(2024) 24:540
Wang et al. BMC Pediatrics
https://doi.org/10.1186/s12887-024-05015-3

BMC Pediatrics

RESEARCH

Open Access

Dysregulated arginine metabolism
in precursor B‑cell acute lymphoblastic
leukemia in children: a metabolomic study
Wenqing Wang1,2†, Liuting Yu2†, Zhen Li1, Yan Xiao2, Hao Jiang4, Yan‑lai Tang3*, Yun Chen2* and Hongman Xue1*

Abstract
Background Precursor B-cell acute lymphoblastic leukemia (B-ALL) is the most common cancers in children. Failureof induction chemotherapy is a major factor leading to relapse and death in children with B-ALL. Given the importance of altered metabolites in the carcinogenesis of pediatric B-ALL, studying the metabolic profile of children
with B-ALL during induction chemotherapy and in different minimal residual disease (MRD) status may contributeto the management of pediatric B-ALL.
Methods We collected paired peripheral blood plasma samples from children with B-ALL at pre- and post-induction
chemotherapy and analyzed the metabolomic profiling of these samples by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Healthy children were included as controls. We selected metabolites
that were depleted in pediatric B-ALL and analyzed the concentrations in pediatric B-ALL samples. In vitro, we study
the effects of the selected metabolites on the viability of ALL cell lines and the sensitivity to conventional chemotherapeutic agents in ALL cell lines.
Results Forty-four metabolites were identified with different levels between groups. KEGG pathway enrichment
analyses revealed that dysregulated linoleic acid (LA) metabolism and arginine (Arg) biosynthesis were closely associated with pediatric B-ALL. We confirmed that LA and Arg were decreased in pediatric B-ALL samples. The treatment
of LA and Arg inhibited the viability of NALM-6 and RS4;11 cells in a dose-dependent manner, respectively. Moreover,Arg increased the sensitivity of B-ALL cells to L-asparaginase and daunorubicin.
Conclusion Arginine increases the sensitivity of B-ALL cells to the conventional chemotherapeutic drugs L-asparaginase and daunorubicin. This may represent a promising therapeutic approach.
Keywords Precursor B-cell acute lymphoblastic leukemia, Minimal residual disease, Metabolomic profiling, Linoleic
acid, Arginine†

Wenqing Wang and Liuting Yu contributed equally to this work.

*Correspondence:
Yan‑lai Tangtangylai@mail.sysu.edu.cn
Yun Chencheny653@mail.sysu.edu.cn
Hongman Xue
xuehm5@mail.sysu.edu.cn
Full list of author information is available at the end of the article© 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 longas 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 the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. 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
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Wang et al. BMC Pediatrics(2024) 24:540

Introduction
Acute lymphoblastic leukemia (ALL), one of the mostcommon malignancies, has been reported to accountfor nearly one-third of all pediatric cancers [1]. According to immunophenotype classification, 85% of all casesof pediatric ALL belong to the Precursor B-cell acutelymphoblastic leukemia (B-ALL) [2]. Despite considerable advances in the treatment of pediatric B-ALL, a highmortality rate has been witnessed among children withrelapsed or refractory cancer [3]. Therefore, novel therapeutic approaches and strategies are needed to further
improve the prognosis of B-ALL and reduce side effects.
Minimal residual disease (MRD) refers to the presenceof leukemia cells in the bone marrow and, less commonly,in the peripheral blood following chemotherapy, immunotherapy, or radiotherapy [4]. Compared to morphological assessment alone, MRD can better estimate thereduction in disease burden following treatment. Therefore, MRD has emerged as a crucial biomarker for risk
stratification and individual risk-guided treatment decisions in B-ALL [5].
Recently, the application of different metabolomic
approaches has been highly efficient in identifying novel
mechanisms of disease pathogenesis and progression.
Furthermore, significant metabolic alterations havebeen identified in several solid tumors and hematological malignancies, such as colorectal cancer [6], myeloma[7], chronic myelogenous leukemia [8], and acute myeloid leukemia [9]. However, the MRD status of pediatric
B-ALL following induction chemotherapy hasn’t beenreported up to now. Studying the specific metabolic profile can be highly effective in understanding the causes ofpoor outcomes and providing insights into disease biology and mechanisms underlying drug resistance [10].
Amino acids are important sources of energy formalignant cells, and alterations in amino acid metabolism are associated with immunosuppressive propertiesof the the tumour microenvironment [11]. It may be a
promising strategy for tumor therapy to adjust aminoacid metabolism and alter the nutrient content of thetumor microenvironment. Studies have shown that Arg
can be used as a sensitiser for radiotherapy, with nearly
78% of patients with glioblastoma metastases gained
achieving or partial remission when given arginine
administration before radiotherapy [12]. Moreover, arginine directly inhibited lymphoma cell growth in vitro[13], as well as Enhances anti-tumour effects in breast
cancer mice by improving immune status [14]. Arg malnutrition in leukaemia and lymphoma in vivo is often
associated with Arg synthase deficiency [15]. Targeting
abnormal arginine metabolism may help treat B-ALL inchildren.

