Abstract
Background
Acute lymphoblastic leukemia (ALL) is the most common hematological
malignancy in pediatrics, and immune‐related genes (IRGs) play crucial
role in its development. Our study aimed to identify prognostic immune
biomarkers of pediatric ALL and construct a risk assessment model.
Methods
Pediatric ALL patients’ gene expression data were downloaded from
Therapeutically Applicable Research to Generate Effective Treatments
(TARGET) database. We screened differentially expressed IRGs (DEIRGs)
between the relapse and non‐relapse groups. Cox regression analysis was
used to identify optimal prognostic genes, then, a risk model was
constructed, and its accuracy was verified in different cohorts.
Results
We screened 130 DEIRGs from 251 pediatric ALL samples. The top three
pathways that DEIRGs may influence tumor progression are NABA
matrisome‐associated, chemotaxis, and antimicrobial humoral response. A
set of 84 prognostic DEIRGs was identified by using univariate Cox
analysis. Then, Lasso regression and multivariate Cox regression
analysis screened four optimal genes (PRDX2, S100A10, RORB, and SDC1),
which were used to construct the prognostic risk model. The risk score
was calculated and the survival analysis results showed that high‐risk
score was associated with poor overall survival (OS)
(p = 3.195 × 10^−7). The time‐dependent survival receiver operating
characteristic curves showed good prediction accuracy (Area Under
Curves for 3‐year, 5‐year OS were 0.892 and 0.89, respectively). And
the predictive performance of our risk model was successfully verified
in testing cohort and entire cohort.
Conclusions
Our prognostic risk model can effectively divide pediatric ALL patients
into high‐risk and low‐risk groups, which may help predict clinical
prognosis and optimize individualized treatment.
Keywords: immune‐related genes, pediatric acute lymphoblastic leukemia,
prognosis, survival analysis
__________________________________________________________________
Differentially expressed immune‐related genes between the relapse and
non‐relapse groups of 251 pediatric acute lymphoblastic leukemia
patients were analyzed. Four optimal genes (PRDX2, S100A10, RORB and
SDC1) were screened, and then an immune‐related prognostic model based
on these four genes were constructed.
graphic file with name MGG3-8-e1404-g009.jpg
1. INTRODUCTION
Acute lymphoblastic leukemia (ALL) is the most frequent malignancy and
the leading cause of cancer‐related deaths in pediatrics. The cure rate
has exceeded 80% in last decade, owing to improved supportive care and
optimized treatment regimens (Brassesco et al., [32]2018; Kato &
Manabe, [33]2018). However, a significant number of patients still
suffer from drug resistance or relapse (Tasian & Hunger, [34]2017),
resulting in treatment failure. In addition, treatment may have to be
discontinued because of its high toxicity (Santiago, Vairy, Sinnett,
Krajinovic, & Bittencourt, [35]2017). Taking these factors into
account, new biomarkers and precise treatment regimens will be a
priority for these patients.
A large number of studies have focused on the development and
application of biomarkers in ALL. For example, some researchers have
suggested a tumor suppressor role of TLE1 in T‐ALL (Brassesco
et al., [36]2018). In addition, mTOR inhibitors have been used in
combination with chemotherapy regiments for the treatment of relapse
ALL (Santiago et al., [37]2017), and bcl‐2 inhibitors have also been
used in the treatment for all subtypes of pediatric ALL (Jones
et al., [38]2016). Recent studies have shown that gene expression in
patients with recurrent leukemia after transplantation is highly
enriched in immune‐related processes (Toffalori et al., [39]2019). It
has also been mentioned that this is related to the escape of tumor
cells from the control of allogeneic immune response (Zeiser &
Vago, [40]2019). These results suggest that immune‐related biomarkers
may be significant signatures for predicting the prognosis of ALL.
With the development of bioinformatics, the immune‐related genes
(IRGs)‐based prognostic signatures have been developed in patients
diagnosed with renal papillary cell carcinoma (Wang et al., [41]2019),
colorectal cancer (Bai, Zhang, Xiang, Zhong, & Xiong, [42]2020), and
lung adenocarcinoma (Song et al., [43]2019), which can predict survival
outcomes. However, the prognostic value of IRGs‐based signatures in
pediatric ALL patients is still unknown.
The purpose of this study was to investigate the clinical significance
of IRGs on the prognosis of pediatric ALL and its biological function.
