AUTHOR=Blanda Adriana , Manitto Rebecca , Pizzamiglio Sara , Sangaletti Sabina , Verderio Paolo 
  
TITLE=Harnessing cutting-edge techniques to identify novel gene expression signatures in acute myeloid leukemia patients
  
JOURNAL=Frontiers in Bioinformatics
  
VOLUME=Volume 6 - 2026
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1805826
  
DOI=10.3389/fbinf.2026.1805826
  
ISSN=2673-7647
  
ABSTRACT=IntroductionAcute Myeloid Leukemia (AML) is a heterogeneous hematological malignancy with poor prognosis, despite therapeutic advances. Gene expression analysis has emerged as a powerful tool for identifying novel biological markers that can aid in predicting patient outcomes. This study is aimed to identify prognostic gene expression signatures from RNA-seq data of 457 AML patients.MethodsFiltering methods were applied to reduce the set of genes, resulting in a subset of 685. Lasso-Cox, Bayesian Model Averaging (BMA), Random Survival Forest (RSF), and a Cox-time Neural Network (CtNN) were implemented to select the most promising prognostic genes. Explainable tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to gain a deeper understanding of neural network outputs. An enrichment analysis was performed to identify enriched biological pathways.ResultsSHAP and LIME interpretations of the CtNN output identified gene sets that differed from those selected by the other algorithms. According to the two-validation metrics the SHAP-derived signature demonstrated superior performance in the testing set. (C-index[95%CI]: 0.639[0.581–0.696] and Integrated Brier Score [95%CI]: 0.172[0.161–0.183]).DiscussionEnrichment analysis revealed structural and developmental pathways, emphasizing the role of microtubule dynamics and ciliary-related signaling in the bone marrow microenvironment.