Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mapping p53 mutations in low-grade glioma: a voxel-based neuroimaging analysis.

ABSTRACT:

Background and purpose

Brain tumor location has proved to be a prognostic factor that may be associated with features of neoplastic origin. Mutation of p53 is an atypical genetic change that occurs during tumorigenesis. Thus, a potential correlation may exist between tumor location and p53 status. The purpose of the current study was to identify anatomic characteristics of mutant p53 expression by using quantitative neuroimaging analyses.

Materials and methods

Preoperative MR images from 182 patients with histologically confirmed low-grade gliomas were retrospectively analyzed. All tumors were manually marked and registered to the standard space. Using a voxel-based lesion-symptom mapping analysis, we located brain regions associated with a high occurrence of p53 mutation and corrected them by using a permutation test. The acquired clusters were further included as a factor in survival analyses.

Results

Statistical analysis demonstrated that the left medial temporal lobe and right anterior temporal lobe were specifically associated with high expression of mutant p53. Kaplan-Meier curves showed that tumors located in these regions were associated with significantly worse progression-free survival compared with tumors occurring elsewhere.

Conclusions

Our voxel-level imaging analysis provides new evidence that genetic changes during cancer may have anatomic specificity. Additionally, the current study suggests that tumor location identified on structural MR images could potentially be used for customized presurgical outcome prediction.

SUBMITTER: Wang YY

PROVIDER: S-EPMC7965905 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Pre-Surgery Cognitive Performance and Voxel-Based Lesion-Symptom Mapping in Patients with Left High-Grade Glioma.

Project description:(1) Background: The literature on the effects of high-grade glioma (HGG) growth on cognition is still scarce. (2) Method: A consecutive series of 85 patients with HGG involving the left hemisphere underwent an extended neuropsychological evaluation prior to surgery. Voxel-based lesion-symptom mapping (VLSM) was used to identify regions related to cognitive performance. (3) Results: The patients' mean level of pre-surgery accuracy was overall high. They showed the greatest difficulties in language with tasks such as naming (42.1% of patients impaired on nouns and 61.4% on verbs), reading (36.3% on words and 32.7% on pseudo-words), auditory lexical decisions (43.9%) and writing (41.3%) being most frequently impaired. VLSM analysis revealed anatomically separated areas along the temporal cortex and the white matter related to impairments on the different tasks, with voxels commonly shared by all tasks restricted to a small region in the ventral superior and middle temporal gyrus. (4) Conclusions: High-grade glioma affects cognition; nonetheless, lesions do not cause diffuse deficits but selectively impact the different language sub-domains along the ventral stream and the dorsal stream for language processing.

| S-EPMC8004913 | biostudies-literature

Localizing seizure-susceptible brain regions associated with low-grade gliomas using voxel-based lesion-symptom mapping.

Project description:BackgroundPatients afflicted with low-grade glioma (LGG) frequently suffer from seizures. The mechanisms of seizure initiation in these patients remain poorly understood. Tumor location has been correlated with seizure initiation. However, these correlative studies relied on dichotomized data analysis based on arbitrary lobe assignments. As a result, the lesion-symptom correlation may be incorrectly interpreted. Here, we present the first study that used a voxel-wise quantitative lesion analysis to investigate the spatial correlation between tumor location and seizure susceptibility.MethodsWe collected the medical records and magnetic resonance images of 410 LGG patients. The dataset was divided into a discovery set and a validation set. A voxel-based lesion-symptom correlative analysis was performed to determine whether tumor location was associated with seizure risk and could be related to the specific type of seizure.ResultsFor all seizure types, increased seizure risks were identified for LGGs that involved the left premotor area. The LGGs that involved the posterior portion of the left inferior and middle frontal gyrus were associated with increased risk of simple partial seizures. LGGs that involved the right temporal-insular region were associated with an increased risk of complex partial seizures. LGGs that involved the left premotor area were more likely to be associated with seizures that generalize. These correlations were consistently observed in both the discovery and the validation datasets.ConclusionsOur quantitative neuroimaging analyses support the concept that the anatomic location of an LGG is a contributing factor in tumor-related seizure.

| S-EPMC4288515 | biostudies-literature

Extrachromosomal driver mutations in glioblastoma and low-grade glioma.

