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Abstract
Melanoma is the most lethal cutaneous cancer. New drugs have recently appeared; however, not all patients obtain a benefit of these new drugs. For this reason, it is still necessary to characterize melanoma at molecular level. The aim of this study was to explore the molecular differences between melanoma tumor subtypes, based on BRAF and NRAS mutational status. Fourteen formalin-fixed, paraffin-embedded melanoma samples were analyzed using a high-throughput proteomics approach, combined with probabilistic graphical models and Flux Balance Analysis, to characterize these differences. Proteomics analyses showed differences in expression of proteins related with fatty acid metabolism, melanogenesis and extracellular space between BRAF mutated and BRAF non-mutated melanoma tumors. Additionally, probabilistic graphical models showed differences between melanoma subgroups at biological processes such as melanogenesis or metabolism. On the other hand, Flux Balance Analysis predicts a higher tumor growth rate in BRAF mutated melanoma samples. In conclusion, differential biological processes between melanomas showing a specific mutational status can be detected using combined proteomics and computational approaches.
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1 Biomedica Molecular Medicine SL, Madrid, Spain
2 Biomedica Molecular Medicine SL, Madrid, Spain; Hospital Universitario La Paz-IdiPAZ, Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Madrid, Spain (GRID:grid.81821.32) (ISNI:0000 0000 8970 9163)
3 Biomedica Molecular Medicine SL, Madrid, Spain (GRID:grid.81821.32); Hospital Universitario La Paz-IdiPAZ, Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Madrid, Spain (GRID:grid.81821.32) (ISNI:0000 0000 8970 9163)
4 Hospital Universitario La Paz-IdiPAZ, Biostatistics Unit, Madrid, Spain (GRID:grid.81821.32) (ISNI:0000 0000 8970 9163)
5 University of Zurich/ETH Zurich, Functional Genomics Center Zurich, Zurich, Switzerland (GRID:grid.7400.3) (ISNI:0000 0004 1937 0650)
6 Hospital Universitario La Paz-IdiPAZ, Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Madrid, Spain (GRID:grid.81821.32) (ISNI:0000 0000 8970 9163)
7 Hospital Universitario Gregorio Marañón, Servicio de Anatomía Patológica, Madrid, Spain (GRID:grid.410526.4) (ISNI:0000 0001 0277 7938)
8 Hospital Universitario Gregorio Marañón, Servicio de Oncología Médica, Madrid, Spain (GRID:grid.410526.4) (ISNI:0000 0001 0277 7938); Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427)
9 Hospital Universitario Ramón y Cajal, Servicio de Oncología Médica, Madrid, Spain (GRID:grid.411347.4) (ISNI:0000 0000 9248 5770)
10 Hospital Universitario La Paz-IdiPAZ, Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Madrid, Spain (GRID:grid.81821.32) (ISNI:0000 0000 8970 9163); Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427)
11 Hospital Universitario La Paz-IdiPAZ, Servicio de Oncología Médica, Madrid, Spain (GRID:grid.81821.32) (ISNI:0000 0000 8970 9163); Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427)