Abstract

TNFAIP8L1 and FLT1 play critical roles in the occurrence and development of tumors, but no in-depth studies have been carried out in cervical cancer. The present study aims to research the correlation between polymorphisms of these two genes and the risk of cervical cancer in the Uygur women. The study involved 342 cervical cancer patients and 498 healthy women. Five single nucleotide polymorphisms (SNPs) from the TNFAIP8L1 gene and the FLT1 gene were selected and genotyped. Odds ratio and 95% CIs were calculated by logistic regression analysis to evaluate the correlation between SNPs and cervical cancer risk. The alleles rs9917028-A (P=0.032), rs10426502-A (P=0.007), and rs1060555-G (P=0.026) of TNFAIP8L1 were associated with a decreased risk of cervical cancer. In the multiple genetic models, these three SNPs were also associated with the risk of cervical cancer. The stratified analysis showed that TNFAIP8L1-rs10426502, -rs1060555, and FLT1-rs9513111 were associated with a decreased risk of cervical cancer amongst people older than 43 years. Moreover, the haplotypes AG (P=0.007) and GC (P=0.026) of linkage disequilibrium block rs10426502|rs1060555 in TNFAIP8L1 were significantly associated with an increased risk of cervical cancer. Our results suggested that the relationships between TNFAIP8L1 and FLT1 polymorphisms and the risk of cervical cancer amongst Uyghur females.

Introduction

Cervical cancer is the third most common cancer amongst women worldwide, with 527,624 new cases and 265,672 deaths in 2012 [1]. It is estimated that by 2020, there will be 609,270 new cases and 317,727 deaths worldwide [2]. While cancer-related mortality has decreased with the implementation of screening programs worldwide, incidence is on the rise in developing countries, where about 80% of cases occur [3]. China is a multiethnic country, the incidence of cervical cancer in all ethnic groups is different, and of which Xinjiang Uygur population incidence is the highest. In recent years, much attention has been paid to the study of cervical cancer in Uygur women [4]. However, the pathogenesis of cervical cancer was not fully understood. Etiological studies have shown that cervical cancer is the result of a combination of factors, including the high-risk human papillomavirus (HPV) infection, environmental, behavioral, and genetic factors [5,6]. In recent years, increasing evidence has emphasized the role of genetic factors in the pathogenesis of cervical cancer. Studies based on single nucleotide polymorphisms (SNPs) and genome-wide association studies have confirmed the relationship between genetic variations and the risk of cervical cancer in differenP-t populations [7–9].

Tumor necrosis factor (TNF)-α- induced protein 8 (TNFAIP8) is a recently identified protein family reported to have important roles in immunity, inflammation, and tumorigenesis. TNFAIP8L1 is a member of the TNFAIP8 family [10]. Past studies have shown a strong correlation between TNFAIP8 protein and the development of a lvariety of cancers, including gynecological cancers such as breast cancer, cervical cancer, ovarian cancer, and endometrial cancer [11]. In addition, the regulation of these proteins has been found to promote basic characteristics of cancer, such as tumor growth, proliferation, inhibition of apoptosis, and angiogenesis [12]. However, the exact function of TNFAIP8L1 in cervical cancer is unknown. The FLT1 gene encodes VEGFR1, a member of the vascular endothelial growth factor receptor (VEGFR) family. The VEGF system is crucial for angiogenesis, which is considered to be the key to the occurrence of malignant tumors [13,14]. A previous study showed that VEGF is an important regulator of endometrial tumor angiogenesis, and FLT1 is a great marker of vascular proliferation [15,16]. At present, the function of FLT1 in cervical cancer has not been deeply studied.

In this case-control study, we investigated the association between SNPs of TNFAIP8L1 and FLT1 genes and cervical cancer risk amongst Xinjiang Uygur females.

Materials and methods

Study population

A total of 342 patients were recruited from the People’s Hospital of Xinjiang Uygur Autonomous Region as preliminary samples. All patients were pathologically confirmed with cervical cancer by at least two pathologists. The International Federation of Gynecology and Obstetrics (FIGO) stage was also recorded for further analysis. Notably, patients who received systemic or topical treatment were excluded from our study. In addition, women eligible for age and ethnicity were recruited continuously from the same hospital health screening center as a control group. Finally, the study included 498 healthy individuals with no history of gynecologic disease or tumors. All participants were from Xinjiang Uygur ethnic group.

