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.

[Genetic mutations of homocysteine metabolism related enzymes in patients with ischemic stroke].

To study genetic mutations of methylenetetrahydrofolate reductase (MTHFR) C677T and cystathionine-beta-synthase (CBS) T833C related to homocysteine metabolism in patients with ischemic stroke, the MTHFR gene C677T gene mutation and the CBS T833C gene mutation were detected by PCR-RFLP or ARMS method in 74 patients with ischemic stroke and 83 normal people for control. Results showed that the frequencies of MTHFR T homogenetic type (2.7%) , heterogenetic type (51.4%) and T allele (28.4%) in ischemic group were higher than those in control group (1.2%, 39.8% and 21.1%, respectively). The frequencies of CBS C homogenetic type (13.5%) and C allele (43.9%) in ischemic group were higher than those in control group (6.0% and 38.0%, respectively). Multiple Logistic Regression analysis showed that together with the T allele in MTHFR, the C allele in CBS and age were related to ischemic stroke (P<0.05). The odds ratios (OR) of the T allele in MTHFR C677T and the C allele in CBS T833C were 1.74 (95%CI 1.06-2.86) and 1.73 (95%CI 1.07-2.81) respectively. The study revealed that the genetic mutations of MTHFR C677T, CBS T833C,were related with the ischemic stroke. The genetic mutations of MTHFR C677T and CBS T833C may be genetic factors for ischemic stroke.