Bipartite network analysis reveals metabolic gene expression profiles that are highly associated with the clinical outcomes of acute myeloid leukemia. (April 2017)

• Record Type: Journal Article
• Authors: Xie, Fanfan; He, Mingxiong; He, Li; Liu, Keqin; Li, Menglong; Hu, Guoquan; Wen, Zhining
• Is Part Of: Computational biology and chemistry. Volume 67(2017)
• Page Start: 150
• Page End: 157
• Publication Date: 2017-04
• Subjects: Metabolic genes -- Gene expression profiles -- Bipartite network -- Acute myeloid leukemia -- Survival analysis; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.01.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Metabolic genes are as important prognostic biomarkers as oncogenes. We found that significant differences exist in metabolic processes of AML patients. We identified 62 metabolic genes that highly associated with the prognosis of AML. Genes ALAS2, BCAT1, BLVRB and HK3 were distinctly expressed in the AML patients. 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.