An Immunogenic Cell Death-Based Risk model Predicts Prognosis and Indicates Immune Infiltration Landscape in Acute Myeloid Leukemia

Abstract Background: Acute myeloid leukemia (AML) is the most common acute leukemia in adults with a high mortality rate. Immunogenic cell death (ICD) plays a crucial role in activation of adaptive immune response and may contribute to the efficacy of cancer immunotherapy. However, the relationship between ICD and AML prognosis is unveiled. Methods and materials: A Pearson correlation analysis was utilized to identified ICD-related lncRNAs. Univariate cox regression analysis and subsequent LASSO analysis were performed to construct an ICD-associated lncRNAs signature. Survival analysis, ROC analysis, univariate and multivariate cox regression were applied to assess the predictive capacity and evaluate prognostic value for AML patients. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were performed to estimate the immune infiltration landscape. Enrichment analysis was used to investigate the biological processes and pathways of the ICD-associated lncRNAs. Results: A predictive risk signature was constructed based on seven ICD-associated lncRNAs (AFF2−IT1, AL5924292, LINC00987, MIR133A1HG, AC022182.2, NORAD and AC244502.1). High risk score was verified as an independent prognostic predictor for poor clinical outcomes in AML patients. Notably, we observed a remarkable difference in immune infiltration landscape, immunotherapy response and drug susceptibility related to risk stratification. In addition, functional enrichment analysis established that immune-related signaling pathways might mediate the role of ICD-related lncRNAs in AML. Conclusions: The signature based on ICD-related lncRNAs can provide guidance to the accurate prediction of AML prognosis and also offer a novel perspective for individualized and precise treatment strategies for AML patients.