Harnessing cutting-edge techniques to identify novel gene expression signatures in acute myeloid leukemia patients

<h4>Introduction</h4>Acute 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.<h4>Methods</h4>Filtering 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.<h4>Results</h4>SHAP 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]).<h4>Discussion</h4>Enrichment analysis revealed structural and developmental pathways, emphasizing the role of microtubule dynamics and ciliary-related signaling in the bone marrow microenvironment.