Screen Necroptosis-related Genes and evaluate the prognostic capacity, clinical value and the affection of their copy number variations in acute myeloid leukemia

Background： Acute myeloid leukemia (AML) is an aggressive hematological neoplasm. Little improvement in survival rates has been achieved over the past few decades. Necroptosis has relationship with certain types of malignancies outcomes. Here, we evaluated the diagnostic ability, prognostic capacity of necroptosis-related genes (NRGs) and the affection of their copy number variations (CNVs) in AML. Methods： Necroptosis-related differentially expressed genes (NRDEGs) were acquired after intersecting Differentially expressed genes (DEGs) from Gene Expression Omnibus(GEO) database with NRGs from GeneCards, Molecular Signatures Database (MsigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. The expression levels of 6 hub-NRDEGs were validated in vitro. mRNA-miRNA and mRNA-TF interaction networks with hub-NRDEGs were screened by Cytoscape ^@ . Single-sample gene set enrichment analysis (ssGSEA) was utilized to calculate correlationships between hub-NRDEGs and immune cells. CNVs analysis on hub-NRDEGs was carried out based on TCGA-LAML datasets from the TCGA database. Kaplan–Meier(K-M) survival analyses was utilized to evaluate the prognostic values along with COX model. Results： 6 hub-NRDEGs ( SLC25A5, PARP1, CTSS, ZNF217, NFKB1, PYGL ) were obtained and their expression changes derived from CNVs in AML were visualized. 65 mRNA-miRNA and 80 mRNA-TF interaction networks with hub-NRDEGs was screened. ssGSEA result showed the expression of RAPR1 is inversely related with CD56 ^dim natural killer cell and CTSS positive with MDSCs in AML. K-M results demonstrated ZNF217 had significant difference in duration of survival in AML patients. Cox regression models revealed hub-NRDEGs had better predictive power at year-1 and year-5. Conclusion： These screened NRDEGs could be exploited as clinical prognosis predictions in AML patients, as well as potential biomarkers for diagnosis and therapeutic targeting.