Search

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.

Glutaminolysis is the metabolic pathway that lyses glutamine to glutamate, alanine, citrate, aspartate, and so on. As partially recruiting reaction steps from the tricarboxylic acid (TCA) cycle and the malate-aspartate shuttle, glutaminolysis takes essential place in physiological and pathological situations. We herein developed a sensitive, rapid, and reproducible liquid chromatography-tandem mass spectrometry method to determine the perturbation of glutaminolysis in human plasma by quantifying 13 involved metabolites in a single 20-min run. A pHILIC column with a gradient elution system consisting of acetonitrile-5 mM ammonium acetate was used for separation, while an electrospray ionization source (ESI) operated in negative mode with multiple reaction monitoring was employed for detection. The method was fully validated according to FDA's guidelines, and it generally provided good results in terms of linearity (the correlation coefficient no less than 0.9911 within the range of 0.05-800 μg/mL), intra- and inter-day precision (less than 18.38%) and accuracy (relative standard deviation between 89.24 and 113.4%), with lower limits of quantification between 0.05 and 10 μg/mL. The new analytical approach was successfully applied to analyze the plasma samples from 38 healthy volunteers and 34 patients with type 2 diabetes (T2D). Based on the great sensitivity and comprehensive capacity, the targeted analysis revealed the imperceptible abnormalities in the concentrations of key intermediates, such as iso-citrate and cis-aconitate, thus allowing us to obtain a thorough understanding of glutaminolysis disorder during T2D. Graphical abstract ᅟ.