Biomarkers in medicine · 2017
Comparison of statistical methods for detection of serum lipid biomarkers for mesothelioma and asbestos exposure.
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Xu Rengyi, Mesaros Clementina, Weng Liwei, Snyder Nathaniel W, Vachani Anil, Blair Ian A, Hwang Wei-Ting
Аннотация
AIM: We compared three statistical methods in selecting a panel of serum lipid biomarkers for mesothelioma and asbestos exposure.
MATERIALS & METHODS: Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance.
RESULTS: Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control.
CONCLUSION: Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.
MATERIALS & METHODS: Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance.
RESULTS: Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control.
CONCLUSION: Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.