Related Experiment Video
Updated: Jun 12, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Elucidating the immune landscape and potential prognostic model in acute myeloid leukemia with TP53 mutation
Gelan Zhu1, Jiayi Cai1, Wanbin Fu1
1Department of Hematology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Objectives:
The TP53 mutation, a prevalent tumor suppressor gene alteration, is linked to chemotherapy resistance, increased relapse rates and diminished overall survival (OS) in acute myeloid leukemia (AML) patients.
Methods:
In this study, we characterize the TP53 mutation phenotypes across various AML cohorts utilizing The Cancer Genome Atlas (TCGA) data. We devised a TP53-related prognostic signature derived from differentially expressed genes between mutated and wild-type TP53 AML specimens. In-depth analyses were conducted, encompassing genetic variation, immune cell infiltration and prognostic stratification.
Results:
A six-gene TP53-related signature was established using least absolute shrinkage and selection operator (LASSO)-Cox regression, demonstrating robust prognostic predictability. This signature exhibited strong performance in both the OHSU validation cohorts, an independent Gene Expression Omnibus (GEO) validation cohort (GSE71014) and proved by results of the in vivo experiment. Finally, we used single cell database (GSE198681) to observe the characteristics of these six genes.
Discussion:
Our study may facilitate the development of efficacious therapeutic approaches and provide a novel idea for future research. Conclusion: The TP53-related signature and pattern hold the potential to refine prognostic stratification and underscore emerging targeted therapies.