Page 2 of 11

In the present study, we assessed the association of
MRD status with different metabolites following induction chemotherapy in pediatric B-ALL. It turned outthat linoleic acid (LA) metabolism and arginine wereassociated with pediatric B-ALL. In vitro, LA and Arg
inhibited the viability of NALM-6 and RS4;11 cells significantly. Furthermore, we found that treatment of arginine
increased the sensitivity of B-ALL cells to conventional
chemotherapeutic agents. Therefore, the application of
LA and Arg can be a promising therapeutic option for
pediatric B-ALL in the future.

Materials and methods
Study population

We enrolled 10 pediatric patients with B-ALL, of whom 5
patients (cohort A) were MRD (-) and 5 patients (cohortB) were MRD (+) at post-induction. Here, we selectedleukemic blasts > 5% as the cut-off value for MRD positivity and < 0.01 for MRD negativity after induction. In
addition, 5 healthy children were included as controls.
The basic characteristics of the children with B-ALL andthe induction chemotherapy regimen are summarisedin Supplementary Tables 1 and 2. The study was conducted according to the guidelines of the Declaration of
Helsinki, and approved by the Ethics Committee of Sun
Yat-sen University (KY-2022-020-01). All participants
under 16 years old provided written informed consentsfrom parents or legal guardians. Samples were collected
at the Affiliated Hospital of Sun Yat-sen University, andall children with B-ALL were treated according to theSouth China Collaborative Group for the Treatment ofPediatric ALL 2016 protocol (SCCCG-2016-ALL), where
induction chemotherapy included prednisone trial,
VDLD induction and CAM intensification. In our study,
we mainly assessed MRD on day 26 after VDLD chemotherapy (post-induction).
Sample collection and preparation

Peripheral blood samples were collected from each subject
at two time points: (1) at diagnosis (pre-induction) on day
0 and (2) after VDLD chemotherapy (post-induction) on
day 26. In total, 3mL of peripheral venous whole blood wascollected to ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes from each participants who had not consumed any food or water. The samples were centrifuged at2000 rpm for 10 min at 4 °C, and the collected supernatant
was stored at − 80 °C. All plasma samples were thawed onice, following which 50 µL of each sample was transferred
to a 1.5 mL polypropylene tube. It was mixed with 150 µL
of methyl alcohol by vortexing for 10 min. Next, the mixture
was centrifuged at 14,000 rpm for 10 min. The supernatants
were transferred to another 1.5 mL polypropylene tube, driedWang et al. BMC Pediatrics(2024) 24:540

with nitrogen, and subsequently dissolved in 80 µL of a 1:1
mixture of methanol and ultrapure water. The samples werevortexed for 5 min and separated by centrifugation at 14,000rpm for 10 min. The QC sample was prepared by mixing anequal aliquot of the supernatant from all of samples. Finally,
supernatants were transferred to autosampler vials.
Metabolomics platform

Metabolomic profiling was performed on a Q Exactive
Plus Hybrid Quadrupole- Orbitrap Mass Spectrometerwith Thermo Vanquish UHPLC (Thermo, MA, USA) at
Bay Laboratory of Shenzhen. All plasma samples wereanalyzed in both positive ion (ESI+) and negative ion(ESI−) modes, with the mass range varying from 100
to 1000 m/z in the full scan mode. Separation was performed on an ACQUITY UPLC BEH C18, 2 μm column;
the mobile phase A was 0.1% formic acid in water, andmobile phase B was acetonitrile. The column temperature was maintained at 25 °C, and elution was performed
at a flow rate of 400 µL/min. A total of 2 µL of the sample
was injected for negative ions and 2 µL for positive ions.
Data processing

