ReviewTrends in cancer2024
'Slicing' glioblastoma drivers with the Swiss cheese model.
Review in Trends in cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- A novel role of exostosin glycosyltransferase 2 (EXT2) in glioblastoma cell metabolism, radiosensitivity and ferroptosis.Cell death and differentiation · 2025Article
- Hypoxia-induced complement component 3 promotes aggressive tumor growth in the glioblastoma microenvironment.JCI insight · 2024Article
- Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives.Journal of hematology & oncology · 2024Review
- Roles of extracellular vesicles in glioblastoma: foes, friends and informers.Frontiers in oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The Swiss cheese model is used to assess risks and explain accidents in a variety of industries. This model can be applied to dissect the homeostatic mechanisms whose cumulative dysregulation contributes to disease states, including cancer. Using glioblastoma (GBM) as an exemplar, we discuss how specific protumorigenic mechanisms collectively drive disease by affecting genomic integrity, epigenetic regulation, metabolic homeostasis, and antitumor immunity. We further highlight how host factors, such as hormonal differences and aging, impact this process, and the interplay between these 'system failures' that enable tumor progression and foster therapeutic resistance. Finally, we examine therapies that consider the interactions between these elements, which may comprise more effective approaches given the multifaceted protumorigenic mechanisms that drive GBM.
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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.