Integrated bioinformatics analysis of the crucial candidate genes and pathways associated with glucocorticoid resistance in acute lymphoblastic leukemia.

Glucocorticoids (GC) are the foundation of the chemotherapy regimen in acute lymphoblastic leukemia (ALL). However, resistance to GC is observed more frequently than resistance to other chemotherapy agents in patients with ALL relapse. Moreover, the mechanism underlying the development of GC resistance in ALL has not yet been fully uncovered. In this study, we used bioinformatic analysis methods to integrate the candidate genes and pathways participating in GC resistance in ALL and subsequently verified the bioinformatics findings with in vitro cell experiments. Ninety-nine significant common differentially expressed genes (DEGs) associated with GC resistance were determined by integrating two gene profile datasets, including GC-sensitive and -resistant samples. Using Kyoto Encyclopedia of Genes and Genomes (KEGG) and REACTOME pathways analysis, the signaling pathways in which DEGs were significantly enriched were clustered. The GC resistance-related biologically functional interactions were visualized as DEG-associated Protein-Protein Interaction (PPI) network complexes, with 98 nodes and 127 edges. MYC, a node which displayed the highest connectivity in all edges, was highlighted as the core gene in the PPI network. Increased C-MYC expression was observed in adriamycin-resistant BALL-1/ADR cells, which we demonstrated was also resistant to dexamethasone. These results outlined a panorama in which the solitary and scattered experimental results were integrated and expanded. The potential promising target of the candidate pathways and genes involved in GC resistance of ALL was concomitantly revealed.