The association of TNF-α promoter polymorphisms with genetic susceptibility to cervical cancer in a Chinese Han population

DOI: https://doi.org/10.21203/rs.3.rs-378408/v1

Abstract

Background

The Tumour necrosis factor-α (TNF-α) gene plays an important role in the host immune response. Recent studies have found that TNF-α also plays an important role in cancer. Polymorphism of the TNF-α promoter region is considered to influence its transcription and be a risk factor for tumorigenesis. In the current study, we evaluated the role of TNF-α promoter region polymorphisms in susceptibility to cervical intraepithelial neoplasia (CIN) and cervical cancer (CC).

Methods

A total of 2,732 subjects, including 1,173 healthy controls, 579 patients with CIN and 980 patients with cervical cancer in a Chinese Han population, were selected for the current study. Five SNPs in the TNF-α promoter, rs1799964 (C > T), rs800630 (A > C), rs1799724 (C > T), rs1800629 (A > G) and rs361525 (A > G), were selected and genotyped using TaqMan Assays. The association of these SNPs with cervical cancer was evaluated among healthy controls and CIN and cervical cancer patients.

Results

The frequency distribution of rs1800629 and rs361525 alleles was significantly different between the cervical cancer group and the control group (P = 0.009 and P = 0.002). The A alleles of rs1800629 and rs361525 were found to be a protective factor (OR = 0.722; 95%CI = 0.564–0.923) and a risk factor (OR = 1.693; 95%CI = 1.205–2.378) for cervical cancer, respectively. In comparison of different pathological types of cervical cancer group and the control group, the distribution of allele frequencies of rs1800629 and rs361525 was significantly different between the squamous cell carcinoma and control groups (P = 0.002 and P < 0.001). The A alleles of rs1800629 and rs361525 were protective (OR = 0.659; 95%CI = 0.502–0.864) and risk (OR = 1.868; 95%CI = 1.317–2.648) factors for cervical squamous cell carcinoma, respectively.

Conclusion

rs1800629 and rs361525 in the TNF-α promoter are associated with susceptibility to cervical cancer and squamous cell carcinoma in the Chinese Han population.

1. Introduction

Cervical cancer is the fourth most common cancer among women worldwide and the second leading cause of cancer death in developing countries [1]. Persistent infection with high-risk human papilloma virus (HR-HPV) is considered to be the main cause of cervical cancer. However, epidemiological studies have shown that most cases of HPV infection can be cleared by the host immune system, with only a small portion of patients chronically infected and further developing cervical intraepithelial neoplasia (CIN) and progressing to cervical cancer [2]. Thus, the host immune system plays an important role in HPV clearance and the development of CIN and cervical cancer [35].

Tumour necrosis factor (TNF) is a proinflammatory cytokine with multiple biological activities, including tumour necrosis factor-α (TNF-α) and tumour necrosis factor-β (TNF-β). TNF-α is mainly produced by monocytes or macrophages and can act as an oncogene or tumour suppressor gene in the human immune response [6]. TNF-α is highly expressed in a variety of tumours and participates in cell transformation [7]. Overall, TNF-α promotes tumour cell proliferation, invasion and metastasis by regulating tumour angiogenesis [810].

The human TNF-α gene is 2.76 kb and located on chromosome 6; it contains 3 introns and 4 exons and is closely related to the major histocompatibility complex region. TNF-α polymorphisms are mainly concentrated in the promoter region and closely related to expression of the gene and the risk of various diseases, such as cervical cancer [8, 1117]. However, results of associations between TNF-α polymorphisms and cervical cancer are still controversial in different populations. For example, in 2001, Jang et al. reported that the A allele of rs361525 is a protective factor for cervical cancer in a Korean population [14]. However, in 2018, Li et al. reported that the A allele of rs361525 is a risk factor for squamous cell carcinoma in a Chinese population from Shandong Province, northern China [18]. Thus, more populations from different regions should be investigated.

In the current study, five single-nucleotide polymorphisms (SNPs) located in the TNF-α promoter, rs1799964 (C > T), rs800630 (A > C), rs1799724 (C > T), rs1800629 (A > G), and rs361525 (A > G), were selected to investigate allele distribution and genotypes in CIN, cervical cancer and healthy controls in a Chinese Han population to clarify the role of these SNPs in the occurrence and development of cervical cancer.

2. Materials And Methods

2.1. Subjects

A total of 2,732 subjects, including 1,173 healthy controls, 579 patients with CIN, and 980 patients with cervical cancer, were recruited to participate in the present study. The CIN and cervical cancer patients were all diagnosed at the Third Affiliated Hospital of Kunming Medical University from October 2018 to May 2020. Inclusion criteria for the patients were as follows: (1) CIN or cervical cancer diagnosed according to the World Health Organization Comprehensive Cervical Cancer Control: A Guide to Essential Practice’ [19] and the International Federation of Gynaecology and Obstetrics, 2009; (2) no other malignancy; and (3) no preoperative neoadjuvant therapy (including chemotherapy and radiotherapy). The exclusion criteria for the patients were as follows: (1) presented with a history of primary cancer other than cervical cancer; (2) malignant tumours other than cervical cancer; and (3) receiving radiotherapy or chemotherapy with unclear pathological diagnosis. Over the same period, 1,173 women from a healthy screening project at the same hospital served as healthy controls in the present study. The inclusion criteria for control individuals were as follows: (1) absence of any malignancy history; (2) absence of any cervical lesion; (3) tested negative for HPV; and (4) no chronic diseases. All subjects were Han Chinese from Yunnan Province (Southwest China) and signed informed consent.

2.2. DNA extraction and SNP genotyping

A total of 5 mL of fasting venous blood was collected from the subjects. Genomic DNA from peripheral blood was extracted using a whole-blood genomic DNA mini kit to extract DNA (QIAamp DNA Blood Mini Kit), and an ultramicro UV-visible spectrophotometer (ND-2000, Thermo Scientific, USA) was used to detect the concentration and purity of the DNA.

