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. 2025 Oct 17;25:1606. doi: 10.1186/s12885-025-14984-2

Association of genetic variants in m1A modification core genes and neuroblastoma risk

Susu Jiang 1,2,#, Siqi Dong 2,#, Yong Li 3,4,#, Lei Lin 2, Liping Chen 2, Wenli Zhang 2, Jinhong Zhu 5, Xinxin Zhang 2, Zhonghua Yang 6, Jiao Zhang 7, Jiwen Cheng 8, Li Li 9, Haixia Zhou 10, Suhong Li 11, Wenhan Yang 12, Jing He 2,✉, Zhenjian Zhuo 1,2,✉
PMCID: PMC12535018  PMID: 41107791

Abstract

Neuroblastoma tightly linked with genetic abnormality. The core genes responsible for RNA N1-methyladenosine (m1A) modification are critical in tumor development. Nevertheless, few reports revealed the function of m1A modification core gene polymorphisms and the neuroblastoma risk. We carried out this study to verify the association of 12 single-nucleotide polymorphisms (SNPs) with neuroblastoma susceptibility. This study recruited 898 cases with newly diagnosed neuroblastoma and 1734 Healthy controls from eight medical centers. We selected 12 SNPs from m1A modification genes ALKBH1, TRMT6, TRMT61B, and TRMT10C, and genotypes were determined by the TaqMan method. We used univariable and multivariable logistic regression models to analyze the association of SNPs with neuroblastoma risk, followed by stratified analysis. Statistical analysis showed that TRMT6 rs236170 GG (AOR = 1.23, 95% CI = 1.02–1.50, P = 0.034), rs451571 CC (AOR = 1.46, 95% CI = 1.01–2.11, P = 0.043), rs236188 AA (AOR = 2.65, 95% CI = 1.16–6.07, P = 0.021), rs236110 AA (AOR = 1.91, 95% CI = 1.29–2.82, P = 0.001), and ALKBH1 rs6494 AA (AOR = 4.27, 95% CI = 1.31–13.93, P = 0.016), rs176942 GG (AOR = 1.98, 95% CI = 1.35–2.89, P = 0.0005) were neuroblastoma risk variants; the ALKBH1 rs1048147 CC (AOR = 0.80, 95% CI = 0.68–0.94, P = 0.007) was inverse associated with neuroblastoma risk. The eQTL analysis showed that functional annotation of rs6494 T > A may be potential function variants through decreasing ALKBH1 gene expression mRNA, rs451571 T > C, rs236188 G > A, rs236110 C > A are associated with neuroblastoma risk through increasing the expression of its nearby genes RP5-967N21.11 and lowering the expression of MCM8. Our research showed some SNPs in the m1A modification core genes are related to neuroblastoma.

Clinical perspectives

(i) Few reports have revealed the function of m1A modification core gene polymorphisms in neuroblastoma risk.

(ii) After genotyping 12 SNPs with potential functions in four m1A modification core genes in children with neuroblastoma and healthy controls, we found several neuroblastoma predisposition loci, including TRMT6 rs236170, rs451571, rs236188, rs236110, and ALKBH1 rs6494, rs176942, and rs1048147. The eQTL assessment demonstrated that rs6494 T > A may be a potential functional variant by decreasing ALKBH1 mRNA expression.

(iii) Our research is the first to reveal m1A modification core gene SNPs and neuroblastoma risk.

Graphical Abstract

graphic file with name 12885_2025_14984_Figa_HTML.jpg

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-025-14984-2.

Keywords: Neuroblastoma, Polymorphism, M1A modification, Susceptibility

Introduction

Neuroblastoma, a common childhood tumor arising in sympathetic nervous tissue, exhibits remarkable clinical Heterogeneity, with high-risk cases showing a dismal 5-year survival rate of less than 50% despite nearly 100% survival in low-to medium-risk groups [1]. The incidence of neuroblastoma in Chinese children is 7.7 to 8.8 cases per million, highlighting the need for improved risk stratification and etiological understanding [2].