The raw data files were imported into Compound Discoverer 3.2, and the intensity of each ion was normalized togenerate a data matrix consisting of the m/z value, retention time, and normalized peak area. The peak intensity
values of each plasma sample were classified according to
the above study group, followed by further analysis.
Metabolic pathway enrichment analysis

The peak intensity values of metabolites with highly different abundance in plasma samples were imported into
the MetaboAnalyst. MetaboAnalyst generates integrated
pathway enrichment and pathway topology analyses tostudy the functions of untargeted metabolites. Metabolite identifiers were converted to the synonyms listed in
the Human Metabolome Database (HMDB 5.0) [16]. The
pathway impact measures represented the sum of important measures of significant metabolites generated by
topology analysis and normalized to important measuresof all metabolites in the associated pathway. In addition,
we used the KEGG database to map significant metabolites to the pathways of interest [17, 18].
ELISA

Peripheral plasma was collected on day 26 of induction
chemotherapy from children with B-ALL, as well as fromPage 3 of 11

healthy children as controls (Supplementary Table 1).
Arginine concentrations were measured using the
Human Arginine (Arg) ELISA KIT (Reed Biotech, Wu
Han) according to the manufacturer’s instructions.
Cell lines

Human B-ALL cell line NALM-6 (ATCC, CRL-3273) andALL cell line RS4;11 (ATCC, CRL-1873) and human normal peripheral blood mononuclear cells (PBMC) wereobtained from the Scientific Research Centre, The SeventhAffiliated Hospital, Sun Yat-sen University (Shenzhen,
China). The cells were cultured in RPMI1640 medium containing 10% fetal bovine serum and 1% penicillin/ streptomycin at 37 °C in a humidified atmosphere of 5% CO2.
Cytotoxicity assay

The cytotoxicity of LA, Arg and chemotherapeutics wereassessed by the CCK8 assay. In 96-well plates, cells weretreated with different concentrations of LA, Arg (0, 100,
200, 300, 400 and 500 µM) and Arg 300µM in combination with different chemotherapeutics for 24 h and then
10 µL CCK8 (5 mg/mL) was added to each well. Afterthe cells were incubated in a 37 °C incubator for 4 h, theabsorbance value was detected at 450 nm by microplate
reader, and the IC50 value was calculated. All of the solutions were adjusted to a pH of 7.4 and stored at 4 °C.
Statistical analysis

The metabolomics data were analyzed using the online
software MetaboAnalyst 5.0 [19]. The supervised
Orthogonal Partial Least Squares Discriminant Analysis(OPLS-DA) model was used to assess metabolic changes
between groups. To assess the risk of overfitting of the
OPLS-DA model, 1000 permutation tests were performed. Pathway enrichment analyses were performed
using the Kyoto Encyclopedia of Genomes (KEGG) database. Qualitative and quantitative differences between
subgroups were analysed using χ2 test or Fisher’s exact
test for categorical parameters, and Student’s t-test or
Mann-Whitney U test was used to compare the difference in two groups as appropriate. Differences in cell
viability were expressed as means ± SDs of triplicate
experiments performed independently. P-value less than
0.05 was considered significant.

Results
Baseline characteristics and metabolite profiling

Plasma samples were collected from 10 pediatricpatients newly diagnosed with B-ALL and five healthy
volunteers of similar age from the same geographicalWang et al. BMC Pediatrics(2024) 24:540

area as controls. The baseline characteristics of patientsare shown in (Supplementary Table 1) All children with
B-ALL were treated with chemotherapy according to theSouth China protocol, a schedule of administration anddosing in the induction chemotherapy regimen is shownin (Supplementary Table 2) Risk stratification revealed
that the MRD(+) group consisted of two high-risk, one
standard-risk, and two low-risk individuals, while the
MRD(-) group consisted of one standard-risk and four
low-risk individuals (Supplementary Table 3). A total of
2,988 features were identified in the plasma samples. We
used HMDB to annotate 274 metabolites in the plasma
and found that 31 metabolites significantly differed
between healthy volunteers and children with B-ALL
at diagnosis, whereas 13 metabolites markedly differed
between children with B-ALL with varied MRD statuses
after induction.
Metabolic alterations in pediatric B‑ALL at pre‑induction
compared with healthy controls

