Qiu et al. BMC Pediatrics(2024) 24:574
https://doi.org/10.1186/s12887-024-05053-x

BMC Pediatrics

RESEARCH

Open Access

TARGET based m6A methylation-related
genes predict prognosis relapsed B-cell acutelymphoblastic leukemia
Kun-yin Qiu1,2†, Xiong-yu Liao1,2†, Jian-pei Fang1,2† and Dun-hua Zhou1,2*
Abstract
Purpose The current study aims to investigate the significance of N6-methyladenosine (m6A) methylationrelated
genes in the clinical prognosis of childhood relapsed B-cell acute lymphoblastic leukemia (B-ALLL) patient.
Methods Transcriptome data and corresponding clinical data on m6A methylation-related genes (including 20
genes) were obtained from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET)
database.
Results The bone marrow (BM) samples of 134 newly diagnosed (naive) and 116 relapsed B-ALL from TARGET wereenrolled in the current study. Three genes (FTO, HNRNPC, RBM15B) showed significant up-regulation in relapsed
B-ALL compared with that in naive B-ALL.The three genes had a significantly worse survival (P < 0.05). The LASSO Cox
regression model was used to select the most predictive genes as prognostic indicators, and YTHDC1 and FTO wereidentified as prognostic factors for relapsed B-ALL. Finally, the results of multivariate regression analysis showed thatthe risk score of m6A methylation-related genes was an independent prognostic factor in relapsed B-ALL (P < 0.05).
Conclusion We found that the expression levels of m6A methylation-related genes were different in naive andrelapsed patients with B-ALL and correlated with survival and prognosis.This implies that m6A methylation-related
genes may be promising prognostic indicators or therapeutic targets for relapsed B-ALL.
Keywords m6A methylation-related genes, Relapsed B-ALL, Children, Prognosis†

Kun-yin Qiu, Xiong-yu Liao and Jian-pei Fang contributed equally tothis work as first authors.
†

Dun-hua Zhou contributed equally to this work as corresponding
author.
*Correspondence:Dun-hua Zhou
zhoudunh@mail.sysu.edu.cn
1
Department of Hematology/Oncology, Children’s Medical Center, Sun
Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou
510120, P.R. China
2
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics
and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen
University, Guangzhou 510120, P.R. China

Introduction
Although the prognosis of childhood B-cell acute lymphoblastic leukemia (B-ALL) has improved significantly
in recent years, 10-15% will experience relapse [1–3]. Thisis partly because pediatric B-ALL tend to have more unfavorable cytogenetic profiles [4, 5]. N6-methyladenosine(m6A) is the most common epigenetic modification on
eukaryotic messenger RNA (mRNA) and plays an important role in many basic biological processes [6, 7]. The
dysregulation of m6Amodification plays a crucial role in
the growth and advancement of several types of cancers,
such as acute myeloid leukemia (AML) [8–10]. Nevertheless, research on the m6A modification in relapse© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Qiu et al. BMC Pediatrics(2024) 24:574

B-ALL is confined. Although the extent of m6A modification in relapse B-ALL remains scantily investigated. Our
objective in this study is to evaluate the activity of m6A
machinery (writers, erasers, and readers) in bone marrow(BM) samples from primary B-ALL patients, both naive
and relapsed. Additionally, we will examine the relationship between gene expression related to m6A methylation and the clinical prognosis of patients. Through thisresearch, our goal is to identify new prognostic biomarkers and potential therapeutic targets for relapsed B-ALL.
We present the following article in accordance with the
MDAR reporting checklist.

Methods and materials
Data collection

We downloaded the transcriptome data and clinical dataof naïve and relapsed B-ALL from The Therapeutically
Applicable Research To Generate Effective Treatments(TARGET) database (https://ocg.cancer.gov/programs/
target). MRNA expression data, as well as clinical information such as age, gender, early treatment response, andfusion gene status, were collected from 134 newly diagnosed (naive) and 116 relapsed B-ALL BM samples. The
results published here are entirely or partially based ondata generated from studies applicable to treatment, inorder to generate effective treatment methods(https://
ocg.cancer.gov/programs/target). The data used for thisanalysis can be found in the https://portal.gdc.cancer.
gov/projects. The Ethics Committee of the Office ofCancer Genomics (OCG) approved this study, and theinformed consent forms were signed by the guardians of
the patients involved. This study followed the guidelines
outlined in the Helsinki Declaration (clinical trial numbers: NCT 00070174, NCT00372593, NCT01371981,
and NCT00002798).
Bioinformatic analysis/statistics

