Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

Jesus Pena-Martin, Maria Belen Garcia-Ortega, Jose Luis Palacios-Ferrer,Caridad Diaz,Maria angel Garcia,Houria Boulaiz,Javier Valdivia,Jose Miguel Jurado, Francisco M. Almazan-Fernandez, Salvador Arias Santiago,Francisca Vicente,Coral del Val,Jose Perez del Palacio,Juan Antonio Marchal

BRITISH JOURNAL OF DERMATOLOGY(2024)

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摘要
Metabolomics using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a powerful tool to identify and quantify metabolites in body fluids for an early diagnosis of malignant melanoma. In this study, LC-HRMS successfully identified lipid metabolites associated with stage I melanoma. Three of these metabolites exhibited exceptional precision and accuracy when they were validated on an independent sample. Background Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.Objectives To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.Methods In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.Results We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.Conclusions These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.
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