Distinct clusters of metabolites were identified in
the peripheral plasma of children with B-ALL at preinduction (cohort A and cohort B) compared to control
individuals, as seen in the discovery cohort by OPLSDA (Fig. 1A, D) and PCA score plots (Supplementary
Fig. 1A). We set up a model that contained metabolomic information by fitting OPLS-DA. We evaluatedits accuracy to classify new samples through 1000 random permutation tests during cross-validation (Fig. 1B,
E). The model was reliable since the goodness of fit (R2)and predictive ability (Q2) were both greater than 0.5
variable Importance in Projection (VIP) scores wereused to plot the changes in the levels of the identified
metabolites in B-ALL compared to healthy controls(Fig. 1C, F). Furthermore, considering the statisticallysignificant differences and the importance of the variables in prediction, we plotted the fold change in levelsof identified metabolites in B-ALL compared to healthy
controls (Supplementary Fig. 1C). Taken together, children with B-ALL experience significant changes in themetabolome of their peripheral plasma. These changes
may be associated with the pathogenesis of B-ALL.
Metabolic changes in distinct MRD status at post‑inductionin pediatric B‑ALL

We also evaluated the metabolic changes of children
with B-ALL in different MRD states at post- induction
chemotherapy. Differential metabolites in the cohort A
and cohort B at post-induction were indicated through
OPLS-DA (Fig. 1G) and PCA score plots (Supplementary Fig. 1B), as previously mentioned. We also fitted
the OPLS-DA model and verified its reliability by crossvalidating the Q2 and R2 values (Fig. 1H). Additionally,

Page 4 of 11

we used VIP scores (Fig. 1I) and volcano plots (Supplementary Fig. 1D) to illustrate the significance and impact
of differential metabolites between the two groups. The
results revealed that metabolic alterations in children
with B-ALL MRD (+) at post-induction chemotherapy
differed from those in MRD (-) patients.
Analysis of pathogenic metabolites in children with B‑ALL
at pre‑induction

To identify metabolic phenotypes that were associatedwith the development of B-ALL, we compared metabolicchanges in plasma samples from B-ALL and healthy controls by hierarchical cluster analysis (Fig. 2A). It turned
out that 12 metabolites were elevated and 13 metabolites
were significantly decreased in children with B-ALL compared to healthy controls. We categorised the top twenty
metabolites (Supplementary Table 4) and found that the
major alterations of metabolites in pediatric B-ALL werefatty acid metabolism and amino acid metabolism, which
was confirmed by KEGG analysis (Fig. 3A).
Metabolite variation in pediatric B‑ALL with different
responses to induction therapy

To investigate metabolite changes in children with B-ALL
with different efficacy after induction chemotherapy, we
compared metabolic changes between the MRD (+) andMRD (-) groups by hierarchical cluster analysis (Fig. 2B).
The results indicated that 10 metabolites appeared to beelevated and 11 metabolites appeared to be decreasedin the MRD (-) group compared to the MRD (+) group.
The top metabolites were categorized in Supplementary
Table 5. To figure out the functional characteristics ofthese metabolites, we performed pathway enrichment
analysis by KEGG and identified significantly enrichedpathways. Among the pathways that were altered, themost changes were observed in the pathways associatedwith arginine and ether lipids (Fig. 3B).
Metabolic characterization of linoleic acid and arginine

Our study revealed significant differences in linoleic acid(LA) levels between pediatric B-ALL patients and healthy
controls, as for arginine (Arg) levels after induction chemotherapy for pediatric B-ALL with different MRD status.
The metabolic pathways of LA and Arg were significantly
enriched in KEGG analysis. Therefore, we further analyzed the mass spectral patterns and the relative abundance of LA and Arg (Fig. 4A). The results demonstrated
that LA was decreased in pediatric B-ALL patients compared to healthy children while Arg was reduced in the
MRD (+) group compared to the MRD (-) groups. More
importantly, the reduction of Arg in MRD (+) group
was confirmed by ELISA. Besides, a notable decrease inWang et al. BMC Pediatrics(2024) 24:540