The probes and primers used for genotyping rs361525 (A > G), rs1799724 (C > T), rs1799964 (C > T), rs1800629 (A > G), and rs800630 (A > C) were all purchased from ABI (http://www.appliedbiosystems.com). The five SNPs were genotyped using the TaqMan fluorescent quantitative PCR method with a QuantStudio™ Real-Time PCR instrument. The TaqMan Genotyping Master Mix used in the typing test was purchased from ABI. The PCR volume was 5 µL, and the reaction conditions were 95 °C predenaturation for 10 minutes, 40 cycles of 95 °C denaturation for 15 seconds and 60 °C annealing for 1 minute, and 60 °C extension for 5 minutes. Deionized water was used to replace the template DNA as a negative control. The PCR experiment data were analysed using TaqMan Genotyper Software (Version 1.3.1). To identify the accuracy of SNP genotyping using the TaqMan assay, samples of each genotype of the five SNPs were sequenced.

2.3. Statistical analysis

Hardy-Weinberg equilibrium (HWE) was calculated using Plink software. Differences in age among the CIN group, cancer group and control group were analysed using one-way ANOVA (Analysis of Variance) with GraphPad Prism 7 software. Differences in alleles and genotypes for the five SNPs were analysed using the SHEsis program [20, 21]. The association between CC, CIN and genotypes of the SNPs was analysed using inheritance model analysis with SNPStats software [22]. The analysed inheritance models included the codominant model, dominant model, recessive model, overdominant model and log-additive model. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were applied to determine the best fit model for each SNP [22]. The inheritance model corresponding to the smallest AIC and BIC was the best fit model. Bonferroni correction was applied in multiple comparisons. A difference was considered statistically significant at P < 0.01 (0.05/5).

3. Results

3.1. Subject characteristics

Table 1 shows the clinical data for the subjects in this study. A total of 1,173 normal healthy women were included in the control group. The cervical cancer group contained 980 patients, including 794 cases of squamous cell carcinoma (SCC), 162 cases of adenocarcinoma (AC), and 24 cases of other types of cancer. According to cervical cancer staging, 679 patients were in stage I, 265 in stage II, and 36 in stage III + IV. The CIN group contained 579 cases, of which 120 were in CIN stage I, 104 in CIN stage II, and 355 in CIN stage III. One-way analysis of variance was used to compare the ages of the subjects among the control, CIN and cervical cancer groups, and a statistically significant difference among the three groups was observed (F = 2.496, P = 0.083).

Table 1

Characteristics of the subjects enrolled in the current study

   

Cervical cancer

CIN

Control

F

P Value

N

980

579

1173

   

Ages (year)

47.51 ± 10.65

47.42 ± 10.02

48.39 ± 11.19

2.496

< 0.083

Pathologic types

SCC (n)

794

       

AC(n)

162

       

Others (n)

24

       

Stages of CC

679

       

265

       

Ⅲ and Ⅳ

36

       

Stages of CIN

 

120

     

 

104

     

 

355

     
Note:SCC, squamous cell carcinoma; AC,Adenocarcinom

3.2. Association analysis of five SNPs in the TNF-αpromoter with CIN and cervical cancer

The alleles and genotype frequencies of the 5 SNPs in the TNF-α promoter are shown in Table 2. The results of HWE showed that rs361525, rs1799964, and rs1800629 were in HWE but that rs1799724 and rs1800630 were not. The allele frequencies of rs1800629 were significantly different between the cervical cancer group and the control group after Bonferroni correction (P = 0.009), which indicates that the A allele of rs1800629 is a protective factor against cervical cancer (OR = 0.722; 95%CI = 0.564–0.923). For rs361525, the allele and genotype frequencies were significantly different between the two groups after Bonferroni correction (P = 0.002 and P = 0.009, respectively). Hence, the A allele of rs361525 may be a risk factor for cervical cancer (OR = 1.693; 95%CI = 1.205–2.378). In contrast, no difference was observed between the CIN and control groups after Bonferroni correction.

Table 2

Hardy-Weinberg equilibrium test of five SNPs

SNPs

Gene

P

rs1799964

TNF-α

0.919

0.338

rs1800630

43.450

4.50E-11

rs1799724

5.494

0.019

rs1800629

0.493

0.483

rs361525

2.245

0.134

3.3. Inheritance model analysis of five SNPs in the TNF-α promoter in CIN and cervical cancer

The results of inheritance model analysis of five SNPs in the TNF-α promoter in cervical cancer is shown in Table 3 and Table 4. Comparison between the cervical cancer and control groups showed that the dominant inheritance model was the best fit model for rs1800630. In this model, the A/C-A/A genotype was related to a reduced risk of cervical cancer (P = 0.006, OR = 0.77; 95%CI = 0.64–0.93). For rs1800629, the overdominant inheritance model was the best fit. In this model, the A/G genotype was related to a reduced risk of cervical cancer (P = 0.008, OR = 0.70; 95%CI = 0.53–0.91). The dominant inheritance model of rs3615252 was the best fit, in which the A/G-A/A genotype was related to an increased risk of cervical cancer (P = 0.008, OR = 1.63; 95%CI = 1.14–2.35). The frequencies of the five SNPs in the inheritance models showed no significant differences between the control group and the CIN group (P > 0.01).

Table 3

The allelic distribution among control, CIN and cervical cancer groups of SNPs in TNF-α gene

SNPs

Alleles n(%)

Control VS CIN

CIN VS CC

Control VS CC

P value

OR[95%CI]

P value

OR[95%CI]

P value

OR[95%CI]

rs1799964

C

T

           

Control

543

(23.1)

1803

(76.9)

0.357

0.924

[0.800-1.094]

0.961

1.004

[0.842–1.198]

0.306

0.928

[0.8038–1.072]

CIN

252

(21.8)

906

(78.2)

CC

428

(21.8)

1532

(78.2)

rs1800630

A

C

           

Control

543

(23.1)

1803

(76.9)

0.106

0.868

[0.7314–1.0304]

0.602

0.953

[0.796–1.141]

0.011

0.827

[0.715–0.959]

CIN

240

(20.7)

918

(79.3)

CC

391

(19.9)

1569

(80.1)

rs1799724

C

T

           

Control

2092

(89.2)

254

(10.8)

0.293

1.134

[0.8974–1.4334]

0.203

0.856

[0.673–1.088]

0.757

0.971

[0.801–1.175]

CIN

1046

(90.3)

112

(9.7)

CC

1742

(88.9)

218

(11.1)

rs1800629

A

G

           