DNA genotyping

The total DNA isolation was performed from the peripheral blood samples provided by the experimental subjects using the GoldMag DNA Purification Kit (GoldMag Co. Ltd, Xi’an City, China). The concentration and quality of the purified DNA were measured with Nanodrop 2000 UV spectrophotometer (Thermo Scientific, Waltham, MA, U.S.A.). Screening the polymorphisms with minor allele frequency (MAF) more than 5% in the 1000 genomes database (http://www.internationalgenome.org/) and the dbSNP database (https://www.ncbi.nlm.nih.gov/projects/SNP/). We eventually selected five SNPs including rs9917028, rs10426502, and rs1060555 of TNFAIP8L1 and rs9513111 and rs677471 of FLT1 for further genotype identification and risk association analysis. The Agena Bioscience Assay Design Suite software, version 2.0 (https://agenacx.com/online-tools/) was applied for MassARRAY assay design. The SNP genotype was identified by the MassARRAY Nanodispenser and MassARRAY iPLEX method (Agena Bioscience, San Diego, CA, U.S.A.) according to the manufacturer’s instructions. Data management and presentation were conducted by the Agena Bioscience TYPER software, version 4.0 [17–19].

Statistical analysis

All the basic statistical analyses were carried out using SPSS 20.0 (SPSS, Chicago, IL, U.S.A.) and Microsoft Excel. Age distribution differences between cases and healthy controls were analyzed by the independent sample t-test. The departure from Hardy-Weinberg equilibrium (HWE) was determined by comparing the observed and expected heterozygosity on controls with the Chi-square test. Statistical significance was defined as P-value less than 0.05. All P-values were two tailed. Furthermore, the association study was performed in multiple inheritance models (co-dominant, dominant, recessive, and additive) using SNPstats software (http://bioinfo.iconcologia.net/SNPstats). Odds ratio (ORs) values and 95% CIs were calculated using an age-adjusted logistic regression to assess the risk of cervical cancer. Haploview 4.2 software (Cambridge, MA, U.S.A.) was used to determine the pairwise linkage disequilibrium (LD), using the standardized D′ and r2 values. The false-positive report probability (FPRP) was calculated to evaluate the significant results using the SAS software 9.4 (SAS Institute, Cary, N.C., U.S.A.). We set 0.2 as the FPRP threshold to detect an OR of 0.67/1.50 (protective/risk effect) associated with genotype and haplotype in the study. The FPRP value of less than 0.2 was considered a significant finding [20,21].

Results

Characteristics of the study subjects

In the present study, the age distribution was matched between the case group and the control group (P>0.05). A total of 342 cervical cancer patients and 498 healthy subjects were included, with the average age of 43.46 and 43.27, respectively (Table 1). Moreover, the frequency distribution of the cervical cancer patients regarding FIGO stage and HPV status were calculated and listed in Table 1.

Table 1
Characteristics of the study population
Variable Cases Controls P-value 
Age (mean ± S.D.) 43.46 ± 13.03 43.27 ± 11.78 0.829 
≤43 166 (49%) 235 (53%)  
>43 176 (51%) 263 (47%)  
Stage    
I–II 132 (39%)   
III–IV 80 (23%)   
Absent 130 (38%)   
HPV status    
Negative 51 (15%)   
Positive 195 (57%)   
Absent 96 (28%)   
Total 342 498  
Variable Cases Controls P-value 
Age (mean ± S.D.) 43.46 ± 13.03 43.27 ± 11.78 0.829 
≤43 166 (49%) 235 (53%)  
>43 176 (51%) 263 (47%)  
Stage    
I–II 132 (39%)   
III–IV 80 (23%)   
Absent 130 (38%)   
HPV status    
Negative 51 (15%)   
Positive 195 (57%)   
Absent 96 (28%)   
Total 342 498  

P-value obtained from independent sample t-test.

Basic information for the candidate SNPs

Basic information and preliminary statistical results of the selected SNPs are showed in Table 2. HWE P-values were obtained with Chi-square tests and all SNPs (rs9917028, rs10426502, rs1060555, rs9513111, and rs677471) were in HWE (P>0.05). The values of the MAF were higher than 5%.

