Discovery of potential therapeutic targets for non-small cell lung cancer using high-throughput metabolomics analysis based on liquid chromatography coupled with tandem mass spectrometry

Abstract

Lung cancer is a severe health problem and threatens a patient's quality of life. The metabolites present in biological systems are expected to be key mediators and the changes in these metabolites play an important role in promoting health. Metabolomics can unravel the global metabolic changes and identify significant biological pathways involved in disease development. However, the role of metabolites in lung cancer is still largely unknown. In the present study, we developed a liquid chromatography coupled with tandem mass spectrometry method for biomarker discovery and identification of non-small cell lung cancer (NSCLC) from metabolomics data sets and aimed to investigate the metabolic profiles of NSCLC samples to identify potential disease biomarkers and to reveal the pathological mechanism. After cell metabolite extraction, the metabolic changes in NSCLC cells were characterized and targeted metabolite analysis was adopted to offer a novel opportunity to probe into the relationship between differentially regulated cell metabolites and NSCLC. Quantitative analysis of key enzymes in the disturbed pathways by proteomics was employed to verify metabolomic pathway changes. A total of 13 specific biomarkers were identified in NSCLC cells related with metabolic disturbance of NSCLC morbidity, which were involved in 4 vital pathways, namely glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis, tyrosine metabolism and sphingolipid metabolism. The proteomics analysis illustrated the obvious fluctuation of the expression of the key enzymes in these pathways, including the downregulation of 3-phosphoglycerate dehydrogenase, phosphoserine phosphatase, tyrosinase and argininosuccinic acid catenase. NSCLC occurrence is mainly related to amino acid and fatty acid metabolic alteration. These findings highlight that the metabolome can provide information on the molecular profiles of cells, which can aid in investigating the metabolite changes to reveal the pathological mechanism.

Fig. 1. A general flow chart of this study based on high-throughput metabolic analysis coupled with proteomics.

Fig. 2. Multivariate analyses of metabolite ions in non-small cell lung cancer. (A) and (B) PCA score plot and 3D OPLS-DA score plot of cell metabolites for clustering the control and model group in positive ion mode. (C) and (D) PCA score plot and 3D OPLS-DA of cell metabolites for clustering the control and model group in negative ion mode.

Fig. 3. Multivariate analyses of metabolite ions in non-small cell lung cancer. (A) and (B) Loading plot and S-plot of the OPLS-DA model for the control and model group in positive ion mode. (C) and (D) Loading plot and S-plot of the OPLS-DA model for the control and model group in negative ion mode.

Fig. 4. Significant changes in potential biomarker candidates between the control and model groups. (A) Heatmap visualization for cell samples from the control and model groups. (B) Unsupervised 2D PCA analysis of the two different groups. (C) VIP scores of the metabolite marker candidates.

Fig. 5. (A) The KEGG global metabolic network associated with NSCLC. (B) The metabolite–metabolite interaction network associated with NSCLC. (C) 3D visualization of complex networks integrating metabolites and genes related with the main metabolites of NSCLC.