The Impact of BCL11A Polymorphisms on Endometrial Cancer Risk Among Chinese Han Females.

Endometrial carcinoma (EC) occurs in the endometrium characterized by a thickening or mass of the endometrium. 1 EC is one of the most common malignant gynecological tumors with an estimated age-standardized incidence risk of 13.1. 2 In China, EC ranks fourth among female cancers in both incidence and mortality, and the incidence increases steadily with decreasing age at diagnosis. 3 , 4 Risk factors for EC are unopposed estrogen, family history of EC, age, excess body weight, diabetes, hypertension, and Lynch syndrome. 5 Although several risk factors have been identified, endometrial carcinogenesis remains poorly understood. Genetic variants such as single nucleotide polymorphisms (SNPs) are known to play important roles in cancer predisposition. 6–8 Many SNPs related to EC susceptibility have been identified in previous genome-wide association studies (GWAS), but many polymorphic loci have not been reported. The B-cell lymphoma/leukemia 11A ( BCL11A ) gene encodes a C2H2 type zinc finger protein transcription factor, and the role of BCL11A in tumors appears to be contextual. In some cancers, it has oncogenic effects, 9 while in some cancers, it may act as a tumor suppressor. 10 For example, BCL11A , as an oncogene, promoted tumor formation, cancer cell mobility and epithelial-mesenchymal transition by activating the Wnt/β-catenin signaling pathway in breast cancer carcinogenesis. 11 Downregulation of BCL11A protein in colorectal cancer cells was related to enhanced radioresistance, supporting BCL11A as a tumor-suppressor role. 12 The expression of BCL11A in ER-/PR-EC was higher than that in normal endometrium, atypical hyperplasia endometrium, and ER+/PR+EC. 13 Moreover, the expression of BCL11A in EC was associated with age, menopause, EC classification, para-aortic lymph node metastasis, tumor differentiation, histological type, ER/PR expression and p53 expression. 13 A targeted next-generation sequencing study displayed that BCL11A mutations were associated with endometriosis and tumor lesions. 14 In EC, consistent with an anti-cancer function, credible variants in BCL11A were associated with reduced BCL11A expression in endometrial tumors. 15 A GWAS study in an Australian population found that BCL11A polymorphism was associated with EC risk. 16 Until now, there were no studies reported the effect of BCL11A genetic variants on EC predisposition in Chinese population. Here, six SNPs (rs7581162, rs10189857, rs1427407, rs766432, rs6729815, and rs2556378) in BCL11A with a minor allele frequency (MAF) > 0.05 in dbSNP database ( http://www.ncbi.nlm.nih.gov/projects/SNP ) and the call rate >95% in our study population were randomly selected and genotyped to evaluate the potential association between BCL11A variants and EC risk among the Chinese Han women at single-SNP or combined SNPs interface.

In the study, we enrolled 509 EC patients (54.94 ± 8.85 years) and 506 healthy controls (54.61 ± 9.07 years), as shown in Table 1 . Age and BMI distributions were not significantly different between cases and controls ( p = 0.553, and p = 0.669, respectively). There were significant difference in the levels of CEA, AFP, CA199, and CA125 between two groups ( p 99.5%, as summarized in Table 2 . The genotype distribution of rs2556378 polymorphism was not inconsistent with HWE ( p = 0.001), therefore, this variant was excluded from subsequent studies. The MAFs of rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 in EC patients were higher than those in healthy controls. BCL11A rs7581162-T (OR = 1.29, 95% CI: 1.06–1.57, p = 0.012), rs10189857-A (OR = 1.26, 95% CI: 1.03–1.54, p = 0.028), rs1427407-T (OR = 1.30, 95% CI: 1.05–1.60, p = 0.015), rs766432-C (OR = 1.27, 95% CI: 1.03–1.56, p = 0.025), and rs6729815-T (OR = 1.32, 95% CI: 1.07–1.62, p = 0.008) was associated with the increased risk of EC. The significance of rs6729815 still existed after Bonferroni correction. The potential function of these polymorphisms by HaploReg v4.1 database and RegulomeDB database was displayed in Table 2 . Based