Department of Hematology, Ningbo First Hospital, Ningbo, Zhejiang Province, China.
Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211004933. doi: 10.1177/15330338211004933.
Acute myeloid leukemia (AML) is a heterogeneous disorder with complex genetic basis and adverse prognosis. Cytogenetics risk, somatic mutations and gene expression profiles are important prognostic factors for AML patients. However, accurate stratification of patient prognosis remains an unsolved problem in AML. This study was to to develop a novel gene profile to accurately classify AML patients into subgroups with different survival probabilities.
Survival-related genes were determined by Kaplan-Meier survival analysis and multivariate analysis using the expression and clinical data of 405 AML patients from Oregon Health & Science University (OHSU) dataset and validated in The Cancer Genome Atlas (TCGA) database. Feature selection was performed by using the Least Absolute Shrinkage and Selection Operator (LASSO) method. With the LASSO model, a prognostic 85-gene score was established and compared with 2 known gene-expression risk scores. The stratification of AML patients was performed by unsupervised hierarchical clustering of 85 gene expression levels to identify clusters of AML patients with different survival probabilities.
The LASSO model comprising 85 genes was considered as the optimal model based on relatively high area under curve value (0.83) and the minimum mean squared error. The 85-gene score was associated with increased mortality in AML patients. Hierarchical clustering analysis of the 85 genes revealed 3 subgroups of AML patients in the OHSU dataset. The cluster1 AML patients were associated with more female cases, higher percent of bone marrow blast cells, 85-gene score, cytogenetics risk, more frequent FLT3-ITD, , mutations, less frequent , mutations, poorer overall survival than cluster2 tumors. The 85-gene score had higher AUC (0.75) than the 5-gene risk score and LSC17 score (0.74 and 0.65).
The 85-gene score is superior to the 2 established prognostic gene signatures in the prediction of prognosis of AML patients.
急性髓系白血病（AML）是一种具有复杂遗传基础和不良预后的异质性疾病。细胞遗传学风险、体细胞突变和基因表达谱是 AML 患者的重要预后因素。然而，AML 患者的准确预后分层仍然是一个未解决的问题。本研究旨在开发一种新的基因谱，以准确地将 AML 患者分为具有不同生存概率的亚组。
使用俄勒冈健康与科学大学（OHSU）数据集的 405 例 AML 患者的表达和临床数据，通过 Kaplan-Meier 生存分析和多变量分析确定与生存相关的基因，并在癌症基因组图谱（TCGA）数据库中进行验证。通过最小绝对收缩和选择算子（LASSO）方法进行特征选择。使用 LASSO 模型建立预后 85 基因评分，并与 2 个已知的基因表达风险评分进行比较。通过 85 个基因表达水平的无监督层次聚类对 AML 患者进行分层，以确定具有不同生存概率的 AML 患者聚类。
基于相对较高的曲线下面积（0.83）和最小均方误差，包含 85 个基因的 LASSO 模型被认为是最佳模型。85 基因评分与 AML 患者死亡率增加相关。在 OHSU 数据集的 85 个基因的层次聚类分析中，发现了 3 个 AML 患者亚组。cluster1 AML 患者与更多的女性病例、更高的骨髓原始细胞百分比、85 基因评分、细胞遗传学风险、更高频的 FLT3-ITD、基因突变、更低频的基因突变、更差的总体生存率相关。与 5 基因风险评分和 LSC17 评分（0.74 和 0.65）相比，85 基因评分具有更高的 AUC（0.75）。
85 基因评分在预测 AML 患者的预后方面优于 2 个已建立的预后基因特征。
