Journal of Cardiothoracic Surgery (Jun 2023)

Combination of CEACAM5, EpCAM and CK19 gene expressions in mediastinal lymph node micrometastasis is a prognostic factor for non-small cell lung cancer

  • Hande Süer,
  • Suat Erus,
  • Ekin E. Cesur,
  • Ömer Yavuz,
  • Orhan Ağcaoğlu,
  • Pınar Bulutay,
  • Tamer T. Önder,
  • Serhan Tanju,
  • Şükrü Dilege

DOI
https://doi.org/10.1186/s13019-023-02297-z
Journal volume & issue
Vol. 18, no. 1
pp. 1 – 10

Abstract

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Abstract Background Lung cancer is known as the most common and highly metastatic form of cancer worldwide. Tumour node metastasis (TNM) staging is the gold standard classification system for the decision-making process for appropriate treatment. Particularly N status has the most important prognostic value in the absence of distant metastasis. Traditional diagnostic methods are capable of detecting metastasis; however, they may fail to detect micrometastasis, which plays a role in disease recurrence and patients' long-term survival. Occult micrometastasis can change the tumour's TNM staging and, consequently, the patient's treatment regimen. Methods The median number of three lymph node tissues were collected from 30 patients who underwent surgery for non-small cell lung cancer. Lymph node tissues were collected from different lymph node stations according to the location of the patient's tumour. CK19, EpCAM and CEACAM5 gene expressions were analysed in tissues using quantitative real-time polymerase chain reaction to detect micrometastasis in distant lymph nodes. Results Triple positivity was seen in 26 out of 30 patients which 19 patients were upstaged from N0 to N2. While survival was not significantly affected between upstaged and non-upstaged patients, patients upstaged with multiple-station N2 had a significantly higher recurrence and lower survival compared to single-station N2. Conclusion A combination of CK19, EpCAM and CEACAM5 gene expressions in lymph nodes can be used to identify micrometastasis which postoperatively may be used as a tool to predict patients’ recurrence and survival.

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