on GTEx Portal database, the genotypes of rs1427407 ( p = 0.00026) was related to the mRNA expression of BCL11A in cell ( Suppl_Figure 1 ). Table 2 Basic Characteristics and Allele Model About Candidate SNPs in the BCL11A Gene SNPs ID Chr:Position Allele (Minor/Major) Call Rate O(HET) E(HET) p a -value for HWE MAF Allele Model Haploreg Regulome DB Case Control OR (95% CI) p b rs7581162 2:60,477,349 T/A 99.9% 0.384 0.362 0.219 0.287 0.238 1.29 (1.06–1.57) 0.012 Promoter histone marks, Enhancer histone marks, Motifs changed, GRASP QTLhits TF binding or DNase peak rs10189857 2:60,486,100 A/G 99.8% 0.345 0.346 1.000 0.264 0.222 1.26 (1.03–1.54) 0.028 DNAse, Motifs changed, NHGRI/EBI GWAS hits TF binding or DNase peak rs1427407 2:60,490,908 T/G 99.8% 0.310 0.321 0.486 0.246 0.200 1.30 (1.05–1.60) 0.015 Enhancer histone marks, Proteins bound, Motifs changed, NHGRI/EBI GWAS hits, GRASP QTL hits TF binding or DNase peak rs766432 2:60,492,835 C/A 100% 0.338 0.335 0.895 0.254 0.212 1.27 (1.03–1.56) 0.025 Enhancer histone marks, DNAse, Motifs changed, NHGRI/EBI GWAS hits, GRASP QTL hits TF binding + any motif + DNase Footprint + DNase peak rs6729815 2:60,496,537 T/C 100% 0.330 0.335 0.790 0.262 0.212 1.32 (1.07–1.62) 0.008* Enhancer histone marks, Motifs Changed, NHGRI/EBI GWAS hits TF binding + DNase peak rs2556378 2:60,535,367 T/G 100% 0.405 0.352 0.001 / / / / Promoter histone marks, Enhancer histone marks, DNAse, NHGRI/EBI GWAS hits, GRASP QTL hits eQTL + TF binding/DNase peak Notes : p a -values for the HWE test were calculated using goodness of fit χ 2 test. p b were calculated using by Fisher’s exact test. Bold indicated that p < 0.05 meant the data was statistically significant. * p indicate that after Bonferroni correction ( p < 0.05/5) means the data is statistically significant. Abbreviations : SNP, single-nucleotide polymorphism; MAF, minor allele frequency; O(HET), observed heterozygosity; E(HET), expected heterozygosity; HWE, Hardy–Weinberg equilibrium. Basic Characteristics and Allele Model About Candidate SNPs in the BCL11A Gene Notes : p a -values for the HWE test were calculated using goodness of fit χ 2 test. p b were calculated using by Fisher’s exact test. Bold indicated that p < 0.05 meant the data was statistically significant. * p indicate that after Bonferroni correction ( p < 0.05/5) means the data is statistically significant. Abbreviations : SNP, single-nucleotide polymorphism; MAF, minor allele frequency; O(HET), observed heterozygosity; E(HET), expected heterozygosity; HWE, Hardy–Weinberg equilibrium. Multiple genetic models were used to evaluate the relationship of BCL11A polymorphisms to EC predisposition. After adjusting for age, rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 were associated with higher EC susceptibility ( Table 3 ). Specifically, the TT genotype of rs7581162 (genotype: TT vs AA, OR = 2.16, 95% CI: 1.27–3.69, p = 0.005; recessive: TT vs AA-AT, OR = 2.03, 95% CI: 1.20–3.42, p = 0.008; and additive: AA+AT+TT, OR = 1.30, 95% CI: 1.07–1.59, p = 0.010) and AA genotype of rs10189857 (genotype: AA vs GG, OR = 1.78, 95% CI: 1.05–3.01, p = 0.033; and additive: GG+GA+AA, OR = 1.26, 95% CI: 1.03–1.54, p = 0.027) contributed to increased EC risk. Rs1427407 had a risk-effect for EC under the genotype (GT vs GG: OR = 1.33, 95% CI: 1.02–1.74, p = 0.033), dominant (GT-TT vs GG: OR = 1.37, 95% CI: 1.06–1.76, p = 0.016) and additive (GG+GT+TT: OR = 1.30, 95% CI: 1.05–1.60, p = 0.014) models. Rs766432 was associated with increased EC predisposition (dominant: AC-CC vs AA, OR = 1.32, 95% CI: 1.03–1.70, p = 0.029; and additive: AA+AC+CC, OR = 1.28, 95% CI: 1.04–1.57, p = 0.022). Moreover, rs6729815 might have higher risk for the occurrence EC in the genotype (TT vs CC: OR = 1.94, 95% CI: 1.14–3.29, p = 0.015), dominant (CT-TT vs CC: OR = 1.32, 95% CI: 1.03–1.70, p = 0.030), recessive (TT vs CC-CT: OR = 1.79, 95% CI: 1.06–3.02, p = 0.028) and additive (TT+CC+CT:OR = 1.31, 95% CI: 1.07–1.61, p = 0.009) models. The significance of rs7581162 in the genotype and recessive models and rs6729815 in the additive model still existed after Bonferroni