Control

177

(7.5)

2169

(92.5)

0.020

0.7054

[0.524–0.948]

0.886

1.024

[0.744–1.408]

0.009

0.722

[0.564–0.923]

CIN

63

(5.4)

1095

(94.6)

CC

109

(5.6)

1851(94.4)

rs361525

A

G

           

Control

59

(2.5)

2287

(97.5)

0.664

1.102

[0.712–1.704]

0.041

1.536

[1.014–2.327]

0.002

1.693

[1.205–2.378]

CIN

32

(3)

1126

(97)

CC

82

(4.2)

1878

(95.8)

Table 4

The genotypic distribution among control, CIN and cervical cancer groups of SNPs in TNF-α gene

SNPs

Alleles n(%)

P value

Control VS CIN

CIN VS CC

Control VS CC

rs1799964

C/C

C/T

T/T

           

Control

57(4.9)

429(36.6)

687(58.6)

0.035

0.485

0.231

CIN

37(6.4)

178(30.7)

364(62.9)

CC

52(5.3)

324(33.1)

604(61.6)

rs1800630

A/A

A/C

C/C

           

Control

103(8.8)

337(28.7)

733(62.5)

0.117

0.882

0.023

CIN

50(8.6)

140(24.2)

389(67.2)

CC

771(78.7)

200(20.4)

9(0.9)

rs1799724

C/C

T/C

T/T

           

Control

925(78.9)

242(20.6)

6(0.5)

0.179

0.058

0.526

CIN

467(80.7)

112(0.19.3)

0(0.00)

CC

771(78.7)

200(20.4)

9(0.9)

rs1800629

A/A

A/G

G/G

           

Control

5(0.4)

167(14.2)

1001(85.3)

0.056

0.979

0.023

CIN

2(0.3)

59(10.2)

518(89.5)

CC

4(0.4)

101(10.3)

875(89.3)

rs361525

A/A

A/G

G/G

           

Control

2(0.2)

55(4.7)

1116(95.1)

0.460

0.099

0.009

CIN

0(0.00)

32(5.5)

547(94.5)

CC

3(0.3)

76(7.8)

901(91.9)

3.4. Association analysis of five SNPs in the TNF-α promoter with different pathological types of cervical cancer

Comparison between patients with different pathological types of cervical cancer and healthy controls is shown in Table 4. The frequency and genotype frequency of rs1800629 were significantly different between the SCC group and the control group (P = 0.002 and P = 0.006, respectively), which indicates that the A allele of this SNP is a protective factor for SCC (OR = 0.659; 95%CI = 0.502–0.864). The frequency of rs361525 was also significantly different between the SCC group and the control group (P < 0.001), and the A allele was a risk factor for SCC (OR = 1.868; 95%CI = 1.317–2.648). The frequencies of the five SNPs showed no significant differences between the AC group and the control group (P > 0.01).

3.5. Inheritance model analysis of five SNPs in the TNF-α promoter with different pathological types of cervical cancer

The results of analysis of different pathological types of cervical cancer under different inheritance models are shown in Table 5. For rs1800629, comparison between the SCC group and the control group showed that the overdominant model was the best fit. In this model, the A/G genotype was related to a reduced risk of SCC (P = 0.002, OR = 0.63; 95%CI = 0.47–0.84). For rs361525, the dominant and log-additive inheritance models were best fitting models. The A/G-A/A genotype was related to an increased risk of SCC in both the dominant model (P = 0.002, OR = 1.80; 95%CI = 1.24–2.61) and the log-additive inheritance model (P = 0.002, OR = 1.74; 95%CI = 1.22–2.48). No significant differences between the AC and control group were found for the frequencies of the five SNPs in the inheritance model (P > 0.01).

Table 5

The allelic distribution among different cervical cancer types (squamous cell carcinoma, adenocarcinoma and other) groups of SNPs in TNF-α gene

SNPs

Alleles

Control

n(%)

SCC

n(%)

SCC vs Control

AC

n(%)

AC vs Control

Other

n(%)

Other vs Control

P value

OR (95% CI)

P value

OR (95% CI)

P value

OR (95% CI)

rs1799964

C

543

(23.1)

360

(22.7)

0.728

0.973

[0.836–1.133]

58

(17.9)

0.034

0.724

[0.536–0.977]

10

(20.8)

0.707

0.8741

[0.433–1.765]

T

1803

(76.9)

1228

(77.3)

266

(82.1)

38

(79.2)

C/C

57

(4.9)

46

(5.8)

0.344

 

5

(3.1)

0.095

 

1

(4.2)

0.926

 

C/T

429

(36.6)

268

(33.8)

48

(29.6)

8

(33.3)

T/T

687

(58.6)

480

(60.5)

109

(67.3)

15

(62.5)

rs1800630

A

543

(23.1)

322

(20.3)

0.033

0.845

[0.723–0.987]

60

(18.5)

0.062

0.755

[0.561–1.015]

9

(18.8)

0.474

0.766

[0.369–1.592]

C

1803

(76.9)

1266

(79.7)

264

(81.5)

39

(81.2)

A/A

103

(8.8)

66

(8.3)

0.044

 

10

(6.2)

0.228

 

0.677

 

A/C

337

(28.7)

190

(23.9)

40

(24.7)

5

(20.8)

C/C

733

(62.5)

538

(67.8)

112

(69.1)

17

(70.8)

rs1799724

C

2092

(89.2)

1401

(88.2)

0.355

0.909

[0.744–1.112]

297

(91.7)

0.170

1.336

[0.882–2.022]

44

(91.7)

0.581

1.336

[0.476–3.748]

T

254

(10.8)

187

(11.8)

27

(8.3)

4

(8.3)

C/C

925

(78.9)

616

(77.6)

0.273

 

135

(83.3)

0.317

 

20

(83.3)

0.834

 

T/C

242

(20.6)

169

(21.3)

27

(16.7)

4

(16.7)

T/T

6

(0.5)

9

(1.1)

0

(0.00)

0

(0.00)

rs1800629

A

177

(7.5)

81

(5.1)

0.002

0.659

[0.502–0.864]

23

(7.1)

0.775

0.936

[0.597–1.470]

5

(10.4)

0.457

1.425

[0.557–3.643]