In this paper, we comprehensively analyzed the expression profile data
and the clinical information of pediatric ALL patients. A prognostic
model based on IRGs was developed and validated in public dataset,
which may be helpful in predicting prognosis and optimizing
individualized treatment.
2. MATERIALS AND METHODS
2.1. Gene expression datasets
The transcriptomic data and corresponding clinical information of 251
pediatric ALL patients were downloaded from the Therapeutically
Applicable Research to Generate Effective Treatments (TARGET) portal
([44]https://ocg.cancer.gov/programs/target) (Kang et al., [45]2010).
And the 2,498 IRG sets were obtained from the ImmPort database
([46]https://www.immport.org/home) (Bhattacharya et al., [47]2014). The
expression data were preprocessed by the following steps: (a) removing
samples with no clinical data; (b) removing samples that expression
data and clinical information did not match; (c) preserving only the
expression profiles of IRGs. Form this, 185 patients with complete gene
expression profiles and clinical information were utilized to further
analyze the model. The data downloaded from the TARGET and ImmPort
databases is publicly available and accessible, therefore, this study
does not require ethics committee approval.
2.2. Identification of DEIRGs
The pediatric ALL samples were divided into relapse group and
non‐relapse group. And the treatment regimens before the study endpoint
events were chemotherapy treatments. The differentially expressed IRGs
(DEIRGs) were screened by using edge R package (Robinson, McCarthy, &
Smyth, [48]2010) in R3.6.2 software. The FDR < 0.05 and |log2
fold‐change [FC]| > 1.5 were cutoff values. Then, the gene expression
values were visualized by pheatmap package (Li, Zhang, Rui, Sun, &
Guo, [49]2018). Enrichment analysis was performed to predict the
biological functions of the DEIRGs by using Metascape
([50]http://metascape.org/), an online bioinformatics pipeline (Zhou
et al., [51]2019).
2.3. Construction of the risk score prognostic model
The 185 samples were randomly divided into a training cohort (n = 93)
and a testing cohort (n = 92). The training cohort was used to build
the risk score prognostic model, the testing and entire TARGET cohorts
were used to test the model. First, univariate Cox analysis was used to
identify possible prognostic DEIRGs (PDEIRGs), and p < .05 was
considered significant. Then, the Lasso regression was applied to
select potential risk genes and eliminate genes that would overfit the
model. Finally, we used multivariate Cox regression analysis to
construct a prognostic risk model.
2.4. Risk score calculation and Model validation
To evaluate the contribution of each gene to prognosis, the
multivariate Cox regression analysis was performed. Then, we obtained a
computational formula that weight the expression values of selected
genes with the regression coefficients as follows:
[MATH: Risk scorepatient=∑i=1
ncoefficientgeneiexpressio
n value ofgenei
:MATH]
The risk model was used to measure the prognostic risk of each
pediatric ALL patient.
We substituted the expression profile data into the model to calculate
the risk score of each sample from the training cohort and entire
TARGET cohort. Then, Kaplan–Meier survival analysis, receiver operating
characteristic (ROC) analysis, risk score distribution, survival
status, and risk gene expression of the training cohort, entire TARGET
cohort were performed to verify our risk score prognostic model.
Multivariate Cox regression analysis was used to assess the independent
prognostic ability of the model.
2.5. Statistical analyses
Statistical analyses were performed using R software
([52]https://www.r‐project.org/) and Perl
([53]https://www.activestate.com/products/perl/). Univariate Cox
regression analysis was used to identify factors affecting the survival
of pediatric ALL patients. Lasso regression was used to evaluate
univariate analysis of the link between PDEIRGS. Multivariate Cox
regression analysis was used to identify prognostic factors.
Kaplan–Meier curves and log‐rank tests were used to analyze the
survival data. An Area Under Curve (AUC) > 0.60 was regarded as
acceptable for predictions, and an AUC > 0.75 was deemed to have
excellent predictive value (Cho et al., [54]2019; Han
et al., [55]2018).
3. RESULTS
3.1. DEIRGs screening based on the pediatric ALL samples
The mRNA expression data of 2,498 IRGs in pediatric ALL (n = 251) from
TARGET database was examined. After screening the expression data by
using edge R package, a total of 130 DEIRGs were obtained from with
relapse group (n = 180) and without group (n = 71). The results showed
that 10.8% (14/130) of DEIRGs were downregulated in relapse group while
89.2% (116/130) of DEIRGs was significantly upregulated (FDR < 0.05,
|log2 fold‐change [FC]| > 1.5) (Figure [56]1a,b).