correction. Table 3 The Effect of BCL11A SNPs on the Susceptibility to Endometrial Cancer SNPs ID Model Genotype Case Control Crude Analysis Adjusted by Age OR (95% CI) p OR (95% CI) p rs7581162 Genotype AA 261 288 1 1 AT 204 194 1.16 (0.90–1.50) 0.259 1.16 (0.90–1.50) 0.257 TT 44 23 2.11 (1.24–3.59) 0.006* 2.16 (1.27–3.69) 0.005* Dominant AA 261 288 1 1 AT-TT 248 217 1.26 (0.98–1.62) 0.066 1.27 (0.99–1.62) 0.063 Recessive AA-AT 465 482 1 1 TT 44 23 1.98 (1.18–3.34) 0.010 2.03 (1.20–3.42) 0.008* Log-additive — — — 1.30 (1.06–1.59) 0.011 1.30 (1.07–1.59) 0.010 rs10189857 Genotype GG 280 305 1 1 GA 189 174 1.18 (0.91–1.54) 0.209 1.18 (0.91–1.54) 0.206 AA 40 25 1.74 (1.03–2.95) 0.038 1.78 (1.05–3.01) 0.033 Dominant GG 280 305 1 1 GA-AA 229 199 1.25 (0.98–1.61) 0.076 1.26 (0.98–1.62) 0.072 Recessive GG-GA 469 479 1 1 AA 40 25 1.63 (0.98–2.74) 0.062 1.66 (0.99–2.79) 0.054 Log-additive — — — 1.25 (1.02–1.53) 0.030 1.26 (1.03–1.54) 0.027 rs1427407 Genotype GG 291 325 1 1 GT 186 156 1.33 (1.02–1.74) 0.034 1.33 (1.02–1.74) 0.033 TT 32 23 1.55 (0.89–2.72) 0.122 1.59 (0.91–2.78) 0.107 Dominant GG 291 325 1 1 GT-TT 218 179 1.36 (1.06–1.75) 0.017 1.37 (1.06–1.76) 0.016 Recessive GG-GT 477 481 1 1 TT 32 23 1.40 (0.81–2.43) 0.228 1.43 (0.82–2.49) 0.204 Log-additive — — — 1.29 (1.05–1.59) 0.016 1.30 (1.05–1.60) 0.014 rs766432 Genotype AA 281 313 1 1 AC 197 171 1.28 (0.99–1.67) 0.061 1.29 (0.99–1.67) 0.058 CC 31 22 1.57 (0.89–2.77) 0.121 1.60 (0.90–2.84) 0.107 Dominant AA 281 313 1 1 AC-CC 228 193 1.32 (1.02–1.69) 0.032 1.32 (1.03–1.70) 0.029 Recessive AA-AC 478 484 1 1 CC 31 22 1.43 (0.81–2.50) 0.214 1.45 (0.83–2.55) 0.194 Log-additive — — — 1.27 (1.03–1.56) 0.025 1.28 (1.04–1.57) 0.022 rs6729815 Genotype CC 283 315 1 1 CT 185 167 1.23 (0.95–1.61) 0.120 1.23 (0.95–1.61) 0.119 TT 41 24 1.90 (1.12–3.23) 0.017 1.94 (1.14–3.29) 0.015 Dominant CC 283 315 1 1 CT-TT 226 191 1.32 (1.03–1.69) 0.031 1.32 (1.03–1.70) 0.030 Recessive CC-CT 468 482 1 1 TT 41 24 1.76 (1.05–2.96) 0.033 1.79 (1.06–3.02) 0.028 Log-additive — — — 1.30 (1.07–1.60) 0.010 1.31 (1.07–1.61) 0.009* Notes : p values were calculated by logistic regression analysis without or with adjustments for age. Bold indicated that p < 0.05 meant the data was statistically significant. * p indicate that after Bonferroni correction ( p < 0.05/5) means the data is statistically significant. Abbreviations : SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval. The Effect of BCL11A SNPs on the Susceptibility to Endometrial Cancer Notes : p values were calculated by logistic regression analysis without or with adjustments for age. Bold indicated that p < 0.05 meant the data was statistically significant. * p indicate that after Bonferroni correction ( p < 0.05/5) means the data is statistically significant. Abbreviations : SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval. We further explored the stratified analysis by age and BMI for the relationships of BCL11A variants with EC risk ( Table 4 ). In the subjects with age > 55 years/BMI ≥ 24 kg/m 2 , no significant association between BCL11A polymorphism with the susceptibility to EC occurrence was observed. Among those aged 55 or younger and those had BMI < 24 kg/m 2 , we found that rs7581162-T, rs10189857-A, rs1427407-T, rs766432-C, and rs6729815-T were risk factors for the development of EC. Table 4 Stratification Analysis by Age and BMI for the Effect of BCL11A SNPs on the Susceptibility to Endometrial Cancer SNPs ID Model Genotype Case Control OR (95% CI) p Case Control OR (95% CI) p Age > 55 years ≤ 55 years rs7581162 Allele A 333 326 1 393 444 1 T 113 96 1.15 (0.84–1.57) 0.373 179 144 1.40 (1.09–1.82) 0.010 Genotype AA 117 123 1 144 165 1 AT 99 80 1.30 (0.88–1.92) 0.185 105 114 1.06 (0.75–1.50) 0.746 TT 7 8 0.92 (0.32–2.61) 0.872 37 15 2.84 (1.50–5.40) 0.001* Dominant AA 117 123 1 144 165 1 AT-TT 106 88 1.27 (0.87–1.85) 0.224 142 129 1.27 (0.91–1.76) 0.158 Recessive AA-AT 216 203 1 249 279 1 TT 7 8 0.82 (0.29–2.30) 0.707 37 15 2.78 (1.49–5.18) 0.001* Log-additive — — — 1.18 (0.84–1.64) 0.343 — — 1.38 (1.08–1.78) 0.012 rs10189857 Allele G 344 331 1 405 453 1 A 102 89 1.10 (0.80–1.52) 