Computational Identification of Co-Expression Networks, Key Genes and Pathways in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is characterized by proliferative, poorly differentiated cells of the hematopoietic system. The aim of this study was to identify biomarkers of AML by gene co-expression network analysis. Microarray dataset was retrieved from Gene Expression Omnibus (GEO) database. Weighted Co-expression Network Analysis (WGCNA) method was applied to evaluate key modules. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) web server. Then, hub genes were identified using Cytoscape software. Finally, expression of hub genes was validated using Gene Expression Profiling Interactive Analysis (GEPIA). A total of 1602 coexpressed genes with highest connectivity were identified in dark red and magenta modules. GO analysis suggested that co-expressed genes were mainly enriched in apoptotic process, signal transduction, cytosol, nucleus, protein binding and ATP binding activity. KEGG pathway analysis revealed that co-expressed genes were significantly enriched in MAPK signaling pathway and pathways in cancer. Protein-Protein Interaction network (PPI) demonstrated that the top 10 hub genes were identified. Survival analysis showed that genes downregulation of ITGB1, JUN and upregulation of ATM, MYC, NOTCH1, PTPN11 were associated with poor overall survival.