Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls

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dc.contributor.authorKim, Julian O.
dc.contributor.authorBalshaw, Robert
dc.contributor.authorTrevena, Connel
dc.contributor.authorBanerji, Shantanu
dc.contributor.authorMurphy, Leigh
dc.contributor.authorDawe, David
dc.contributor.authorTan, Lawrence
dc.contributor.authorSrinathan, Sadeesh
dc.contributor.authorBuduhan, Gordon
dc.contributor.authorKidane, Biniam
dc.contributor.authorQing, Gefei
dc.contributor.authorDomaratzki, Michael
dc.contributor.authorAliani, Michel
dc.date.accessioned2022-11-01T04:20:46Z
dc.date.issued2022-10-12
dc.date.updated2022-11-01T04:20:46Z
dc.description.abstractAbstract Background Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development. Methods Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates. Results A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5′-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol. Conclusions Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.
dc.identifier.citationCancer & Metabolism. 2022 Oct 12;10(1):16
dc.identifier.urihttps://doi.org/10.1186/s40170-022-00294-9
dc.identifier.urihttp://hdl.handle.net/1993/36967
dc.language.rfc3066en
dc.rightsopen accessen_US
dc.rights.holderThe Author(s)
dc.titleData-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls
dc.typeJournal Article
local.author.affiliationRady Faculty of Health Sciencesen_US
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