Open AccessImmune‐relatedlncRNAs can predict the prognosis of acute myeloid leukemia
Author(s) -
Li Ran,
Wu Shishuang,
Wu Xiaolu,
Zhao Ping,
Li Jingyi,
Xue Kai,
Li Junmin
Publication year - 2022
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.4487
Subject(s) - nomogram , proportional hazards model , univariate , oncology , receiver operating characteristic , myeloid leukemia , multivariate statistics , medicine , immune system , multivariate analysis , survival analysis , immunology , computer science , machine learning
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.