Integration of a 4-gene risk score model enhances prognostic accuracy in acute myeloid leukemia

The clinical outcomes of acute myeloid leukemia (AML) patients exhibit substantial heterogeneity, with relapse posing a formidable challenge. Herein, we developed a risk score model by integrating relapse-related genes through Cox regression analysis. The relapse-related genes were identified via differential gene expression analysis of 15 matched diagnosed and relapsed AML samples retrieved from the Gene Expression Omnibus (GEO) database. These genes include SCN9A, CFH, CD34, and CALCRL. Our findings demonstrate that higher risk scores were significantly associated with an unfavorable ELN2017 risk classification, leukemic transformation, as well as FLT3-ITD and RUNX1 mutations. Conversely, lower risk scores were linked to NPM1 mutation. Patients with higher risk scores had a shorter overall survival (OS). Furthermore, we integrated the risk score model with the European LeukemiaNet (ELN) risk classification to establish a novel composite risk classification scheme. Patients were classified into three new risk groups based on composite risk classification showing significantly distinct OS. In summary, the four-gene risk score holds promise in predicting the OS of AML patients, and the composite risk classification shows greater potential in predicting the outcomes of AML patients. These four genes may represent potential therapeutic targets in the treatment of AML.