0.551 167 135 1.38 (1.06–1.80) 0.016 Genotype GG 128 131 1 152 174 1 GA 88 69 1.31 (0.88–1.94) 0.190 101 105 1.10 (0.78–1.56) 0.588 AA 7 10 0.72 (0.26–1.94) 0.512 33 15 2.53 (1.33–4.85) 0.005* Dominant GG 128 131 1 152 174 1 GA-AA 95 79 1.23 (0.84–1.81) 0.291 134 120 1.28 (0.92–1.78) 0.142 Recessive GG-GA 216 200 1 253 279 1 AA 7 10 0.65 (0.24–1.74) 0.389 33 15 2.44 (1.29–4.60) 0.006* Log-additive — — — 1.11 (0.80–1.55) 0.540 — — 1.36 (1.05–1.75) 0.020 rs1427407 Allele G 353 342 1 415 464 1 T 93 80 1.13 (0.81–1.57) 0.485 157 122 1.44 (1.10–1.89) 0.008* Genotype GG 134 140 1 157 185 1 GT 85 62 1.43 (0.96–2.15) 0.081 101 94 1.27 (0.89–1.80) 0.187 TT 4 9 0.46 (0.14–1.54) 0.209 28 14 2.37 (1.21–4.67) 0.012 Dominant GG 134 140 1 157 185 1 GT-TT 89 71 1.31 (0.89–1.94) 0.177 129 108 1.41 (1.01–1.97) 0.043 Recessive GG-GT 219 202 1 258 279 1 TT 4 9 0.41 (0.12–1.35) 0.142 28 14 2.18 (1.12–4.23) 0.022 Log-additive — — — 1.14 (0.80–1.60) 0.472 — — 1.41 (1.08–1.83) 0.011 rs766432 Allele A 350 338 1 409 459 1 C 96 86 1.08 (0.78–1.50) 0.653 163 129 1.42 (1.09–1.85) 0.010 Genotype AA 130 135 1 151 178 1 AC 90 68 1.37 (0.92–2.04) 0.118 107 103 1.23 (0.87–1.74) 0.248 CC 3 9 0.34 (0.09–1.30) 0.114 28 13 2.55 (1.27–5.09) 0.008* Dominant AA 130 135 1 151 178 1 AC-CC 93 77 1.25 (0.85–1.84) 0.254 135 116 1.37 (0.99–1.91) 0.059 Recessive AA-AC 220 203 1 258 281 1 CC 3 9 0.30 (0.08–1.14) 0.078 28 13 2.35 (1.19–4.64) 0.014 Log-additive — — — 1.09 (0.77–1.53) 0.644 — — 1.40 (1.08–1.83) 0.012 rs6729815 Allele C 347 338 1 404 459 1 T 99 86 1.12 (0.81–1.55) 0.490 168 129 1.48 (1.14–1.93) 0.004* Genotype CC 131 135 1 152 180 1 CT 85 68 1.29 (0.87–1.92) 0.212 100 99 1.20 (0.84–1.70) 0.316 TT 7 9 0.80 (0.29–2.21) 0.668 34 15 2.70 (1.42–5.14) 0.003* Dominant CC 131 135 1 152 180 1 CT-TT 92 77 1.23 (0.84–1.81) 0.289 134 114 1.39 (1.00–1.94) 0.050 Recessive CC-CT 216 203 1 252 279 1 TT 7 9 0.73 (0.27–2.00) 0.540 34 15 2.52 (1.34–4.74) 0.004* Log-additive — — — 1.13 (0.81–1.58) 0.477 — — 1.43 (1.11–1.85) 0.006* BMI ≥ 24 kg/m 2 < 24 kg/m 2 rs7581162 Allele A 293 287 1 433 483 1 T 105 93 1.11 (0.80–1.53) 0.541 187 147 1.42 (1.10–1.83) 0.006* Genotype AA 109 105 1 152 183 1 AT 75 77 0.94 (0.62–1.42) 0.756 129 117 1.33 (0.95–1.85) 0.092 TT 15 8 1.77 (0.71–4.37) 0.218 29 15 2.41 (1.24–4.68) 0.009* Dominant AA 109 105 1 152 183 1 AT-TT 90 85 1.01 (0.68–1.51) 0.948 158 132 1.45 (1.06–1.99) 0.022 Recessive AA-AT 184 182 1 281 300 1 TT 15 8 1.82 (0.75–4.41) 0.188 29 15 2.14 (1.12–4.08) 0.022 Log-additive — — — 1.10 (0.79–1.53) 0.569 — — 1.44 (1.11–1.86) 0.005* rs10189857 Allele G 296 289 1 453 495 1 A 102 91 1.09 (0.79–1.52) 0.587 167 133 1.37 (1.06–1.78) 0.017 Genotype GG 114 108 1 166 197 1 GA 68 73 0.88 (0.58–1.35) 0.564 121 101 1.43 (1.02–2.00) 0.037 AA 17 9 1.76 (0.75–4.14) 0.196 23 16 1.75 (0.89–3.42) 0.104 Dominant GG 114 108 1 166 197 1 GA-AA 85 82 0.98 (0.65–1.46) 0.917 144 117 1.47 (1.07–2.03) 0.018 Recessive GG-GA 182 181 1 287 298 1 AA 17 9 1.85 (0.80–4.28) 0.153 23 16 1.52 (0.79–2.95) 0.212 Log-additive — — — 1.09 (0.79–1.50) 0.620 — — 1.38 (1.06–1.78) 0.016 rs1427407 Allele G 304 299 1 464 507 1 T 94 81 1.14 (0.81–1.60) 0.442 156 121 1.41 (1.08–1.84) 0.012 Genotype GG 118 117 1 173 208 1 GT 68 65 1.04 (0.68–1.59) 0.867 118 91 1.57 (1.12–2.21) 0.009* TT 13 8 1.56 (0.62–3.95) 0.344 19 15 1.57 (0.77–3.18) 0.214 Dominant GG 118 117 1 173 208 1 GT-TT 81 73 1.09 (0.73–1.64) 0.664 137 106 1.57 (1.13–2.17) 0.007* Recessive GG-GT 186 182 1 291 299 1 TT 13 8 1.54 (0.62–3.85) 0.352 19 15 1.33 (0.66–2.68) 0.419 Log-additive — — — 1.13 (0.81–1.58) 0.478 — — 1.41 (1.08–1.84) 0.012 rs766432 Allele A 303 298 1 156 499 1 C 95 84 1.11 (0.80–1.55) 0.533 164 131 1.37 (1.05–1.78) 0.019 Genotype AA 71 70 1 165 199 1 AC 12 7 1.00 (0.65–1.52) 0.985 126 101 1.52 (1.09–2.12) 0.014 CC 116 114 1.64 (0.62–4.34) 0.321 19 15 1.58 (0.78–3.21) 0.208 Dominant AA 83 77 1 165 199 1 AC-CC 187 184 1.05 (0.70–1.58) 0.799 145 116 1.53 (1.11–2.10) 0.010 Recessive AA-AC 12 7 1 291 300 1 CC — — 1.64 (0.63–4.29) 0.313 19 15 1.34 (0.67–2.69) 0.411 Log-additive — 71 70 1.11 (0.79–1.55) 0.559 — — 1.39 (1.07–1.82) 0.015 rs6729815 Allele C 300 193 1 451 