Cargando…

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

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 potentia...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Chong, Yang, Shaoxin, Lu, Wei, Liu, Jiali, Wei, Yanyu, Guo, Hezhou, Zhang, Yanjie, Shi, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728998/
https://www.ncbi.nlm.nih.gov/pubmed/33329712
http://dx.doi.org/10.3389/fgene.2020.573124
_version_ 1783621366728097792
author Zhao, Chong
Yang, Shaoxin
Lu, Wei
Liu, Jiali
Wei, Yanyu
Guo, Hezhou
Zhang, Yanjie
Shi, Jun
author_facet Zhao, Chong
Yang, Shaoxin
Lu, Wei
Liu, Jiali
Wei, Yanyu
Guo, Hezhou
Zhang, Yanjie
Shi, Jun
author_sort Zhao, Chong
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7728998
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-77289982020-12-15 Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells Zhao, Chong Yang, Shaoxin Lu, Wei Liu, Jiali Wei, Yanyu Guo, Hezhou Zhang, Yanjie Shi, Jun Front Genet Genetics 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. Frontiers Media S.A. 2020-11-27 /pmc/articles/PMC7728998/ /pubmed/33329712 http://dx.doi.org/10.3389/fgene.2020.573124 Text en Copyright © 2020 Zhao, Yang, Lu, Liu, Wei, Guo, Zhang and Shi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zhao, Chong
Yang, Shaoxin
Lu, Wei
Liu, Jiali
Wei, Yanyu
Guo, Hezhou
Zhang, Yanjie
Shi, Jun
Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells
title Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells
title_full Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells
title_fullStr Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells
title_full_unstemmed Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells
title_short Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells
title_sort increased nfatc4 correlates with poor prognosis of aml through recruiting regulatory t cells
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728998/
https://www.ncbi.nlm.nih.gov/pubmed/33329712
http://dx.doi.org/10.3389/fgene.2020.573124
work_keys_str_mv AT zhaochong increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT yangshaoxin increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT luwei increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT liujiali increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT weiyanyu increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT guohezhou increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT zhangyanjie increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells
AT shijun increasednfatc4correlateswithpoorprognosisofamlthroughrecruitingregulatorytcells