The differential expression of 20 m6A methylation-related
genes, including writers (METTL3, METTL14, VIRMA,
RBM15, RBM15B, RMBX, WTAP, and ZC3H13), readers (HNRNPC, HNRNPA2B1, YTHDC1, YTHDC2,YTHDF1, YTHDF2, YTHDF3, IGF2BP1, IGF2BP2,
IGF2BP3), and erasers (ALKBH5, FTO), in samplesof naive and relapse B-ALL BM were assessed using R
sofeware.
Statistical analyses

Univariate Cox regression was used to analyze the setof 20 identified genes. Candidate genes were chosen
based on whether they met the screening requirementof P<0.05. Following that, we employed LASSO regression on high-dimensional data in order to select themost pertinent prognostic factors using package in R
software. Using this method, we identified five genes

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and calculated their corresponding risk scores. Basedon the median expression of m6A methylation-related
genes, patients were categorized into high-risk and lowrisk groups. We then utilized the K-M survival approach
to examine the association between these m6A-related
genes and the survival rates. The P-value of the K-M
survival curves was determined using log-rank tests. To
assess the accuracy of the model, we generated a receiver
operating characteristic (ROC) curve. Univariate andmultivariate Cox regression analyses were carried out toidentify prognostic factors for relapsed B-ALL.
The regression coefficients of 5 optimal prognostic
genes were derived from the multivariate Cox proportional hazards regression model. Subsequently, a linear
combination method was adopted to assemble expression
level and coefficient of each gene to get a risk score formula. The samples of BM were divided into two groups
using “sample” function of R software. Heatmap of ALL
was plotted using “pheatmap” R package with zero-mean
normalization. PCA was used to estimate batch effectand clustering result using “ggfortify” R package. Twogroups of boxplot were analyzed using Wilcoxon-test.
For Kaplan-Meier curves, p-values and hazard ratio (HR)with 95% confidence interval (CI) were generated bylog-rank tests and univariate Cox proportional hazards
regression. All analytical methods above and R packages
were performed using R software version 3.6.1 (The R
Foundation for Statistical Computing, 2019). All statistical tests were two-sided. P < 0.05 was considered as statistically significant.

Results
TARGET dataset and patients’ characteristics

The current study included samples of 134 newly diagnosed (naive) B-ALL and 116 relapsed B-ALL extracted
from the BM collection of TARGET. After excluding
samples with incomplete clinical data, a group of 116
patients diagnosed with relapsed B-ALL (consisting of 64
males and 52 females) were included for analysis. The age
range from 1.1 years to 18.7 years and the mean age ofthe individuals in the dataset was 8.4 years. The available
clinical information encompassed demographic characteristics, initial response to treatment, and the presenceof fusion genes.
Expression of m6A methylation-related genes in relapsed
B-ALL

A gene expression heatmap was created using 20 m6A
methylation-related genes to gain a comprehensive
understanding of their expression in naive and relapsed
B-ALL samples. Analysis of the TARGET data revealed
that three genes (FTO, HNRNPC, RBM15B) were significantly up-regulated, while four genes (METTL14,YTHDC1, YTHDC2, RBMX) were significantlyQiu et al. BMC Pediatrics(2024) 24:574

down-regulated in relapsed B-ALL compared to naive
B-ALL (Fig. 1A-B). Based on Fig. 1C, statistical analysis
using Pearson correlation confirmed positive correlations among four genes.The correlation coefficient of 0
indicates a strong positive correlation between METTL3
and YTHDC1.66.METTL4 and YTHDC2, as well as
YTHDC1 and YTHDC2, exhibited robust positive correlations with correlation coefficients of 0.64 and 0.56.
Survival analysis of m6A methylation-related genes

The results revealed that patients with elevated expression of three genes (FTO, HNRNPC, RBM15B) experienced considerably poorer survival rates P < 0.05).
Conversely, individuals with decreased expression of
two genes (YTHDC1, YTHDC2) exhibited significantly

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better survival outcomes (P < 0.05). The remaining two
genes demonstrated no significant difference in terms ofsurvival (P > 0.05) (Fig. 2A-E).
Construction of LASSO model

The univariate Cox regression was used to analyze 20
genes and five candidate genes with P < 0.05 were as
screening criteria (Fig. 3A). We used the LASSO Cox
regression model to select the most predictive genes as
prognostic indicators. When the median of the sum ofsquared residuals is the smallest, select λ (Fig. 3B–C).
YTHDC1 and FTO have been identified as prognostic factors for relapsed ALL. We also calculated the risk
scores for these two genes. According to the combination
model of the cutoff values of the median expression ofFig. 1 Expression, correlation, and prognostic information of m6A methylation-related genes. (A) Heatmaps of m6A methylationrelated genes expressedin naive and relapsed B-ALL. (***P < 0.001, **P < 0.01, *P < 0.05) (B) The expression distribution of m6A in naive and relapsed B-ALL.The abscissa represents
different m6A, and the ordinate represents the expression distribution of gene, different colors represent different groups. *P < 0.05, **P < 0.01,***P < 0.001,
asterisks (*) stand for significance levels. The statistical difference of two groups was compared through the Wilcox test, significance difference of twogroups was tested with Kruskal-Wallis test. (C) Correlation matrix of interaction in m6A methylation-related genes. Correlation coefficients are plotted withnegative correlation (gray) and positive correlation (yellow)