Project description:Alteration of the number of copies of double minutes (DMs) with oncogenic EGFR mutations in response to tyrosine kinase inhibitors is a novel adaptive mechanism of glioblastoma. Here we provide evidence that such mutations in DMs, called here amplification-linked extrachromosomal mutations (ALEMs), originate extrachromosomally and could therefore be completely eliminated from the cancer cells. By exome sequencing of seven glioblastoma patients we reveal ALEMs in EGFR, PDGFRA and other genes. These mutations together with DMs are lost by cancer cells in culture. We confirm the extrachromosomal origin of such mutations by showing that wild-type and mutated DMs may coexist in the same tumour. Analysis of 4,198 tumours suggests the presence of ALEMs across different tumour types with the highest prevalence in glioblastomas and low-grade gliomas. The extrachromosomal nature of ALEMs explains the observed drastic changes in the amounts of mutated oncogenes (like EGFR or PDGFRA) in glioblastoma in response to environmental changes.

| S-EPMC4338529 | biostudies-literature

Low-grade glioma microglia

Project description:To examine the effect of Nf1 inactivation in astrocytes on normal microglia in the optic nerve, microglia from Nf1 flox/flox (FF) and Nf1 flox/flox; GFAP-Cre (FFC) were flow sorted. High throughput RNA-seq was employed to compare the expression profiles, and upregulated genes in microglia supportive to Nf1 deficient astrocytes were verified.

2020-07-03 | GSE65865 | GEO

Survival analysis of immune-related lncRNA in low-grade glioma.

Project description:BackgroundLow-grade glioma is grade I-II glioma. Immunotherapy is a promising way of tumor killing. Research on immune molecular mechanisms in low-grade gliomas and discovery of new immune checkpoints for low-grade gliomas are of great importance.MethodsGene sequencing data and clinical data of low-grade glioma were downloaded from TCGA database. Prognosis related lncRNAs were identified by Cox regression and their possible functions were found by gene enrichment set analysis.ResultsA total of 529 low-grade glioma samples and 5 non-tumor brain tissue samples are obtained from the TCGA database. Two hundred forty-seven immune-associated lncRNAs are screened. Cox regression showed that 16 immune-related lncRNAs are associated with low-grade glioma prognosis, and 7 lncRNAs are independent risk factors. Gene set enrichment analysis suggests that these molecules are enriched in extracellular region, sequence-specific DNA binding, neuropeptide signaling pathway, transcriptional misregulation in cancer, cytokine-cytokine receptor interaction, protein digestion and absorption, chemokine signaling pathway, etc. CONCLUSION: The identification of immune-related lncRNA may provide new targets for the research of the molecular mechanisms and treatment of low-grade glioma.

| S-EPMC6697914 | biostudies-literature

Management of pediatric low-grade glioma.

Project description:Purpose of reviewPediatric low-grade gliomas (pLGGs) have been treated with similar therapies for the last 30 years. Recent biological insights have allowed a new generation of targeted therapies to be developed for these diverse tumors. At the same time, technological advances may redefine the late toxicities associated with radiation therapy. Understanding recent developments in pLGG therapy is essential to the management of these common pediatric tumors.Recent findingsIt is now well understood that aberrations of the mitogen-activated protein kinase pathway are key to oncogenesis in low-grade gliomas. This understanding, along with the development of available targeted agents, have heralded a new era of understanding and treatment for these patients. Promising, sustained responses are now being seen in early phase trials among patients with multiply recurrent/progressive disease. Also, newer and highly conformal radiation approaches such as proton beam radiotherapy maintain efficacy of radiation but limit radiation-associated toxicities.SummaryNovel therapies offer the potential for tumor control with greatly reduced toxicities. However, late effects of these therapies are just now being explored. Improved radiation approaches and targeted agents have the potential to redefine traditional therapy for pLGG.

| S-EPMC6664811 | biostudies-literature

Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging.

Project description:PURPOSE:PET and SPECT voxel kinetics are highly noised. To our knowledge, no study has determined the effect of denoising on the ability to detect differences in binding at the voxel level using Statistical Parametric Mapping (SPM). METHODS:In the present study, groups of subject-images with a 10%- and 20%- difference in binding of [123I]iomazenil (IMZ) were simulated. They were denoised with Factor Analysis (FA). Parametric images of binding potential (BPND) were produced with the simplified reference tissue model (SRTM) and the Logan non-invasive graphical analysis (LNIGA) and analyzed using SPM to detect group differences. FA was also applied to [123I]IMZ and [11C]flumazenil (FMZ) clinical images (n = 4) and the variance of BPND was evaluated. RESULTS:Estimations from FA-denoised simulated images provided a more favorable bias-precision profile in SRTM and LNIGA quantification. Simulated differences were detected in a higher number of voxels when denoised simulated images were used for voxel-wise estimations, compared to quantification on raw simulated images. Variability of voxel-wise binding estimations on denoised clinical SPECT and PET images was also significantly diminished. CONCLUSION:In conclusion, noise removal from dynamic brain SPECT and PET images may optimize voxel-wise BPND estimations and detection of biological differences using SPM.

| S-EPMC6124809 | biostudies-literature

Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis.

