ArticleScientific reports2019
Melanoma proteomics suggests functional differences related to mutational status.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Mitochondrial dysfunction and immune suppression in BRAF V600E-mutated metastatic melanoma.Clinical and translational medicine · 2024Article
- AnnexinA6: a potential therapeutic target gene for extracellular matrix mineralization.Frontiers in cell and developmental biology · 2023Review
- BRAF activation by metabolic stress promotes glycolysis sensitizing NRASNature communications · 2022Article
- Relevant proteins for the monitoring of engraftment phases after allogeneic hematopoietic stem cell transplantation.Clinics (Sao Paulo, Brazil) · 2022Article
- Towards Precision Dermatology: Emerging Role of Proteomic Analysis of the Skin.Dermatology (Basel, Switzerland) · 2022Review
- The human melanoma proteome atlas-Defining the molecular pathology.Clinical and translational medicine · 2021Article
- Proteogenomic analysis of melanoma brain metastases from distinct anatomical sites identifies pathways of metastatic progression.Acta neuropathologica communications · 2020Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
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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