A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Abstract

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 ( $P<0.001$ ). 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 ( $P<0.001$ ) 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.