Table 2
Basic information regarding candidate SNPs
SNP Chromosome Position Alleles Gene Role MAF HWE OR P-value 
      Case Control P-value (95% CI)  
rs9917028 Chr19 4640971 G/A TNFAIP8L1 Intron 0.330 0.382 0.107 0.80 (0.65-0.98) 0.032 
rs10426502 Chr19 4651257 G/A TNFAIP8L1 Intron 0.037 0.067 0.154 0.53 (0.33-0.84) 0.007 
rs1060555 Chr19 4652810 C/G TNFAIP8L1 3′UTR 0.238 0.287 0.154 0.78 (0.62-0.97) 0.026 
rs9513111 Chr13 28423426 C/T FLT1 Intron 0.326 0.674 0.766 0.91 (0.74–1.12) 0.385 
rs677471 Chr13 28489675 C/T FLT1 Intron 0.355 0.645 0.160 1.08 (0.88–1.33) 0.448 
SNP Chromosome Position Alleles Gene Role MAF HWE OR P-value 
      Case Control P-value (95% CI)  
rs9917028 Chr19 4640971 G/A TNFAIP8L1 Intron 0.330 0.382 0.107 0.80 (0.65-0.98) 0.032 
rs10426502 Chr19 4651257 G/A TNFAIP8L1 Intron 0.037 0.067 0.154 0.53 (0.33-0.84) 0.007 
rs1060555 Chr19 4652810 C/G TNFAIP8L1 3′UTR 0.238 0.287 0.154 0.78 (0.62-0.97) 0.026 
rs9513111 Chr13 28423426 C/T FLT1 Intron 0.326 0.674 0.766 0.91 (0.74–1.12) 0.385 
rs677471 Chr13 28489675 C/T FLT1 Intron 0.355 0.645 0.160 1.08 (0.88–1.33) 0.448 

P-values were calculated with Chi-square tests. P<0.05 indicates statistical significance.

SNPs and cervical cancer risk

The differences in allele frequency between cases and controls were compared by χ2 test (Table 2). The allele with lower frequency of each SNP was regarded as a risk factor. The results indicated that alleles rs9917028-A (P=0.032), rs10426502-A (P=0.007), and rs1060555-G (P=0.026) of TNFAIP8L1 were risk alleles for cervical cancer amongst Xinjiang Uygur female.

As showed in Table 3, significant associations were existed between TNFAIP8L1 rs9917028, and decreased risk of cervical cancer in allele model, co-dominant model, and additive model (allele model: OR = 0.80, 95% CI: 0.65–0.98, P=0.032; co-dominant model: OR = 0.64, 95% CI: 0.42–0.99, P=0.044; additive model: OR = 0.81, 95% CI: 0.66–0.99, P=0.039). TNFAIP8L1 rs10426502 and rs1060555 were decreased risk of cervical cancer in allele model, co-dominant model, dominant model, and additive model (rs10426502: allele model: OR = 0.53, 95% CI: 0.33–0.84, P=0.007; co-dominant model: OR = 0.47, 95% CI: 0.28–0.76, P=0.003; dominant model: OR = 0.49, 95% CI: 0.30–0.79, P=0.004; additive model: OR = 0.52, 95% CI: 0.32–0.83, P=0.007. rs1060555: allele model: OR = 0.78, 95% CI: 0.62–0.97, P=0.026; co-dominant model: OR = 0.70, 95% CI: 0.52–0.93, P=0.015; dominant model: OR = 0.70, 95% CI: 0.53–0.93, P=0.012; additive model: OR = 0.77, 95% CI: 0.61–0.97, P=0.024).