Figure 1.
Figure 1
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Differentially expressed immune‐related genes. (a) Heatmap of DEIRGs;
the green spectrum means low gene expression while the red means high
gene expression. (b) Volcano plot of DEIRGs; the green dots indicate
downregulated IRGs, the red dots indicate upregulated IRGs, and the
black dots represent IRGs that were not significantly differentially
expressed. DEIRGs, differentially expressed IRGs; IRGs, immune‐related
genes
Then, we conducted enrichment analysis to identify the possible
biological functions of DEIRGs. Data showed that the top three
signaling pathways affected by DEIRGs were NABA matrisome‐associated,
chemotaxis, and antimicrobial humoral response [Figure [58]2a,b]. All
these three signaling pathways were reported to be associated with
tumor progression (Chen, Lin, Wu, Her, & Hui, [59]2009; Naba
et al., [60]2012; Shields et al., [61]2007), providing evidence for
further study on the mechanism of pediatric ALL progression.
Figure 2.
Figure 2
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Biological functions of DEIRGs. (a) Significantly enriched pathways of
the DEIRGs. (b) Network of enriched pathways. Each node represents an
enriched GO term and node size represents the number of gene in the
pathways. DEIRGs, differentially expressed IRGs
3.2. Identification of prognostic DEIRGs
The 185 samples were randomly divided into a training cohort (n = 93)
and a testing cohort (n = 92), see Table [63]1. To identify possible
prognostic DEIRGs, we performed a univariate Cox regression analysis of
the expression of each DEIRG in the training cohort. As a result, 84
PDEIRGs were found to be significantly associated with the overall
survival (OS) of pediatric ALL patients (p < .05).
Table 1.
Clinical information of pediatric ALL patients in the training and
validation cohorts
Training cohort (n = 93) Testing cohort (n = 92) Entire TARGET cohort
(n = 185)
Sex
Male 47 (50.5%) 44 (47.8%) 91 (49.2%)
Female 46 (49.5%) 48 (52.2%) 94 (50.8%)
Age at diag (years)
<10 64 (68.8%) 60 (65.2%) 124 (67%)
≥10 29 (31.2%) 32 (34.8%) 61 (33%)
WBC at diag (×10^9/L)
<50 60 (64.5%) 57 (62%) 117 (63.2%)
≥50 33 (35.5%) 35 (38%) 68 (36.8%)
CNS status at diag
CNS1 73 (78.5%) 76 (82.6%) 149 (80.5%)
CNS2 19 (20.4%) 14 (15.2%) 33 (17.8%)
CNS3 1 (1.1%) 2 (2.2%) 3 (1.7%)
First event
Relapse 57 (61.3%) 64 (69.6%) 121 (65.4%)
None 36 (38.7%) 28 (30.4%) 64 (34.6%)
Vital status
Dead 39 (41.9%) 43 (46.7%) 82 (44.3%)
Alive 54 (58.1%) 49 (53.3%) 103 (55.7%)
[64]Open in a new tab
Abbreviations: ALL, acute lymphoblastic leukemia; CNS, central nervous
system; CNS1: no lymphoblasts in CSF; CNS2: present lymphoblasts in
CSF, WBC count of the CSF < 5 cells/µl; CNS3: present lymphoblasts in
CSF or a cranial nerve palsy, WBC count of the CSF ≥ 5 cells/µl; diag,
diagnosis; TARGET, Therapeutically Applicable Research to Generate
Effective Treatments; WBC, white blood cell count.
3.3. Screening prognostic genes for constructing risk model
We further analyzed and screened PDEIRGs for constructing cox
regression hazard model. First, to avoid model overfitting, we used
Lasso regression to remove PDEIRGs that are highly correlated to each
other. Therefore, we obtained seven candidate PDEIRGs
(Figure [65]3a,b). Then, multivariate Cox proportional risk regression
analysis was performed (with forward selection and backward selection).
Finally, we obtained four optimal PDEIRGs (risk genes) to incorporate
into the prognostic risk model: PRDX2, S100A10, RORB, and SDC1. These
four genes were identified as high‐risk genes (predicting a poor
prognosis) in terms of the OS of patients (Figure [66]4).
Figure 3.
Figure 3
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(a and b) PDEIRGs screened through Lasso regression
Figure 4.
