Neuroblastoma risk decreased by NSUN3 rs7653521 C>T polymorphism in Chinese children

To the Editor: As a heterogeneous tumor, neuroblastoma accounts for 7–8% of childhood cancers, as well as approximately 15% of pediatric deaths induced by malignancy.[1,2] Among patients with neuroblastoma, 60% are diagnosed in high-risk, of whom the prognosis after intensive treatment remains poor.[3] It is vital to discover risk-related genotypes and molecular markers of neuroblastoma, which would be helpful for establishing early diagnosis or treatment methods. NOP2/Sun RNA methyltransferase (NSUN) family members, including NSUN1–7, are important methyltransferases responsible for RNA 5-methylcytosine (m5C) modifications.[4] Previously, our group reported that single nucleotide polymorphisms (SNPs) in NSUN2 gene contribute to reducing neuroblastoma risk.[5] This study aimed to investigate the relationship between NSUN3 polymorphisms and neuroblastoma risk in Chinese children. The scientific protocols we applied in this study were approved by the Institutional Review Boards of the Children’s Hospital of Nanjing Medical University (No. 202112141-1) and the Guangzhou Women and Children’s Medical Center (No. 202016601). Written informed consent was obtained from all participants’ guardians. In this study, Children who were diagnosed with neuroblastoma by histology or biopsy in the Children’s Hospital of Nanjing Medical University were assigned to the case group during Jan 30, 2008 to Nov 2, 2021. Exclusion criteria were as follows: other types of cancer, secondary/recurrent malignancies, and receipt of chemotherapy or radiotherapy before recruitment. Meanwhile, people without tumors were randomly recruited to the control group who visited the hospital at the same time. Totally, 402 neuroblastoma patients and 473 controls were recruited. However, one patient was excluded due to the genotyping failed. Finally, 401 neurobalstoma patients and 473 controls participated in this study. The demographic characteristics of the included subjects in the final analysis are shown in Supplementary Table 1, https://links.lww.com/CM9/C349. The two groups were well matched according to similar population characteristics, such as age (case: 40.85 ± 35.42 years; control: 40.88 ± 29.76 years) and gender [case: 52.62% (211/401) in girls, 47.38% (190/401) in boys ; control: 52.43% (248/473) in girls, 47.57% (225/473) in boys] [Supplementary Table 1, https://links.lww.com/CM9/C349]. We retrieved functional SNPs as potential candidates from the National Center for Biotechnology Information (NCBI) Single Nucleotide Polymorphism Database (dbSNP) database and SNPinfo Web Server. According to the selection standard, NSUN3 SNPs should be located in the 5′-flanking region, 5′-untranslated region, exon, intron, or 3′-untranslated region. Moreover, the minor allele frequency (MAF) of the chosen SNPs in the Chinese Han population exceeds 5%. Meanwhile, there should be low linkage disequilibrium (R2 <0.80) among selected SNPs. Consequently, we selected three SNPs (rs17855824 C>T, rs61739170 G>C, and rs7653521 C>T) in the NSUN3 gene as candidates. The TIANamp Blood DNA Kit (TianGen Biotech Co. Ltd., Beijing, China) was used for the extraction of genomic DNA from peripheral blood specimens. Then, the TaqMan real-time polymerase chain reaction method was applied to genotype NSUN3 SNPs in these two groups. Finally, approximately 10% of the DNA samples were randomly selected to repeat the genotyping. A 100% consistency was proved for each duplicated SNP set. The deviation from Hardy–Weinberg equilibrium (HWE) for each SNP in the control group was estimated by χ2 test. Then, a bilateral chi-squared test was applied to assess the differences in genotypic frequencies and demographic variables between all the