Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells.

Chong Zhao,Yanjie Zhang,Wei Lu,Jun Shi,Hezhou Guo,Jiali Liu,Yanyu Wei,Shaoxin Yang

doi:10.3389/fgene.2020.573124

Abstract

Despite that immune responses play important roles in acute myeloid leukemia (AML), immunotherapy is still not widely used in AML due to lack of an ideal target. Therefore, we identified key immune genes and cellular components in AML by an integrated bioinformatics analysis, trying to find potential targets for AML. Eighty-six differentially expressed immune genes (DEIGs) were identified from 751 differentially expressed genes (DEGs) between AML patients with fair prognosis and poor prognosis from the TCGA database. Among them, nine prognostic immune genes, including NCR2, NPDC1, KIR2DL4, KLC3, TWIST1, SNORD3B-1, NFATC4, XCR1, and LEFTY1, were identified by univariate Cox regression analysis. A multivariable prediction model was established based on prognostic immune genes. Kaplan–Meier survival curve analysis indicated that patients in the high-risk group had a shorter survival rate and higher mortality than those in the low-risk group (P < 0.001), indicating good effectiveness of the model. Furthermore, nuclear factors of activated T cells-4 (NFATC4) was recognized as the key immune gene identified by co-expression of differentially expressed transcription factors (DETFs) and prognostic immune genes. ATP-binding cassette transporters (ABC transporters) were the downstream KEGG pathway of NFATC4, identified by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). To explore the immune responses NFATC4 was involved in, an immune gene set of T cell co-stimulation was identified by single-cell GSEA (ssGSEA) and Pearson correlation analysis, positively associated with NFATC4 in AML (R = 0.323, P < 0.001, positive). In order to find out the immune cell types affected by NFATC4, the CIBERSORT algorithm and Pearson correlation analysis were applied, and it was revealed that regulatory T cells (Tregs) have the highest correlation with NFATC4 (R = 0.526, P < 0.001, positive) in AML from 22 subsets of tumor-infiltrating immune cells. The results of this study were supported by multi-omics database validation. In all, our study indicated that NFATC4 was the key immune gene in AML poor prognosis through recruiting Tregs, suggesting that NFATC4 might serve as a new therapy target for AML.

Highlights

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults, which often confronts high recurrence risk and low 5-years survival after diagnosis (Li et al, 2020)
We identified key immune genes correlated with AML prognosis and explored the associated immune gene set and immune cells by stimulation was identified by single-cell GSEA (ssGSEA) and CIBERSORT algorithm with the expression profiles from the The Cancer Genome Atlas (TCGA) database, trying to find novel targets for immunotherapy
Immune genes are involved in immune responses via affecting immune cells; we identified AML prognosisrelated immune gene sets and immune cells by ssGSEA and CIBERSORT algorithm

Summary

Introduction

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults, which often confronts high recurrence risk and low 5-years survival after diagnosis (Li et al, 2020). Immune therapy which has gained significant clinical impact on other neoplastic diseases still faces great challenges in AML. This indicates us to pay more attention to immune regulation in AML. The progression of AML is closely associated with immune imbalance. As important participants in immune responses, changes in the type and proportion of immune cells are involved in cancer progression. Increased Treg phenotype may promote disease progression and lead to poor prognosis in AML through contributing to immune evasion (Govindaraj et al, 2014; Arandi et al, 2018). How immune cells involved in immune imbalance are regulated in AML remains unclear

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