4.3 Article

CD44/CD24 immunophenotypes on clinicopathologic features of salivary glands malignant neoplasms

Journal

DIAGNOSTIC PATHOLOGY
Volume 8, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/1746-1596-8-29

Keywords

Salivary gland; Salivary gland neoplasm; CD44; CD24; Clinicopathologic features

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Funding

  1. Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Brazil [FAPESP 2011/18587-2]
  2. FAPESP, Brazil

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Background: Salivary Glands Malignant Neoplasms (SGMNs) account for 3-6% of head and neck cancers and 0.3% of all cancers. Tumor cells that express CD44 and CD24 exhibit a stem-cell-like behavior. CD44 is the binding site for hyaluronic acid, and CD24 is a receptor that interacts with P-selectin to induce metastasis and tumor progression. The present study aims to evaluate the expression of CD44 and CD24 on SGMNs and correlated these data with several clinicopathologic features. Methods: Immunohistochemical stains for CD44 and CD24 were performed on tissue microarrays containing SGMN samples from 69 patients. The CD44, CD24 and CD44/CD24 expression phenotypes were correlated to patient clinicopathologic features and outcome. Results: CD44 expression was associated with the primary site of neoplasm (p = 0.046). CD24 was associated with clinical stage III/IV (p = 0.008), T stage (p = 0,27) and lymph node (p = 0,001). The CD44/CD24 profiles were associated with the primary site of injury (p = 0.005), lymph node (p = 0.011) and T stage (p = 0.023). Univariate analysis showed a significant relationship between clinical staging and disease-free survival (p = 0.009), and the overall survival presents relation with male gender (p = 0.011) and metastasis (p = 0.027). Conclusion: In summary, our investigation confirms that the clinical stage, in accordance with the literature, is the main prognostic factor for SGMN. Additionally, we have presented some evidence that the analysis of isolated CD44 and CD24 immunoexpression or the two combined markers could give prognostic information associated to clinicopathologic features in SGMN. Virtual Slides: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1284611098470676.

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