Qiu et al. BMC Pediatrics(2024) 24:574

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Fig. 2 (A-E) Prognostic information for five genes, which had a significantly survival rate (P < 0.05)

two candidate genes, patients are divided into high-riskand low-risk groups. The prognosis of the low-risk groupis always better than that of the high-risk group. Draw
the survival curve using the K-M method (Fig. 3D-E). We
also compared the prognostic efficiency of risk factors

using receiver operating characteristic (ROC) curves.
The results showed that the area under the curve (5-yeararea under the curve (AUC)) was 67.2% (Fig. 3F), indicating that m6A methylation related genes can serve as biomarkers for the prognosis of relapsed B-ALL.

Qiu et al. BMC Pediatrics(2024) 24:574

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Fig. 3 Gene selection and survival analysis in relapsed B-ALL prognosis prediction. (A) Forest plots for hazard ratios (HRs) of survival-associated mA methylation-related genes in relapsed B-ALL. (B) Coefficients of selected features are shown by lambda parameter. (C) Partial likelihood deviance versus log(λ) was drawn using LASSO Cox regression model. (D) The Riskscore, survival time and survival status of selected dataset. The top scatterplot represents
the Riskscore from low to high. Different colors represent different groups. The scatter plot distribution represents the Riskscore of different samples correspond to the survival time and survival status. The buttom heatmap is the gene expression from the signature. (E) Kaplan-Meier survival analysis of therisk model from dataset, comparison among different groups was made by log-rank test. HR(High exp) represents the hazard ratio of the low-expression
samples relatives to the high-expression samples. HR > 1 indicates the gene is a risk factor, and HR < 1 indicates the gene is a protective factor. HR(95%Cl),
the median survival time (LT50) for different groups, in years. (F) The ROC curve and AUC of the gene. The higher values of AUC corresponding to higher
predictive powerQiu et al. BMC Pediatrics(2024) 24:574

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Table 1 Univariate analysis for OS among pediatric patients withrelapsed B-ALL
Variables
GenderMale
FemaleAge (mean ± SD)
WBC (mean ± SD)
D29 MRD
Negative
Positive
D43 MRD
Negative
Positive
ETV6/RUNX1 Status
Negative
Positive
KMT2A Status
Negative
Positive
TCF3 PBX1 Status
Negative
Positive
BCR/ABL Status
Negative
Positive
RISKSCORE (mean ± SD)

OS
Total

HR(95%CI)

64 (55.2%)
52 (44.8%)
8.4 ± 5.1
60.3 ± 134.3

1.0
0.6 (0.4, 1.0)
1.1 (1.0, 1.1)
1.0 (1.0, 1.0)

85 (73.9%)30 (26.1%)

1.0
1.1 (0.7, 1.8)

112 (96.6%)
4 (3.4%)1.0
0.8 (0.2, 3.4)

93(85.3%)
16(14.7%)

1.0
0.8 (0.3, 2.1)

86 (95.6%)
4 (4.4%)1.0
1.4 (0.4, 4.6)

65 (86.7%)
10 (13.3%)

1.0
1.5 (0.7, 3.2)

115 (99.1%)
1 (0.9%)-3.0 ± 0.2

1.0
3.9 (0.5, 29.1)
5.9 (2.2, 15.4)

P value
0.035<0.001
0.002
0.719

0.795

0.682

0.542

0.306

0.178

< 0.001

Table 2 Multivariate analysis for OS among pediatric patientswith relapsed B-ALL
OutcomeOS

Variable
GenderAgeWBC
D29 MRD
D43 MRD
ETV6/RUNX1 Status
KMT2A Status
TCF3 PBX1 Status
BCR/ABL Status
Risk score

HR (95% CI)
0.6 (0.4, 1.1)
1.3 (0.7, 2.3)
1.0 (1.0, 1.0)
1.0 (0.6, 1.6)
1.8 (1.0, 3.3)
0.9 (0.4, 2.2)
0.9 (0.2, 3.6)
1.1 (1.0, 1.2)
4.4 (0.5, 38.7)
1.7 (1.1, 2.6)

P value
0.130
0.342
0.467
0.937
0.071
0.790
0.859
0.153
0.187
0.014

Prognostic value of the m6A methylation-related genes

Univariate analysis indicated that several factors, including gender, age, white blood cell counts (WBC), andthe risk score of m6A methylation-related genes, had an
impact on patient prognosis (P<0.05). Despite the lackof correlation between the prognosis of relapsed B-ALL
and the early treatment response and fusion gene status,
the P-value was greater than 0.05 (Table 1). The findings from the analysis of multiple variables revealed thatthe risk score associated with m6A methylation-related
genes played a significant role in predicting prognosis

for relapsed B-ALL patients, showing its independence(P < 0.05) (Table 2).