G

2169

(92.5)

1507

(94.9)

301

(92.9)

43

(89.6)

A/A

5

(0.4)

3

(0.4)

0.006

 

1

(0.6)

0.861

 

0

(0.00)

0.631

 

A/G

167

(14.2)

75

(9.4)

21

(13.0)

5

(20.8)

G/G

1001

(85.3)

716

(90.2)

140

(86.4)

19

(79.2)

rs361525

A

59

(2.5)

73

(4.6)

< 0.001

1.868

[1.317–2.648]

7

(2.2)

0.700

0.856

[0.388–1.890]

2

(4.2)

0.472

1.685

[0.400-7.107]

G

2287

(97.5)

1515

(95.4)

317

(97.8)

46

(95.8)

A/A

2

(0.2)

3

(0.4)

0.002

 

0

(0.00)

0.851

 

0

(0.00)

0.695

 

A/G

55

(4.7)

67

(8.4)

7

(4.3)

2

(8.3)

G/G

1116

(95.1)

724

(91.2)

155

(95.7)

22

(91.7)

Table 6

Inheritance model analysis of SNPs in TNF-α gene between control and cervical cancer groups

SNPs

Model

Genotype

Control n(%)

CC n(%)

OR (95% CI)

P value

AIC

BIC

rs1799964

Codominant

T/T

687 (58.6)

604 (61.6)

1

0.170

2852.2

2874.9

C/T

429 (36.6)

324 (33.1)

0.84 (0.70–1.01)

C/C

57 (4.9)

52 (5.3)

1.02 (0.68–1.53)

Dominant

T/T

687 (58.6)

604 (61.6)

1

0.098

2851

2868

C/T-C/C

486 (41.4)

376 (38.4)

0.86 (0.72–1.03)

Recessive

T/T-C/T

1116 (95.1)

928 (94.7)

1

0.680

2853.6

2870.6

C/C

57 (4.9)

52 (5.3)

1.09 (0.73–1.62)

Overdominant

T/T-C/C

744 (63.4)

656 (66.9)

1

0.059

2850.2

2867.2

C/T

429 (36.6)

324 (33.1)

0.84 (0.70–1.01)

Log-additive

---

---

---

0.91 (0.79–1.06)

0.220

2852.3

2869.3

rs1800630

Codominant

C/C

733 (62.5)

667 (68.1)

1

0.020

2847.9

2870.6

A/C

337 (28.7)

235 (24)

0.76 (0.62–0.93)

A/A

103 (8.8)

78 (8)

0.81 (0.59–1.12)

Dominant

C/C

733 (62.5)

667 (68.1)

1

0.006

2846.1

2863.1

A/C-A/A

440 (37.5)

313 (31.9)

0.77 (0.64–0.93)

Recessive

C/C-A/C

1070 (91.2)

902 (92)

1

0.430

2853.1

2870.1

A/A

103 (8.8)

78 (8)

0.88 (0.64–1.21)

Overdominant

C/C-A/A

836 (71.3)

745 (76)

1

0.013

2847.5

2864.6

A/C

337 (28.7)

235 (24)

0.78 (0.64–0.95)

Log-additive

---

---

---

0.85 (0.74–0.97)

0.017

2848

2865

rs1799724

Codominant

C/C

925 (78.9)

771 (78.7)

1

0.590

2854.7

2877.4

C/T

242 (20.6)

200 (20.4)

0.95 (0.77–1.18)

T/T

6 (0.5)

9 (0.9)

1.63 (0.56–4.78)

Dominant

C/C

925 (78.9)

771 (78.7)

1

0.770

2853.7

2870.7

C/T-T/T

248 (21.1)

209 (21.3)

0.97 (0.78–1.20)

Recessive

C/C-C/T

1167 (99.5)

971 (99.1)

1

0.360

2852.9

2869.9

T/T

6 (0.5)

9 (0.9)

1.65 (0.56–4.82)

Overdominant

C/C-T/T

931 (79.4)

780 (79.6)

1

0.630

2853.5

2870.5

C/T

242 (20.6)

200 (20.4)

0.95 (0.76–1.18)

Log-additive

---

---

---

0.99 (0.81–1.21)

0.920

2853.7

2870.8

rs1800629

Codominant

G/G

1001 (85.3)

875 (89.3)

1

0.030

2848.7

2871.4

A/G

167 (14.2)

101 (10.3)

0.70 (0.53–0.91)

A/A

5 (0.4)

4 (0.4)

1.10 (0.29–4.23)

Dominant

G/G

1001 (85.3)

875 (89.3)

1

0.010

2847.1

2864.1

A/G-A/A

172 (14.7)

105 (10.7)

0.71 (0.54–0.92)

Recessive

G/G-A/G

1168 (99.6)

976 (99.6)

1

0.840

2853.7

2870.7

A/A

5 (0.4)

4 (0.4)

1.15 (0.30–4.42)

Overdominant

G/G-A/A

1006 (85.8)

879 (89.7)

1

0.008

2846.7

2863.7

A/G

167 (14.2)

101 (10.3)

0.70 (0.53–0.91)

Log-additive

---

---

---

0.73 (0.57–0.95)

0.016

2847.9

2864.9

rs361525

Codominant

G/G

1116 (95.1)

901 (91.9)

1

0.028

2848.6

2871.3

A/G

55 (4.7)

76 (7.8)

1.62 (1.12–2.35)

A/A

2 (0.2)

3 (0.3)

1.90 (0.31–11.74)

Dominant

G/G

1116 (95.1)

901 (91.9)

1

0.008

2846.6

2863.6

A/G-A/A

57 (4.9)

79 (8.1)

1.63 (1.14–2.35)

Recessive

G/G-A/G

1171 (99.8)

977 (99.7)

1

0.500

2853.3

2870.3

A/A

2 (0.2)

3 (0.3)

1.85 (0.30-11.41)

Overdominant

G/G-A/A

1118 (95.3)

904 (92.2)

1

0.010

2847.1

2864.1

A/G

55 (4.7)

76 (7.8)

1.62 (1.12–2.34)

Log-additive

---

---

---

1.59 (1.13–2.24)

0.008

2846.7

2863.7

Table 7

Inheritance model analysis of SNPs in TNF-α gene between control and CIN groups

SNPs

Model

Genotype

Control n(%)

