Original Articles: Basic ScienceMetabolomic characterisation of progression and spontaneous regression of melanoma in the melanoma-bearing Libechov minipig modelKertys, Martina,,*; Grendar, Marianb,,*; Horak, Vratislavc,,*; Zidekova, Nelaa; Kupcova Skalnikova, Helenac; Mokry, Juraja; Halasova, Erikab; Strnadel, Janb Author Information aDepartment of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava bBiomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic cInstitute of Animal Physiology and Genetics of the Czech Academy of Sciences, v.v.i., Libechov, Czech Republic *Martin Kertys, Marian Grendar and Vratislav Horak contributed equally to the writing of this article. Received 18 September 2020 Accepted 12 January 2021 Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website, www.melanomaresearch.com. Correspondence to Jan Strnadel, Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin Comenius University in Bratislava, Mala Hora 4D 036 01, Martin, Slovak Republic, Tel: +421 43 2633652; e-mail: [email protected] Melanoma Research: April 2021 - Volume 31 - Issue 2 - p 140-151 doi: 10.1097/CMR.0000000000000722 Buy SDC Metrics Abstract Melanoma-bearing Libechov minipig (MeLiM) represents a large animal model for melanoma research. This model shows a high incidence of complete spontaneous regression of melanoma – a phenomenon uncommon in humans. Here, we present the first metabolomic characterisation of the MeLiM model comparing animals with progressing and spontaneously regressing melanomas. Plasma samples of 19 minipigs with progression and 27 minipigs with evidence of regression were analysed by a targeted metabolomic assay based on mass spectrometry detection. Differences in plasma metabolomics patterns were investigated by univariate and multivariate statistical analyses. Overall, 185 metabolites were quantified in each plasma sample. Significantly altered metabolomic profile was found, and 42 features were differentially regulated in plasma. Besides, the machine learning approach was used to create a predictive model utilising Arg/Orn and Arg/ADMA ratios to discriminate minipigs with progressive disease development from minipigs with regression evidence. Our results suggest that progression of melanoma in the MeLiM model is associated with alteration of arginine, glycerophospholipid and acylcarnitines metabolism. Moreover, this study provides targeted metabolomics characterisation of an animal model of melanoma with progression and spontaneous regression of tumours. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.