Genetic factors play a critical role in neuroblastoma pathogenesis, with known alterations including MYCN amplification, 17q gain, and deletion of 1p and 11q in high risk cases [3]. Candidate gene studies have linked mutations in ATRX [4], TERT [5], ALK [6], and RAS [7], to disease risk, while genome-wide association studies (GWASs) have identified susceptibility single nucleotide polymorphisms (SNPs) in genes like CASC15 [8], IL-6 [9], BARD1 [10], LMO1 [11], ERCC1 and XPF genes polymorphisms [12]. However, GWAS-based discoveries are limited by stringent multiple testing corrections that may overlook SNPs with moderate effects, coupled with publication bias favoring strongly positive associations. This leaves significant gaps in our understanding of neuroblastoma genetic susceptibility.

N1-methyladenosine (m1A) modification, a key post-transcriptional regulator of gene expression, has emerged as a critical player in tumorigenesis, making it a promising therapeutic target [13, 14]. The m1A regulatory machinery has core components: writers (TRMT6/61A, TRMT61B, TRMT10C) [15–17], erasers (ALKBH1/3/7 and FTO) [18], and readers (YTHDF1/2/3 and YTHDC1) [19, 20]. These core components orchestrate mRNA methylation [21, 22], processing [23], stability [24], and translation [22]. Mounting evidence links m1A-related genes like TRMT10C, TRMT6, and ALKBH1 to human cancers [25–28], with ALKBH1 recently implicated in neuroblastoma via tRNA cleavage regulation [29]. Notably, TRMT61B, a key m1A writer, has no reported association with neuroblastoma risk to date. Our previous single-center study identified associations between TRMT10C, TRMT6 SNPs and neuroblastoma risk, but this requires validation in larger multicenter cohorts [30].

To address these knowledge gaps, we conducted a multicenter study across eight hospitals to investigate the association between SNPs in m1A modification core genes and neuroblastoma risk in Chinese children.

Materials and methods

Study subjects

We conducted a case-control study with eight participating medical centers in eight cities in China (Guangzhou, Xi’an, Wenzhou, Zhengzhou, Kunming, Taiyuan, Shenyang, and Changsha). This study included 898 patients with newly diagnosed neuroblastoma and 1734 cancer-free controls. We used structured questionnaires to collect the epidemiological data. The patient was histologically diagnosed via biopsy. Control subjects were randomly selected from among healthy volunteers.Case subjects were required to meet the following criteria: aged 0–14 years, histologically confirmed diagnosis of primary neuroblastoma, no history of other organ Malignancies, and no prior chemotherapy before sample collection. Control subjects were required to be Healthy children aged 0–14 years with no history of tumors, neurological disorders, congenital genetic disorders, infectious diseases, or other relevant conditions. These control volunteers were matched with the case subjects by age and sex. Control and case subjects were recruited at the same time. Written informed consent was obtained from the guardians of all participants. This study was approved by the institutional review board of Guangzhou Women and Children’s Medical Center, as described previously. We extracted DNA from both the tumor tissue and blood samples of the case and control subjects, and subsequently performed genotyping analysis. We confirm all of these human tissue samples that all experiments were performed in accordance with relevant guidelines and regulations [31].

SNP selection and genotyping

Four m1A modification core genes were included in the current study: tRNA methyltransferase 10 C (TRMT10C), tRNA methyltransferase 61 B (TRMT61B), tRNA methyltransferase 6 non-catalytic subunit (TRMT6), and alkB homolog 1 (ALKBH1). We first selected candidate SNPs by searching the NCBI dbSNP database and then filtered out potentially functional SNPs using SNPinfo (https://snpinfo.niehs.nih.gov/). The qualified SNPs met the following selection criteria: (1) located at the end of TRMT10C, TRMT61B, TRMT6, ALKBH1 genes, such as the 5’ flanking regions, 5’ untranslated regions (5’ UTR), 3’ UTR, and exons. (2) For the Chinese Han subjects, the minor allele frequency (MAF) was > 5%. (3) Linkage disequilibrium (LD) of SNPs was lower than 0.8 (R2 < 0.8). Finally, there were 12 SNPs selected. Specifically, 4 TRMT10C, 1 TRMT61B, 4 TRMT6, and 3 ALKBH1 SNPs were genotyped. The TIANamp Blood DNA Kit was used to process the donated peripheral blood and extract genomic DNA. These selected SNPs were genotyped using a standard TaqMan real-time PCR method, and we chose a subset of 10% of the samples for secondary genotyping analysis. The secondary genotyping analysis yielded a concordance rate of 100%. Detailed procedures can be found in a previous publication [32].