Figure 4
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Risk genes of the prognostic risk model. *, p < .05; ***, p < .001
3.4. Construction of prognostic risk model in training cohort
Based on the results of multivariate Cox regression analysis, we
constructed a model to assess the significance of risk genes in
predicting prognosis in pediatric ALL patients. The computational
formula was as follows:
[MATH: Training cohort risk
score=0.1615×expression of
PRDX2+0.3387×expression of
S100A10+0.0903×expression
of RORB+0.1940×expression of
SDC1. :MATH]
We calculated the risk score of each patient in the training cohort
using the risk model, and patients were sorted into a high‐risk group
(n = 46) and a low‐risk group (n = 47). To investigate the difference
in prognosis between the high‐risk and low‐risk groups, we created a
Kaplan–Meier curve based on the log‐rank test. The prognosis was better
in the low‐risk group than in the high‐risk group (p = 3.195 × 10^–7)
(Figure [69]5a). The OS rates at 3 years and 5 years for the high‐risk
group in the training cohort were 46.3% and 33.2%, respectively, while
the corresponding rates for the low‐risk group were 91.5% and 86.9%,
respectively. Then, we tested the predictive accuracy of the model for
3‐year and 5‐year OS through the time‐dependent ROC curves. The AUC
values for the prognostic model were 0.892 at 3 years and 0.89 at
5 years (Figure [70]5b,c). We then sorted and analyzed the distribution
of patients’ risk scores in the training cohort (Figure [71]6a). The
survival status of each patient in the training cohort is marked on the
dot plot in Figure [72]6b. The heatmap we completed showed the
expression of risk genes in both risk groups (Figure [73]6c). In the
high‐risk group of the training cohort, four high‐risk genes (PRDX2,
S100A10, RORB, and SDC1) were upregulated. In the low‐risk group, the
expression of these risk genes was downregulated.
Figure 5.
Figure 5
[74]Open in a new tab
Prognosis analysis of training cohort. (a) Kaplan–Meier curve analysis
of the high‐risk and low‐risk groups. Time‐dependent ROC curve analysis
for the predictive accuracy of the risk model for 3‐year (b) and 5‐year
OS (c). OS, overall survival; ROC, receiver operating characteristic
Figure 6.
Figure 6
[75]Open in a new tab
Prognosis analysis of training cohort. (a) Risk score distribution of
patients based on the prognostic risk model. (b) Survival status of
patients in different groups. (c) Heatmap of expression profiles of
risk genes
3.5. Verification of the performance of the prognostic model
To validate the predictive ability of the prognostic risk model, we
used it to analyze the testing cohort (the remaining 92 patients from
the 185 total) and the entire TARGET cohort. First, the risk score for
each patient in the testing cohort and the entire TARGET cohort was
calculated according to the coefficient value of the four risk genes.
Patients were divided into high‐risk and low‐risk groups with the
median risk score of the training cohort utilized as the cutoff value.
In the testing cohort, 52 patients were divided as high risk and 40
were divided as low risk. In the entire TARGET cohort, 98 patients were
classified as high risk and 87 were classified as low risk.
Then, Kaplan–Meier survival analysis was performed for both the testing
cohort and the entire TARGET cohort. Patients of high risk were with
poor OS compared with those of low risk in both the testing cohort
(p = 1.427 × 10^–3) and the entire TARGET cohort (p = 3.255 × 10^–9)
(Figure [76]7a,d). In the testing cohort, the OS rates at 3 years and
5 years for the high‐risk group were 59.1% and 45.2%, respectively,
while the corresponding rates for the low‐risk group were 87.5% and
77.3%, respectively. In the entire TARGET cohort, the OS rates at
3 years and 5 years for the high‐risk group were 54.3% and 40.7%,
respectively, while the corresponding rates for the low‐risk group were
89.7% and 82.5%, respectively. To evaluate the accuracy in prognosis
prediction of our four‐gene model, we performed time‐dependent ROC
curve analysis. In the testing cohort, the AUCs at 3 and 5 years were
0.814 and 0.751, respectively (Figure [77]7b,c). In the entire TARGET
cohort, the AUCs at 3 and 5 years were 0.852 and 0.819, respectively
(Figure [78]7e,f).
Figure 7.
Figure 7
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Prognosis analysis of testing cohort and entire TARGET cohort.
