p ¼ 0.00140)

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and patients who were non-drinkers (AT + AA versus TT, adjusted OR ¼ 0.463, 95% CI ¼ 0.273–0.786, p ¼ 0.00027) (Table 6).

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Discussion Esophageal carcinoma is one of the most common malignant cancers, especially in the Chinese, and its incidence is increasing. Among the main known causes of esophageal cancer, genetic aberration is of key importance. With the growing number of identified SNPs and the improved development of SNP detection methods, offerings for cancer susceptibility analysis are increasing. In our study, a casecontrol design was used to explore the relationship between CAV1 C239A, G14713A, G21985A and T29107A genotypes and the susceptibility to ESCC in Anyang, a Chinese district with a high incidence of esophageal cancer. CAV1 is important for cell signaling regulation and acts as a scaffold upon which to concentrate a variety of proteins involved in different signaling pathways. For example, the caveolin-scaffolding domain (CSD) negatively regulates the activity of numerous signaling molecules, including endothelial nitric oxide synthase (eNOS) (Ju et al., 1997), Src tyrosine kinases (Li et al., 1996), EGFR (Couet et al., 1997) and H-Rs (Engelman et al., 1997). Notably, some molecular changes induced during tumorigenesis can minimize the tumor suppressing activity of CAV1 and produce a ‘‘tolerant’’ cellular environment in which CAV1 could operate in the reverse manner, potentially leading to a high rate of metastasis, multidrug resistance (MDR) and a low rate of survival (Quest et al., 2008). Li et al. showed that CAV1 secreted by prostate cancer cell lines promoted proliferative and oncogenic activities through the phosphoinositide-3-kinase (PI3K)-protein kinase B (AKT)-endothelial nitric oxide synthase (eNOS) signaling pathway and inhibition of the serine/threonine protein phosphatases PP1 and PP2A (Li et al., 2001, 2009). Moreover, CAV1 acted as a conditional ‘‘oncogene’’ in colon cancer whose activity was determined by mutation of K-RAS, and the K-RAS-CAV1 pathway was suggested to potentially form a positive feedback loop to promote the migration and metastasis of tumor cells, particularly in advanced colorectal cancer (Roy et al., 2013). The ability of CAV1 to modulate signals is therefore a significant factor in tumor progression. In our results, the CAV1 G14713A genotype was associated with a significantly increased risk for ESCC, while the T29107A genotype was associated with a significantly decreased risk for ESCC. There was no association between any of the other tested SNPs and ESCC risk. These results were consistent with other studies of CAV1 genetic polymorphism-related susceptibility to breast, colorectal and gastric cancers (Liu et al., 2011; Yang et al., 2010; Zhang et al., 2014). There was also an ethnic difference in the frequencies of genetic polymorphisms. In the current study, frequency of the A allele in CAV1 G14713A was 19.79% in the controls, which is in agreement with reliable data reported by the International HapMap Project for Han Chinese (16.7%) and Japanese (22.2%) populations and is higher than that of European (10.0%) and Nigerian (0.8%) populations. Frequency of the A allele in CAV1 T29017A was 28.81%

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in the control group, which is also consistent with data from the International HapMap Project for Han Chinese (21.6%) and Japanese (31.8%) populations, and is slightly lower than that of the European (38.3%) population. CAV1 G14713 and T29107A are located in introns on the CAV1 gene, and their polymorphism variations do not directly result in amino acid sequence change. However, it seems plausible to suspect that these intron polymorphisms may influence CAV1 expression levels and protein function by chaining with nearby polymorphisms, alternative splicing and affecting the stability of mRNA during cancer progression. To support this view, a study on hepatocellular carcinoma found that carriers of CAV1 G14713A AG and AA genotypes expressed higher levels of CAV1 mRNA and protein compared with those having the GG genotype (Hsu et al., 2013). Specifically, the A allele of CAV1 G14713 could have elevated the expression of CAV1 protein and increased the risk of hepatocellular carcinoma by coding for a higher level of mRNA in some way. The role of CAV1 polymorphisms in the pathogenesis of ESCC thus requires further study. One of the most important histopathological features of CAV1 is its altered expression impact on progression of different kinds of cancer. In lung SCC, CAV1 overexpression has been suggested to be involved in tumor extension (Kato et al., 2004). Ando et al. showed that high expression of CAV1 in ESCC significantly correlated with several clinicopathological factors including T factor, lymphatic invasion and differentiation, and that high level of CAV1 expression was an independent prognosis factor (Ando et al., 2007). In a study by Seki et al., CAV1 was significantly down-regulated by miR-133a overexpression in HNSCC cell lines and CAV1 contains sits targeted by miR-133a (Nohata et al., 2011). These studies suggested that CAV1 may serve both as biomarkers in detecting cancer and as possible molecular targets in cancer therapeutics. In our study, the role of CAV1 polymorphisms in ESCC susceptibility was analyzed in the Chinese population. The results revealed that genetic polymorphisms of CAV1 G14713A and T29107A might affect an individual’s susceptibility to developing ESCC. The strengths of the study include the relative large sample size, involving ESCC patients with age- and sex- matched controls. Moreover, this study population of China was mostly from the same area of high incidence of ESCC, and the study participants were relatively homogeneous in terms of genetic background and environmental risk factors. If further studies investigate the association between the CAV1 polymorphisms and gene expression at the mRNA and protein levels, this could be an explanation of links between these polymorphisms and major clinical tumor characteristics. Furthermore, previous researches provided genetic evidence showing that CAV1 mutation may participate in the pathogenesis of cancer. Han et al. (2004) investigated the sequence of CAV1 exons 1 and 3 in 74 oral SCCs and 15 oral cancer cell lines to explore the role of CAV1 in oral cancer identifying 1 missense and 4 silent mutations, each in exon 3. In 2006, Lisanti et al. reported that the CAV1 P132L mutation was present in 19% of estrogen receptor (ER)-positive breast cancers, and 6 novel CAV1 mutations were associated with ER-positive breast cancers (W128 Stop, Y118H, S136R, I141T, Y148H and Y148S) (Li et al., 2006). Conversely, in Haeusler et al.’s

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(2005) study, no mutation was found within the open reading frame or the promoter of CAV1 in prostate cancer. The effects of CAV1 mutation in ESCC thus requires further study. Several limitation of this case-control study need to be addressed. Firstly, in the process of pathological diagnosis, the inter-rater and intra-rater agreements were not calculated. This may influence the uniformity and reliability of the diagnostic outcomes. Secondly, the sample size determination was not based on power calculation, which may affect the accuracy of the results to some extent. Thirdly, the lack of a prior power calculation might increase false positive rate in the regression models. Fourthly, despite a relatively large sample size in our study, the sample size gets relatively small after stratification, which may weaken the statistical power of our study. Finally, the fact is that, our study participants were restricted to a Chinese population. As the role of genetic polymorphism in tumor risk may be different with different ethnic populations, future researches with larger sample from other ethnicities are needed. In conclusion, we have shown the potential of CAV1 G14713A and T29107A to act as efficient genetic susceptibility markers of ESCC in Chinese and the potential of targeting the A alleles for development modes of early diagnosis, prediction and individualized therapy.

Declaration of interest This paper was supported by the Program of Shandong Province’s Pharmaceutical Health and Technology Development (No HZ068). The authors report no declarations of interest.

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Functional polymorphisms of caveolin-1 variants as potential biomarkers of esophageal squamous cell carcinoma.

To investigate the association of caveolin-1 (CAV1) genetic variants (C239A (rs1997623), G14713A (rs3807987), G21985A (rs12672038), T29107A (rs7804372...
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