Gene Signature-Based Prognostic Model for Acute Myeloid Leukemia: The Role of BATF, EGR1, PD-1, PD-L1, and TIM-3.

Abstract

Background: Acute myeloid leukemia (AML) is a malignancy of hematopoietic stem and progenitor cells, with T cell exhaustion linked to poor outcomes. Our previous research has shown that basic leucine zipper ATF-like transcription factor (BATF) and early growth response 1 (EGR1) play a role in chimeric antigen receptor T (CAR-T) cell exhaustion during AML tumor elimination. However, the roles of BATF and EGR1 and their association with immune checkpoint genes in AML prognosis remain underexplored. Methods: Bone marrow (BM) samples from 92 newly diagnosed AML patients at our clinical center (JUN-dataset) were analyzed to detect the expression levels of BATF, EGR1, programmed cell death 1 (PD-1), programmed death-ligand 1 (PD-L1), T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) together with conducting a prognostic assessment. Our findings were validated using RNA sequencing data from 155 AML patients from the TCGA database and 199 AML patients from the Beat-AML database. Results: High BATF expression correlated with poor overall survival (OS) (P = 0.030), whereas high EGR1 expression indicated a favorable prognosis (P = 0.040). Patients with high BATF and low EGR1 expression had worst outcomes (P < 0.001). Among those receiving allogenic hematopoietic stem cell transplantation (allo-HSCT), high BATF expression was linked to shorter OS (P = 0.004). Moreover, a prognostic model incorporating BATF, EGR1, PD-1, PD-L1, and TIM-3 calculated a risk score, with high-risk patients demonstrating significantly shorter OS than low-risk patients in both total AML patients and allo-HSCT recipients (P < 0.001). Similar results were found in both the TCGA and Beat-AML datasets. Conclusions: We establish a prognostic model based on BATF, EGR1, PD-1, PD-L1, and TIM-3 expression that effectively predicts survival outcomes for AML patients and allo-HSCT recipients. This model may provide valuable insights for prognosis assessment and treatment strategies.