Statistical analysis

We applied a goodness-of-fit χ2 test to check the Hardy-Weinberg equilibrium (HWE) of each SNP in controls. A two-sided χ2 test was performed to analyze the differences in categorical variables between cases and controls, whereas the t-test was used for continuous variables. The contributions of candidate SNPs to neuroblastoma susceptibility were tested using logistic regression analysis, and adjusted odds ratios (OR) and 95% confidence intervals (CI) were determined using multivariable logistic regression analysis with adjustment for age, sex, clinical stage, and tumor site. Age and clinical stages were classified based on references [33, 34]. All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina). SNPs and nearby gene expression levels were evaluated using eQTLs analysis. eQTLs results were obtained from the GTEx portal website (https://www.gtexportal.org/home/). Statistical significance was set at P < 0.05.

Results

Association study

The characteristics of the study population are presented in Table S1. Detailed information about these participants was provided in our previous research [31]. Finally, we genotyped 12 SNPs selected from the four key genes responsible for the m1A modification core, and single-locus analysis was used to determine the association of each SNP with neuroblastoma risk (Table 1). In the controls, none of these SNPs deviated from HWE (P > 0.05). In the dominant model, only TRMT6 rs236188 (adjusted OR = 0.76, 95% CI = 0.61–0.97, P = 0.024) and ALKBH1 rs1048147 (adjusted OR = 0.80, 95% CI = 0.68–0.94, P = 0.007) were protective factors for neuroblastoma. Under recessive model, TRMT6 rs236170 (adjusted OR = 1.23, 95% CI = 1.02–1.50, P = 0.034), TRMT6 rs451571 (adjusted OR = 1.46, 95% CI = 1.01–2.11, P = 0.043), TRMT6 rs236188 (adjusted OR = 2.65, 95% CI = 1.16–6.07, P = 0.021), TRMT6 rs236110 (adjusted OR = 1.91, 95% CI = 1.29–2.82, P = 0.0021), ALKBH1 rs6494 (adjusted OR = 4.27, 95% CI = 1.31–13.93, P = 0.016), and ALKBH1 rs176942 (adjusted OR = 1.98, 95% CI = 1.35–2.89, P = 0.0005) were associated with neuroblastoma predisposition. More detailed genotype frequency distributions of TRMT6 SNPs in cases and controls are shown in Table S2, and those of ALKBH1SNPs are shown in Table S3.

Table 1.