Kaplan–Meier curve analysis of the high‐risk and low‐risk groups ((a)
for testing cohort, (d) for entire TARGET cohort). Time‐dependent ROC
curve analysis for the predictive accuracy of the risk model for 3‐year
((b) for testing cohort, (e) for entire TARGET cohort) and 5‐year OS
((c) for testing cohort, (f) for entire TARGET cohort). ROC, receiver
operating characteristic; OS, overall survival; TARGET, Therapeutically
Applicable Research to Generate Effective Treatments
The risk score distribution, survival status, and risk gene expression
in the testing cohort and the entire TARGET cohort are shown in
Figure [80]8a–f. Similar to the results in the training cohort, risk
gene levels were lower in the low‐risk group than in the high‐risk
group. These results suggested that our prognostic risk model can
accurately predict the prognosis of pediatric ALL patients.
Figure 8.
Figure 8
[81]Open in a new tab
Prognosis analysis of testing cohort and entire TARGET cohort. Risk
score distribution of patients based on the prognostic risk model ((a)
for testing cohort, (d) for entire TARGET cohort). Survival status of
patients in different groups ((b) for testing cohort, (e) for entire
TARGET cohort). Heatmap of expression profiles of risk genes ((c) for
testing cohort, (f) for entire TARGET cohort). TARGET, Therapeutically
Applicable Research to Generate Effective Treatments
The univariate and multivariate Cox analysis of risk score generated by
our model and clinical parameters in entire TARGET cohort is shown in
Table [82]2. The univariate analysis indicated that the variables of
age, minimal residual disease (MRD) status at day 29 of induction
therapy, and risk score were associated with the prognosis of pediatric
ALL patients. And in the multivariate analysis, the risk score can
serve as an independent prognostic factor for OS in the entire TARGET
cohort (p < .05). These results suggested that our prognostic risk
model can be independently used to predict the prognosis of pediatric
ALL patients. In addition, the variables of age, MRD status at day 29
also had important prognostic value in the multivariate analysis
(p < .05).
Table 2.
Univariate and multivariate Cox regression analyses of the entire
TARGET cohort
Variables Univariate analysis Multivariate analysis
HR (95% CI) p value HR (95% CI) p value
Risk score (from risk model)
High versus low 1.25 (1.19–1.32) 6.07E‐19 1.29 (1.22–1.36) 4.29E‐20
Age at diagnosis
≥10 versus <10 years old 2.15 (1.38–3.34) 6.72E‐04 2.24 (1.43–3.51)
4.27E‐04
Gender
Male versus female 1.40 (0.90–2.18) 0.131 1.69 (1.07–2.65) 2.37E‐02
WBC at diagnosis
＞50 versus ≤50 × 10^9/L 0.93 (0.59–1.47) 0.751 0.98 (0.61–1.57) 0.927
CNS status at diagnosis
CNS3/CNS2 versus CNS1 1.05 (0.60–1.84) 0.871 1.05 (0.59–1.87) 0.87
MRD day 29
≥10^−4 versus ＜10^−4 1.80 (1.15–2.79) 9.46E‐03 2.06 (1.30–3.25)
2.09E‐03
[83]Open in a new tab
MRD day 29, minimal residual disease status in bone marrow, by flow
cytometry, at day 29 of induction therapy.
Abbreviations: CI, confidence interval; CNS, central nervous system;
CNS1: no lymphoblasts in CSF; CNS2: present lymphoblasts in CSF, WBC
count of the CSF < 5 cells/µl; CNS3: present lymphoblasts in CSF or a
cranial nerve palsy, WBC count of the CSF ≥ 5 cells/µl; HR, hazard
ratio; MRD, minimal residual disease; TARGET, Therapeutically
Applicable Research to Generate Effective Treatments; WBC, white blood
cell count.
4. DISCUSSION
Although cure rate of pediatric ALL have improved recently, some
patients still suffer from relapse and refractory. With the development
of second‐generation sequencing, researchers expect to improve clinical
outcomes through more accurate risk stratification and
molecular‐targeted therapies (Tasian & Hunger, [84]2017). Studies have
shown that gene expression profiles of patients with relapse leukemia
are highly enriched in immune‐related processes (Toffalori
et al., [85]2019). In addition, some tumor relapses are associated with
cancer cells mimicking the IRGs of healthy cells (van der Bruggen
et al., [86]1991; Knuth, Danowski, Oettgen, & Old, [87]1984;
Old, [88]1981; Sahin et al., [89]1995; Schreiber, Old, &
Smyth, [90]2011). Therefore, immune‐related biomarkers may be an
important indicator of prognosis in pediatric ALL.
