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Cited by 159

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

Multitasking in Tumor Progression: Signaling Functions of Cell Adhesion Molecules

CAVALLARO, UGO, CHRISTOFORI, GERHARD·2004·Annals of the New York Academy of Sciences

Abstract

A bstract: Approximately 90% of all cancer deaths arise from metastasis formation. Hence, understanding the molecular mechanisms underlying tumor progression, local invasion, and the formation of tumor metastases represents one of the great challenges in exploratory cancer research. Recent experimental results indicate that changes in cell adhesion play a critical role in tumor progression. Cell adhesion molecules of varying classes and functions, including cadherins, cell adhesion molecules of the immunoglobulin family (Ig‐CAMs), CD44, and integrins, can interact with and modulate the signaling function of receptor tyrosine kinases (RTKs). Conversely, signaling by RTKs can directly affect the adhesive function of adhesion molecules. Loss of E‐cadherin and gain of mesenchymal cadherin function as well as changes in the expression of Ig‐CAMs during the progression of many cancer types exemplify such functional implicatons: cell adhesion molecules not only define a tumor cell's adhesive repertoire, but also directly influence classic signal transduction pathways, thereby modulating the metastatic behavior of tumor cells.

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Metadata

Authors
CAVALLARO, U., & CHRISTOFORI, G.
Venue
Annals of the New York Academy of Sciences
Publisher
Wiley
Published
2004-04
Volume
1014
Issue
1
Pages
58-66
ISSN
  • 0077-8923 (ISSN-L)
  • 1749-6632
OpenAlex
W2057947988
Cited by
159
References
3
FWCI
5.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multitasking in Tumor Progression: Signaling Functions of Cell Adhesion Molecules. https://app.cochat.ai/papers/10.1196/annals.1294.006
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CAVALLARO, U., & CHRISTOFORI, G. (2004). Multitasking in Tumor Progression: Signaling Functions of Cell Adhesion Molecules. Annals of the New York Academy of Sciences. https://doi.org/10.1196/annals.1294.006

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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.