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

doi:10.21203/rs.3.rs-378408/v1

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.

Cancer Biology

Immunology

Oncology

Tumour necrosis factor-α (TNF-α)

Promoter

Cervical cancer (CC)

Cervical intraepithelial neoplasia (CIN)

Single-nucleotide polymorphism (SNP)

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 [3–5].

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 [8–10].

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, 11–17]. 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.

2.1. 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).

3.1. 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	Ⅰ	679
	Ⅱ	265
	Ⅲ and Ⅳ	36
Stages of CIN	Ⅰ		120
	Ⅱ		104
	Ⅲ		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	x²	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
SNPs	Alleles n(%)		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
SNPs	Alleles n(%)			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
	Dominant	C/T-C/C	486 (41.4)	376 (38.4)	0.86 (0.72–1.03)	0.098	2851	2868
	Recessive	T/T-C/T	1116 (95.1)	928 (94.7)	1	0.680	2853.6	2870.6
	Recessive	C/C	57 (4.9)	52 (5.3)	1.09 (0.73–1.62)	0.680	2853.6	2870.6
	Overdominant	T/T-C/C	744 (63.4)	656 (66.9)	1	0.059	2850.2	2867.2
	Overdominant	C/T	429 (36.6)	324 (33.1)	0.84 (0.70–1.01)	0.059	2850.2	2867.2
	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
	Dominant	A/C-A/A	440 (37.5)	313 (31.9)	0.77 (0.64–0.93)	0.006	2846.1	2863.1
	Recessive	C/C-A/C	1070 (91.2)	902 (92)	1	0.430	2853.1	2870.1
	Recessive	A/A	103 (8.8)	78 (8)	0.88 (0.64–1.21)	0.430	2853.1	2870.1
	Overdominant	C/C-A/A	836 (71.3)	745 (76)	1	0.013	2847.5	2864.6
	Overdominant	A/C	337 (28.7)	235 (24)	0.78 (0.64–0.95)	0.013	2847.5	2864.6
	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
	Dominant	C/T-T/T	248 (21.1)	209 (21.3)	0.97 (0.78–1.20)	0.770	2853.7	2870.7
	Recessive	C/C-C/T	1167 (99.5)	971 (99.1)	1	0.360	2852.9	2869.9
	Recessive	T/T	6 (0.5)	9 (0.9)	1.65 (0.56–4.82)	0.360	2852.9	2869.9
	Overdominant	C/C-T/T	931 (79.4)	780 (79.6)	1	0.630	2853.5	2870.5
	Overdominant	C/T	242 (20.6)	200 (20.4)	0.95 (0.76–1.18)	0.630	2853.5	2870.5
	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
	Dominant	A/G-A/A	172 (14.7)	105 (10.7)	0.71 (0.54–0.92)	0.010	2847.1	2864.1
	Recessive	G/G-A/G	1168 (99.6)	976 (99.6)	1	0.840	2853.7	2870.7
	Recessive	A/A	5 (0.4)	4 (0.4)	1.15 (0.30–4.42)	0.840	2853.7	2870.7
	Overdominant	G/G-A/A	1006 (85.8)	879 (89.7)	1	0.008	2846.7	2863.7
	Overdominant	A/G	167 (14.2)	101 (10.3)	0.70 (0.53–0.91)	0.008	2846.7	2863.7
	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
	Dominant	A/G-A/A	57 (4.9)	79 (8.1)	1.63 (1.14–2.35)	0.008	2846.6	2863.6
	Recessive	G/G-A/G	1171 (99.8)	977 (99.7)	1	0.500	2853.3	2870.3
	Recessive	A/A	2 (0.2)	3 (0.3)	1.85 (0.30-11.41)	0.500	2853.3	2870.3
	Overdominant	G/G-A/A	1118 (95.3)	904 (92.2)	1	0.010	2847.1	2864.1
	Overdominant	A/G	55 (4.7)	76 (7.8)	1.62 (1.12–2.34)	0.010	2847.1	2864.1
	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
