# Derivation of a novel multi‐gene prognostic model based on regulated cell death pathways in acute myeloid leukemia: A comprehensive bioinformatic analysis integrating gene expression, mutation profiling, and immune infiltration

## TL;DR

A new four-gene model based on cell death pathways helps predict survival in acute myeloid leukemia patients and reveals genetic and immune patterns linked to prognosis.

## Contribution

A novel multi-gene prognostic model based on regulated cell death pathways in AML, validated across cohorts and linked to genomic and immune features.

## Key findings

- A four-gene model (ARHGEF35, GSN, ELANE, AKT3) accurately predicts AML patient survival with high AUC values.
- High-risk patients show enrichment of DNMT3A and RUNX1 mutations and increased M2 macrophage infiltration.
- Low-risk patients are associated with KIT mutations and reduced resting mast cells.

## Abstract

Acute myeloid leukemia (AML) is a highly aggressive hematologic malignancy with dismal survival outcomes, where dysregulation of regulated cell death (RCD) pathways plays a pivotal role in leukemogenesis and therapeutic resistance.

Differential expression analyses were performed comparing AML samples with healthy bone marrow. Diagnostic differentially expressed genes (DEGs) were then intersected with curated gene sets representing apoptosis, pyroptosis, autophagy, necroptosis, and ferroptosis to derive an RCD-based gene signature. Prognostic markers were identified by univariate Cox regression, and these markers were refined using LASSO regression to construct a multi-gene prognostic model that generated an individual risk score (RS) for each patient. The performance of the model was validated internally through Kaplan–Meier survival analyses and receiver operating characteristic (ROC) curves for 1-, 3-, and 5-year survival, and externally confirmed in an independent TARGET-AML cohort. In addition, mutation analysis was conducted using the maftools package, and immune infiltration profiling was performed with CIBERSORT and xCell to characterize the molecular landscape of the risk groups.

Our integrative approach yielded a four-gene prognostic model incorporating ARHGEF35, GSN, ELANE, and AKT3. High RS was strongly associated with adverse overall survival, with Kaplan–Meier analyses showing p-value < 0.0001 in the training cohort and p-value = 0.0026 in the testing cohort. The model demonstrated robust predictive accuracy with AUC values of 82%, 87%, and 91% for 1-, 3-, and 5-year survival in the training set, and 65%, 81%, and 94% in the testing set. Mutation analysis revealed that DNMT3A and RUNX1 mutations were significantly enriched in high-RS patients (p-value = 0.0015 and p-value = 0.0086, respectively), whereas KIT mutations were more prevalent in low-RS patients (p-value = 0.0058). Immune profiling indicated that high-RS patients had increased M2 macrophage infiltration (p-value = 0.0027) and reduced resting mast cells (p-value = 0.0033).

These findings establish that an RCD-based multi-gene risk model can robustly stratify AML patients by prognosis and illuminate underlying genomic and immunologic mechanisms, thereby offering promising avenues for personalized therapeutic strategies.