CIN n(%)

OR (95% CI)

P value

AIC

BIC

rs1799964

Codominant

T/T

687 (58.6)

364 (62.9)

1

0.032

1977.5

1999.4

C/T

429 (36.6)

178 (30.7)

0.75 (0.59–0.94)

C/C

57 (4.9)

37 (6.4)

1.11 (0.69–1.78)

Dominant

T/T

687 (58.6)

364 (62.9)

1

0.036

1978

1994.4

C/T-C/C

486 (41.4)

215 (37.1)

0.79 (0.63–0.99)

Recessive

T/T-C/T

1116 (95.1)

542 (93.6)

1

0.380

1981.6

1998

C/C

57 (4.9)

37 (6.4)

1.23 (0.78–1.96)

Overdominant

T/T-C/C

744 (63.4)

401 (69.3)

1

0.010

1975.7

1992.1

C/T

429 (36.6)

178 (30.7)

0.74 (0.59–0.93)

Log-additive

---

---

---

0.88 (0.73–1.06)

0.170

1980.5

1996.9

rs1800630

Codominant

C/C

733 (62.5)

389 (67.2)

1

0.078

1979.3

2001.2

A/C

337 (28.7)

140 (24.2)

0.76 (0.59–0.97)

A/A

103 (8.8)

50 (8.6)

0.84 (0.57–1.24)

Dominant

C/C

733 (62.5)

389 (67.2)

1

0.027

1977.6

1994

A/C-A/A

440 (37.5)

190 (32.8)

0.78 (0.62–0.97)

Recessive

C/C-A/C

1070 (91.2)

529 (91.4)

1

0.630

1982.2

1998.6

A/A

103 (8.8)

50 (8.6)

0.91 (0.62–1.33)

Overdominant

C/C-A/A

836 (71.3)

439 (75.8)

1

0.037

1978.1

1994.5

A/C

337 (28.7)

140 (24.2)

0.77 (0.60–0.99)

Log-additive

---

---

---

0.86 (0.72–1.01)

0.067

1979.1

1995.5

rs1799724

Codominant

C/C

925 (78.9)

467 (80.7)

1

0.043

1978.1

2000

C/T

242 (20.6)

112 (19.3)

0.84 (0.65–1.10)

T/T

6 (0.5)

0 (0)

0.00 (0.00-NA)

Dominant

C/C

925 (78.9)

467 (80.7)

1

0.150

1980.4

1996.8

C/T-T/T

248 (21.1)

112 (19.3)

0.82 (0.63–1.08)

Recessive

C/C-C/T

1167 (99.5)

579 (100)

1

0.030

1977.7

1994.1

T/T

6 (0.5)

0 (0)

0.00 (0.00-NA)

Overdominant

C/C-T/T

931 (79.4)

467 (80.7)

1

0.230

1980.9

1997.4

C/T

242 (20.6)

112 (19.3)

0.85 (0.65–1.11)

Log-additive

---

---

---

0.81 (0.62–1.05)

0.110

1979.8

1996.2

rs1800629

Codominant

G/G

1001 (85.3)

518 (89.5)

1

0.071

1979.1

2001

A/G

167 (14.2)

59 (10.2)

0.68 (0.49–0.95)

A/A

5 (0.4)

2 (0.4)

0.72 (0.13–4.12)

Dominant

G/G

1001 (85.3)

518 (89.5)

1

0.021

1977.1

1993.5

A/G-A/A

172 (14.7)

61 (10.5)

0.68 (0.49–0.95)

Recessive

G/G-A/G

1168 (99.6)

577 (99.7)

1

0.750

1982.3

1998.7

A/A

5 (0.4)

2 (0.4)

0.75 (0.13–4.31)

Overdominant

G/G-A/A

1006 (85.8)

520 (89.8)

1

0.023

1977.3

1993.7

A/G

167 (14.2)

59 (10.2)

0.68 (0.49–0.95)

Log-additive

---

---

---

0.70 (0.51–0.96)

0.024

1977.3

1993.7

rs361525

Codominant

G/G

1116 (95.1)

547 (94.5)

1

0.580

1983.3

2005.2

A/G

55 (4.7)

32 (5.5)

1.07 (0.66–1.74)

A/A

2 (0.2)

0 (0)

0.00 (0.00-NA)

Dominant

G/G

1116 (95.1)

547 (94.5)

1

0.860

1982.4

1998.8

A/G-A/A

57 (4.9)

32 (5.5)

1.04 (0.65–1.69)

Recessive

G/G-A/G

1171 (99.8)

579 (100)

1

0.320

1981.4

1997.8

A/A

2 (0.2)

0 (0)

0.00 (0.00-NA)

Overdominant

G/G-A/A

1118 (95.3)

547 (94.5)

1

0.780

1982.3

1998.7

A/G

55 (4.7)

32 (5.5)

1.07 (0.66–1.74)

Log-additive

---

---

---

1.02 (0.64–1.62)

0.950

1982.4

1998.8

Table 8

Inheritance model analysis of SNPs in TNF-α gene between control and different pathologic types (SCC, AC and other) groups

SNPs

Model

Genotype

Control n(%)

SCC

SCC vs Control

AC n(%)

AC vs Control

Other n(%)

Other vs Control

OR (95% CI)

P value

AIC

BIC

OR (95% CI)

P value

AIC

BIC

OR (95% CI)

P value

AIC

BIC

rs1799964

Codominant

T/T

687 (58.6)

480 (60.5)

1

0.280

2560.9

2583.3

109 (67.3)

1

0.061

953.1

973.9

15 (62.5)

1

0.870

234.6

255

C/T

429 (36.6)

268 (33.8)

0.87 (0.72–1.06)

48 (29.6)

0.69 (0.48-1.00)

8 (33.3)

0.82 (0.34–1.96)

C/C

57 (4.9)

46 (5.8)

1.14 (0.75–1.73)

5 (3.1)

0.49 (0.19–1.28)

1 (4.2)

0.69 (0.09–5.41)

Dominant

T/T

687 (58.6)

480 (60.5)

1

0.300

2560.4

2577.1

109 (67.3)

1

0.024

951.6

967.2

15 (62.5)

1

0.610

232.6

247.9

C/T-C/C

486 (41.4)

