Zhang Shuai, Wang Qianqian, Xia Haoran, Liu Hui
Department of Hematology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Bejing, China.
Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
J Oncol. 2022 Nov 21;2022:7727424. doi: 10.1155/2022/7727424. eCollection 2022.
Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( < 0.001). The median overall survival (mOS) of subgroups 1-3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( < 0.001) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.
急性髓系白血病（AML）是一种预后较差的恶性血液系统疾病。AML患者的风险分层主要基于细胞遗传学和分子遗传学特征；然而，具有良好遗传学特征的患者预后可能较差。在此，我们聚焦于AML人群中免疫和标志性基因集的活性变化。基于基因集变异分析（GSVA）得出的基因集富集分数，我们在TCGA队列中通过非负矩阵分解（NMF）算法鉴定出三种AML亚型。亚组1中的AML患者总生存期（OS）比亚组2和亚组3更差（<0.001）。亚组1至3的中位总生存期（mOS）分别为0.4年、2.2年和1.7年。各亚组之间的临床特征，包括年龄和FAB分类，存在显著差异。使用最小绝对收缩和选择算子（LASSO）回归方法，我们发现了三个预后基因集，并基于它们建立了最终的预后模型。在TCGA队列中，高危组患者的OS明显短于低危组患者（<0.001），低危组和高危组的mOS分别为2.2年和0.7年。结果在GSE146173和GSE12417队列中得到进一步验证。我们使用蛋白质-蛋白质相互作用网络进一步鉴定了预后基因集的关键基因。总之，该研究建立并验证了一种用于AML风险分层的新型预后模型，为准确的预后评估提供了新的视角。