Table 3
The relationship between TNFAIP8L1 gene polymorphism and cervical cancer
SNP Model Genotype Case Control Adjusted by age 
     OR (95% CI) P-value 
rs9917028 Co-dominant GG 157 (46%) 199 (40%)  
  GA 144 (42%) 218 (44%) 0.84 (0.62–1.13) 0.241 
  AA 41 (12%) 81 (16%) 0.64 (0.42–0.99) 0.044 
 Dominant GG 157 (46%) 199 (40%)  
  GA–AA 185 (54%) 299 (60%) 0.78 (0.59–1.04) 0.088 
 Recessive GG-GA 301 (88%) 417 (84%)  
  AA 41 (12%) 81 (16%) 0.70 (0.47–1.05) 0.087 
 Additive — — — 0.81 (0.66–0.99) 0.039 
rs10426502 Co-dominant GG 317 (93%) 431 (83%)  
  GA 23 (6%) 67 (17%) 0.47 (0.28–0.76) 0.003 
  AA 1 (1%) 0 (0%) — 0.999 
 Dominant GG 317 (93%) 431 (83%)  
  GA-AA 24 (7%) 67 (17%) 0.49 (0.30–0.79) 0.004 
 Recessive GG-GA 341 (91.90%) 498 (100%)  
  AA 1 (1%) 0 (0%) — 0.999 
 Additive — — — 0.52 (0.32–0.83) 0.007 
rs1060555 Co-dominant CC 199 (58%) 246 (49%)  
  CG 123 (36%) 218 (44%) 0.70 (0.52–0.93) 0.015 
  GG 20 (6%) 34 (7%) 0.73 (0.41–1.30) 0.285 
 Dominant CC 199 (58%) 246 (49%)  
  CG-GG 143 (42%) 252 (57%) 0.70 (0.53–0.93) 0.012 
 Recessive CC-CG 322 (94%) 464 (93%)  
  GG 20 (6%) 34 (7%) 0.85 (0.48–1.50) 0.571 
 Additive — — — 0.77 (0.61–0.97) 0.024 
SNP Model Genotype Case Control Adjusted by age 
     OR (95% CI) P-value 
rs9917028 Co-dominant GG 157 (46%) 199 (40%)  
  GA 144 (42%) 218 (44%) 0.84 (0.62–1.13) 0.241 
  AA 41 (12%) 81 (16%) 0.64 (0.42–0.99) 0.044 
 Dominant GG 157 (46%) 199 (40%)  
  GA–AA 185 (54%) 299 (60%) 0.78 (0.59–1.04) 0.088 
 Recessive GG-GA 301 (88%) 417 (84%)  
  AA 41 (12%) 81 (16%) 0.70 (0.47–1.05) 0.087 
 Additive — — — 0.81 (0.66–0.99) 0.039 
rs10426502 Co-dominant GG 317 (93%) 431 (83%)  
  GA 23 (6%) 67 (17%) 0.47 (0.28–0.76) 0.003 
  AA 1 (1%) 0 (0%) — 0.999 
 Dominant GG 317 (93%) 431 (83%)  
  GA-AA 24 (7%) 67 (17%) 0.49 (0.30–0.79) 0.004 
 Recessive GG-GA 341 (91.90%) 498 (100%)  
  AA 1 (1%) 0 (0%) — 0.999 
 Additive — — — 0.52 (0.32–0.83) 0.007 
rs1060555 Co-dominant CC 199 (58%) 246 (49%)  
  CG 123 (36%) 218 (44%) 0.70 (0.52–0.93) 0.015 
  GG 20 (6%) 34 (7%) 0.73 (0.41–1.30) 0.285 
 Dominant CC 199 (58%) 246 (49%)  
  CG-GG 143 (42%) 252 (57%) 0.70 (0.53–0.93) 0.012 
 Recessive CC-CG 322 (94%) 464 (93%)  
  GG 20 (6%) 34 (7%) 0.85 (0.48–1.50) 0.571 
 Additive — — — 0.77 (0.61–0.97) 0.024 

P-values were calculated with wald-test. P<0.05 indicates statistical significance.

Based on age stratification, we tried to determine the effect of these variants on the risk of cervical cancer, as showed in Table 4. In accordance with our statistically significant findings of the allele model, the minor allele of rs10426502, and rs1060555 in TNFAIP8L1 played roles in reducing the risk of cervical cancer in Uygur women over 43 years old (rs10426502: OR = 0.44, 95% CI: 0.22–0.88, P=0.017; rs1060555: OR = 0.72, 95% CI: 0.53–0.97, P=0.033). Amongst Uygur women under 43 years of age, the rs9917028-‘AA’ genotype of TNFAIP8L1 reduced the risk of cervical cancer in genotype model and additive model (genotype model: OR = 0.45, 95% CI: 0.22–0.91, P=0.026; additive model: OR = 0.47, 95% CI: 0.24–0.91, P=0.025). Amongst Uygur women over 43 years of age, individuals carrying the heterozygous genotype rs1060555-‘CG’ in TNFAIP8L1 (OR = 0.40, 95% CI: 0.17–0.92, P=0.017) were less likely to suffer from cervical cancer when compared with the homozygous ‘CC’. In addition, rs10426502 and rs1060555 of TNFAIP8L1 were significantly associated with reduced risk of cervical cancer in the dominant and additive models, as well as rs9513111 of FLT1 was significantly associated with reduced risk of cervical cancer in the recessive model (P<0.05).

