Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes.

Medulloblastoma, a common pediatric malignant tumor, has been recognized to have four molecular subgroups [wingless (WNT), sonic hedgehog (SHH), group 3, group 4], which are defined by the characteristic gene transcriptomic and DNA methylomic profiles, and has distinct clinical features within each subgroup. The tumor immune microenvironment is integral in tumor initiation and progression and might be associated with therapeutic responses. However, to date, the immune infiltrative landscape of medulloblastoma has not yet been elucidated. Thus, we proposed MethylCIBERSORT to estimate the degree of immune cell infiltration and weighted correlation network analysis (WGCNA) to find modules of highly correlated genes. Synthesizing the hub genes in the protein-protein interaction (PPI) network and modules of the co-expression network, we identify three candidate biomarkers [GRB2-associated-binding protein 1 (GAB1), Abelson 1 (ABL1), and CXC motif chemokine receptor type 4 (CXCR4)] via the molecular profiles of medulloblastoma. Given this, we investigated the correlation between these three immune hub genes and immune checkpoint blockade response and the potential of drug prediction further. In addition, this study demonstrated a higher presence of endothelial cells and infiltrating immune cells in Group 3 tumor bulk. The above results will be conducive to better comprehending the immune-related pathogenesis and treatment of medulloblastoma.

Low-frequency collective motion of DNA-binding domain defines p53 function.

Most mutations in the DNA-binding domain (DBD) of p53 inactivate or rescue the protein function interacting with the minor groove of DNA. However, how the conformation changes propagating from the mutation sites result in distinct molecular recognition is still not well understood. As the protein mobility is an intrinsic property encrypted in its primary structure, we examined if different structures of wild-type and mutant p53 core domains display any unique patterns of intrinsic mobility. Normal mode calculation was employed to characterize the collective dynamics of DBD in p53 monomer and tetramer as well as their mutants. Intriguingly, the low-frequency collective motions of DBD show similar patterns between the wild-type protein and the rescued mutants. The analysis on atomic backbone fluctuations and low-frequency vibration mode statistics does further support the correlation between the intrinsic collective motion of DBD and the p53 protein function. The mutations in the DBD influence the low-frequency vibration of the p53 tetramer via the change of the collective motions among its four monomers. These findings thus provide new insights for understanding the physical mechanism of p53 protein structure-function relationship and help find the small molecule drug to modulate protein dynamic for disease therapy.

Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS: Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS: A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS: A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.