Association of m1A modification genes and neuroblastoma risk in Chinese children

Gene Polymorphism Allele Case (N = 898)a Control (N = 1734)a AOR (95% CI)b P b P c AOR (95% CI)d P d P c HWE
A B AA AB BB AA AB BB
TRMT10C rs7641261 C T 510 309 58 1013 622 92 1.02 (0.87–1.21) 0.790 1 1.26 (0.90–1.78) 0.177 1 0.784
TRMT10C rs2303476 T C 558 278 45 1076 568 83 0.96 (0.81–1.13) 0.621 1 1.05 (0.72–1.53) 0.793 1 0.471
TRMT10C rs4257518 A G 293 395 190 558 825 344 0.96 (0.81–1.15) 0.674 1 1.13 (0.92–1.38) 0.243 1 0.217
TRMT10C rs3762735 C G 688 167 24 1316 379 32 0.89 (0.73–1.08) 0.242 1 1.51 (0.88–2.59) 0.131 1 0.440
TRMT61B rs4563180 G C 540 298 55 1028 610 89 0.97 (0.82–1.14) 0.697 1 1.23 (0.87–1.73) 0.251 1 0.903
TRMT6 rs236170 A G 282 389 215 562 809 356 1.03 (0.86–1.22) 0.756 1 1.23 (1.02–1.50) 0.034 0.816 0.039
TRMT6 rs451571 T C 528 306 52 1039 617 71 1.02 (0.86–1.20) 0.832 1 1.46 (1.01–2.11) 0.043 1 0.082
TRMT6 rs236188 G A 770 103 13 1442 275 10 0.76 (0.61–0.97) 0.024 0.576 2.65 (1.16–6.07) 0.021 0.504 0.424
TRMT6 rs236110 C A 557 277 52 1102 570 55 1.04 (0.88–1.23) 0.684 1 1.91 (1.29–2.82) 0.001 0.024 0.069
ALKBH1 rs1048147 C A 489 308 84 863 709 155 0.80 (0.68–0.94) 0.007 0.168 1.07 (0.81–1.41) 0.646 1 0.588
ALKBH1 rs6494 T A 783 89 9 1497 226 4 0.82 (0.64–1.06) 0.131 1 4.27 (1.31–13.93) 0.016 0.384 0.135
ALKBH1 rs176942 A G 610 216 55 1172 499 56 0.94 (0.79–1.12) 0.460 1 1.98 (1.35–2.89) 0.0005 0.012 0.746
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Single genotype models (AB vs. AA; BB vs. AA), additive model (AA vs. AB vs. BB), dominant model (AB + BB vs. AA), and recessive model (BB vs. AA + AB)

Values in boldface indicate statistically significant

AOR adjusted odds ratio, CI confidence interval, HWE Hardy–Weinberg equilibrium

aThere were missing values for the genotyping failed

bAdjusted for age and gender for dominant model

cBonferroni correction for multiple testing

dAdjusted for age and gender for recessive model

Stratified analysis

We then analyzed the association between TRMT6 and ALKBH1 SNPs and susceptibility to neuroblastoma risk in subgroups divided by demographic variables, clinical stages, and tumor origins (Tables 2 and 3). For the TRMT6 gene, rs451571 CC increased the risk of neuroblastoma at other sites of origin (adjusted OR = 2.42, 95% CI = 1.21–4.85, P = 0.013). The rs236188 GG/GA polymorphism was associated with decreased neuroblastoma risk in children aged > 18 months, male sex, and clinical stages III and IV. rs236110 AA was associated with increased neuroblastoma risk in children aged over 18 months, all sexes, all sites of origin except the mediastinum, and all clinical stages. rs451571 CC, rs236188 AA, rs236110 AA, and rs236170 GG were identified as risk genotypes. When compared with 0 risk genotypes, children with 1–4 risk genotypes were more Likely to develop neuroblastoma risk in children aged over 18 months, male sex, retroperitoneal subtype, and other sites of origin.

Table 2.

Stratification analysis for the association between TRMT6 gene genotypes and neuroblastoma susceptibility

