Αρχειοθήκη ιστολογίου

Αλέξανδρος Γ. Σφακιανάκης
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5
Άγιος Νικόλαος Κρήτη 72100
2841026182
6032607174

Παρασκευή 21 Ιουλίου 2017

Comparative metabolic analysis in head and neck cancer and the normal gingiva

Abstract

Objectives

Chronic accumulation of lactate in malignant tumor tissue is associated with increased malignancy and radioresistance. For this study, biopsies of primary head and neck squamous cell carcinoma (HNSCC) and of the normal gingiva of the same patient were compared via metabolic profiling to the healthy gingiva from cancer-free patients.

Materials and methods

Cryobiopsies of 140 HNSCC patients were used to determine ATP, lactate, and glucose concentrations of the tumor and normal gingiva via induced metabolic bioluminescence imaging (imBI). Additionally, these metabolites were quantified in a collective of 79 healthy (non-tumor-bearing) patients. Furthermore, tumor samples were analyzed via immunofluorescence imaging and quantitative real-time PCR for the expression of lactate and glucose transporters.

Results

There were significant differences in ATP concentrations detectable between the tumor, normal gingiva of tumor patients, and gingiva from healthy patients. Lactate concentrations were significantly increased in tumor tissue compared to the normal gingiva of tumor patients as well as the gingiva from healthy patients. Concerning glucose, there was a significant decrease in glucose concentrations detectable in the tumor biopsies compared to the normal gingiva of tumor patients. On the other hand, tumor samples from patients revealed significantly elevated relative expression levels of monocarboxylate transporters (MCT-1 and MCT-4), as well as glucose transporters (GLUT-1 and GLUT-3) compared to the corresponding normal gingiva of each patient.

Conclusions

We could demonstrate that the lactate concentration in HNSCC correlates with primary tumor (T) stage.

Clinical relevance

The aim of this study was to identify metabolic parameters to improve early cancer diagnosis, allow predictions on the degree of malignancy, and contribute to a personalized tumor therapy.



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