Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Children Health Care, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China.
Cancer Med. 2022 Feb;11(3):888-899. doi: 10.1002/cam4.4487. Epub 2021 Dec 14.
The immune microenvironment in acute myeloid leukemia (AML) is closely related to patients' prognosis. Long noncoding RNAs (lncRNAs) are emerging as key regulators in immune systems. In this study, we established a prognostic model using an immune-related lncRNA (IRL) signature to predict AML patients' overall survival (OS) through Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis. Kaplan-Meier analysis, receiver operating characteristic (ROC) analysis, univariate Cox regression, and multivariate Cox regression analyses further illustrated the reliability of our prognostic model. An IRL signature-based nomogram consisting of other clinical features efficiently predicted the OS of AML patients. The incorporation of the IRL signature improved the ELN2017 risk stratification system's prognostic accuracy. In addition, we found that monocytes and metabolism-related pathways may play a role in AML progression. Overall, the IRL signature appears as a novel effective model for evaluating the OS of AML patients and may be implemented to contribute to the prolonged OS in AML patients.
急性髓系白血病（AML）中的免疫微环境与患者的预后密切相关。长链非编码 RNA（lncRNA）作为免疫系统的关键调节因子而备受关注。本研究通过最小绝对值收缩和选择算子（LASSO）和多变量 Cox 回归分析，建立了一个基于免疫相关 lncRNA（IRL）特征的预后模型，以预测 AML 患者的总生存期（OS）。Kaplan-Meier 分析、受试者工作特征（ROC）分析、单变量 Cox 回归和多变量 Cox 回归分析进一步说明了我们预后模型的可靠性。一个由其他临床特征组成的基于 IRL 特征的列线图可以有效地预测 AML 患者的 OS。IRL 特征的纳入提高了 ELN2017 风险分层系统的预后准确性。此外，我们发现单核细胞和代谢相关途径可能在 AML 进展中发挥作用。总的来说，IRL 特征似乎是评估 AML 患者 OS 的一种新的有效模型，可能有助于延长 AML 患者的 OS。
