Predictive value for lymph node metastasis of epithelial-mesenchymal transition and cancer stem cell marker expression in early gastric cancer

초록

Background: Minimally invasive therapies, including endoscopic mucosal resection or sentinel node navigation surgery, have been widely applied in early gastric cancer because of their benefits in promoting patient quality of life. However, lymph node dissection is beyond the capability of endoscopic therapy, and in sentinel node navigation surgery, the potential for skip metastasis is not negligible. Therefore, the possibility of lymph node metastasis is the most important factor to consider when deciding whether to apply the minimally invasive therapies. In the present study, the significance of epithelial mesenchymal transition and stem cell marker expression in lymph node metastasis in early gastric cancer was investigated. Methods: We evaluated the significance of the expression of 5 epithelial mesenchymal transition-related markers (E-cadherin, MMP7, S100A, Snail-1, and HGF) and 6 stem cell markers (ALDH1, SOX2, CD24, CD44, CD54, and CD133) in 119 early gastric cancer specimens using immunohistochemistry. Because protein expression is heterogeneous in gastric cancer, we analyzed the expression of these markers in two selected regions (one each at the superficial zone and the deep invasive front). Results: Expression of E-cadherin, MMP7, HGF, and CD133 at the deep invasive front was associated with the absence of lymph node metastasis (P = 0.013, 0.018, <0.001, and 0.026, respectively). Presence of diffuse-type component, lymphatic invasion, and lack of expression of HGF and CD133 at the deep invasive front were independent predictive markers of lymph node metastasis (P = 0.019, <0.001, 0.015, and 0.047, respectively). Conclusions: Lymph node metastasis is strongly associated with expression status of HGF and CD133 at the deep invasive front, suggesting the usefulness of these proteins as independent predictive markers of lymph node metastasis in early gastric cancer. (C) 2017 Elsevier GmbH. All rights reserved.

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