Clinical significance of epithelial–mesenchymal transition-related markers expression in the micrometastatic sentinel lymph node of NSCLC

• Published in: Clinical & translational oncology 2020-03, Vol.22 (3), p.381-391
• Main Authors: Lafuente-Sanchis, A., Olmo, A., Carretero, J., Alcacer Fernandez-Coronado, J., Estors-Guerrero, M., Martínez-Hernández, N. J., Cremades, A., Zúñiga, A., Alcacer, J., Farras, R., Cuenca, M., Galbis-Caravajal, J. M.
• Format: Article
• Language: English
• Subjects: Medicine; Medicine & Public Health; Oncology; Research Article
• ISSN: 1699-048X; 1699-3055
• DOI: 10.1007/s12094-019-02138-3

Objectives Metastatic lymph node affectation is the main prognostic factor in localised lung cancer. However, the pathological study of lymph nodes reveals tumour relapse for 20% of patients after oncological curative surgery. Recently, EMT (epithelial-mesenchymal transition) has been established as one of the main factors related to lymphatic dissemination and metastasis. This study evaluated the prognostic value of EMT-related gene expression in micrometastatic sentinel lymph nodes (SLN) of non-small cell lung cancer (NSCLC) patients. Methods The presence of genes CDH1, CDH2, VIM, TWIST1, SNAI1, SNAI2, ZEB1, and ZEB2 in mRNA was analysed in tumours and in the SLN of NSCLC patients for whom surgery was planned for treatment. The significant association between the expression level of EMT-related markers and patients’ clinicopathological characteristics and relapse was assessed. Results Of the 96 patients, 56 (58.33%) presented molecular micrometastasis in SLN, which showed higher CDH1 , CDH2, and VIM expressions than non-micrometastatic ones. An association linking a low CDH1/CDH2 ratio in SLN with molecular micrometastasis, adenocarcinoma, and non-smoking patients was found. The multivariate Cox regression analysis proved the prognostic accuracy of the CDH1 / CDH2 ratio in SLN. Conclusions The molecular EMT status of SLN could be used as an independent prognosis predictor in early stage NSLCL patients, and as a new tool to better stratify and predict patient outcomes.