504 1 T 98 89 1.08 (0.77–1.49) 0.665 169 126 1.50 (1.15–1.95) 0.003* Genotype CC 117 110 1 166 205 1 CT 66 73 0.85 (0.56–1.30) 0.456 119 94 1.57 (1.12–2.21) 0.009* TT 16 8 1.84 (0.76–4.50) 0.179 25 16 1.99 (1.03–3.87) 0.042 Dominant CC 117 110 1 166 205 1 CT-TT 82 81 0.95 (0.63–1.42) 0.802 144 110 1.63 (1.18–2.26) 0.003* Recessive CC-CT 183 183 1 285 299 1 TT 16 8 1.96 (0.81–4.72) 0.134 25 16 1.68 (0.88–3.23) 0.116 Log-additive — — — 1.07 (0.77–1.48) 0.694 — — 1.49 (1.15–1.93) 0.003* Notes : p values were calculated by Fisher’s exact test or logistic regression analysis with adjustments for age. Bold indicated that p < 0.05 meant the data was statistically significant. * p indicate that after Bonferroni correction ( p < 0.05/5) means the data is statistically significant. Abbreviations : SNP, single nucleotide polymorphism; BMI, body mass index; OR, odds ratio; 95% CI, 95% confidence interval. Stratification Analysis by Age and BMI for the Effect of BCL11A SNPs on the Susceptibility to Endometrial Cancer Notes : p values were calculated by Fisher’s exact test or logistic regression analysis with adjustments for age. Bold indicated that p < 0.05 meant the data was statistically significant. * p indicate that after Bonferroni correction ( p < 0.05/5) means the data is statistically significant. Abbreviations : SNP, single nucleotide polymorphism; BMI, body mass index; OR, odds ratio; 95% CI, 95% confidence interval. The results of age stratification (age ≤ 55 years) were as follows: in the allele (rs7581162, OR = 1.40, p = 0.010; rs10189857, OR = 1.38, p = 0.016; rs1427407, OR = 1.44, p = 0.008; rs766432, OR = 1.42, p = 0.010; and rs6729815, OR = 1.48, p = 0.004, respectively), genotype (rs7581162, OR = 2.84, p = 0.001; rs10189857, OR = 2.53, p = 0.005; rs1427407, OR = 2.37, p = 0.012; rs766432, OR = 2.55, p = 0.008; and rs6729815, OR = 2.70, p = 0.003, respectively), dominant (rs1427407, OR = 1.41, p = 0.043), recessive (rs7581162, OR = 2.78, p = 0.001; rs10189857, OR = 2.44, p = 0.006; rs1427407, OR = 2.18, p = 0.022; rs766432, OR = 2.35, p = 0.014; rs6729815, OR = 2.52, p = 0.004, respectively) and additive (rs7581162, OR = 1.38, p = 0.012; rs10189857, OR = 1.36, p = 0.020; rs1427407, OR = 1.41, p = 0.011; rs766432, OR = 1.40, p = 0.012; rs6729815, OR = 1.43, p = 0.006, respectively) models. The significance of rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 among those aged 55 or younger still existed after Bonferroni correction. The associated results of BMI stratification (BMI < 24 kg/m 2 ) were as follows: rs7581162 in the allele (OR = 1.42, p = 0.006), genotype (OR = 2.41, p = 0.009), dominant (OR = 1.45, p = 0.022), recessive (OR = 2.14, p = 0.022), and additive (OR = 1.44, p = 0.005) models; rs10189857 in the allele (OR = 1.37, p = 0.017), genotype (OR = 1.43, p = 0.037), dominant (OR = 1.47, p = 0.018), and additive (OR = 1.38, p = 0.016) models; rs1427407 in the allele (OR = 1.41, p = 0.012), genotype (OR = 1.57, p = 0.009), dominant (OR = 1.57, p = 0.007), and additive (OR = 1.41, p = 0.012) models; rs766432 in the allele (OR = 1.37, p = 0.019), genotype (OR = 1.52, p = 0.014), dominant (OR = 1.53, p = 0.010), and additive (OR = 1.39, p = 0.015) models; rs6729815 in the allele (OR = 1.50, p = 0.003), genotype (OR = 1.57, p = 0.009, and OR = 1.99, p = 0.042), dominant (OR = 1.63, p = 0.003), and additive (OR = 1.49, p = 0.003) models. The significance of rs7581162, rs1427407, and rs6729815 among those with BMI < 24 kg/m 2 still existed after Bonferroni correction. The association of these SNPs with the stage of EC patients was assessed ( Suppl_Table 3 ). However, no significant association was detected between BCL11A SNPs and at age in EC patients. Moreover, haplotype analysis was performed to estimate the association between BCL11A haplotypes and EC susceptibility. As shown in Figure 1 , rs10189857 and rs1427407 are in linkage disequilibrium. Haplotype analysis revealed that A rs10189857 T rs1427407 (adjusted OR = 1.30, 95% CI: 1.05–1.60, p = 0.016) and G rs10189857 G rs1427407 (adjusted OR = 1.26, 95% CI: 1.03–1.54, p = 0.023) haplotypes were related