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Cited by 268

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Elevated levels of IGF-1 receptor convey invasive and metastatic capability in a mouse model of pancreatic islet tumorigenesis

Open access
Lopez, Theresa, Hanahan, Douglas·2002·Cancer Cell

Abstract

In a prototypical model of multistage tumorigenesis involving pancreatic islets in RIP1-Tag2 transgenic mice, activation of insulin-like growth factor II (IGF-II) was previously shown to serve as a survival factor that inhibited apoptosis. Now IGF-1R, the receptor tyrosine kinase for IGF-II, has been found to be variably upregulated, first uniformly in dysplastic and angiogenic progenitors and then focally at the margins and in invasive regions of carcinomas. When the levels of IGF-1R were forcibly elevated throughout islet tumorigenesis, progression was accelerated at all stages in the pathway, although apoptosis was not differentially suppressed. Notably, encapsulated tumors were absent; instead, invasive carcinomas with downregulated E-cadherin were prevalent, and the majority of mice had local lymph node metastasis.

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Metadata

Authors
Lopez, T., & Hanahan, D.
Venue
Cancer Cell
Publisher
Elsevier BV
Published
2002-05
Volume
1
Issue
4
Pages
339-353
ISSN
1535-6108 (ISSN-L)
OpenAlex
W1972486056
Cited by
268
References
3
FWCI
7.43
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Elevated levels of IGF-1 receptor convey invasive and metastatic capability in a mouse model of pancreatic islet tumorigenesis. https://app.cochat.ai/papers/10.1016/s1535-6108(02)00055-7
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Lopez, T., & Hanahan, D. (2002). Elevated levels of IGF-1 receptor convey invasive and metastatic capability in a mouse model of pancreatic islet tumorigenesis. Cancer Cell. https://doi.org/10.1016/s1535-6108(02)00055-7

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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.