	Dominant	C/T-C/C	486 (41.4)	215 (37.1)	0.79 (0.63–0.99)	0.036	1978	1994.4
	Recessive	T/T-C/T	1116 (95.1)	542 (93.6)	1	0.380	1981.6	1998
	Recessive	C/C	57 (4.9)	37 (6.4)	1.23 (0.78–1.96)	0.380	1981.6	1998
	Overdominant	T/T-C/C	744 (63.4)	401 (69.3)	1	0.010	1975.7	1992.1
	Overdominant	C/T	429 (36.6)	178 (30.7)	0.74 (0.59–0.93)	0.010	1975.7	1992.1
	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
	Dominant	A/C-A/A	440 (37.5)	190 (32.8)	0.78 (0.62–0.97)	0.027	1977.6	1994
	Recessive	C/C-A/C	1070 (91.2)	529 (91.4)	1	0.630	1982.2	1998.6
	Recessive	A/A	103 (8.8)	50 (8.6)	0.91 (0.62–1.33)	0.630	1982.2	1998.6
	Overdominant	C/C-A/A	836 (71.3)	439 (75.8)	1	0.037	1978.1	1994.5
	Overdominant	A/C	337 (28.7)	140 (24.2)	0.77 (0.60–0.99)	0.037	1978.1	1994.5
	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
	Dominant	C/T-T/T	248 (21.1)	112 (19.3)	0.82 (0.63–1.08)	0.150	1980.4	1996.8
	Recessive	C/C-C/T	1167 (99.5)	579 (100)	1	0.030	1977.7	1994.1
	Recessive	T/T	6 (0.5)	0 (0)	0.00 (0.00-NA)	0.030	1977.7	1994.1
	Overdominant	C/C-T/T	931 (79.4)	467 (80.7)	1	0.230	1980.9	1997.4
	Overdominant	C/T	242 (20.6)	112 (19.3)	0.85 (0.65–1.11)	0.230	1980.9	1997.4
	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
	Dominant	A/G-A/A	172 (14.7)	61 (10.5)	0.68 (0.49–0.95)	0.021	1977.1	1993.5
	Recessive	G/G-A/G	1168 (99.6)	577 (99.7)	1	0.750	1982.3	1998.7
	Recessive	A/A	5 (0.4)	2 (0.4)	0.75 (0.13–4.31)	0.750	1982.3	1998.7
	Overdominant	G/G-A/A	1006 (85.8)	520 (89.8)	1	0.023	1977.3	1993.7
	Overdominant	A/G	167 (14.2)	59 (10.2)	0.68 (0.49–0.95)	0.023	1977.3	1993.7
	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
	Dominant	A/G-A/A	57 (4.9)	32 (5.5)	1.04 (0.65–1.69)	0.860	1982.4	1998.8
	Recessive	G/G-A/G	1171 (99.8)	579 (100)	1	0.320	1981.4	1997.8
	Recessive	A/A	2 (0.2)	0 (0)	0.00 (0.00-NA)	0.320	1981.4	1997.8
	Overdominant	G/G-A/A	1118 (95.3)	547 (94.5)	1	0.780	1982.3	1998.7
	Overdominant	A/G	55 (4.7)	32 (5.5)	1.07 (0.66–1.74)	0.780	1982.3	1998.7
	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
Haplotypes	Control n(%)	CIN n(%)	CC n(%)	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
Haplotypes	Control n(%)	SCC n(%)	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]

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 [23–28]. 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 [31–34]. Moreover, several studies have found that rs1800629 and rs361525 are associated with breast cancer, liver cancer, gastric cancer and cervical cancer [12, 13, 15, 35–39]. 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.

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.

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

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusion

1. Introduction

2. Materials And Methods

2.1. Subjects

2.2. DNA extraction and SNP genotyping

2.3. Statistical analysis

3. Results

3.1. Subject characteristics

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

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

4. Discussion

Abbreviations

Declarations

References

Status:

Journal Publication

Version 1