to the increased EC risk ( Suppl_Table 2 ). Furthermore, AT (adjusted OR = 1.42, 95% CI: 1.09–1.85, p = 0.010) and GG (adjusted OR = 1.36, 95% CI: 1.05–1.75, p = 0.019) haplotypes also conferred higher EC predisposition among subjects with age ≤ 55 years. Figure 1 LD plots of six SNPs in the BCL11A gene. The number in the diamond indicates the D’ value of pairwise LD between SNPs. LD plots of six SNPs in the BCL11A gene. The number in the diamond indicates the D’ value of pairwise LD between SNPs. MDR analysis was applied to explore the effect of BCL11A SNP-SNP interaction on EC risk. The dendrogram ( Figure 2A ) and Fruchterman Reingold ( Figure 2B ) showed that the BCL11A SNP-SNP interaction had a strong redundant effect. The best single-locus and multi-locus models of BCL11A SNPs for EC susceptibility were summarized in Table 5 . In the single-locus model, rs1427407 was the most influential attributor for EC risk (testing accuracy = 0.5356, cross-validation consistency (CVC) = 10/10). In the multi-locus model, the best combination was a two-locus model containing rs7581162 and rs766432 (testing accuracy= 0.5336, CVC= 5/10). Table 5. SNP–SNP Interaction Models of the BCL11A Gene the Predisposition of Endometrial Cancer Model Training Bal. Acc. Testing Bal. Acc. CVC OR (95% CI) p rs1427407 0.5356 0.5356 10/10 1.35 (1.05–1.74) 0.0277 rs7581162, rs766432 0.5412 0.5336 5/10 1.42 (1.08–1.86) 0.0106 rs7581162, rs10189857, rs766432 0.5447 0.5287 4/10 1.44 (1.12–1.86) 0.0047 rs7581162, rs10189857, rs1427407, rs766432 0.5470 0.5208 4/10 1.47 (1.14–1.90) 0.0029 rs7581162, rs10189857, rs1427407, rs766432, rs6729815 0.5479 0.5178 10/10 1.46 (1.14–1.88) 0.0029 Notes : p values were calculated using χ 2 tests. Bold indicated that p < 0.05 meant the data was statistically significant. Abbreviations : MDR, multifactor dimensionality reduction; Bal. Acc., balanced accuracy; CVC, cross-validation consistency; OR, odds ratio; CI, confidence interval. Figure 2 The dendrogram ( A ) and Fruchterman Reingold ( B ) of BCL11A SNP-SNP interaction for EC risk. ( A ) Short connections among nodes represent stronger redundant interactions. Green and blue color indicated weak interactions. ( B ) This graphical model describes the percent entropy explained by each SNP. Values in nodes represent the information gains of individual attribute (main effects). Values between nodes are information gains of each pair of attributes (interaction effects). Positive percent entropy indicates synergy whereas the negative percent entropy indicates redundancy. SNP–SNP Interaction Models of the BCL11A Gene the Predisposition of Endometrial Cancer Notes : p values were calculated using χ 2 tests. Bold indicated that p < 0.05 meant the data was statistically significant. Abbreviations : MDR, multifactor dimensionality reduction; Bal. Acc., balanced accuracy; CVC, cross-validation consistency; OR, odds ratio; CI, confidence interval. The dendrogram ( A ) and Fruchterman Reingold ( B ) of BCL11A SNP-SNP interaction for EC risk. ( A ) Short connections among nodes represent stronger redundant interactions. Green and blue color indicated weak interactions. ( B ) This graphical model describes the percent entropy explained by each SNP. Values in nodes represent the information gains of individual attribute (main effects). Values between nodes are information gains of each pair of attributes (interaction effects). Positive percent entropy indicates synergy whereas the negative percent entropy indicates redundancy. FPRP analysis was carried out to interrogate whether the significant findings were deserving attention ( Suppl_Table 4 ). At the prior probability level of 0.1, the significant association for rs7581162, rs10189857, rs1427407, rs766432 and rs6729815 remained noteworthy in the overall analysis (FPRP 85% under the allele model. In the subgroup at age < 55 years, significant findings remained noteworthy for these variants. The relationship of BCL11A SNPs with CEA, AFP, CA199, and CA125 among EC patients/healthy controls were assessed, as displayed in Suppl_Table 5 . However, no statistically association was observed.