control and neuroblastoma cases. Subsequently, to calculate odds ratio (OR) and 95% confidence interval (CI) values adjusted for age and gender, we utilized multiple logistic regression analysis. The OR and 95% CI values were applied to evaluate the significance of the associations between SNPs in NSUN3 gene and neuroblastoma risk. Then, all the subjects were divided into several subgroups according to age, gender, tumor origin site, and clinical stage for further hierarchical analysis. The SAS statistical package (version 9.4, SAS Institute, Cary, NC, USA) was used for the statistical analysis. All tests for statistical significance are bilateral. P <0.05 indicates statistical significance. As shown in Table 1, three polymorphisms in NSUN3 (rs17855824 C>T, rs61739170 G>C, and rs7653521 C>T) were in accordance with HWE in the control subjects (rs17855824: PHWE = 0.268; rs61739170: PHWE = 0.094; rs7653521: PHWE = 0.325). In fact, no obvious statistically significant associations were detected between neuroblastoma risk and rs17855824 C>T or rs61739170 G>C according to the dominant models (rs17855824, CT/TT vs. CC: adjusted OR = 0.85, 95% CI = 0.55–1.32, P = 0.477; rs61739170, CC/GC vs. GG: adjusted OR = 1.10, 95% CI = 0.79–1.53, P = 0.572) or additive models (rs17855824, CC vs. CT vs. TT: adjusted OR = 0.89, 95% CI = 0.60–1.32, P = 0.549; rs61739170, GG vs. GC vs. CC: adjusted OR = 1.09, 95% CI = 0.82–1.45, P = 0.548) [Table 1]. However, rs7653521 C>T was statistically significant related to neuroblastoma susceptibility (CT/TT vs. CC: adjusted OR = 0.69, 95% CI = 0.52–0.92, P = 0.012; CT vs. CC: adjusted OR = 0.62, 95% CI = 0.45–0.84, P = 0.002). Table 1 - Association of NSUN3 gene polymorphisms with neuroblastoma risk in children from Jiangsu province. Genotypes Cases (n = 401) Controls (n = 473) P value† Crude OR (95% CI) P value Adjusted OR (95% CI) P value‡ rs17855824 C>T (P HWE = 0.268) CC 362 (90.27) 420 (88.79) 1.00 1.00 CT 36 (8.98) 50 (10.57) 0.54 (0.53–1.31) 0.434 0.54 (0.53–1.31) 0.434 TT 3 (0.75) 3 (0.63) 1.16 (0.23–5.78) 0.856 1.16 (0.23–5.79) 0.857 Additive 0.550 0.89 (0.60–1.32) 0.550 0.89 (0.60–1.32) 0.549 Dominant 39 (9.73) 53 (11.21) 0.478 0.85 (0.55–1.32) 0.478 0.85 (0.55–1.32) 0.477 CC/CT 398 (99.25) 470 (99.37) 1.00 1.00 TT 3 (0.75) 3 (0.63) 0.839 1.18 (0.24–5.89) 0.839 1.18 (0.24–5.89) 0.840 rs61739170 G>C (P HWE = 0.094) GG 316 (78.80) 380 (80.34) 1.00 1.00 GC 76 (18.95) 84 (17.76) 1.09 (0.77–1.54) 0.631 1.09 (0.77–1.54) 0.629 CC 9 (2.24) 9 (1.90) 1.20 (0.47–3.07) 0.698 1.21 (0.47–3.08) 0.696 Additive 0.551 1.09 (0.82–1.45) 0.550 1.09 (0.82–1.45) 0.548 Dominant 85 (21.20) 93 (19.66) 0.574 1.10 (0.79–1.53) 0.574 1.10 (0.79–1.53) 0.572 GG/GC 392 (97.76) 464 (98.10) 1.00 1.00 CC 9 (2.24) 9 (1.90) 0.723 1.19 (0.47–3.01) 0.722 1.19 (0.47–3.02) 0.720 rs7653521 C>T (P HWE = 0.325) CC 142 (35.41) 130 (27.48) 1.00 1.00 CT 166 (41.40) 246 (52.01) 0.62 (0.45–0.84) 0.002 0.62 (0.45–0.84) 0.002 TT 93 (23.19) 97 (20.50) 0.88 (0.61–1.27) 0.491 0.88 (0.61–1.27) 0.491 Additive 0.284 0.90 (0.75–1.09) 0.284 0.90 (0.75–1.09) 0.285 Dominant 259 (64.59) 343 (72.52) 0.012 0.69 (0.52–0.92) 0.012 0.69 (0.52–0.92) 0.012 CC/CT 308 (76.81) 376 (79.49) 1.00 1.00 TT 93 (23.19) 97 (20.50) 0.338 1.17 (0.85–1.62) 0.338 1.17 (0.85–1.62) 0.338 Combine protective genotypes* 0–2 154 (38.40) 142 (30.02) 1.00 1.00 3 247 (61.60) 331 (69.98) 0.009 0.68 (0.52–0.91) 0.009 0.69 (0.52–0.91) 0.009 Data were shown as n (%). Additive model was defined as CC vs. CT vs. TT (rs17855824); GG vs. GC vs. CC (rs61739170); CC vs. CT vs. TT (rs7653521). Dominant model was defined as CT/TT vs. CC (rs17855824); CC/GC vs. GG (rs61739170); CT/TT vs. CC (rs7653521). CI: Confidence interval; HWE: Hardy–Weinberg equilibrium; NSUN: NOP2/Sun RNA methyltransferase; OR: Odds ratio. *Combined