We analyzed the differential immune gene expression between the relapse
and non‐relapse groups of 251 pediatric ALL patients and screened 130
DEIRGs. Pathway enrichment analysis was performed to explore the
potential biological mechanisms of them. And the top three pathways
were NABA matrisome‐associated, chemotaxis, and antimicrobial humoral
response, which were reported to be involved in tumor development (Chen
et al., [91]2009; Naba et al., [92]2012; Shields et al., [93]2007).
Based on comprehensive analysis, we identified four optimal genes
(PRDX2, S100A10, RORB, and SDC1) and used them to conduct a prognostic
risk model for pediatric ALL patients. The model was able to classify
pediatric ALL patients into two subgroups with statistically different
survival outcomes, which were validated in both the testing cohort and
the entire TARGET cohort. In addition, we verified and analyzed the
risk score distribution, survival status, and risk gene expression of
testing cohort and entire TARGET cohort. We came to the conclusion that
low‐risk group had lower levels of the risk gene than high‐risk group,
which is similar to that of the training cohort. These results suggest
that the model may represent the risk status of pediatric ALL patients
and provide reliable prognostic value for them. And the multivariate
Cox regression analysis confirmed that our model could independently
predict the prognosis of pediatric ALL patients.
We identified four optimal signatures from IRGs: PRDX2, S100A10, RORB,
and SDC1. PRDX2 can regulate oxidative and metabolic stress, whose
carcinogenic role in several solid cancers has been reported (Kim
et al., [94]2000; Stresing et al., [95]2013). PRDX2 has also been shown
to induce the growth of lymphoma cells (Trzeciecka et al., [96]2016).
And S100A10 can promote the invasion and metastasis of cancer by
increasing the production of fibrinolytic enzyme (Choi, Fogg, Yoon, &
Waisman, [97]2003; Madureira et al., [98]2016; O'Connell, Madureira,
Berman, Liwski, & Waisman, [99]2011; Zhang, Fogg, &
Waisman, [100]2004). RORB regulates Wnt pathway activity, which may be
correlated with tumorigenesis and tumor stages (Wen et al., [101]2017).
In addition, SDC1 has been reported to play an important role in the
malignant progression of tumors (Li et al., [102]2019). At present, no
reports concerning these genes were published in ALL, so the role of
them in pediatric ALL needs further investigation.
Many researches focused on the relapse and prognosis of leukemia.
Cristina Toffalori et al. found that the gene expression profile of
patients with recurrence was highly enriched in immune‐related
processes by analyzing the genome of patients with acute myeloid
leukemia transplantation, and frequent new genomic changes in patients
who relapsed after transplantation were observed (Toffalori
et al., [103]2019), which was consistent with Miguel et al's report
(Waterhouse et al., [104]2011). Joanna et al. suggested that the most
striking characteristics were pathways leading to drug‐resistant
phenotypes in ALL relapsed patients with high‐resolution genomic
techniques, which can be targeted to prevent or treat relapse (Pierro,
Hogan, Bhatla, & Carroll, [105]2017). Plenty of studies on prognosis of
leukemia, nevertheless, no immune gene‐related prognostic research of
pediatric ALL has been carried out. Therefore, we focused on the
pediatric ALL sample data from public dataset TARGET, which includes
comprehensive clinical information and sequencing data. We used
multiple algorithms (including univariate Cox, multivariate Cox, and
Lasso regression) at the genome‐wide level to construct a risk model
for predicting the prognosis of pediatric ALL patients. And the model
was successfully verified in testing cohort and entire TARGET cohort.
Therefore, the research data are comprehensive and research method is
reliable. Our predictive model can represent the risk status and
provide reliable prognostic value for the whole cohort and subgroups of
pediatric ALL patients. Still, our survey has some limitations. We used
retrospective data that were not validated in prospective clinical
trials. In addition, further studies are needed on the mechanism by
which IRGs affect pediatric ALL prognosis.
5. CONCLUSIONS
In conclusion, we identified and verified four risk signatures based on
IRGs. Then based them a risk model for pediatric ALL patients was
developed, which can classify patients into high‐risk and low‐risk
groups. These findings may provide insights for predicting clinical
outcomes and individualized treatment based on risk scores.
CONFLICT OF INTERESTS
The authors declare that they have no competing interests.
AUTHOR CONTRIBUTIONS
XQ and NZ designed the study and downloaded the data. XQ performed the
data analyses. XQ, YC, and HZ wrote the manuscript. JD revised the
manuscript. All authors read and approved the final manuscript.
ACKNOWLEDGMENTS