Variables rs451571 (case/control) AOR (95% CI)a P a rs236188 (case/control) AOR (95% CI)a P a rs236110 (case/control) AOR (95% CI)a P a Risk genotypes (case/control) AOR (95% CI)a P a
TT/TC CC GG GA/AA CC/CA AA 0 1–4
Age, month
 ≤ 18 319/678 20/31 1.40 (0.78–2.50) 0.256 292/590 47/119 0.80 (0.56–1.15) 0.231 320/687 19/22 1.87 (1.00-3.51) 0.051 257/565 82/144 1.26 (0.92–1.71) 0.149
 > 18 515/978 32/40 1.52 (0.94–2.45) 0.086 478/852 69/166 0.74 (0.54–0.997) 0.048 514/985 33/33 1.97 (1.20–3.24) 0.007 404/803 143/215 1.34 (1.05–1.71) 0.019
Sex
 Female 374/707 27/34 1.56 (0.92–2.63) 0.096 340/620 61/121 0.94 (0.67–1.32) 0.729 380/719 21/22 1.88 (1.02–3.46) 0.044 305/590 96/151 1.23 (0.92–1.65) 0.160
 Male 460/949 25/37 1.39 (0.83–2.34) 0.214 430/822 55/164 0.64 (0.46–0.89) 0.007 454/953 31/33 1.96 (1.18–3.24) 0.009 356/778 129/208 1.35 (1.05–1.74) 0.020
Sites of origin
 Adrenal gland 231/1656 16/71 1.66 (0.95–2.91) 0.077 217/1442 30/285 0.71 (0.47–1.06) 0.093 231/1672 16/55 2.12 (1.19–3.78) 0.011 188/1368 59/359 1.19 (0.87–1.63) 0.277
 Retroperitoneal 296/1656 18/71 1.43 (0.84–2.44) 0.187 274/1442 40/285 0.74 (0.52–1.06) 0.100 295/1672 19/55 1.95 (1.14–3.33) 0.015 232/1368 82/359 1.34 (1.02–1.78) 0.037
 Mediastinum 201/1656 8/71 0.91 (0.43–1.92) 0.807 177/1442 32/285 0.91 (0.61–1.36) 0.658 202/1672 7/55 1.06 (0.48–2.37) 0.880 163/1368 46/359 1.08 (0.76–1.53) 0.661
 Others 95/1656 10/71 2.42 (1.21–4.85) 0.013 92/1442 13/285 0.71 (0.39–1.29) 0.263 96/1672 9/55 2.88 (1.38-6.00) 0.005 72/1368 33/359 1.75 (1.14–2.68) 0.011
Clinical stages
 I + II + 4s 453/1656 29/71 1.48 (0.95–2.31) 0.086 415/1442 67/285 0.81 (0.61–1.09) 0.159 454/1672 28/55 1.88 (1.18-3.00) 0.008 361/1368 121/359 1.28 (1.01–1.63) 0.040
 III + IV 368/1656 21/71 1.39 (0.84–2.31) 0.202 342/1442 47/285 0.71 (0.51–0.99) 0.042 367/1672 22/55 1.84 (1.10–3.08) 0.020 291/1368 98/359 1.28 (0.99–1.67) 0.059
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AOR adjusted odds ratio, CI confidence interval

aAdjusted for age and sex, omitting the corresponding stratify factor

Values in boldface indicate statistically significant

Table 3.

Stratification analysis for the association between ALKBH1 gene genotypes and neuroblastoma susceptibility

Variables rs1048147 (case/control) AOR (95% CI)a P a rs176942 (case/control) AOR (95% CI)a P a Risk genotypes (case/control) AOR (95% CI)a P a
CC CA/AA AA/AG GG 0 1–3
Age, month
 ≤ 18 190/367 148/342 0.84 (0.65–1.09) 0.187 320/687 18/22 1.76 (0.93–3.33) 0.083 146/341 192/368 1.22 (0.94–1.58) 0.144
 > 18 299/496 244/522 0.78 (0.63–0.95) 0.019 506/984 37/34 2.09 (1.30–3.38) 0.003 239/521 304/497 1.33 (1.08–1.64) 0.008
Sex
 Female 227/366 173/375 0.74 (0.58–0.94) 0.015 372/717 28/24 2.24 (1.28–3.93) 0.005 170/374 230/367 1.39 (1.09–1.78) 0.009
 Male 262/497 219/489 0.85 (0.68–1.06) 0.144 454/954 27/32 1.77 (1.05–2.99) 0.033 215/488 266/498 1.21 (0.97–1.51) 0.085
Sites of origin
 Adrenal gland 140/863 106/804 0.75 (0.58–0.99) 0.039 229/1671 17/56 2.18 (1.24–3.82) 0.007 105/862 141/865 1.35 (1.03–1.76) 0.032
 Retroperitoneal 156/863 154/804 0.98 (0.77–1.25) 0.891 299/1671 11/56 1.08 (0.56–2.09) 0.815 152/862 158/865 1.04 (0.82–1.32) 0.757
 Mediastinum 127/863 81/804 0.64 (0.47–0.85) 0.003 190/1671 18/56 2.86 (1.64–4.98) 0.0002 79/862 129/865 1.63 (1.22–2.20) 0.001
 Others 55/863 50/804 0.91 (0.61–1.35) 0.638 97/1671 8/56 2.46 (1.14–5.31) 0.022 48/862 57/865 1.18 (0.80–1.75) 0.412
Clinical stages
 I + II + 4s 268/863 212/804 0.79 (0.65–0.97) 0.024 450/1671 30/56 1.99 (1.26–3.14) 0.003 211/862 269/865 1.27 (1.04–1.56) 0.022
 III + IV 210/863 176/804 0.83 (0.67–1.04) 0.111 361/1671 25/56 2.01 (1.23–3.29) 0.005 170/862 216/865 1.28 (1.02–1.60) 0.033
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AOR adjusted odds ratio, CI confidence interval

