Dataset Information

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment

ABSTRACT: Lung cancer causes more deaths in men and women than any other cancer related disease. Currently, few effective strategies exist to predict how patients will respond to treatment. We evaluated the serum metabolomic profiles of 25 lung cancer patients undergoing chemotherapy ± radiation to evaluate the feasibility of metabolites as temporal biomarkers of clinical outcomes . Methods: Serial serum specimens collected prospectively from lung cancer patients were analyzed using both nuclear magnetic resonance (1H-NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Multivariate statistical analysis consisted of unsupervised principal component analysis or orthogonal partial least squares discriminant analysis with significance assessed using a cross-validated ANOVA. Results: The metabolite profiles were reflective of the temporal distinction between patient samples before during and after receiving therapy (1H-NMR p<0.001 and GC-MS p<0.01). Disease progression and survival were strongly correlative with the GC-MS metabolite data whereas stage and cancer type were associated with 1H-NMR data. Metabolites such as hydroxylamine, tridecan-1-ol, octadecan-1-ol, were indicative of survival (GC-MS p<0.05) and metabolites such as tagatose, hydroxylamine, glucopyranose, and threonine that were reflective of progression (GC-MS p<0.05). Conclusions: Metabolite profiles have the potential to act as prognostic markers of clinical outcomes for lung cancer patients. Serial 1H-NMR measurements appear to detect metabolites diagnostic of tumor pathology, while GC-MS provided data better related to prognostic clinical outcomes warranting further study in a larger cohort and with various treatment options.

INSTRUMENT(S): GCT Premier (Waters), Bruker

SUBMITTER: Farshad Farshidfar

PROVIDER: MTBLS200 | MetaboLights | 2017-01-19

REPOSITORIES: MetaboLights

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Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment.

Hao Desirée D Sarfaraz M Omair MO Farshidfar Farshad F Bebb D Gwyn DG Lee Camelia Y CY Card Cynthia M CM David Marilyn M Weljie Aalim M AM

Metabolomics : Official journal of the Metabolomic Society 20160227

Lung cancer causes more deaths in men and women than any other cancer related disease. Currently, few effective strategies exist to predict how patients will respond to treatment. We evaluated the serum metabolomic profiles of 25 lung cancer patients undergoing chemotherapy ± radiation to evaluate the feasibility of metabolites as temporal biomarkers of clinical outcomes. Serial serum specimens collected prospectively from lung cancer patients were analyzed using both nuclear magnetic resonance ...[more]

PMID: 27073350

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Project description:Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting outcomes and therapeutic efficacy. However, no studies have reported a systematic analysis of lipid metabolism characterizations in gastric cancer (GC). Experimental design: In this study, we comprehensively estimated the lipid metabolism characteristics of 367 GC patients and systematically correlated lipid metabolism with genomic characteristics, clinicopathologic features, and clinical outcomes of GC using two proposed computational algorithms (TGGA-STAD and GSE84437). Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore as a biomarker for GC. Finally, a total of 90 serum as well as 30 tumor tissues and adjacent tissues were for pseudotargeted metabolomics analysis via UHPLC-Q-TOF/MS to determine the lipid metabolism characteristics of the GC patients. Results: A total of 3104 DEGs were determined, among which five lipid metabolism signature genes were identified. The high LMscore subgroup had a positive correlation with the progression of GC, which indicated that the LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic agents. Additionally, significant alterations of sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue by pseudotargeted metabolomics. Conclusions: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the lipid metabolism characteristics of GC may, therefore, help to provide insights into the pathogenesis of GC and interpret the responses of gastric tumors to therapies, which might be helpful to achieve better outcomes in the treatment of GC.

Dataset Information

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment

Publications

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment.

Similar Datasets

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