Project description:This study aims to evaluate deep learning (DL) performance in differentiating low- and high-grade glioma. Search online database for studies continuously published from 1st January 2015 until 16th August 2022. The random-effects model was used for synthesis, based on pooled sensitivity (SE), specificity (SP), and area under the curve (AUC). Heterogeneity was estimated using the Higgins inconsistency index (I2). 33 were ultimately included in the meta-analysis. The overall pooled SE and SP were 94% and 93%, with an AUC of 0.98. There was great heterogeneity in this field. Our evidence-based study shows DL achieves high accuracy in glioma grading. Subgroup analysis reveals several limitations in this field: 1) Diagnostic trials require standard method for data merging for AI; 2) small sample size; 3) poor-quality image preprocessing; 4) not standard algorithm development; 5) not standard data report; 6) different definition of HGG and LGG; and 7) poor extrapolation.

| S-EPMC10209541 | biostudies-literature

Management of low-grade glioma: a systematic review and meta-analysis.

Project description:BackgroundOptimum management of low-grade gliomas remains controversial, and widespread practice variation exists. This evidence-based meta-analysis evaluates the association of extent of resection, radiation, and chemotherapy with mortality and progression-free survival at 2, 5, and 10 years in patients with low-grade glioma.MethodsA quantitative systematic review was performed. Inclusion criteria included controlled trials of newly diagnosed low-grade (World Health Organization Grades I and II) gliomas in adults. Eligible studies were identified, assigned a level of evidence for every endpoint considered, and analyzed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The relative risk of mortality and of progression at 2, 5, and 10 years was calculated for patients undergoing resection (gross total, subtotal, or biopsy), radiation, or chemotherapy.ResultsGross total resection was significantly associated with decreased mortality and likelihood of progression at all time points compared to subtotal resection. Early radiation was not associated with decreased mortality; however, progression-free survival was better at 5 years compared to patients receiving delayed or no radiation. Chemotherapy was associated with decreased mortality at 5 and 10 years in the high-quality literature. Progression-free survival was better at 5 and 10 years compared to patients who did not receive chemotherapy. In patients with isocitrate dehydrogenase 1 gene (IDH1) R132H mutations receiving chemotherapy, progression-free survival was better at 2 and 5 years than in patients with IDH1 wild-type gliomas.ConclusionsResults from this review, the first to quantify differences in outcome associated with surgery, radiation, and chemotherapy in patients with low-grade gliomas, can be used to inform evidence-based management and future clinical trials.

| S-EPMC6660818 | biostudies-literature

OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma.

Project description:Glioma is the most frequent primary brain tumor that causes high mortality and morbidity with poor prognosis. There are four grades of gliomas, I to IV, among which grade II and III are low-grade glioma (LGG). Although less aggressive, LGG almost universally progresses to high-grade glioma and eventual causes death if lacking of intervention. Current LGG treatment mainly depends on surgical resection followed by radiotherapy and chemotherapy, but the survival rates of LGG patients are low. Therefore, it is necessary to use prognostic biomarkers to classify patients into subgroups with different risks and guide clinical managements. Using gene expression profiling and long-term follow-up data, we established an Online consensus Survival analysis tool for LGG named OSlgg. OSlgg is comprised of 720 LGG cases from two independent cohorts. To evaluate the prognostic potency of genes, OSlgg employs the Kaplan-Meier plot with hazard ratio and p value to assess the prognostic significance of genes of interest. The reliability of OSlgg was verified by analyzing 86 previously published prognostic biomarkers of LGG. Using OSlgg, we discovered two novel potential prognostic biomarkers (CD302 and FABP5) of LGG, and patients with the elevated expression of either CD302 or FABP5 present the unfavorable survival outcome. These two genes may be novel risk predictors for LGG patients after further validation. OSlgg is public and free to the users at http://bioinfo.henu.edu.cn/LGG/LGGList.jsp.

| S-EPMC7381343 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data