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Updated: May 13, 2025
Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
Published on: March 26, 2018
Gene Signature-Based Prognostic Model for Acute Myeloid Leukemia: The Role of BATF, EGR1, PD-1, PD-L1, and TIM-3
Yupei Zhang1,2, Zhixi Chen1,2, Jiamian Zheng1,2
1Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou 510632, China.
Insights
This study reveals that high basic leucine zipper ATF-like transcription factor (BATF) expression predicts poor outcomes in acute myeloid leukemia (AML), while high early growth response 1 (EGR1) indicates a favorable prognosis. A new model using BATF, EGR1, and immune checkpoint genes predicts AML survival.
Area of Science:
- Hematology
- Immunology
- Oncology
Background:
- Acute myeloid leukemia (AML) is a cancer of blood stem cells.
- T cell exhaustion is associated with poor AML prognosis.
- Basic leucine zipper ATF-like transcription factor (BATF) and early growth response 1 (EGR1) are implicated in CAR-T cell exhaustion during AML treatment.
Purpose of the Study:
- To investigate the roles of BATF and EGR1 in AML prognosis.
- To explore the association between BATF, EGR1, and immune checkpoint genes in AML.
- To develop a prognostic model for AML patients.
Main Methods:
- Expression levels of BATF, EGR1, PD-1, PD-L1, and TIM-3 were analyzed in 92 newly diagnosed AML patients.
- Prognostic assessments were performed.
- Findings were validated using RNA sequencing data from TCGA and Beat-AML databases.
Main Results:
- High BATF expression correlated with poor overall survival (OS) in AML patients (P=0.030).
- High EGR1 expression was associated with a favorable prognosis (P=0.040).
- A prognostic model incorporating BATF, EGR1, PD-1, PD-L1, and TIM-3 accurately predicted survival outcomes in AML patients and allo-HSCT recipients across multiple datasets.
Conclusions:
- A novel prognostic model based on BATF, EGR1, PD-1, PD-L1, and TIM-3 expression effectively predicts survival in AML patients.
- This model can aid in prognosis assessment and guide treatment strategies for AML and allo-HSCT recipients.
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.