314 (39.5)

0.91 (0.75–1.09)

53 (32.7)

0.67 (0.47–0.95)

9 (37.5)

0.80 (0.35–1.86)

Recessive

T/T-C/T

1116 (95.1)

748 (94.2)

1

0.390

2560.7

2577.5

157 (96.9)

1

0.200

955.1

970.7

23 (95.8)

1

0.760

232.8

248.1

C/C

57 (4.9)

46 (5.8)

1.20 (0.79–1.81)

5 (3.1)

0.56 (0.22–1.44)

1 (4.2)

0.74 (0.10–5.69)

Overdominant

T/T-C/C

744 (63.4)

526 (66.2)

1

0.140

2559.3

2576.1

114 (70.4)

1

0.078

953.6

969.2

16 (66.7)

1

0.700

232.7

248

C/T

429 (36.6)

268 (33.8)

0.87 (0.71–1.05)

48 (29.6)

0.73 (0.50–1.04)

8 (33.3)

0.84 (0.36-2.00)

Log-additive

---

---

---

0.96 (0.82–1.12)

0.590

2561.2

2577.9

---

0.70 (0.51–0.95)

0.018

951.1

966.7

---

0.82 (0.40–1.68)

0.590

232.6

247.9

rs1800630

Codominant

C/C

733 (62.5)

538 (67.8)

1

0.044

2557.2

2579.5

112 (69.1)

1

0.160

955

975.8

17 (70.8)

1

0.620

233.9

254.3

A/C

337 (28.7)

190 (23.9)

0.76 (0.62–0.95)

40 (24.7)

0.77 (0.52–1.14)

5 (20.8)

0.63 (0.23–1.72)

A/A

103 (8.8)

66 (8.3)

0.86 (0.61–1.21)

10 (6.2)

0.59 (0.29–1.17)

2 (8.3)

0.74 (0.17–3.28)

Dominant

C/C

733 (62.5)

538 (67.8)

1

0.016

2555.6

2572.3

112 (69.1)

1

0.077

953.6

969.2

17 (70.8)

1

0.340

232

247.2

A/C-A/A

440 (37.5)

256 (32.2)

0.79 (0.65–0.96)

50 (30.9)

0.73 (0.51–1.04)

7 (29.2)

0.65 (0.27–1.60)

Recessive

C/C-A/C

1070 (91.2)

728 (91.7)

1

0.670

2561.3

2578

152 (93.8)

1

0.160

954.8

970.4

22 (91.7)

1

0.810

232.8

248.1

A/A

103 (8.8)

66 (8.3)

0.93 (0.67–1.29)

10 (6.2)

0.63 (0.32–1.25)

2 (8.3)

0.84 (0.19–3.66)

Overdominant

C/C-A/A

836 (71.3)

604 (76.1)

1

0.019

2556

2572.7

122 (75.3)

1

0.300

955.6

971.2

19 (79.2)

1

0.380

232.1

247.4

A/C

337 (28.7)

190 (23.9)

0.78 (0.63–0.96)

40 (24.7)

0.82 (0.56–1.20)

5 (20.8)

0.65 (0.24–1.76)

Log-additive

---

---

---

0.86 (0.75-1.00)

0.048

2557.5

2574.3

---

0.77 (0.58–1.01)

0.055

953

968.6

---

0.76 (0.39–1.50)

0.420

232.2

247.5

rs1799724

Codominant

C/C

925 (78.9)

616 (77.6)

1

0.420

2561.7

2584

135 (83.3)

1

0.120

954.5

975.3

20 (83.3)

1

0.690

234.2

254.5

C/T

242 (20.6)

169 (21.3)

1.01 (0.81–1.27)

27 (16.7)

0.70 (0.45–1.09)

4 (16.7)

0.69 (0.23–2.05)

T/T

6 (0.5)

9 (1.1)

2.04 (0.70–5.96)

0 (0)

0.00 (0.00-NA)

0 (0)

0.00 (0.00-NA)

Dominant

C/C

925 (78.9)

616 (77.6)

1

0.740

2561.3

2578.1

135 (83.3)

1

0.081

953.7

969.3

20 (83.3)

1

0.460

232.3

247.6

C/T-T/T

248 (21.1)

178 (22.4)

1.04 (0.83–1.30)

27 (16.7)

0.68 (0.44–1.06)

4 (16.7)

0.67 (0.23-2.00)

Recessive

C/C-C/T

1167 (99.5)

785 (98.9)

1

0.190

2559.7

2576.5

162 (100)

1

0.210

955.1

970.7

24 (100)

1

0.610

232.6

247.9

T/T

6 (0.5)

9 (1.1)

2.04 (0.70–5.94)

0 (0)

0.00 (0.00-NA)

0 (0)

0.00 (0.00-NA)

Overdominant

C/C-T/T

931 (79.4)

625 (78.7)

1

0.960

2561.4

2578.2

135 (83.3)

1

0.110

954.2

969.7

20 (83.3)

1

0.500

232.4

247.7

C/T

242 (20.6)

169 (21.3)

1.01 (0.80–1.26)

27 (16.7)

0.70 (0.45–1.10)

4 (16.7)

0.70 (0.23–2.07)

Log-additive

---

---

---

1.06 (0.86–1.31)

0.570

2561.1

2577.9

---

0.67 (0.44–1.04)

0.065

953.3

968.9

---

0.67 (0.23–1.94)

0.440

232.3

247.5

rs1800629

Codominant

G/G

1001 (85.3)

716 (90.2)

1

0.007

2553.5

2575.8

140 (86.4)

1

0.840

958.4

979.2

19 (79.2)

1

0.610

233.9

254.3

A/G

167 (14.2)

75 (9.4)

0.63 (0.47–0.84)

21 (13)

0.92 (0.56–1.50)

5 (20.8)

1.62 (0.59–4.41)

A/A

5 (0.4)

3 (0.4)

0.96 (0.22–4.14)

1 (0.6)

1.73 (0.20-15.28)

0 (0)

0.00 (0.00-NA)

Dominant

G/G

1001 (85.3)

716 (90.2)

1

0.002

2551.8

2568.5

140 (86.4)

1

0.790

956.6

972.2

19 (79.2)

