Leveraging Deep Learning to Construct a Programmed Cell Death-Driven Prognostic Signature in Acute Myeloid Leukemia

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Abstract
Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by profound molecular heterogeneity and high relapse rates, posing significant clinical challenges. Programmed cell death (PCD), encompassing diverse regulated modalities such as apoptosis, necroptosis, and ferroptosis, plays a key role in leukemogenesis and therapeutic response; however, a comprehensive prognostic framework integrating multi-modal PCD pathways in AML remains elusive. In this study, we performed a systematic transcriptomic analysis of 1624 genes associated with 13 distinct PCD forms. A novel computational pipeline combining a variational autoencoder (VAE) for dimensionality reduction and a multilayer perceptron (MLP) for classification was employed to identify robust PCD-related biomarkers, interpreted via SHapley Additive exPlanations (SHAP) analysis. This approach identified 48 candidate genes with discriminative potential between AML and normal bone marrow. Unsupervised consensus clustering based on these genes delineated two molecular subtypes exhibiting divergent clinical outcomes and immune microenvironment profiles. The subtype demonstrated an immunosuppressive phenotype, characterized by enriched regulatory T cells, M2 macrophages, and elevated expression of inhibitory immune checkpoints, correlating with inferior survival. We developed an 8-gene prognostic signature (SORL1, PIK3R5, RIPK3, ELANE, GPX1, VNN1, CD74, and IL3RA) that effectively categorized patients into high- and low-risk groups with notable survival differences, validated across independent cohorts. A prognostic nomogram combining the risk score, age, and cytogenetic risk enhanced the prediction accuracy for overall survival. Our study presents an integrative model that connects multi-modal PCD pathways to AML prognosis, offering a new molecular subtyping system and a clinically applicable risk assessment tool for improved prognostication and personalized treatment strategies.
Funding Information
  • National Natural Science Foundation of China https://ror.org/01h0zpd94 (62302088)
  • National Natural Science Foundation of China https://ror.org/01h0zpd94 (62373093)
  • National Natural Science Foundation of China https://ror.org/01h0zpd94 (62202094)
  • Natural Science Foundation of Heilongjiang Province (YQ2024F001)
  • the Postdoctoral Program of Heilongjiang Province of China (LBH-TZ2303)
  • the Postdoctoral Program of Heilongjiang Province of China (LBH-Z23066)
  • the Postdoctoral Program of Heilongjiang Province of China (LBH-Z22065)

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