Metabolic profiling of potential lung cancer biomarkers using bronchoalveolar lavage fluid and the integrated direct infusion/ gas chromatography mass spectrometry platform

Abstract

24Lung cancer is one of the ten most common causes of death worldwide, so that the search for 25 early diagnosis biomarkers is a very challenging task. Bronchoalveolar lavage fluid (BALF) provides 26 information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and 27 no previous metabolomic studies have been performed with BALF samples from patients with lung 28 cancer. Therefore, this fluid has been explored looking for new contributions in lung cancer metabolism. 29In this way, two complementary metabolomics techniques based on direct infusion high resolution mass 30 spectrometry (DI-ESI-QTOF-MS) and gas chromatography mass spectrometry (GC-MS) have been 31 applied to compare statistically differences between lung cancer (LC) and control (C) BALF samples, 32 using partial least square discriminant analysis (PLS-DA) in order to find and identify potential 33 biomarkers of the disease. A total of 42 altered metabolites were found in BALF from LC. The metabolic 34 pathway analysis showed that glutamate and glutamine metabolism pathway was mainly altered by this 35 disease. In addition, we assessed the biomarker specificity and sensitivity according to the area under the 36 receiver operator characteristic (ROC) curves, indicating that glycerol and phosphoric acid were potential 37 sensitive and specific biomarkers for lung cancer diagnosis and prognosis.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

“…Indeed, it is now well established that cancer cells produce distinct chemical signatures that can be seen in tissues and biofluids [16]. As a result, a number of metabolite biomarkers that are specific to lung cancer have been discovered in a variety of biofluids, including serum/plasma, bronchial fluid, or sputum [17][18][19]. These biomarkers have frequently been discovered via metabolomics.…”

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

“…18,[20][21][22][23][24] One of the main fields using MS data is metabolomics, which aims to identify and quantify small molecules involved in metabolic reactions. 25 Metabolomics studies have been applied in several areas, especially cancer.…”

“…25 Various types of chemometric algorithms have been reported for pattern recognition and classification of MS data, especially for discriminating between healthy control and cancer samples, or discriminating cancer subtypes. For instance, there are several papers reporting the use of partial least squares discriminant analysis (PLS-DA), 14,18,20 hierarchical cluster analysis (HCA), 14,27,28 principal component analysis (PCA), 14,29 support vector machines (SVM), 17,29 artificial neural networks (ANN), 28 principalcomponent analysis followed by linear discriminant analysis (PCA-LDA), 15 principal component directed partial least squares (PC-PLS), 30 and backward variable elimination partial least squares discriminant analysis (BVE-PLSDA). 31 Principal component analysis (PCA) is a method of exploratory analysis capable of reducing the original data into a few variables.…”

“…[7][8][9] MS is usually coupled with chromatography such as liquid chromatography (LC-MS) and gas chromatography (GC-MS). Other techniques such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) are often used in MS applications, including disease screening and diagnosis.5 Some examples of MS applications includes toxicology screening and toxic drug quantification using quadrupole MS/MS; 10 identification of inborn errors in metabolism or genetic defects in newborns for prenatal screening programs using electrospray tandem MS; 11 detection of drug-induced hepatotoxicity using MS-based metabolomics; 12 and identification and quantification of bleomycin in serum and tumor tissue by high resolution LC-MS. 13 MS-based techniques have been largely employed for cancer identification, such as for breast cancer, 14 prostate cancer, 15,16 ovarian cancer, 17 lung cancer, 18 and pancreatic cancer; 19 as well as for identifying many biomarkers. 18,[20][21][22][23][24] One of the main fields using MS data is metabolomics, which aims to identify and quantify small molecules involved in metabolic reactions.…”

“…For instance, there are several papers reporting the use of partial least squares discriminant analysis (PLS-DA), 14,18,20 hierarchical cluster analysis (HCA), 14,27,28 32 PCA reduces the data into a few principal components (PCs), where each one represents a piece of the original information. The first PC has the largest explained variance; therefore, they represent most of the information present in the original data.…”