1

0.400

232.2

247.4

A/G-A/A

172 (14.7)

78 (9.8)

0.64 (0.48–0.85)

22 (13.6)

0.94 (0.58–1.52)

5 (20.8)

1.58 (0.58–4.30)

Recessive

G/G-A/G

1168 (99.6)

791 (99.6)

1

0.990

2561.4

2578.2

161 (99.4)

1

0.630

956.5

972.1

24 (100)

1

0.680

232.7

248

A/A

5 (0.4)

3 (0.4)

1.01 (0.23–4.37)

1 (0.6)

1.75 (0.20-15.45)

0 (0)

0.00 (0.00-NA)

Overdominant

G/G-A/A

1006 (85.8)

719 (90.5)

1

0.002

2551.5

2568.2

141 (87)

1

0.720

956.6

972.2

19 (79.2)

1

0.370

232.1

247.3

A/G

167 (14.2)

75 (9.4)

0.63 (0.47–0.84)

21 (13)

0.91 (0.56–1.50)

5 (20.8)

1.62 (0.59–4.43)

Log-additive

---

---

---

0.66 (0.50–0.87)

0.003

2552.6

2569.4

---

0.96 (0.61–1.53)

0.870

956.7

972.3

---

1.49 (0.57–3.89)

0.440

232.3

247.6

rs361525

Codominant

G/G

1116 (95.1)

724 (91.2)

1

0.008

2553.7

2576.1

155 (95.7)

1

0.750

958.1

978.9

22 (91.7)

1

0.780

234.4

254.8

A/G

55 (4.7)

67 (8.4)

1.78 (1.22–2.60)

7 (4.3)

0.84 (0.37–1.91)

2 (8.3)

1.70 (0.38–7.47)

A/A

2 (0.2)

3 (0.4)

2.35 (0.38–14.48)

0 (0)

0.00 (0.00-NA)

0 (0)

0.00 (0.00-NA)

Dominant

G/G

1116 (95.1)

724 (91.2)

1

0.002

2551.8

2568.6

155 (95.7)

1

0.630

956.5

972.1

22 (91.7)

1

0.530

232.5

247.8

A/G-A/A

57 (4.9)

70 (8.8)

1.80 (1.24–2.61)

7 (4.3)

0.82 (0.36–1.85)

2 (8.3)

1.65 (0.38–7.27)

Recessive

G/G-A/G

1171 (99.8)

791 (99.6)

1

0.370

2560.6

2577.4

162 (100)

1

0.520

956.3

971.9

24 (100)

1

0.810

232.8

248.1

A/A

2 (0.2)

3 (0.4)

2.27 (0.37–13.97)

0 (0)

0.00 (0.00-NA)

0 (0)

0.00 (0.00-NA)

Overdominant

G/G-A/A

1118 (95.3)

727 (91.6)

1

0.003

2552.6

2569.4

155 (95.7)

1

0.680

956.5

972.1

22 (91.7)

1

0.510

232.5

247.7

A/G

55 (4.7)

67 (8.4)

1.77 (1.21–2.59)

7 (4.3)

0.84 (0.37–1.91)

2 (8.3)

1.70 (0.39–7.48)

Log-additive

---

---

---

1.74 (1.22–2.48)

0.002

2551.8

2568.6

---

0.80 (0.36–1.79)

0.580

956.4

972

---

1.57 (0.38–6.52)

0.560

232.6

247.8

Table 9

The distribution of the haplotypes constructed by SNPs in TNF-α gene

Haplotypes

Control

n(%)

CIN

n(%)

CC

n(%)

Control vs CIN

CIN VS CC

Control vs CC

P value

OR[95%CI]

P value

OR[95%CI]

P value

OR[95%CI]

C-A

479.43

(0.204)

215.38

(0.186)

344.33

(17.6)

0.171

0.882 [0.737 ~ 1.056]

0.488

0.935 [0.774 ~ 1.130]

0.015

0.825 [0.707 ~ 0.963]

C-C

63.57

(0.027)

36.61

(0.032)

83.67

(0.043)

0.467

1.165 [0.771 ~ 1.761]

0.118

1.370 [0.922 ~ 2.034]

0.005

1.596 [1.145 ~ 2.225]

T-C

1739.43

(0.741)

881.39

(0.761)

1485.33

(0.758)

0.311

1.092 [0.921 ~ 1.294]

0.931

0.992 [0.832 ~ 1.184]

0.277

1.083 [0.938 ~ 1.252]

Table 10

The distribution of the haplotypes constructed by SNPs in TNF-α gene

Haplotypes

Control n(%)

SCC n(%)

Control VS SCC

P value

OR[95%CI]

C-A

479.43(0.204)

283.46(0.179)

0.039

0.842 [0.715 ~ 0.992]

C-C

63.57(0.027)

76.54(0.048)

< 0.0005

1.814 [1.292 ~ 2.546]

T-C

1739.43(0.741)

1189.46(0.749)

0.690

1.031 [0.886 ~ 1.201]

4. Discussion

Cervical cancer is a gynaecological malignancy with a high incidence worldwide that seriously influences women's lives. Many studies to date have shown that host genetic variation has a certain association with susceptibility to cervical cancer [2328]. In the current study, we found that rs1800629 and rs361525 in the TNF-α gene promoter are related to susceptibility to cervical cancer in a Chinese Han population.