aAdjusted for age and sex, omitting the corresponding stratify factor

Values in boldface indicate statistically significant

For ALKBH1 genotypes, rs1048147 CA/AA was associated with a reduced risk of neuroblastoma in patients aged > 18 months, female sex, origin from the adrenal gland and mediastinum, and clinical stages I + II + 4s. rs176942 GG was associated with a higher risk of neuroblastoma in patients aged > 18 months, all sexes, common sites of origin except retroperitoneal, and all clinical stages. rs1048147 CC, rs176942 GG, and rs6494 AA were defined as the risk genotypes. When compared with 0 risk genotypes, children with 1–3 risk genotypes had a higher risk of neuroblastoma in patients aged > 18 months, females, origin sites from the adrenal gland and mediastinum, and all tumor clinical stages.

Expression quantitative trait loci (eQTL) analyses

We further assessed the statistical significance of the SNPs’ mRNA expression of its host gene and neighboring genes. Finally, four SNPs (rs6494, rs451571, rs236188, and rs236110) were included. Samples with the rs6494 A genotype had lower ALKBH1 mRNA levels in whole blood than samples with the rs6494 T genotype (Fig. 1A). However, we found that tissues with the rs6494 A genotype presented higher mRNA levels of nearby genes, such as ADCK1 and SNW1 (Fig. 1B-D).

Fig. 1.

Fig. 1

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Functional implication of ALKBH1 gene rs6494 polymorphism based on the public database GTEx portal. A The genotype of rs6494 and expression of the ALKBH1 gene in whole blood. B-D The genotype of rs6494 and expression of its neighboring genes ADCK1 and SNW1 in different tissues

rs236110 AA/CA was significantly associated with higher expression in RP5-967N21.11 and lower expression in the MCM8 gene (Fig. 2). We also found that the rs236188 AA/GA genotype had significantly higher levels of RP5-967N21.11 expression and lower expression of the MCM8 gene (Fig. 3). This phenomenon in rs236188 AA/GA and rs236110 AA/CA also existed in the rs451571 CC/TC genotypes (Fig. 4).

Fig. 2.

Fig. 2

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Functional relevance of rs236110 polymorphism on genes expression in GTEx database

Fig. 3.

Fig. 3

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Functional relevance of rs236188 polymorphism on genes expression in GTEx database

Fig. 4.

Fig. 4

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Functional relevance of rs451571 polymorphism on genes expression in GTEx database

Discussion

TRMT6 polymorphisms and neuroblastoma risk

Our study identified four TRMT6 SNPs (rs236170 A > G, rs451571 T > C, rs236188 G > A, and rs236110 C > A) as significant risk factors for neuroblastoma in Chinese children. This finding aligns with the established role of TRMT6 in tumorigenesis [35, 36], as the gene encodes tRNA methyltransferase 6 family proteins linked to poor prognosis in multiple cancers [37–39]including glioma [40], colorectal cancer [41], hepatocellular carcinoma (HCC) [37], and gastrointestinal cancer (GI) [38]. Mechanistically, TRMT6 forms complexes with TRMT61A to regulate m1A modification of tRNAiMet at position 58, which connects PKCα signaling to translational control and promotes glioma cell proliferation and malignant transformation [40]. Such pathways may similarly contribute to neuroblastoma development when perturbed by TRMT6 genetic variants.

