The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Choosing the right cell line for renal cell cancer research
Open accessAbstract
Cell lines are still a tool of choice for many fields of biomedical research, including oncology. Although cancer is a very complex disease, many discoveries have been made using monocultures of established cell lines. Therefore, the proper use of in vitro models is crucial to enhance our understanding of cancer. Therapeutics against renal cell cancer (RCC) are also screened with the use of cell lines. Multiple RCC in vitro cultures are available, allowing in vivo heterogeneity in the laboratory, but at the same time, these can be a source of errors. In this review, we tried to sum up the data on the RCC cell lines used currently. An increasing amount of data on RCC shed new light on the molecular background of the disease; however, it revealed how much still needs to be done. As new types of RCC are being distinguished, novel cell lines and the re-exploration of old ones seems to be indispensable to create effective in vitro tools for drug screening and more.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774 · cited 1×
“This in vivo heterogeneity of tumors can be mimicked in vitro with use of a panel of cell lines in the place of multiple biopsy derived samples [87].”
BackgroundThe tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis
Maxwell, P. H., Wiesener, M. S., Chang, G.-W., Clifford, S. C., Vaux, E. C., Cockman, M. E., Wykoff, C. C., Pugh, C. W., Maher, E. R., & Ratcliffe, P. J. (1999) · Nature · doi:10.1038/20459 · Cited by 5,180
The Principles of Humane Experimental Technique
(1960) · Medical Journal of Australia · doi:10.5694/j.1326-5377.1960.tb73127.x · Cited by 4,912
Characteristics of a human cell line transformed by DNA from human adenovirus type 5
Graham, F. L., Russell, W. C., Smiley, J., & Nairn, R. (1977) · Journal of General Virology · doi:10.1099/0022-1317-36-1-59 · Cited by 4,793
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Research Network (2013) · Nature · doi:10.1038/nature12222 · Cited by 3,485
Monks, A., Scudiero, D., Skehan, P., Shoemaker, R., Paull, K., Vistica, D., Hose, C., Langley, J., Cronise, P., Vaigro-Wolff, A., et al. (1991) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/83.11.757 · Cited by 3,289
The NCI60 human tumour cell line anticancer drug screen
Shoemaker, R. H. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1951 · Cited by 2,620
COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer
Forbes, S. A., Bindal, N., Bamford, S., Cole, C., Kok, C. Y., Beare, D., Jia, M., Shepherd, R., Leung, K., Menzies, A., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq929 · Cited by 2,278
Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
Escudier, B., Porta, C., Schmidinger, M., Algaba, F., Patard, J. J., Khoo, V., Eisen, T., & Horwich, A. (2014) · Annals of Oncology · doi:10.1093/annonc/mdu259 · Cited by 2,251
Self-citationSchmidt, L., Duh, F.-M., Chen, F., Kishida, T., Glenn, G., Choyke, P., Scherer, S. W., Zhuang, Z., Lubensky, I., Dean, M., et al. (1997) · Nature Genetics · doi:10.1038/ng0597-68 · Cited by 1,524
Cited by
Models of renal cell carcinoma used to investigate molecular mechanisms and develop new therapeutics
Shapiro, D. D., Virumbrales-Muñoz, M., Beebe, D. J., & Abel, E. J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.871252 · Cited by 28
Menke, J., Eckmann, P., Ozyurt, I. B., Roelandse, M., Anderson, N., Grethe, J., Gamst, A., & Bandrowski, A. (2022) · Journal of Medical Internet Research · doi:10.2196/37324 · Cited by 25
Microphysiological model of renal cell carcinoma to inform anti-angiogenic therapy
Virumbrales-Muñoz, M., Ayuso, J. M., Loken, J. R., Denecke, K. M., Rehman, S., Skala, M. C., Abel, E. J., & Beebe, D. J. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121454 · Cited by 20
Sakolish, C., Tsai, H.-H. D., Lin, H.-C., Bajaj, P., Villenave, R., Ferguson, S. S., Stanko, J. P., Becker, R. A., Hewitt, P., Chiu, W. A., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13030563 · Cited by 11
Kidney cancer models for pre-clinical drug discovery: challenges and opportunities
Pohl, L., Friedhoff, J., Jurcic, C., Teroerde, M., Schindler, I., Strepi, K., Schneider, F., Kaczorowski, A., Hohenfellner, M., Duensing, A., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.889686 · Cited by 5
Metadata
- Authors
- Brodaczewska, K. K., Szczylik, C., Fiedorowicz, M., Porta, C., & Czarnecka, A. M.
- Venue
- Molecular Cancer
- Published
- 2016-12
- Volume
- 15
- Issue
- 1
- Pages
- 83-83
- ISSN
- 1476-4598 (ISSN-L)
- OpenAlex
- W2565408756
- Cited by
- 267
- References
- 154
- FWCI
- 15.36
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Choosing the right cell line for renal cell cancer research. https://app.cochat.ai/papers/10.1186/s12943-016-0565-8
- The paper
- Brodaczewska, K. K., Szczylik, C., Fiedorowicz, M., Porta, C., & Czarnecka, A. M. (2016). Choosing the right cell line for renal cell cancer research. Molecular Cancer. https://doi.org/10.1186/s12943-016-0565-8
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
