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. 2021 Apr 19:2021:5550116.
doi: 10.1155/2021/5550116. eCollection 2021.

An Immune Checkpoint-Related Gene Signature for Predicting Survival of Pediatric Acute Myeloid Leukemia

Feng Jiang  1   2 , Xin-Yu Wang  1 , Ming-Yan Wang  1 , Yan Mao  1 , Xiao-Lin Miao  1 , Chu-Yan Wu  3 , Guo-Ping Zhou  1
Affiliations

An Immune Checkpoint-Related Gene Signature for Predicting Survival of Pediatric Acute Myeloid Leukemia

Feng Jiang et al. J Oncol. .

Abstract

Objective: The aim of this research was to create a new genetic signature of immune checkpoint-associated genes as a prognostic method for pediatric acute myeloid leukemia (AML).

Methods: Transcriptome profiles and clinical follow-up details were obtained in Therapeutically Applicable Research to Generate Effective Treatments (TARGET), a database of pediatric tumors. Secondary data was collected from the Gene Expression Omnibus (GEO) to test the observations. In univariate Cox regression and multivariate Cox regression studies, the expression of immune checkpoint-related genes was studied. A three-mRNA signature was developed for predicting pediatric AML patient survival. Furthermore, the GEO cohort was used to confirm the reliability. A bioinformatics method was utilized to identify the diagnostic and prognostic value.

Results: A three-gene (STAT1, BATF, EML4) signature was developed to identify patients into two danger categories depending on their OS. A multivariate regression study showed that the immune checkpoint-related signature (STAT1, BATF, EML4) was an independent indicator of pediatric AML. By immune cell subtypes analyses, the signature was correlated with multiple subtypes of immune cells.

Conclusion: In summary, our three-gene signature can be a useful tool to predict the OS in AML patients.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Analysis process flowchart in this study.
Figure 2
Figure 2
Participant immune checkpoint genes recognition of the TARGET cohort. (a) Univariate Cox regression testing that defines HR prediction variables with 95% CI and p values. (b) Selecting LASSO regression algorithm input variables. (c) Constructing immune checkpoint-related gene model by multivariate Cox regression in TARGET cohort.
Figure 3
Figure 3
Signature prediction value in pediatric AML. (a) A heat map showing the three patterns of gene expression linked to immune checkpoint in both the TARGET and GEO categories of high and low risk. (b) The distribution of risk score. (c) Distribution of status in high- and low-risk AML patients. The dot indicates the condition of the patient by the rising risk. The x-axis consists of the number of patients with a y-axis of time of survival. (d) The death rates of all the risk categories. (e), (f) The general survival curves of Kaplan–Meier patients allocated to high- and low-risk groups depending on the median score of the risk score.
Figure 4
Figure 4
Low-risk and high-risk GSEA enrichment.
Figure 5
Figure 5
Immune cell subtypes distribution and visualization of patients with AML. (a) Overview of 22 immune cell subtypes in dataset of TARGET predicted compositions. (b) Distinctions between low- and high-risk groups with 22 immune cell subtypes. (c) Description of the 22 immune cell subtypes of the GEO cohort estimated compositions. (d) Contrast between low- and high-risk groups in 22 subtypes of immune cells. Colors blue and red represent low-risk and high-risk samples.
Figure 6
Figure 6
STAT1, BATF, and EML4 association and infiltration of immune cells in pediatric AML.
Figure 7
Figure 7
Comparison of risk score and three immune checkpoint gene expressions in TARGET cohort. (a) Correlation between PD-L1 expression and risk score. (b) PD-L1 expression in high and low-risk group. (c) Correlation between PD1 expression and risk score. (d) PD1 expression in high- and low-risk group. (e) Correlation between CTLA4 expression and risk score. (f) CTLA4 expression in high- and low-risk group.
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