Peer-reviewed veterinary case report
Metabolite profiling helps diagnose and predict dog oral melanoma
By Kawabe, Mifumi et al.·Published in The Journal of veterinary medical science·2015·Graduate School of Life and Environmental Sciences, Japan·View original on PubMed →
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Original publication title: Profiling of plasma metabolites in canine oral melanoma using gas chromatography-mass spectrometry.
- Species:
- dog
Plain-English summary
A dog with oral melanoma, a serious type of mouth cancer, was studied to find better ways to diagnose and predict outcomes after treatment. Researchers looked at blood samples from healthy dogs and those with melanoma to identify specific metabolites that could indicate the disease's severity. The findings suggested that certain metabolites could help determine how well a dog might respond to treatments like radiation therapy. Dogs predicted to be healthy based on these tests showed no advanced cancer after treatment, indicating that this method could be a valuable tool for veterinarians in managing oral melanoma in dogs.
People also search for: dog oral melanoma prognosis · canine mouth cancer treatment · blood test for dog cancer
Abstract
Malignant melanoma is one of the most common and aggressive tumors in the oral cavity of dog. The tumor has a poor prognosis, and methods for diagnosis and prediction of prognosis after treatment are required. Here, we examined metabolite profiling using gas chromatography-mass spectrometry (GC-MS) for development of a discriminant model for evaluation of prognosis. Metabolite profiles were evaluated in healthy and melanoma plasma samples using orthogonal projection to latent structure using discriminant analysis (OPLS-DA). Cases that were predicted to be healthy using the OPLS discriminant model had no advanced lesions after radiation therapy. These results indicate that metabolite profiling may be useful in diagnosis and prediction of prognosis of canine malignant melanoma.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/25843037/