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EgoNet identifies differential ego-modules and pathways related to prednisolone resistance in childhood acute
Jian Jiang1, Xiang-Yun Yin1, Xue-Wen Song2
1a Department of Pediatrics , The Affiliated Hospital of Qingdao University , Qingdao , Shandong , People's Republic of China.
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.
Insights
This study identified a key ego-network module in childhood acute lymphoblastic leukemia (ALL) resistant to prednisolone. This finding may help uncover mechanisms of prednisolone resistance in ALL.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Childhood acute lymphoblastic leukemia (ALL) poses significant treatment challenges, particularly cases resistant to prednisolone.
- Understanding the molecular mechanisms underlying prednisolone resistance is crucial for improving therapeutic outcomes.
Purpose of the Study:
- To identify critical ego-modules and pathways associated with prednisolone resistance in childhood ALL.
- To elucidate the underlying mechanisms contributing to treatment resistance.
Main Methods:
- Utilized the EgoNet algorithm to construct differential co-expression networks (DCN) and identify candidate ego-network modules.
- Performed statistical significance testing and functional enrichment analysis using the Reactome database.
- Validated findings using Support Vector Machine (SVM) for classification accuracy and the attract method for reliability assessment.
Main Results:
- A significant ego-network module (module 30) was identified, enriched in the APC/C-mediated degradation of cell cycle proteins pathway.
- This pathway demonstrated high classification accuracy (AUC = 0.915) in distinguishing resistant cases.
- The identified module showed superior gene node representation and classification ability compared to the attract method.
Conclusions:
- A novel differential ego-network module, identified through DCN and EgoNet, is implicated in prednisolone resistance in childhood ALL.
- This finding offers potential insights into the mechanisms driving treatment resistance and may guide future therapeutic strategies.
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