Notably, our results contrast with a study by Ma et al., who reported that TRMT6 rs236170 A > G, rs236188 G > A, and rs236110 C > A were inversely associated with hepatoblastoma risk [42]. This discrepancy highlights potential tissue-specific or cancer-type differences in the functional impact of TRMT6 SNPs, emphasizing the need for cancer-specific genetic susceptibility studies.

Functional insights from eQTL analysis further support our findings: rs236110 C > A, rs236188 G > A, and rs451571 T > C were associated with lower expression of MCM8 and higher expression of the uncharacterized lncRNA RP5-967N21.11.

MCM8, a key player in DNA replication and cell cycle regulation [43], is overexpressed in HCC, glioma, and cholangiocarcinoma, where it correlates with poor prognosis [43–45]. Our observation of reduced MCM8 expression linked to TRMT6 risk alleles suggests a distinct mechanism in neuroblastoma, where downregulated MCM8 may disrupt genomic stability and promote tumorigenesis. The role of RP5-967N21.11 remains unknown, warranting further investigation to clarify its potential involvement in neuroblastoma pathogenesis.

ALKBH1 polymorphisms and neuroblastoma risk

In ALKBH1, we found that rs6494 T > A and rs176942 A > G increased neuroblastoma risk, while rs1048147 CC acted as a protective allele. ALKBH1, a DNA demethylase that targets m1A in cytosolic tRNAs, exhibits context-dependent roles in cancer: acting as a tumor suppressor in pancreatic cancer (where upregulation correlates with favorable prognosis and lower clinical stage) [46] and as an oncogene in stomach adenocarcinoma, liver hepatocellular carcinoma, and colon adenocarcinoma (where overexpression predicts poor survival) [38]. This duality may explain the complex associations observed in our study.

Our finding that ALKBH1 rs6494 T > A is a risk factor partially aligns with Archer NP et al., who reported a modest role for rs6494 in childhood B-cell acute lymphoblastic leukemia [47]. However, Li Y et al. found that ALKBH1 rs1048147 reduced gastric cancer risk in older adults [48], consistent with our observation of rs1048147 CC as a neuroblastoma-protective allele. Notably, our previous study found no association between these ALKBH1 SNPs and glioma risk [49], reinforcing cancer-type specificity in genetic associations.

Functional analysis revealed that the rs6494 A allele reduces ALKBH1 expression in whole blood, potentially contributing to neuroblastoma risk given ALKBH1’s oncogenic roles in multiple cancers [28, 48]. Additionally, rs6494 correlates with expression changes in adjacent genes ADCK1 and SNW1: ADCK1 promotes colony formation and cancer cell infiltration [50, 51], while SNW1 interacts with RBPJ to regulate Notch signaling in neuroblastoma [52]. These downstream effects suggest that rs6494 may influence neuroblastoma risk through both ALKBH1-dependent and independent pathways.

Knowledge gaps and study limitations

This study addresses a critical gap: prior to our work, no studies had explored associations between core m1A modification gene SNPs and neuroblastoma risk in Chinese children. Our multicenter design, encompassing 898 cases and 1734 controls, strengthens the reliability of these findings. However, limitations remain. First, our focus on Chinese children necessitates replication in diverse ethnic groups to account for allele frequency differences. Second, the absence of environmental or lifestyle data prevents us from evaluating gene-environment interactions. Third, while eQTL data provide mechanistic clues, experimental validation is needed to confirm the functional roles of identified SNPs in neuroblastoma pathogenesis. Finally, the role of RP5-967N21.11, a novel candidate linked to TRMT6 SNPs, requires further investigation to fully interpret our results.