Recent studies have shown that TNF-α may promote the spread and metastasis of cancer, resulting in increased serum TNF-α levels in patients with advanced cervical cancer [29, 30]. In addition, studies have shown that TNF-α gene promoter polymorphisms are associated with TNF-α expression. For example, rs1800629 and rs361525 influence transcription of the TNF-α gene [3134]. Moreover, several studies have found that rs1800629 and rs361525 are associated with breast cancer, liver cancer, gastric cancer and cervical cancer [12, 13, 15, 3539]. In 2005, Duarte et al. reported that the A allele of rs1800629 is associated with an increased risk of invasive cervical cancer in the Portuguese population [13]. Singh et al. and Du et al. also found the rs1800629 A allele to be associated with an increased risk of cervical cancer in an Indian population and a Chinese population from Sichuan Province, southwestern China, respectively [12, 15]. However, in 2012, Wang et al. reported that rs1800629 was not significantly associated with cancer in a Chinese population from Liaoning Province, northeastern China [40]. The results of the current study show that the A allele of rs1800629 is a protective factor for cervical cancer, especially SCC, in the Chinese Han population. Our findings are similar to those of Zidi et al. in Tunisia [41]. In general, association results vary among different populations and even the same population. One of the reasons for such inconsistency is the different genetic backgrounds of different populations, even the same population. For example, the samples included in Du et al., Wang et al. and our study are from different provinces of China. Our previous study reported that the Han population in China could be categorized into southern Han and northern Han according to their distinction in genetic background [42]. Although Sichuan and Yunnan are located in southwestern China, the population from Sichuan Province belongs to the southern Chinese population, and the population from Yunnan Province has special genetic characteristics between the southern and northern Chinese Han populations. Other reasons for inconsistency among populations may be different sample sizes. For example, the sample in Du et al. involved 522 cervical cancer patients and 550 healthy individuals, whereas 1,173 healthy controls, 579 patients with CIN, and 980 patients with cervical cancer were enrolled in the current study.

In 2001, Jang et al. reported that the A allele of rs361525 is a protective factor for cervical cancer in a Korean population [14]. Nevertheless, in 2015, Zidi et al. showed that the A/A and G/A-A/A genotypes of rs361525 are related to the progression of CIN but not to the eventual occurrence of cervical cancer in Tunisia [41]. In 2018, Li et al. reported that the rs361525 A allele is a risk factor for cervical cancer in a Chinese population from Shandong Province, northern China [18], and in 2019, Du et al. showed that it is a protective factor against cervical cancer in Sichuan Province, southwestern China [12]. In the current study, the A allele of rs361525 was found to be a risk factor for cervical cancer in a Chinese population. Moreover, in subgroup analysis, the A allele was a risk factor for SCC but not AC. Our results are similar to those of Li et al., in which the rs361525 A allele was a risk factor for cervical cancer. One of the reasons for the discrepancy between the studies of Li et al. and Du et al. and ours may be the different pathological types of cervical cancer included; we found that the A allele of rs361525 is a risk factor for SCC, which indicates that the A allele has different roles in pathological types of cervical cancer. SCC is the most common pathological type of cervical cancer, accounting for approximately 75–80%, followed by AC, which accounts for approximately 10.0–25%, and adenosquamous carcinoma, which accounts for approximately 3–5% [43]. Therefore, in the future, it is necessary to study the role of rs361525 in the different pathological types of cervical cancer.

To date, there are no reports on the association between rs1800630 and cervical cancer. In the current study, we found that rs1800630 was associated with cervical cancer, but not CIN, in the Chinese Han population (P = 0.011). However, after Bonferroni correction, there was no significant difference between the control and cervical cancer groups. In inheritance model analysis, the A/C-A/A genotype was related to a reduced risk of cervical cancer (P = 0.006, OR = 0.77; 95% CI = 0.64–0.93) in the dominant model. Previous studies have shown that rs1800630 is associated with gastric cancer and hepatocellular carcinoma [44, 45], with the A allele of rs1800630 being a protective factor in the former but a risk factor in the latter. Our results show that the A allele of rs1800630 is a protective factor against cervical cancer (OR = 0.827; 95% CI = 0.715–0.959). The reason for this difference between gastric cancer and hepatocellular carcinoma may be the different cancer types. In 1998, Higuchi et al. reported that the A allele of rs1800630 increased the level of TNF-α in peripheral blood mononuclear cells [46], yet in 1999, Skoog et al. reported that it may decrease transcription of the TNF-α gene in HepG2 cells [47]. Therefore, the role of the A allele of rs1800630 differs among cells. More samples should be included In future studies, and the role of rs1800630 in cervical cancer should be investigated.

In the current study, we found that rs1800629 and rs361525 in the TNF-α promoter gene are associated with cervical cancer in a Chinese Han population. The A allele of rs1800629 is a protective factor for cervical cancer and SCC, whereas the A allele of rs361525 is a risk factor for cervical cancer and SCC. Nonetheless, association results vary among different populations. Thus, multicentre and more samples from different regions should be evaluated to study the association between TNF-α gene polymorphisms and cervical cancer. Moreover, the function of polymorphisms should be investigated in the future.

Abbreviations

Tumour necrosis factor-α: TNF-α; cervical intraepithelial neoplasia CIN; cervical cancer CC; tumour necrosis factor-β: TNF-β; Single nucleotide polymorphisms: SNPs; ANOVA: Analysis of Variance; Human papillomavirus: HPV; Hardy-Weinberg equilibrium: HWE; Odds ratios: ORs; Cervical intraepithelial neoplasia: CIN; squamous cell carcinoma: SCC; Adenocarcinoma: AC; Confidence intervals: CIs; Linkage disequilibrium: LD; Akaike information criterion: AIC; Bayesian information criterion: BIC.

Declarations

Ethics approval and consent to participate

The current study was approved by the Institutional Review Boards of the No. 3 Affiliated Hospitals of Kunming Medical University and was performed in accordance with the principles of the Declaration of Helsinki. All individuals enrolled in this study provided written informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The data generated during the current study are available to any scientist wishing to use them for non-commercial purpose from the corresponding author on reasonable request. However, the clinical data might be available without the privacy data of participates in the current study.

Competing interests

The authors declare that they have no competing interests.

Funding

The current study was supported by grant from Yunnan Applied Basic Research Projects (2016FA034); Special Funds for High-level Healthy Talents of Yunnan Province (D-201669 and L-201615); Yunnan Provincial Science and Technology Department (2019HC0060), Association Foundation Program of Yunnan Provincial Science and Technology Department and Kunming Medical University (2017FR467-077); Special Funds for high-level health talents of Yunnan Province (H-2018014); The PUMC Youth Fund (3332019111). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Authors' contributions

LS and YFY designed the current study; JY and YYW finished the main part of experiment and data analysis of the current study; ZLY finished the sample clinical diagnose and collection; SYL and CYL were responsible for the collection of venous blood; WPL and XWZ participated in the genomic DNA extraction; YYW drafted the manuscript; LS and YFY revised the manuscript. And all authors have read and approved the manuscript.

Acknowledgement

Our great gratitude was expressed to the participation of the patients and control subjects in current study.

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