Dataset Information

Elevated TNFRSF4 gene expression is a predictor of poor prognosis in non-M3 acute myeloid leukemia.

ABSTRACT:

Background

We used bioinformatic tools to dichotomize 157 non-M3 AML patients from the TCGA dataset based on the presence or absence of TP53 mutations, and screened out a key gene related to TP53 mutation for future analysis.

Methods

DEGs were analyzed by R package "DESeq2" and then run GSEA, GO enrichment, KEGG pathway and PPI network. Hub genes were selected out according to MCC. Log-rank (Mantel-Cox) test was used for survival analysis. Mann-Whitney U's nonparametric t test and Fisher's exact test was used for continuous and categorical variables respectively. p value< 0.05 was considered to be statistical significance.

Results

TNFRSF4 was final screened out as a key gene. Besides TP53 mutation (p = 0.0118), high TNFRSF4 was also associated with FLT3 mutation (p = 0.0102) and NPM1 mutation (p = 0.0024). Elevated TNFRSF4 was significantly related with intermediate (p = 0.0004) and poor (p = 0.0011) risk stratification as well as relapse statute (p = 0.0099). Patients with elevated TNFRSF4 expression had significantly shorter overall survival (median survival: 2.35 months vs. 21 months, p < 0.0001). Based on our clinical center data, TNFRSF4 expression was significantly higher in non-M3 AML patients than HDs (p = 0.0377) and MDS patients (EB-1, 2; p = 0.0017).

Conclusions

Elevated TNFRSF4 expression was associated with TP53, FLT3 and NPM1 mutation as well as poor clinical outcome. TNFRSF4 expression was significantly higher in non-M3 AML patients than HDs and MDS (EB-1, 2) patients. TNFRSF4 is need for future functional and mechanistic studies to investigate the role in non-M3 AML.

SUBMITTER: Gu S

PROVIDER: S-EPMC7197135 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Elevated <i>TNFRSF4</i> gene expression is a predictor of poor prognosis in non-M3 acute myeloid leukemia.

Gu Siyu S Zi Jie J Han Qi Q Song Chunhua C Ge Zheng Z

Cancer cell international 20200504

<h4>Background</h4>We used bioinformatic tools to dichotomize 157 non-M3 AML patients from the TCGA dataset based on the presence or absence of <i>TP53</i> mutations, and screened out a key gene related to <i>TP53</i> mutation for future analysis.<h4>Methods</h4>DEGs were analyzed by R package "DESeq2" and then run GSEA, GO enrichment, KEGG pathway and PPI network. Hub genes were selected out according to MCC. Log-rank (Mantel-Cox) test was used for survival analysis. Mann-Whitney U's nonparamet ...[more]

PMID: 32390761

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