COMPUTATIONAL IDENTIFICATION OF CO-EXPRESSION NETWORKS, KEY GENES AND PATHWAYS IN ACUTE MYELOID LEUKEMIA (Applied Biological Research, 2023)
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 co expressed 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  signalingpathway 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.
Keywords: Acute myeloid leukemia; biomarker; disease biology; gene co-expression; key genes