Human MGMT peptide

We attempted to establish a magnetic resonance imaging (MRI)-based radiomic model for stratifying prognostic subgroups of newly diagnosed glioblastoma (GBM) patients and predicting O (6)-methylguanine-DNA methyltransferase promotor methylation (pMGMT-met) status of the tumor. Preoperative MRI scans from 201 newly diagnosed GBM patients were included in this study. A total of 489 texture features including the first-order feature, second-order features from 162 datasets, and location data from 182 datasets were collected. Supervised principal component analysis was used for prognostication and predictive modeling for pMGMT-met status was performed based on least absolute shrinkage and selection operator regression. 22 radiomic features that were correlated with prognosis were used to successfully stratify patients into high-risk and low-risk groups (p = 0.004, Log-rank test). The radiomic high- and low-risk stratification and pMGMT status were independent prognostic factors. As a matter of fact, predictive accuracy of the pMGMT methylation status was 67% when modeled by two significant radiomic features. A significant survival difference was observed among the combined high-risk group, combined intermediate-risk group (this group consists of radiomic low risk and pMGMT-unmet or radiomic high risk and pMGMT-met), and combined low-risk group (p = 0.0003, Log-rank test). Radiomics can be used to build a prognostic score for stratifying high- and low-risk GBM, which was an independent prognostic factor from pMGMT methylation status. On the other hand, predictive accuracy of the pMGMT methylation status by radiomic analysis was insufficient for practical use.

Object Mitochondrial pyruvate carrier (MPC) proteins MPC1 and MPC2 form a transporter complex to control rate-limiting pyruvate transportation through the inner mitochondrial membrane. MPC1 plays a crucial role in the tumor metabolite and biosynthesis process. However, the role of MPC1 in glioblastoma (GBM) is unknown. Methods The Cancer Genome Atlas (TCGA) data set, which included 631 cases of GBM with genomic and clinical data, was obtained from the UCSC Xena browser. The clinical data set contained demographic, survival rate, and histological and pathological information. The association between MPC1 gene copy number segments and GBM patient overall survival was analyzed by Kaplan-Meier survival analysis, which was performed using the R2 web-based platform to identify the best cut-off. GraphPad Prism 7 was used to compare the differences in MPC1 gene copy number segments between various groups and subtypes. Results A total of 631 patients with glioblastoma (mean age 57.78 +/- 14.36 years, 59% of males) were examined in this study, including 438 cases with MPC1 intact (MPC1 copy number segments > - 0.1, 69.4%) and 157 cases with MPC1 deletion (24.9%) tumors. Among the four GBM subtypes, the proneural group had the highest MPC1 copy number segments and GBM patients diagnosed with proneural subtype showed the best outcome. The expression of MPC1 transcripts was different in the TCGA-GBM dataset compared with the GTEx dataset. MPC1 copy number segments showed a significant correlation with MGMT copy number segments (r = 0.1322, p = 0.0012). MGMT gene expression level in MPC1 intact tumors was significantly lower than that in MPC1 deletion tumors (p = 0.0003). Significant relevancy was observed between better OS and the MPC1 intact group compared with the MPC1 deletion group (p = 0.020). Moreover, patients with MPC1 deletion tumors treated with temozolomide (TMZ) had worse survival than patients with MPC1 intact tumors (p = 0.027). Conclusions Our results suggest a role of decreased MPC1 copy number segments in reducing overall survival in glioblastoma. MPC1 deletion is associated with poor response to TMZ chemotherapy in GBM.