Scientific Reports (May 2018)

Expressional analysis of disease-relevant signalling-pathways in primary tumours and metastasis of head and neck cancers

  • Dorothee Goesswein,
  • Negusse Habtemichael,
  • Aslihan Gerhold-Ay,
  • Johanna Mazur,
  • Désirée Wünsch,
  • Shirley K. Knauer,
  • Julian Künzel,
  • Christoph Matthias,
  • Sebastian Strieth,
  • Roland H. Stauber

DOI
https://doi.org/10.1038/s41598-018-25512-7
Journal volume & issue
Vol. 8, no. 1
pp. 1 – 14

Abstract

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Abstract Head and neck squamous cell carcinoma (HNSCC) often metastasize to lymph nodes resulting in poor prognosis for patients. Unfortunately, the underlying molecular mechanisms contributing to tumour aggressiveness, recurrences, and metastasis are still not fully understood. However, such knowledge is key to identify biomarkers and drug targets to improve prognosis and treatments. Consequently, we performed genome-wide expression profiling of 15 primary HNSSCs compared to corresponding lymph node metastases and non-malignant tissue of the same patient. Differentially expressed genes were bioinformatically exploited applying stringent filter criteria, allowing the discrimination between normal mucosa, primary tumours, and metastases. Signalling networks involved in invasion contain remodelling of the extracellular matrix, hypoxia-induced transcriptional modulation, and the recruitment of cancer associated fibroblasts, ultimately converging into a broad activation of PI3K/AKT-signalling pathway in lymph node metastasis. Notably, when we compared the diagnostic and prognostic value of sequencing data with our expression analysis significant differences were uncovered concerning the expression of the receptor tyrosine kinases EGFR and ERBB2, as well as other oncogenic regulators. Particularly, upregulated receptor tyrosine kinase combinations for individual patients varied, implying potential compensatory and resistance mechanisms against specific targeted therapies. Collectively, we here provide unique transcriptional profiles for disease predictions and comprehensively analyse involved signalling pathways in advanced HNSCC.