Mapping p53 Mutations in Low-Grade Glioma: A Voxel-Based Neuroimaging Analysis

Wang, Y. Y.; Zhang, T.; Li, S. W.; Qian, Tianyi; Fan, Xing; Peng, Xiaoxia; Ma, Jun; Wang, L.; Jiang, Tao

doi:10.3174/ajnr.a4065

Cited by 26 publications

(16 citation statements)

References 34 publications

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“…Tumor location is an influential factor associated with LGG-related epilepsy. Several MRI-based studies indicate the association between the involvement of eloquent (42), cortical (45), and insular regions (43) with epilepsy occurrence, along with the probabilistic risk atlas of LGG-related epilepsy (44). However, there is a need to investigate and predict the type of epilepsy.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

et al. 2020

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Purpose: The majority of patients with low-grade gliomas (LGGs) experience tumor-related epilepsy during the disease course. Our study aimed to build a radiomic prediction model for LGG-related epilepsy type based on magnetic resonance imaging (MRI) data.Methods: A total of 205 cases with LGG-related epilepsy were enrolled in the retrospective study and divided into training and validation cohorts (1:1) according to their surgery time. Seven hundred thirty-four radiomic features were extracted from T2-weighted imaging, including six location features. Pearson correlation coefficient, univariate area under curve (AUC) analysis, and least absolute shrinkage and selection operator regression were adopted to select the most relevant features for the epilepsy type to build a radiomic signature. Furthermore, a novel radiomic nomogram was developed for clinical application using the radiomic signature and clinical variables from all patients.Results: Four MRI-based features were selected from the 734 radiomic features, including one location feature. Good discriminative performances were achieved in both training (AUC = 0.859, 95% CI = 0.787-0.932) and validation cohorts (AUC = 0.839, 95% CI = 0.761-0.917) for the type of epilepsy. The accuracies were 80.4 and 80.6%, respectively. The radiomic nomogram also allowed for a high degree of discrimination. All models presented favorable calibration curves and decision curve analyses. Conclusion:Our results suggested that the MRI-based radiomic analysis may predict the type of LGG-related epilepsy to enable individualized therapy for patients with LGG-related epilepsy.

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Section: Discussionmentioning

confidence: 99%

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

et al. 2020

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“…Previous studies have indicated a growing number of molecular mechanisms contributing to TMZ resistance in GBM including genetic and epigenetic, such as MGMT methylation (22), IDH mutations (23), aberrant ABC transporter expression (24,25), p53 mutations and deletions (26), DNA repair deregulation (27,28) and miRNAs (29,30). Furthermore, evidence is now beginning to demonstrate lncRNAs as having important roles in cancer therapy (31,32).…”

Section: Discussionmentioning

confidence: 99%

Genomic profiling of long non-coding RNA and mRNA expression associated with acquired temozolomide resistance in glioblastoma cells

Zeng¹,

Xu²,

Liu

et al. 2017

International Journal of Oncology

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Temozolomide (TMZ) is an alkylating chemotherapeutic agent widely used in anti-glioma treatment. However, acquired TMZ resistance represents a major clinical challenge that leads to tumor relapse or progress. This study investigated the genomic profiles including long non-coding RNA (lncRNA) and mRNA expression associated with acquired TMZ resistance in glioblastoma (GBM) cells in vitro. The TMZ-resistant (TR) of GBM sub-cell lines were established through repetitive exposure to increasing TMZ concentrations in vitro. The differentially expressed lncRNAs and mRNAs between the parental U87 and U87TR cells were detected by human lncRNA microarray method. In this study, we identified 2,692 distinct lncRNAs demonstrating >2-fold differential expression with 1,383 lncRNAs upregulated and 1,309 lncRNAs downregulated. Moreover, 4,886 differential mRNAs displayed 2,933 mRNAs upregulated and 1,953 mRNAs downregulated. Further lncRNA classification and subgroup analysis revealed the potential functions of the lncRNA-mRNA relationship associated with the acquired TMZ resistance. Gene ontology and pathway analysis on mRNAs showed significant biological regulatory genes and pathways involved in acquired TMZ resistance. Moreover, we found the ECM-receptor interaction pathway was significantly downregulated and ECM related collagen I, fibronectin, laminin and CD44 were closely associated with the TR phenotype in vitro. Our findings indicate that the dysregulated lncRNAs and mRNAs identified in this work may provide novel targets for overcoming acquired TMZ resistance in GBM chemotherapy.

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“…For example, IDH1 mutation was commonly seen in left frontal lobe, where TERT mutation only exists as well[ 2 , 5 , 23 ]. Such investigations were performed in the context of MGMT and TP53[ 24 , 25 ]. In our study, we used a panel composed of IDH1/1p19q/TERT which is worldwide recognized in the precise diagnosis of glioma to demonstrate the anatomic distribution of different molecular subsets.…”

Section: Discussionmentioning

confidence: 99%

Anatomic mapping of molecular subtypes in diffuse glioma

Tang

Lian

et al. 2017

BMC Neurol

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BackgroundTumor location served as an important prognostic factor in glioma patients was considered to postulate molecular features according to cell origin theory. However, anatomic distribution of unique molecular subtypes was not widely investigated. The relationship between molecular phenotype and histological subgroup were also vague based on tumor location. Our group focuses on the study of glioma anatomic location of distinctive molecular subgroups and histology subtypes, and explores the possibility of their consistency based on clinical background.MethodsWe retrospectively reviewed 143 cases with both molecular information (IDH1/TERT/1p19q) and MRI images diagnosed as cerebral diffuse gliomas. The anatomic distribution was analyzed between distinctive molecular subgroups and its relationship with histological subtypes. The influence of tumor location, molecular stratification and histology diagnosis on survival outcome was investigated as well.ResultsAnatomic locations of cerebral diffuse glioma indicate varied clinical outcome. Based on that, it can be stratified into five principal molecular subgroups according to IDH1/TERT/1p19q status. Triple-positive (IDH1 and TERT mutation with 1p19q codeletion) glioma tended to be oligodendroglioma present with much better clinical outcome compared to TERT mutation only group who is glioblastoma inclined (median overall survival 39 months VS 18 months). Five molecular subgroups were demonstrated with distinctive locational distribution. This kind of anatomic feature is consistent with its corresponding histological subtypes.DiscussionEach molecular subgroup in glioma has unique anatomic location which indicates distinctive clinical outcome. Molecular diagnosis can be served as perfect complementary tool for the precise diagnosis. Integration of histomolecular diagnosis will be much more helpful in routine clinical practice in the future.

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Mapping p53 Mutations in Low-Grade Glioma: A Voxel-Based Neuroimaging Analysis

Cited by 26 publications

References 34 publications

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

Genomic profiling of long non-coding RNA and mRNA expression associated with acquired temozolomide resistance in glioblastoma cells

Anatomic mapping of molecular subtypes in diffuse glioma

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