The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Functional links between clustered microRNAs: suppression of cell-cycle inhibitors by microRNA clusters in gastric cancer
Open accessAbstract
microRNAs (miRNAs) play integral roles in diverse processes including tumorigenesis. miRNA gene loci are often found in close conjunction, and such clustered miRNA genes are transcribed from a common promoter to generate polycistronic primary transcript. The primary transcript (pri-miRNA) is then processed by two RNase III proteins to release the mature miRNAs. Although it has been speculated that the miRNAs in the same cluster may play related biological functions, this has not been experimentally addressed. Here we report that the miRNAs in two clusters (miR-106b approximately 93 approximately 25 and miR-222 approximately 221) suppress the Cip/Kip family members of Cdk inhibitors (p57(Kip2), p21(Cip1) and p27(Kip1)). We show that miR-25 targets p57 through the 3'-UTR. Furthermore, miR-106b and miR-93 control p21 while miR-222 and miR-221 regulate both p27 and p57. Ectopic expression of these miRNAs results in activation of Cdk2 and facilitation of G1/S phase transition. Consistent with these results, both clusters are abnormally upregulated in gastric cancer tissues compared to the corresponding normal tissues. Ectopic expression of miR-222 cluster enhanced tumor growth in the mouse xenograft model. Our study demonstrates the functional associations between clustered miRNAs and further implicates that effective cancer treatment may require a combinatorial approach to target multiple oncogenic miRNA clusters.
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
Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20107 · Cited by 54,679
Global Cancer Statistics, 2002
Parkin, D. M., Bray, F., Ferlay, J., & Pisani, P. (2005) · CA: A Cancer Journal for Clinicians · doi:10.3322/canjclin.55.2.74 · Cited by 18,196
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
MicroRNA expression profiles classify human cancers
Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005) · Nature · doi:10.1038/nature03702 · Cited by 9,539
MicroRNA signatures in human cancers
Calin, G. A., & Croce, C. M. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1997 · Cited by 7,590
CDK inhibitors: positive and negative regulators of G1-phase progression
Sherr, C. J., & Roberts, J. M. (1999) · Genes & Development · doi:10.1101/gad.13.12.1501 · Cited by 6,115
A microRNA expression signature of human solid tumors defines cancer gene targets
Volinia, S., Calin, G. A., Liu, C.-G., Ambs, S., Cimmino, A., Petrocca, F., Visone, R., Iorio, M., Roldo, C., Ferracin, M., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510565103 · Cited by 5,697
(2008) · doi:10.1038/nrg2290 · Cited by 5,199
miRBase: tools for microRNA genomics
Griffiths-Jones, S., Saini, H. K., van Dongen, S., & Enright, A. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm952 · Cited by 4,382
MicroRNA genes are transcribed by RNA polymerase II
Lee, Y., Kim, M., Han, J., Yeom, K., Lee, S., Baek, S. H., & Kim, V. N. (2004) · The EMBO Journal · doi:10.1038/sj.emboj.7600385 · Cited by 4,373
Self-citation
Cited by
miR-221 overexpression contributes to liver tumorigenesis
Pineau, P., Volinia, S., McJunkin, K., Marchio, A., Battiston, C., Terris, B., Mazzaferro, V., Lowe, S. W., Croce, C. M., & Dejean, A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907904107 · Cited by 699
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Schaub, F. X., Dhankani, V., Berger, A. C., Trivedi, M., Richardson, A. B., Shaw, R., Zhao, W., Zhang, X., Ventura, A., Liu, Y., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.03.003 · Cited by 431
“…and miR-222, which have been proposed to promote cell proliferation and tumorigenesis, at least in part via p27-Kip repression (Kedde et al., 2010; Kim et al., 2009; Korpal et al., 2008; le Sage et al., 2007), positively correlated with MYC levels in PCPG, BLCA, CESC, and TGCT, but negatively…”
Self-citationExamination of the expanding pathways for the regulation of p21 expression and activity
Jung, Y.-S., Qian, Y., & Chen, X. (2010) · Cellular Signalling · doi:10.1016/j.cellsig.2010.01.013 · Cited by 423
PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes
Ruepp, A., Kowarsch, A., Schmidl, D., Buggenthin, F., Brauner, B., Dunger, I., Fobo, G., Frishman, G., Montrone, C., & Theis, F. J. (2010) · Genome Biology · doi:10.1186/gb-2010-11-1-r6 · Cited by 284
microRNA strand selection: Unwinding the rules
Medley, J. C., Panzade, G., & Zinovyeva, A. Y. (2021) · WIREs RNA · doi:10.1002/wrna.1627 · Cited by 225
Metadata
- Authors
- Kim, Y.-K., Yu, J., Han, T. S., Park, S.-Y., Namkoong, B., Kim, D. H., Hur, K., Yoo, M.-W., Lee, H.-J., Yang, H.-K., & Kim, V. N.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-01-19
- Volume
- 37
- Issue
- 5
- Pages
- 1672-1681
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2107268184
- Cited by
- 470
- References
- 32
- FWCI
- 13.89
- 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: Functional links between clustered microRNAs: suppression of cell-cycle inhibitors by microRNA clusters in gastric cancer. https://app.cochat.ai/papers/10.1093/nar/gkp002
- The paper
- Kim, Y.-K., Yu, J., Han, T. S., Park, S.-Y., Namkoong, B., Kim, D. H., Hur, K., Yoo, M.-W., Lee, H.-J., Yang, H.-K., & Kim, V. N. (2009). Functional links between clustered microRNAs: suppression of cell-cycle inhibitors by microRNA clusters in gastric cancer. Nucleic Acids Research. https://doi.org/10.1093/nar/gkp002
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.