Page 5 of 11

Fig. 1 Metabolic changes were observed in the peripheral plasma of pediatric B-ALL. A, D OPLS-DA score plots for comparison between healthy
controls and pediatric B-ALL in cohort A and cohort B at pre-induction, respectively. G OPLS-DA score plots for comparison between cohort
A and cohort B in B-ALL at post-induction. B, E, H The cross-validation results, tested with a permutation method repeated 1000 times. Both R2
and Q2 are greater than 0.5. C, F The VIP score plots for showing the impacts of differential metabolites in B-ALL compared to healthy controls. I The
VIP score plots for showing the impacts of altered metabolites on MRD status after B-ALL induction chemotherapy

Fig. 2 The identification of pathogenic metabolites in pediatric B-ALL. Hierarchical cluster analysis was performed for metabolite differencesin (A) children with B-ALL at pre-induction compared to healthy controls and (B) children with B-ALL of different MRD status at post-induction
chemotherapy. The red color represents high relative concentrations of metabolites in each group, while the blue color represents low relative
concentrations of metabolites in each groupWang et al. BMC Pediatrics(2024) 24:540

Page 6 of 11

Fig. 3 KEGG pathway enrichment analysis. Significant changes of the metabolites were observed in (A) children with B-ALL at the time of diagnosis
compared to healthy controls, and (B) children with B-ALL of different MRD status after induction chemotherapy

arginine levels was observed in children with B-ALL compared to healthy controls (Supplementary Fig. 2).
Linoleic acid and arginine inhibited the viability of ALL celllines

Through evaluation the levels of metabolites in different groups, we found that linoleic acid (LA) and arginine(Arg) were significantly decreased in pediatric B-ALL
compared to healthy controls in the discovery cohort at
pre-induction and post-induction, respectively. To confirm the role of LA and Arg in ALL, the ALL cell lines
were treated with LA and Arg, respectively. Compared tophosphate buffer saline (PBS), both LA and Arg remarkably inhibited cell viability in NALM-6 and RS4;11 celllines in a dose-dependent manner (Fig. 5A) without negative effects on normal PBMC. These results indicatedthat LA and Arg exhibited anti-ALL activity with highspecificity.
Arginine increased the sensitivity of ALL cells
to conventional chemotherapeutic agents

As arginine levels were diverse in pediatric B-ALL
patients with different MRD states following induction
chemotherapy and arginine inhibited ALL cell viability
in vitro, we wondered whether arginine could be usedin combination with the most commonly used chemotherapeutics (VDLD) in B-ALL treatment. To this end,
ALL cell lines NALM-6 and RS4;11 were treated with
Arg in combination with chemotherapeutic agents for
24 h (i.e. dexamethasone, L-asparaginase, daunorubicin,
vincristine). These agents were normally applied to treat

pediatric ALL patients. The results showed that combination of Arg and L-asparaginase (L-ASP) and daunorubici (DNR) respectively at fixed molar combination
ratios exert greater effects on inhibiting cell viability thanthat of L-Asp or DNR alone (Fig. 6A). Therefore, Arg
could effectively increase the sensitivity of B-ALL cells to
L-ASP and DNR.

Discussion
B-ALL has been reported as the most common pediatric cancer [20]. Although conventional chemotherapy
contributes to disease-free survival in more than 85%of pediatric patients with ALL, survival after relapse
is very poor. MRD, which is often used to study hematological cancer, serves as a primary indicator to assess
relapsed ALL in a post-induction setting [21]. In recent
years, researchers have recognized altered metabolism as
a cornerstone mechanism underlying oncogenesis and ahallmark of cancer [22]. Furthermore, tumor metabolism
represents a readily druggable molecular space for identifying vulnerable metabolic targets in tumors [23]. For
instance, a metabolite with a causal effect on the disease
indicates the possibility of the involvement of an enzyme
in its biosynthetic pathway that could serve as a therapeutic target [24]. Therefore, metabolomic insights canpave the way toward discovering novel therapeutic strategies for B-ALL, especially for relapsed and refractory
B-ALL.
In this study, we performed an in-depth metabolomic analysis of peripheral plasma from B-ALL children at pre-induction chemotherapy to determine theWang et al. BMC Pediatrics(2024) 24:540