A total of 509 patients with EC and 506 matched healthy women were recruited from Hainan General Hospital ( Table 1 ). All participants were genetically unrelated Han Chinese women. Patients were diagnosed with EC based on histopathologic biopsies using the guidelines of the International Federation of Obstetrics and Gynecology (FIGO) criteria. Patients who received preoperative chemotherapy, radiotherapy, or hormone therapy were excluded. Patients with immunological diseases or other cancers were also excluded. Age-matched healthy control underwent routine gynecologic examination at the same hospital. Selection criteria included no malignancy, no history of cancer and no chronic or acute disease. Demographical and clinical characteristics were collected from medical records. This study was approved by the ethics committee of Hainan General Hospital (Ethical approval No.: Med-Eth-Re [2018] 14), in compliance with the Declaration of Helsinki. Informed consent was obtained from all recruited participants. Table 1 Basic Information of Endometrial Cancer and Health Controls Characteristics Cases (n = 509) Control (n = 506) p -value Age (years, mean ± SD) 54.94 ± 8.85 54.61 ± 9.07 0.553 BMI (kg/m 2 )  ≥ 24 199 (39.1%) 191 (37.7%) 0.699  < 24 310 (60.9%) 315 (62.3%) CEA (ng/mL, mean ± SD) 11.20 ± 2.09 2.08 ± 2.22 < 0.001 AFP (ng/mL, mean ± SD) 8.96 ± 5.32 2.86 ± 1.17 < 0.001 CA199 (U/mL, mean ± SD) 17.87± 20.99 12.79 ± 10.45 < 0.001 CA125 (U/mL, mean ± SD) 24.22 ± 39.00 12.97 ± 9.51 < 0.001 Stage I–II 261 (51.3%) III–IV 93 (18.3%) Missing 155 (30.5%) Notes : p values were calculated using χ 2 tests or Student’s t -test. Bold indicated that p < 0.05 meant the data was statistically significant. Abbreviations : SD, standard deviation; BMI, body mass index; CEA, carcinoembryonic antigen; AFP, alpha fetoprotein; CA, carbohydrate antigen. Basic Information of Endometrial Cancer and Health Controls Notes : p values were calculated using χ 2 tests or Student’s t -test. Bold indicated that p < 0.05 meant the data was statistically significant. Abbreviations : SD, standard deviation; BMI, body mass index; CEA, carcinoembryonic antigen; AFP, alpha fetoprotein; CA, carbohydrate antigen. Six SNPs (rs7581162, rs10189857, rs1427407, rs766432, rs6729815, and rs2556378) in the BCL11A gene with minor allele frequency (MAF) > 0.05 in the dbSNP database ( http://www.ncbi.nlm.nih.gov/projects/SNP ) and call rate > 95% were selected. The potential functions of these polymorphisms were evaluated through the HaploReg v4.1 ( https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php ), RegulomeDB ( https://regulome.stanford.edu/regulome-search/ ) and GTEx Portal databases ( https://gtexportal.org/home/ ). Venous blood (5 mL) was collected from each subject in an EDTA vacutainer tube. Genomic DNA was isolated using a commercially available GoldMag DNA Purification Kit (GoldMag Co. Ltd, Xi′an, China). Genotyping was performed by the Agena MassARRAY system (Agena, San Diego, CA, USA) as previously described. Primers design ( Suppl_Table 1 ) and data interpretation were performed by the corresponding supporting software following the manufacturer’s instructions. The MassARRAY platform is based on MALDI-TOF (matrix-assisted laser desorption/ionization—time of flight) mass spectrometry. The analytical accuracy of MALDI-TOF MS is quite high, 0.1–0.01% of the determined mass. 17 , 18 Furthermore, 5% of the samples were used for re-genotyping to quality control, and the consistency rate was 100%. Age and clinical characteristics were expressed as mean ± standard deviation (SD), and differences between EC patients and health controls were evaluated by Student’s t -test. The difference of body mass index (BMI) between cases and controls were analyzed by χ 2 -test Hardy–Weinberg equilibrium (HWE) was assessed to compare the genotype frequencies in controls using the goodness-of-fit chi-square test. Comparison of genotype and allele frequencies in cases and controls were performed by χ 2 -test. The major-type allele was used as a reference, and minor-type allele was used as a risk factor. Risk assessment of BCL11A polymorphisms for EC risk was performed by calculating odds ratios (OR) with 95% confidence intervals (CI) using logistic regression models. D’ values for pairwise linkage disequilibrium (LD) plots were generated by Haploview software (version 4.2). The relationship of BCL11A haplotypes with EC susceptibility was evaluated by χ 2 -test and logistic regression model. Multifactor dimensionality reduction (MDR) analysis was performed using MDR_3.0.2 software to identify high-order interaction models for EC predisposition. False-positive report probability (FPRP) analysis was used to evaluate the noteworthy associations and statistical power of the significant findings. 19 , 20 We set 0.2 as a FPRP threshold and assigned a prior probability of 0.1 for an association with genotypes under investigation. Analysis of Variance (ANOVA) was performed for the correlation of BCL11A variants with CEA, AFP, CA199, CA125 of EC patients and healthy controls. Data analysis was performed using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) and PLINK version 1.0.7 software. p -value was two-tailed and p < 0.05 was defined statistical significance, whereas adjusted p < 0.05/5 was considered significant after Bonferroni correction.

