Novel Prognostic Signature for Acute Myeloid Leukemia: Bioinformatics Analysis of Combined CNV-Driven and Ferroptosis-Related Genes

Abstract

Background: Acute myeloid leukemia (AML), which has a difficult prognosis, is the most common hematologic malignancy. The role of copy number variations (CNVs) and ferroptosis in the tumor process is becoming increasingly prominent. We aimed to identify specific CNV-driven ferroptosis-related genes (FRGs) and establish a prognostic model for AML. Methods: The combined analysis of CNV differential data and differentially expressed genes (DEGs) data from The Cancer Genome Atlas (TCGA) database was performed to identify key CNV-driven FRGs for AML. A risk model was constructed based on univariate and multivariate Cox regression analysis. The Gene Expression Omnibus (GEO) dataset was used to validate the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs and CNV-driven FRGs. Results: We identified a total of 6828 AML-related DEGs, which were shown to be significantly associated with cell cycle and immune response processes. After a comprehensive analysis of CNVs and corresponding DEGs and FRGs, six CNV-driven FRGs were identified, and functional enrichment analysis indicated that they were involved in oxidative stress, cell death, and inflammatory response processes. Finally, we screened 2 CNV-driven FRGs (DNAJB6 and HSPB1) to develop a prognostic risk model. The overall survival (OS) of patients in the high-risk group was significantly shorter in both the TCGA and GEO (all p < 0.05) datasets compared to the low-risk group. Conclusion: A novel signature based on CNV-driven FRGs was established to predict the survival of AML patients and displayed good performance. Our results may provide potential targets and new research ideas for the treatment and early detection of AML.