Table 4
Relationships between TNFAIP8L1 and FLT1 polymorphism and cervical cancer risk according to the stratification by age
SNP Model Genotype ≤43 >43 
Gene   OR (95% CI) P-value OR (95% CI) P-value 
rs9917028 Allele   
TNFAIP8L1  0.75 (0.56–1.02) 0.064 0.84 (0.64–1.12) 0.238 
 Genotype GG   
  GA 0.94 (0.61–1.43) 0.766 0.75 (0.50–1.15) 0.187 
  AA 0.45 (0.22–0.91) 0.026 0.80 (0.46–1.40) 0.434 
 Dominant GG   
  GA-AA 0.81 (0.54–1.21) 0.305 0.77 (0.52–1.13) 0.180 
 Recessive GG-GA   
  AA 0.47 (0.24–0.91) 0.025 0.92 (0.55–1.55) 0.757 
 Additive — 0.75 (0.56–1.02) 0.065 0.86 (0.66–1.13) 0.281 
rs10426502 Allele   
TNFAIP8L1  0.62 (0.33–1.19) 0.149 0.44 (0.22–0.88) 0.017 
 Genotype GG   
  GA 0.53 (0.26–1.07) 0.077 — — 
  AA — 0.999 — — 
 Dominant GG   
  GA-AA 0.57 (0.29–1.14) 0.112 0.42 (0.21–0.86) 0.017 
 Recessive GG-GA   
  AA — 0.999 — — 
 Additive — 0.63 (0.33–1.22) 0.173 0.42 (0.21–0.86) 0.017 
rs1060555 Allele   
TNFAIP8L1  0.86 (0.62–1.19) 0.355 0.72 (0.53–0.97) 0.033 
 Genotype CC   
  CG 0.94 (0.62–1.42) 0.760 0.54 (0.36–0.82) 0.003 
  GG 0.59 (0.22–1.60) 0.303 0.79 (0.38–1.64) 0.527 
 Dominant CC   
  CG-GG 0.89 (0.60–1.34) 0.583 0.58 (0.39–0.85) 0.005 
 Recessive CC-CG   
  GG 0.61 (0.23–1.62) 0.321 1.03 (0.50–2.10) 0.935 
 Additive — 0.87 (0.61–1.22) 0.406 0.71 (0.52–0.97) 0.030 
rs9513111 Allele   
FLT1  1.05 (0.78–1.41) 0.773 0.81 (0.61–1.08) 0.144 
 Genotype TT   
  TC 0.89 (0.58–1.36) 0.581 1.02 (0.68–1.54) 0.913 
  CC 1.38 (0.69–2.76) 0.359 0.55 (0.29–1.04) 0.067 
 Dominant TT   
  TC-CC 0.96 (0.64–1.44) 0.852 0.89 (0.61–1.31) 0.561 
 Recessive TT-TC   
  CC 1.47 (0.76–2.83) 0.250 0.54 (0.29–0.99) 0.049 
 Additive — 1.06 (0.78–1.45) 0.695 0.82 (0.62–1.09) 0.166 
SNP Model Genotype ≤43 >43 
Gene   OR (95% CI) P-value OR (95% CI) P-value 
rs9917028 Allele   
TNFAIP8L1  0.75 (0.56–1.02) 0.064 0.84 (0.64–1.12) 0.238 
 Genotype GG   
  GA 0.94 (0.61–1.43) 0.766 0.75 (0.50–1.15) 0.187 
  AA 0.45 (0.22–0.91) 0.026 0.80 (0.46–1.40) 0.434 
 Dominant GG   
  GA-AA 0.81 (0.54–1.21) 0.305 0.77 (0.52–1.13) 0.180 
 Recessive GG-GA   
  AA 0.47 (0.24–0.91) 0.025 0.92 (0.55–1.55) 0.757 
 Additive — 0.75 (0.56–1.02) 0.065 0.86 (0.66–1.13) 0.281 
rs10426502 Allele   
TNFAIP8L1  0.62 (0.33–1.19) 0.149 0.44 (0.22–0.88) 0.017 
 Genotype GG   
  GA 0.53 (0.26–1.07) 0.077 — — 
  AA — 0.999 — — 
 Dominant GG   
  GA-AA 0.57 (0.29–1.14) 0.112 0.42 (0.21–0.86) 0.017 
 Recessive GG-GA   
  AA — 0.999 — — 
 Additive — 0.63 (0.33–1.22) 0.173 0.42 (0.21–0.86) 0.017 
rs1060555 Allele   
TNFAIP8L1  0.86 (0.62–1.19) 0.355 0.72 (0.53–0.97) 0.033 
 Genotype CC   
  CG 0.94 (0.62–1.42) 0.760 0.54 (0.36–0.82) 0.003 
  GG 0.59 (0.22–1.60) 0.303 0.79 (0.38–1.64) 0.527 
 Dominant CC   
  CG-GG 0.89 (0.60–1.34) 0.583 0.58 (0.39–0.85) 0.005 
 Recessive CC-CG   
  GG 0.61 (0.23–1.62) 0.321 1.03 (0.50–2.10) 0.935 
 Additive — 0.87 (0.61–1.22) 0.406 0.71 (0.52–0.97) 0.030 
rs9513111 Allele   
FLT1  1.05 (0.78–1.41) 0.773 0.81 (0.61–1.08) 0.144 
 Genotype TT   
  TC 0.89 (0.58–1.36) 0.581 1.02 (0.68–1.54) 0.913 
  CC 1.38 (0.69–2.76) 0.359 0.55 (0.29–1.04) 0.067 
 Dominant TT   
  TC-CC 0.96 (0.64–1.44) 0.852 0.89 (0.61–1.31) 0.561 
 Recessive TT-TC   
  CC 1.47 (0.76–2.83) 0.250 0.54 (0.29–0.99) 0.049 
 Additive — 1.06 (0.78–1.45) 0.695 0.82 (0.62–1.09) 0.166 

