A systems biology approach to find representative genes in Acute Myeloid Leukemia.

In this study, we modeled gene expression profile data from Acute Myeloid Leukemia (AML) and healthy cases. At first, the GEO-GSE9476 dataset was processed, and a total of 341 genes were identified as differentially expressed genes (DEGs) in patients, and 599 DEGs in healthy individuals. Gene Ontology and pathway analysis on DEGs led to the identification of 5 Transcription Factors for patients and 3 for healthy cases. Analysis of the respective metabolic pathways revealed a common region in the metabolic pathway between AML and Tuberculosis (TB) that confirmed the validity of our procedure due to the consistency with similar reports. Upon PPI network analysis, Hub genes and three modules containing 41 up-regulated and down-regulated genes in AML patients were identified. Survival analysis on these genes results in reducing the number of identified effective genes into 3 upregulated (ITGAM, ITGAL and CD163) and 5 downregulated genes (MCM2, MCM3, RFC4, RFC5 and FEN1). Finally, drug sensitivity analysis was performed on these genes demonstrating complexity in drug-resistance due to the pattern of gene expression. This knowledge could potentially enable personalized treatment approaches based on individual patient responses due to the epigenetics and life style which affect gene expression pattern.