Page 7 of 11

Fig. 4 The mass spectral patterns and the relative abundance of linoleic acid and arginine. A-B Fragmentation profiles of LA and Arg on MS,
respectively. C Comparison of the relative abundance of linoleic acid between Cohort A and Cohort B at pre-induction and healthy controls. D
Comparison of the relative abundance of Arg in cohort A and cohort B at post-induction in B-ALL

differences in metabolites between B-ALL children andhealthy subjects, as well as the differences in metabolites
in B-ALL children with different MRD states at postinduction chemotherapy [25]. In total, 2988 metabolites
were annotated in all collected samples, out of which
273 metabolites were best matched to the BioCyc [26],
HMDB, or KEGG databases. Significant changes in themetabolomic composition of children with B-ALL wereobserved during induction chemotherapy. These observations suggested that metabolic reprogramming occurredin children with B-ALL to overcome inadequate nutrient
supply and to generate the energy required for cell proliferation and survival. The metabolic reprogramming may

be a potential driver of hematological tumor pathogenicity in children with B-ALL.
The findings of this study indicate that the presence of
peripheral blast cells exerts an influence on the metabolic
activity of the organism. Firstly, a comparative analysis
was conducted between the pre-induction B-ALL groupand healthy controls, which revealed more pronounced
alterations in fatty acid metabolism in children with ALL
than in healthy controls. Furthermore, notable alterations
in arginine metabolism were identified in patients withdisparate MRD statuses following induction chemotherapy. Through comparison of samples of healthy controls
and pediatric B-ALL patients at pre-induction, 31 metabolites were selected which showed significant enrichment

Wang et al. BMC Pediatrics(2024) 24:540

Page 8 of 11

Fig. 5 LA and Arg inhibited the viability of ALL cell lines. A-C Arg and (D-F) LA inhibited the viability of NALM-6, RS4;11 cell lines rather than normal
PBMC, as determined by CCK8 assay. Data are expressed as mean ± SEM of three independent experiments. *p < .05, **p < .01, ***p < .001,****p < .0001

Fig. 6 Effects of Arg treatment alone and in combination with different chemotherapic drugs in (A-D) RS4;11 and in (E-H) NALM-6 cells. Cells
were treated with indicated concentrations and fixed molar ratios and viability assessed by CCK8 assay after 24 h incubation. Data are expressed
as mean ± SEM of three independent experiments. *p < .05, **p < .01, ***p < .001

for several processes, including LA metabolism, D-glutamine and D-glutamate metabolism, and nitrogenmetabolism. Besides, 13 metabolites were identified withdifferent levels in pediatric B-ALL samples with different
MRD status post-induction. These metabolites exhibited distinct enrichment for processes including arginine
metabolism, ether- lipid metabolism and glutathione

metabolism. Dysregulation of fatty acid and amino acidmetabolism, particularly glutamine, serine, and glycine,has been reported to support the growth of cancer cells[27]. Aberrant regulation of amino acids and extracellular matrix (ECM) has been known to promote pro-tumor
activity [28]. The functions of these newly identified differential metabolites need to be further investigated.