Our study identified some weak associations (AOR ~ 1.2, ~ 20% increased risk) between specific SNPs and neuroblastoma, but these involve core methylation-modifying genes with established roles in neuroblastoma pathogenesis, enhancing their biological plausibility beyond statistical significance. Clinically, these SNPs can complement high-impact factors like MYCN amplification to refine risk stratification: aid in identifying progression risk in low-risk patients or optimizing high-risk monitoring and may offer insights into epigenetic therapy responses if their functional effects are validated. Consistent with field consensus, small effects (AOR 1.1–1.3) in methylation pathways are common in multi-factor risk models. However, standalone clinical utility is limited; larger, diverse cohort validation and functional studies to confirm methylation regulatory mechanisms are needed to rule out chance and strengthen clinical relevance.

Conclusion

In conclusion, we identified that TRMT6 rs236170, rs451571, rs236188, rs236110, and ALKBH1 rs6494 and rs176942 are associated with increased neuroblastoma risk, and ALKBH1 rs1048147 can reduce the risk of neuroblastoma. These SNPs may be used to screen children at a high risk of developing neuroblastoma.

Supplementary Information

Supplementary Material 1. (143.6KB, pdf)

Acknowledgments

Role of the funder/sponsor

The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Prior presentations

The article has not been published in other journals.

Abbreviations

m1A

N1-methyladenosine

SNP

Single nucleotide polymorphism

GWAS

Genome-wide association study

INSS

International Neuroblastoma Staging System

HWE

Hardy-Weinberg equilibrium

AOR

Adjusted odds ratio

OR

Odds ratio

CI

Confidence interval

SD

Standard deviation

NA

Not available

Authors’ contributions

Dr Jing He, Zhenjian Zhuo had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Susu Jiang, Yong Li, Siqi Dong, contributed equally as co–first authors.Concept and design: Jing He, Zhenjian Zhuo.Acquisition, analysis, or interpretation of data: Susu Jiang, Yong Li, Lei Lin, Jing He, Zhenjian Zhuo.Drafting of the manuscript: Liping Chen, Wenli Zhang, Jinhong Zhu.Critical revision of the manuscript for important intellectual content: Xinxin Zhang, Zhonghua Yang, Jiao Zhang, Jiwen Cheng, Li Li, Haixia Zhou, Suhong Li, Wenhan Yang.Statistical analysis: Susu Jiang, Yong Li, Lei Lin.Obtained funding: Jing He, Zhenjian Zhuo.Administrative, technical, or material support: Xinxin Zhang, Zhonghua Yang, Jiao Zhang, Jiwen Cheng, Li Li, Haixia Zhou, Suhong Li, Wenhan Yang.Supervision: Jing He, Zhenjian Zhuo.

Funding

This study was supported by grants from the National Natural Science Foundation of China (No: 82173593, 81973063), Shenzhen Municipal Commission of Science and Technology Innovation (No: JCYJ20220531093213030), Major Science and Technology Special Project of Wenzhou (No: ZY2020021), Guangzhou Science and Technology Project (No: 202201020622), Guangzhou Science and Technology Project (No: 2021BS051) and the Guangdong Basic and Applied Basic Research Foundation (No: 2023A1515220053).

Data availability

All the data were available upon request from the correspondence authors.

Declarations

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Guangzhou Woman and Children’s Medical Center (Ethical Approval No. 202016601), and written informed consent was obtained from each participant. We confirm All of these human tissue samples that all experiments were performed in accordance with relevant guidelines and regulations.

Consent for publication

No identified individual data was used. All participants signed an informed consent to use their de-identified data.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Susu Jiang, Siqi Dong and Yong Li contributed equally to this work.

Contributor Information

Jing He, Email: hejing198374@gmail.com.

Zhenjian Zhuo, Email: zhenjianzhuo@pku.edu.cn, Email: zhenjianzhuo@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (143.6KB, pdf)

Data Availability Statement

All the data were available upon request from the correspondence authors.

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