P-values were calculated with wald-test. P<0.05 indicates statistical significance.

Haplotype analyses and false-positive report

Furthermore, haplotype analyses were performed and a LD block was found in the TNFAIP8L1 gene, formed by rs10426502-rs1060555 (Figure 1). The haplotypes AG (OR = 1.94, 95% CI: 0.20–3.13, P=0.007) and GC (OR = 1.30, 95% CI: 1.03–1.63, P=0.026) were significantly associated with an increased risk for cervical cancer in Uyghur population (Table 5).

LD block construction

Figure 1
LD block construction

Block rs10426502-rs1060555 was detected in TNFAIP8L1. (D′ = 1.0, r2 = 0.159).

Figure 1
LD block construction

Block rs10426502-rs1060555 was detected in TNFAIP8L1. (D′ = 1.0, r2 = 0.159).

Table 5
Haplotype frequencies of TNFAIP8L1 SNPs and the association with cervical cancer risk
Gene Haplotype Haplotype frequency OR (95% CI) P-value 
  Case Control   
 rs10426502|rs1060555     
TNFAIP8L1 _AG_ 0.9633 0.933 1.94 (1.20–3.13) 0.007 
 _GG_ 0.7977 0.780 1.11 (0.87–1.42) 0.386 
 _GC_ 0.761 0.713 1.30 (1.03–1.63) 0.026 
Gene Haplotype Haplotype frequency OR (95% CI) P-value 
  Case Control   
 rs10426502|rs1060555     
TNFAIP8L1 _AG_ 0.9633 0.933 1.94 (1.20–3.13) 0.007 
 _GG_ 0.7977 0.780 1.11 (0.87–1.42) 0.386 
 _GC_ 0.761 0.713 1.30 (1.03–1.63) 0.026 

P-values were calculated with Pearson’s Chi-square tests. P<0.05 indicates statistical significance.

FPRP values at different prior probability levels were calculated to evaluate significance results (Supplementary Table S1). For a prior probability of 0.1, the FPRP values were less than 0.2 for the associations of rs10426502, rs1060555 alleles, and genotypes with decreased risk of cervical cancer (rs10426502: allele-A: 0.094; GA: 0.045; GA-AA: 0.062. rs1060555: allele-G: 0.187 CG: 0.112; CG-GG: 0.112). And the FPRP values were also less than 0.2 for the associations of haplotypes with increased risk of cervical cancer (rs10426502|rs1060555: AG: 0.098; GC: 0.172). In contrast, we observed greater FPRP values for the significant associations between rs9917028 and cervical cancer risk, suggesting some possible bias in the findings due to limited sample size, which need further validation in larger sample.

