Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage of disease, it has an excellent prognosis, but the mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge to improve patient survival.

To identify novel blood-circulating biomarkers that may be useful for the diagnosis of MM to guide patient counselling and appropriate disease management.

In this study, a total of 105 serum samples from 26 healthy patients (HC) and 79 MM patients (MMP) were analysed using an untargeted approach by liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) to compare the metabolomic profile of both conditions. Resulting data were subjected to both univariate (UVA) and multivariate (MVA) statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 stage I MMP.

The study successfully identified several lipidic metabolites differentially expressed in Stage I MM patients in comparison to HC. Three of these metabolites were utilized to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in body fluids that could serve as potential early diagnostic markers for MM.