Lung cancer (Amsterdam, Netherlands) · 2021
Metabolomic profiling for second primary lung cancer: A pilot case-control study.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Aredo Jacqueline V, Purington Natasha, Su Li, Luo Sophia J, Diao Nancy, Christiani David C, Wakelee Heather A, Han Summer S
Аннотация
OBJECTIVES: Lung cancer survivors have a high risk of developing a second primary lung cancer (SPLC). While national screening guidelines have been established for initial primary lung cancer (IPLC), no consensus guidelines exist for SPLC. Furthermore, the factors that contribute to SPLC risk have not been established. This study examines the potential for using serum metabolomics to identify metabolite biomarkers that differ between SPLC cases and IPLC controls.
MATERIAL AND METHODS: In this pilot case-control study, we applied an untargeted metabolomics approach based on ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) to serum samples of 82 SPLC cases and 82 frequency matched IPLC controls enrolled in the Boston Lung Cancer Study. Random forest and unconditional logistic regression models identified metabolites associated with SPLC. Candidate metabolites were integrated into a SPLC risk prediction model and the model performance was evaluated through a risk stratification approach.
RESULTS: The untargeted analysis detected 1008 named and 316 unnamed metabolites among all study participants. Metabolites that were significantly associated with SPLC (False Discovery Rate q-value < 0.2) included 5-methylthioadenosine (odds ratio [OR] = 2.04, 95 % confidence interval [CI] 1.39-3.01; P = 2.8 × 10-4) and phenylacetylglutamine (OR = 2.65, 95 % CI 1.56-4.51; P = 3.2 × 10-4), each exhibiting approximately 1.5-fold increased levels among SPLC cases versus IPLC controls. In stratifying the study participants across quartiles of estimated SPLC risk, the risk prediction model identified a significantly higher proportion of SPLC cases in the fourth compared to the first quartile (68.3 % versus 39.0 %; P = 0.044).
CONCLUSION: SPLC cases may have distinct metabolomic profiles compared to those in IPLC patients without SPLC. A risk stratification approach integrating metabolomics may be useful for distinguishing patients based on SPLC risk. Prospective validation studies are needed to further evaluate the potential for leveraging metabolomics in SPLC surveillance and screening.
MATERIAL AND METHODS: In this pilot case-control study, we applied an untargeted metabolomics approach based on ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) to serum samples of 82 SPLC cases and 82 frequency matched IPLC controls enrolled in the Boston Lung Cancer Study. Random forest and unconditional logistic regression models identified metabolites associated with SPLC. Candidate metabolites were integrated into a SPLC risk prediction model and the model performance was evaluated through a risk stratification approach.
RESULTS: The untargeted analysis detected 1008 named and 316 unnamed metabolites among all study participants. Metabolites that were significantly associated with SPLC (False Discovery Rate q-value < 0.2) included 5-methylthioadenosine (odds ratio [OR] = 2.04, 95 % confidence interval [CI] 1.39-3.01; P = 2.8 × 10-4) and phenylacetylglutamine (OR = 2.65, 95 % CI 1.56-4.51; P = 3.2 × 10-4), each exhibiting approximately 1.5-fold increased levels among SPLC cases versus IPLC controls. In stratifying the study participants across quartiles of estimated SPLC risk, the risk prediction model identified a significantly higher proportion of SPLC cases in the fourth compared to the first quartile (68.3 % versus 39.0 %; P = 0.044).
CONCLUSION: SPLC cases may have distinct metabolomic profiles compared to those in IPLC patients without SPLC. A risk stratification approach integrating metabolomics may be useful for distinguishing patients based on SPLC risk. Prospective validation studies are needed to further evaluate the potential for leveraging metabolomics in SPLC surveillance and screening.