Discussion

In the present study, selected SNPs in TNFAIP8L1 and FLT1 and their association with cervical cancer risk were investigated for the first time. Three TNFAIP8L1 variants and one variant of FLT1 were associated with reduced cervical cancer susceptibility amongst females from Xinjiang Uyghur Autonomous Region of China.

TNFAIP8L1 plays an important role in malignant tumor. Immunohistochemistry and western blot analysis showed that TNFAIP8L1-specific antibodies were expressed in both male and female reproductive organs and germ cell tissues of mice. In addition, elevated TNFAIP8L1 mRNA levels were detected in human gynecological cancer cells, including cervical and ovarian cancer cells [22]. As a regulator of cell death, TNFAIP8L1 plays an indispensable role in tumor cell necrosis. Studies have shown that TNFAIP8L1 can induce hepatocellular carcinoma cell apoptosis by interacting with Rac1 [23,24]. In support of this view, TNFAIP8L1 expression was down-regulated in hepatocellular carcinoma tissues compared with adjacent normal tissues. In addition, TNFAIP8L1 was found to significantly reduce tumor burden in vivo and cell proliferation in vitro. Similar to hepatocellular carcinoma, down-regulation of TNFAIP8L1 has also been observed in lung cancer [12]. Our study showed that these three candidate SNPs (rs9917028, rs10426502, and rs1060555) were risk factors for cervical cancer amongst Uygur women in Xinjiang. We hypothesized that these three SNPs might affect the expression and function of TNFAIP8L1 in the development of cervical cancer.

The development and metastasis of primary tumors require angiogenesis, growth, movement, and detachment. Therefore, angiogenesis, the formation of blood vessels, has been identified as the key to the occurrence and development of malignant tumors. VEGF is considered as an important regulator of angiogenesis [15,25]. VEGF receptor 1 (FLT1) is one of the targets of VEGF, which can regulate the growth and migration of endothelial cells and regulate angiogenesis. Recent studies have shown that FLT1 present and functional in different human cancer cells, and VEGF activation of FLT1 can be involved in the process of tumor progression [26–28]. FLT1 has been shown to be a marker of angiogenesis in endometrial cancer, but its function as a predictor has not been determined. Daniel et al. believed that the FLT1-snp allele might be an important risk factor for angiogenesis-related disease [15]. Currently, VEGFA and its receptor-FLT1, as pro-angiogenic growth factor, are related to the promotion of angiogenesis, vascular permeability, cell migration and gene expression, and have become the targets of anticancer treatment [29]. Our findings suggest that FLT1 rs9513111 may serve as a clinical predictor in Uygur women over 43 years of age. Our experimental results should be further verified in a larger sample size

There are still some limitations in the present study. First, this work detected the effect of SNP of two genes on cervical cancer susceptibility, and the specific molecular mechanism needs to be further explored. Second, lack of other clinical information. Therefore, further studies with a larger sample size and functional experiments are required.

Our study validated the relationship between TNFAIP8L1 and FLT1 variations and the risk of cervical cancer in Uygur women. These SNPs (rs9917028, rs10426502, rs1060555, and rs9513111) are expected to be new targets for early diagnosis and prevention of cervical cancer in Uygur women.

Acknowledgments

We appreciated the participants involved in the present study and the medical staff from the People’s Hospital of Xinjiang Uygur Autonomous Region for sample collection.

Ethics statement

Our study was approved by the ethics committee of the People’s Hospital of Xinjiang Uygur Autonomous Region. All procedures were carried out in accordance with the ethical standards of the ethics committee and with the 1964 Helsinki declaration, and its later amendments. Informed consent was signed by each subject prior to blood samples collection.

Author Contribution

M.N. and L.W. conceived and designed the experiments. L.H., S.H., and C.M. performed the experiments. L.H. analyzed the data. S.H. drafted the manuscript. All authors read and approved the final manuscript.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

Xinjiang Tianshan Youth Program [grant number 2017Q008].

Abbreviations

     
  • FIGO

    International Federation of Gynecology and Obstetrics

  •  
  • FPRP

    false-positive report probability

  •  
  • HPV

    high-risk human papillomavirus

  •  
  • HWE

    Hardy-Weinberg equilibrium

  •  
  • LD

    linkage disequilibrium

  •  
  • MAF

    minor allele frequency

  •  
  • TNF

    tumor necrosis factor

  •  
  • VEGFR

    vascular endothelial growth factor receptor

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Supplementary data