ArticleiScience2020
A Clinical Genomics-Guided Prioritizing Strategy Enables Selecting Proper Cancer Cell Lines for Biomedical Research.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Plant biotechnology research with single-cell transcriptome: recent advancements and prospects.Plant cell reports · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selecting appropriate cell lines to represent a disease is crucial for the success of biomedical research, because the usage of less relevant cell lines could deliver misleading results. However, systematic guidance on cell line selection is unavailable. Here we developed a clinical Genomics-guided Prioritizing Strategy for Cancer Cell Lines (CCL-cGPS) and help to guide this process. Statistical analyses revealed CCL-cGPS selected cell lines were among the most appropriate models. Moreover, we observed a linear correlation between the drug response and CCL-cGPS score of cell lines for breast and thyroid cancers. Using RT4 cells selected by CCL-GPS, we identified mebendazole and digitoxin as candidate drugs against bladder cancer and validate their promising anticancer effect through
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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.