EgoNet identifies differential ego-modules and pathways related to prednisolone resistance in childhood acute lymphoblastic leukemia.

Purpose: To extract feature ego-modules and pathways in childhood acute lymphoblastic leukemia (ALL) resistant to prednisolone treatment, and further to explore the mechanisms behind prednisolone resistance.

Materials And Methods: EgoNet algorithm was used to identify candidate ego-network modules, mainly via constructing differential co-expression network (DCN); selecting ego genes; collecting ego-network modules; refining candidate modules. Afterwards, statistical significance was calculated for these candidate modules. Biological functions of differential ego-network modules were identified using Reactome database. To verify this proposed method can lead to truly positive findings in clinical settings, support vector machine (SVM) was utilized to compute the AUC values for each significant pathway using 3-fold cross-validation method. To predict the reliability of our findings, another established method (attract) was used to identify the differential attractor modules using the same microarray profile.

Results: After eliminating the modules with classification accuracy < 0.9 and node number < 15, only ego-network module 30 was eligible. After significance calculation, module 30 was significant. Module 30 was enriched in APC/C-mediated degradation of cell cycle proteins. The AUC for the significant pathway of APC/C-mediated degradation of cell cycle proteins was 0.915. Although the attract method obtained more modules, the module identified by our proposed method owned more gene nodes, and had more classification ability (AUC = 0.915).

Conclusion: One differential ego-network module identified in childhood ALL resistance to prednisolone based on DCN and EgoNet, might be helpful to reveal the mechanisms underlying prednisolone resistance in childhood ALL.