为小儿急性髓性白血病开发并验证前景良好的 5 基因预后模型。

IF 6.3 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular biomedicine Pub Date : 2024-01-02 DOI:10.1186/s43556-023-00162-y
Yu Tao, Li Wei, Norio Shiba, Daisuke Tomizawa, Yasuhide Hayashi, Seishi Ogawa, Li Chen, Hua You
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引用次数: 0

摘要

小儿急性髓性白血病(P-AML)的风险分级对于个性化治疗至关重要。因此,我们旨在为P-AML患者建立一个风险分级工具,并最终指导个体化治疗。我们将 TARGET 数据库中具有认可 mRNA-seq 数据的 256 例 P-AML 患者分为训练数据集和内部验证数据集。通过单变量Cox分析、LASSO回归分析、Kaplan-Meier(K-M)生存率和多变量Cox分析,构建了基于基因表达的总生存率(OS)预后评分。从 5 个基因(ZNF775、RNFT1、CRNDE、COL23A1 和 TTC38)的表达水平生物信息学推导出的 P-AML-5G 预后评分将训练数据集中的 P-AML 患者分为高风险组(高于最佳临界值)和低风险组(低于最佳临界值),前者的 OS 更短(P 3、P 4、P 5)。
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Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia.

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

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