ArticleOncotarget2018
Comparative molecular characterization of typical and exceptional responders in glioblastoma.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Article
- Cellular and molecular features related to exceptional therapy response and extreme long-term survival in glioblastoma.Cancer medicine · 2023Review
- Space flight and central nervous system: Friends or enemies? Challenges and opportunities for neuroscience and neuro-oncology.Journal of neuroscience research · 2022Review
- Impairing proliferation of glioblastoma multiforme with CD44+ selective conjugated polymer nanoparticles.Scientific reports · 2022Article
- Bioinformatic Analyzes of the Association Between Upregulated Expression ofFrontiers in medicine · 2022Article
- The Exceptional Responders Initiative: Feasibility of a National Cancer Institute Pilot Study.Journal of the National Cancer Institute · 2021Article
- AKIP1 promotes glioblastoma viability, mobility and chemoradiation resistance via regulating CXCL1 and CXCL8 mediated NF-κB and AKT pathways.American journal of cancer research · 2021Article
- DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.Medical & biological engineering & computing · 2020Article
- Bidirectional Relation Between Parkinson's Disease and Glioblastoma Multiforme.Frontiers in neurology · 2020Review
- Case Report of Complete Radiological Response of a Thalamic Glioblastoma After Treatment With Proton Therapy Followed by Temozolomide and Tumor-Treating Fields.Frontiers in oncology · 2020Article
- The Genetic Landscape of Human Glioblastoma and Matched Primary Cancer Stem Cells Reveals Intratumour Similarity and Intertumour Heterogeneity.Stem cells international · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Glioblastoma (GBM) is the most common and the deadliest type of primary brain tumor, with a median survival time of only 15 months despite aggressive treatment. Although most patients have an extremely poor prognosis, a relatively small number of patients survive far beyond the median survival time. Investigation of these exceptional responders has sparked a great deal of interest and is becoming an important focus in the field of cancer research. To investigate the molecular differences between typical and exceptional responders in GBM, comparative analyses of somatic mutations, copy number, methylation, and gene expression datasets from The Cancer Genome Atlas were performed, and the results of these analyses were integrated via gene ontology and pathway analyses to assess the functional significance of the differential aberrations. Less severe copy number loss of
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.