Wang et al. BMC Pediatrics(2024) 24:540

Of interest, two depleted metabolites LA and Arg wereobserved during induction chemotherapy. The reductionof LA and Arg in the peripheral blood of pediatric B-ALL
patients indicated that they played as a potential protective role in B-ALL progression. LA is an n-6 polyunsaturated fatty acid [29], which is enzymatically oxidized
to different derivatives involved in cell signaling [30]. It
inhibited colorectal cancer cell growth by inducing apoptosis [31]. Supplementation of LA has been a potential
adjuvant treatment for breast cancer [32, 33]. Long-term
treatment of cancer cells with LA induced a dormancylike phenotype [34]. However, LA has not been used asa treatment option for leukemia. In this regard, our work
provides a new link between altered LA metabolism and
B-ALL pathogenesis. Amino acids play crucial roles incancer progression beyond their function as building
blocks for protein synthesis [35]. Arg is a semi-essential
amino acid that that is involved in immune regulation.
Arg-derived NO activated cytotoxic T cells [36] andmacrophages [37], thus promoting the immune response
against tumor cells. Geiger Roger et al. reported that
arginine revealed anti-tumor activity by enhancing theproliferation, differentiation and survival of T cells [38].
Engineered bacteria can produce lots of arginine when
implanted into tumor and exhibit synergistic effects
with PD-L1 blockers on augmenting the infiltration ofcytotoxic T cells and decreasing the infiltration of Treg
cells in the tumor [39]. In addition, Arg was reported toinhibit the growth of gastric cancer cells [40] and micemelanoma cells [41]. The administration of arginine
enhanced the therapeutic effects of radiation in patientswith brain metastases [42].
Arginine depletion has been shown to be effective inthe treatment of tumours [43–45]. Such as, arginine
depletion using BCT-100 a pegylated (PEG) recombinant human arginase reduced disease burden andenhanced the survival in ALL murine xenografts [46].
However, our study suggests that high levels of Arg
may increase the response of B-ALL cells to induction
chemotherapy. This is similar to some studies suggesting that arginine may enhancing anti-tumour activity.
[38, 40, 41, 47]. Given the limitations of the current
study, further research is required to investigate theimplications for the underlying molecular mechanismsof arginine in pediatric B-ALL.
However, this study has a few limitations. Firstly, thefusion genes in the sample were diverse and it was not possible to observe the effect of a particular fusion gene on
metabolism in isolation. Secondly, the study did not include
patients whose end-of-induction MRD levels ranged from0.01 to 5%. In addition, we used a small sample size andalthough several significant findings were identified, alarger sample size may have revealed additional differences.

Page 9 of 11

Therefore, we may miss some potentially relevant metabolites. Larger-scale functional studies in vivo and in vitro areneeded to determine the relationship between metabolic
alterations and children with B-ALL.

Conclusions
Overall, the present study identified novel metabolic
alterations in children with B-ALL by performing metabolomic analysis of peripheral blood before and after
induction chemotherapy. Our findings indicate that Arg
is deficient in B-ALL and that increasing arginine may
potentially enhance the response of B-ALL cells to induction chemotherapy. Therefore, targeting Arg in chemotherapy may help treat children with B-ALL.
Abbreviations
B-ALL	Precursor B-cell acute lymphoblastic leukemia
MRD	Minimal residual disease
UHPLC-MS	Ultra-high performance liquid chromatography-mass spectrometryLA	Linoleic acidArg	Arginine
OPLS-DA	Orthogonal partial least squares discriminant analysis
VIP	Variable Importance in Projection
KEGG	Kyoto encyclopedia of genomes
L-ASP	L-asparaginase
DNR	Daunorubici
ECM	Extracellular matrix

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12887-​024-​05015-3.
Supplementary Material 1.
Supplementary Material 2.
Acknowledgements
Not applicable.
Authors’ contributions
Conceptualization, W.W. and Y.C.; methodology, W.W.; software, Y.X.; validation,
W.W., Z.L.; Y.X. and H.J.; resources, Y.T. and Z.L.; data curation, W.W.; original draft
preparation, W.W. and L.Y.; review and editing, Y.C.; L.Y. and H.X.; supervision,
Y.C and H.X.; All authors have read and agreed to the published version of themanuscript.
Funding
This study was supported by Shenzhen Science and Technology Innovation
Commission (JCYJ 20190809160609727); Sanming Project of Medicine inShenzhen (SZSM202011004).
Availability of data and materials
The raw data used to support the findings of this study are available from thecorresponding author upon reasonable request.

Declarations
Ethics approval and consent to participate
The study was approved by the Institutional Review Board of The SeventhAffiliated Hospital of Sun Yat-Sen University. All participants under 16 years oldprovided written informed consents from parents or legal guardians. The data
presented in this study are available in the article.

Wang et al. BMC Pediatrics(2024) 24:540

Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital of Sun Yat-Sen University,
Shenzhen, Guangdong 518107, China. 2 Edmond H. Fischer Translational
Medical Research Laboratory, Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
3
Department of Pediatrics, , The First Affiliated Hospital, Sun Yat-sen University,
Guangzhou, Guangdong 510080, China. 4 Medical laboratory science, The
Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong
518107, China.
Received: 2 April 2024 Accepted: 14 August 2024

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