protective genotypes were carriers with rs17855824 CC/CT, rs61739170 GG/GC, and rs7653521 TT/CT genotypes. †χ2 test for genotype distributions between neuroblastoma patients and cancer-free controls. ‡Adjusted for age and sex. Then, the protective genotypes of the SNPs were determined based on their influence on neuroblastoma susceptibility (OR <1.00), regardless of whether the association was significant. Therefore, the rs17855824 CC/CT, rs61739170 GG/GC, and rs7653521 CT/TT genotypes were chosen for the analysis of combined protective genotypes [Table 1]. Neuroblastoma susceptibility was significantly lower in carriers with 3 protective genotypes than in carriers with 0–2 protective genotypes (adjusted OR = 0.69, 95% CI = 0.52–0.91, P = 0.009). To obtain more details about the influences of NSUN3 rs7653521 C>T polymorphism and combined protective genotypes on neuroblastoma risk, stratification analysis was performed by age, gender, tumor origin site, and clinical stage [Supplementary Table 2, https://links.lww.com/CM9/C349]. The rs7653521 CT/TT genotype was clearly associated with a reduced neuroblastoma risk compared with the rs7653521 CC genotype in cases aged 18 months to 14 years (adjusted OR = 0.65, 95% CI = 0.46–0.92, P = 0.014), girls (adjusted OR = 0.62, 95% CI = 0.41–0.95, P = 0.027), and subjects with tumors in the mediastinum (adjusted OR = 0.54, 95% CI = 0.36–0.82, P = 0.004) or other sites (adjusted OR = 0.31, 95% CI = 0.12–0.79, P = 0.015). Moreover, we observed a statistically significant association between three combined protective genotypes and reduced neuroblastoma risk in older children (adjusted OR = 0.66, 95% CI = 0.47–0.93, P = 0.016), girls (adjusted OR = 0.64, 95% CI = 0.42–0.96, P = 0.030), cases with mediastinal neuroblastoma (adjusted OR = 0.59, 95% CI = 0.39–0.90, P = 0.013), patients with tumors in other sites (adjusted OR = 0.35, 95% CI = 0.13–0.90, P = 0.029), and patients with stage III/IV (adjusted OR = 0.68, 95% CI = 0.47–0.98, P = 0.040). Our data showed that patients with the NSUN3 rs7653521 CT and TT/CT genotypes exhibited obviously lower neuroblastoma risk than those with the rs7653521 CC genotype. Moreover, the carriers with 3 protective genotypes were at a profoundly lower risk of neuroblastoma than the patients with 0–2 protective genotypes. Nevertheless, several limitations of this research should be addressed. First, the sample size was small in this study as all subjects were enrolled from the Children’s Hospital of Nanjing Medical University, which might restrict the statistical power. Therefore, multicenter studies to confirm the influence of NSUN3 SNPs on neuroblastoma risk should be performed. Second, we only enrolled research subjects from Jiangsu Province, China. The statistical results of this study cannot be applied to other populations. It is essential and helpful to investigate the roles of these SNPs in different regions and ethnic groups. Finally, the number of SNPs included in this study is relatively small. All functional SNPs in NSUN3 gene should be evaluated to yield a more powerful conclusion. In summary, we demonstrated that the rs7653521 C>T polymorphism in NSUN3 gene is significantly associated with a lower risk of neuroblastoma in Chinese children. Given that NSUN3 encodes a methyltransferase for RNA m5C modification, further study of the potential biological mechanisms of NSUN3 rs7653521 C>T and m5C modification in neuroblastoma is warranted. Funding This study was supported by grants from the National Natural Science Foundation of China (Nos. 82173593 and 82002635), Guangzhou Science and Technology Project (No. 202201020622), Postdoctoral Science Foundation of China (No. 2021M691649), and Postdoctoral Science Foundation of Jiangsu Province (No. 2021K524C). Conflicts of interest None.