Discussion
Relapsed childhood patients with B-ALL have become amajor global public health issue. Based on bioinformatics analysis conducted on the TARGET database, three
m6A methylation-related genes showing up-regulationin relapsed B-ALL were identified, and their association
with markedly poorer survival was established.The widespread distribution of m6A methylation-related genes inleukemic BM samples was revealed by our research, suggesting their significant implications in predicting the
prognosis of relapsed B-ALL.Furthermore, there was anotable interconnection observed among m6A methylation-related genes within regulatory networks, indicating
their collaborative involvement in the progression of leukemia. Additionally, the deleterious impact on patientswith relapsed B-ALL may be attributed to the association of FTO, HNRNPC, and RBM15B with poorer survival outcomes.The indicated results suggest that m6Amodulators could serve as potential targets for treating
relapsed B-ALL.
The LASSO algorithm examines multiple independent
variables at once and identifies the most impactful ones.
With the LASSO algorithm, it is possible to concurrently
evaluate numerous independent variables and pinpoint
the variables with the greatest influence.According to
the traditional regression methods are considerably lessprecise.Based on LASSO Cox analysis, prognostic factors
for relapsed B-ALL were identified, including RBM15B,
YTHDC1, YTHDC2, FTO, and HNRNPC among the 20
genes analyzed [11]. The ROC curve was used to evaluate the impact of m6A methylation related genes on the
prognostic outcome of relapsed B-ALL.The findings indicated that genes associated with m6A methylation played
a role in the survival of relapsed B-ALL.The risk score ofm6A methylation related genes (YTHDC1 and FTO) may
be a powerful biomarker for the survival rate of relapsed
B-ALL. The high expression level of FTO indicates poor
prognosis, while YTHDC1 can be considered a protective
gene. We conducted a comprehensive biological analysisof the 20 most important m6A methylation related genes,
which is more comprehensive than previous studies on
the impact of individual genes on diseases. Due to theinteractions between m6A methylation related genes, ourstudy more accurately reflects their impact on relapsed
B-ALL.
Totally, the combination of m6A methylation related
genes and clinical parameters may have better predictable
power than a single biomarker. Recently, m6A methylation related genes have shown bigger potential in predicting cancer prognosis [12–15]. Our research mainly
showed that the expression levels of m6A methylation

Qiu et al. BMC Pediatrics(2024) 24:574

related genes had a vital part in relapsed B-ALL andmay serve as a prognostic factor for the type of leukemia. The expression of m6A methylation related genes ishighly related with the clinical characteristics of relapsed
B-ALL, which can predict its prognosis and guide precise
treatment. This present study provides vital rules for the
future detection of the role of m6A methylation among
relapsed B-ALL.
Acknowledgements
The study was supported by grant from Guangdong Science and Technology
Department (2020B1212060018).
Author contributions
KY.Q wrote the manuscript and XY.L perform the study. JP.F and DH.Z reviewed
the manuscript.All authors reviewed the manuscript.
Funding
This work was supported by Guangdong Basic and Applied Basic Research
Foundation (NO.2024A1515012445), Special topic of science and technology
for agriculture and social development of Guangzhou key R & D plan
(2024B03J1247), Bethune Medical Scientific Research Fund Project (No.
SCE111DS), Guangdong Medical Scientific Research Foundation (A2024057),
Basic Research Project (Dengfeng Hospital) jointly funded by Universities(Institutes) in Guangzhou (No.202201020310), Guangzhou Basic and Applied
Basic Research Foundation (2024A04J4686), Yat-sen Excellent Young Scientists
Fund (2024A03J1185), Young Teacher Training Project of Sun Yat-sen University(24pnpy314) and Sun Yat-sen Pilot Scientific Research Fund (YXQH202205).
Data availability
The data in the current study are available from the corresponding author on
reasonable request.

Declarations
Ethics approval and consent to participate
The Ethics Committee of the Office of Cancer Genomics (OCG) approved thisstudy, and the informed consent forms were signed by the guardians of thepatients involved.
Consent for publication
Not applicable.
Footnote
Reporting checklist: The authors have completed the MDAR reporting
checklist.
Competing interests
The authors declare no competing interests.
Received: 22 May 2024 / Accepted: 3 September 2024

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