ArticleCancer medicine2018
Identification of transcriptome signature for predicting clinical response to bevacizumab in recurrent glioblastoma.
Article in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Congress of neurological surgeons systematic review and evidence-based guidelines update on the role of neuropathology in the management of progressive glioblastoma in adults.Journal of neuro-oncology · 2022Pooled it
- The role of c-Met and VEGFR2 in glioblastoma resistance to bevacizumab.Scientific reports · 2021Trial
- The expression pattern and prognostic relevance of p120-catenin, COL4A2 and SOX10 in glioma.Frontiers in oncology · 2026Article
- Transcriptomic analysis identifies a potential target for bevacizumab resistant glioblastoma.Scientific reports · 2025Article
- Collagen VI deposition primes the glioblastoma microenvironment for invasion through mechanostimulation of β-catenin signaling.PNAS nexus · 2024Article
- Glioma Cells Secrete Collagen VI to Facilitate Invasion.bioRxiv : the preprint server for biology · 2023Article
- Identification of Implications of Angiogenesis and m6A Modification on Immunosuppression and Therapeutic Sensitivity in Low-Grade Glioma by Network Computational Analysis of Subtypes and Signatures.Frontiers in immunology · 2022Article
- Identification of transcriptome signature for predicting clinical response to bevacizumab in recurrent glioblastoma.Cancer medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastomas are among the most fatal brain tumors. Although no effective treatment option is available for recurrent glioblastomas (GBMs), a subset of patients evidently derived clinical benefit from bevacizumab, a monoclonal antibody against vascular endothelial growth factor. We retrospectively reviewed patients with recurrent GBM who received bevacizumab to identify biomarkers for predicting clinical response to bevacizumab. Following defined criteria, the patients were categorized into two clinical response groups, and their genetic and transcriptomic results were compared. Angiogenesis-related gene sets were upregulated in both responders and nonresponders, whereas genes for each corresponding angiogenesis pathway were distinct from one another. Two gene sets were made, namely, the nonresponder angiogenesis gene set (NAG) and responder angiogenesis gene set (RAG), and then implemented in independent GBM cohort to validate our dataset. A similar association between the corresponding gene set and survival was observed. In NAG, COL4A2 was associated with a poor clinical outcome in bevacizumab-treated patients. This study demonstrates that angiogenesis-associated gene sets are composed of distinct subsets with diverse biological roles and they represent different clinical responses to anti-angiogenic therapy. Enrichment of a distinct angiogenesis pathway may serve as a biomarker to predict patients who will derive a clinical benefit from bevacizumab.
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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.