Bipartite network analysis reveals metabolic gene expression profiles that are highly associated with the clinical outcomes of acute myeloid leukemia

Abstract

Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR)=1.58, p=0.038) and in two test sets (HR=1.69 and 1.56 and p=0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.