A novel approach utilizing rapid thin-film microextraction method for salivary metabolomics studies in lung cancer diagnosis

• Published in: Microchemical journal Vol. 207; p. 112069
• Main Authors: Pelit, Fusun, Erbas, Ilknur, Mert Ozupek, Nazli, Gul, Merve, Sakrak, Esra, Ocakoglu, Kasim, Pelit, Levent, Ozdemir, Durmus, Goksel, Tuncay, Basbinar, Yasemin, Goksel, Ozlem
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2024
• Subjects: Biomarker; Liquid Chromatography-Tandem Mass Spectrometry; Metabolomics; Saliva; Thin Film Microextraction; Metabolomics; Biomarker; Saliva; Liquid Chromatography-Tandem Mass Spectrometry; Thin Film Microextraction
• ISSN: 0026-265X
• DOI: 10.1016/j.microc.2024.112069

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•The TFME method was used to investigate 18 targeted metabolites as a novel diagnostic approach for lung cancer diagnosis.•Response surface methodology was used to optimize the analysis conditions.•The method was applied to the saliva samples of 40 patients of lung cancer and 38 healthy controls.•The efficacy of metabolites for lung cancer diagnosis was determined by in silico methods.
This study investigated the potential of targeted salivary metabolomics as a convenient diagnostic tool for lung cancer (LC), utilizing a rapid TFME-based method. It specifically examines TFME blades modified with SiO2 nanoparticles, which were produced using a custom-made coating system. Validation of the metabolite biomarker analysis was performed by these blades using liquid chromatography-tandem mass spectroscopy (LC-MS/MS).
The extraction efficiencies of SiO2 nanoparticle/polyacrylonitrile (PAN) composite-coated blades were compared for 18 metabolites. Response surface methodology (RSM) was used to optimize the analysis conditions. Linear calibration plots were obtained for all metabolites at concentrations between 0.025 to 4.0  μg/mL in the presence of internal standard, with correlation coefficients (R2) ranging from 0.9975 to 0.9841. The limit of detection (LOD) and limit of quantitation (LOQ) were in the range of 0.014 to 0.97μg mL−1 and 0.046 to 3.20 μgmL−1, respectively. The %RSD values for all analytes were within the acceptable range (less than 20 %) for the proposed method.
The method was applied to the saliva samples of 40 patients with LC and 38 healthy controls. The efficacy of metabolites for LC diagnosis was determined by in silico methods and the results reveal that phenylalanine and purine metabolism metabolites (e.g., hypoxanthine) are of great importance for LC diagnosis. Furthermore, potentially significant biomarker analysis results from the ROC curve data reveal that proline, hypoxanthine, and phenylalanine were identified as potential biomarkers for LC diagnosis.