In this study, we found that rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 in BCL11A were associated with EC susceptibility in Chinese Han women, especially in subjects aged ≤ 55 years and BMI <24 kg/m 2 . Haplotype analysis showed that rs10189857 and rs1427407 were in linkage disequilibrium, and the A rs10189857 T rs1427407 and G rs10189857 G rs1427407 haplotypes were related to the increased EC risk. MDR analysis indicated that rs1427407 was the most influential attributor on EC risk in the single-locus model, and the best combination was a two-locus model containing rs7581162 and rs766432. Our study is the first to report that BCL11A variants were risk factors for EC in Chinese Han females. The BCL11A gene, located on 2p16, spans 102 kb, which is considered to be an oncogene in malignant haematological diseases, and is first detected in B-cell chronic lymphocytic leukaemia. 21 BCL11A can cause transcriptional repression of mammalian target genes by binging to DNA motifs and promoting the deacetylation of H3/H4 histone. 22 BCL11A may participate in cell cycle and cell growth by inhibiting of P21 induction. 23 In addition, BCL11A is involved in cell apoptosis by upregulating the expression of BCL2, BCL2-xL, and MDM2and inhibiting the activity of P53. 24 These studies support the possible involvement of BCL11A in tumorigenesis. Studies have found that BCL11A is abnormally expressed in EC tissues, which is related to EC classification, lymph node metastasis, tumor differentiation, histological type, ER/PR expression. 13 Targeted next-generation sequencing evaluated BCL11A mutations in patients with endometriosis. 14 These lines of evidence have led us to formulate the hypothesis that BCL11A could be of pathogenic importance in EC. Genetic variations of BCL11A gene may be associated with gene expression, thereby affecting the occurrence and development of disease. To date, BCL11A polymorphisms have been reported to be associated with various cancer, such as pancreatic cancers, laryngeal squamous cell carcinoma, and chronic lymphocytic leukemia. 25–27 Previous study have revealed that BCL11A rs7579014 is a risk locus for EC occurrence and is related to the expression of BCL11A in endometrial tumors. 15 BCL11A rs148261157 has been reported to increase the risk of EC. 16 However, the association between rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 in BCL11A and EC susceptibility has not been reported. Our study displayed that the MAFs of these five SNPs in BCL11A were higher in EC patients than in healthy controls, and had the increased risk effect on EC predisposition. To date, the functions of these SNPs have not been reported. In bioinformatics analysis, results from HaploReg v4.1 database displayed that these SNPs may be associated with promoter/Enhancer histone marks, DNAse, proteins bound, motifs changed, NHGRI/EBI GWAS hits, and/or GRASP QTL hits. 28 Previously, rs766432 in the intronic regions of BCL11A gene may affect the binding of protein to this region. 29 Rs1427407 in the second intron of the trans-acting element BCL11A is associated with the expression of BCL11A . 30 Based on GTEx Portal database, the genotypes of rs1427407 was related to the mRNA expression of BCL11A in cell. These results suggest that these loci may be involved in EC carcinogenic by affecting the expression or function of BCL11A , which requires further experimental confirmation. Age is a risk factor for the development of EC. 31 A study reported that median age at diagnosis of endometrial cancer was 55 years. 32 Besides, the mean ages of EC patients and the controls were respectively 54.94 ± 8.85 years and 54.61 ± 9.07 years in the study. In order to explore the contribution of age, we have divided the cases and controls into two groups as ≤ 55 years and > 55 years. The results of age-stratified analysis displayed that rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 were associated with EC susceptibility in subjects aged ≤ 55 years, but not in subjects aged > 55 years, suggesting that the risk association between BCL11A variants and EC predisposition might be age-dependent. Obesity is a risk factor for endometrial cancer risk and mortality. 33 Using body mass index (BMI) as a measure of obesity, we assessed the association of BCL11A variants with BMI. Among those aged 55 or younger and those had BMI < 24 kg/m 2 , we found that rs7581162-T, rs10189857-A, rs1427407-T, rs766432-C, and rs6729815-T were risk factors for the development of EC. The underlying mechanism of this correlation awaits further study. Haplotype-based analysis may be more effective than single-locus analysis when there is linkage disequilibrium among SNPs. 34 Haplotype analysis revealed that rs10189857 and rs1427407were in linkage disequilibrium, and the A rs10189857 T rs1427407 and G rs10189857 G rs1427407 haplotypes were related to an increased risk of EC. MDR analysis was used to identify specific combination effects of genetic variants with high-order interactions. 35 In this study, we found that rs1427407 was the most influential attributor for EC risk in the single-locus model, and the best combination was the two-locus model incorporating rs7581162 and rs766432 in BCL11A . Although our results provided evidence on the relationship between BCL11A polymorphisms and EC predisposition, several limitations should not be neglected. First, our subjects were recruited from a hospital that may contribute to selection bias. Second, due to the lack of adequate personal information, this study failed to assess the impact of gene-environment interactions on EC susceptibility and the association of genetic variants with clinicopathological data of EC patients. In the future, we would like to enlarge sample size and complete the clinicopathological data to evaluate the relationship. Third, although our findings suggested that BCL11A variants were associated with increased the risk of EC, the potential mechanisms and functions of these SNPs underlying the association have not been revealed. Fourth, the expression data of BCL11A is missing. In subsequent research, we would design detailed experiments to further explore the expression data of BCL11A and the potential mechanisms and functions of these SNPs in EC. In conclusion, our study first provides evidence that rs7581162, rs10189857, rs1427407, rs766432, and rs6729815 in BCL11A are risk factors for EC occurrence in Chinese Han women. These findings increased our understanding of the role of BCL11A gene in EC pathogenesis. However, the expression data of BCL11A and the association between SNPs and BCL11A expression need further explore in the more detailed experiments.