BACKGROUND/AIM: Canine mammary gland tumors (MGTs), as a potential model of human breast cancer, have a well-defined histological classification system. MicroRNA (miRNA) expression is a key part of the molecular signatures of both MGTs and human breast cancer, although the signatures alone do not yet provide a sufficient basis for definitive diagnosis. In this study, we investigated the association between miRNA expression patterns and histological classification. MATERIALS AND METHODS: Mammary gland tissue was collected from healthy dogs (n=7) and dog patients (n=80). Further samples (n=5) were obtained from established MGT cell lines. We targeted miRNAs differentially expressed in metastatic tumor tissue versus non-metastatic and normal tissue. A subset of samples was analyzed using small RNA next generation sequencing (NGS) with subsequent qPCR. RESULTS: Six differentially expressed miRNAs were selected from the NGS analysis and submitted for large-scale qPCR. The large-scale qPCR analysis revealed greater alternations in miRNA expression. Large-scale analysis, based on 79 samples, revealed a hierarchical clustering based on selected miRNAs that did not strikingly match the histopathological subtype classification. CONCLUSION: We successfully investigated the large-scale miRNA expression pattern in canine MGT and provided the whole miRNA expression. The selected miRNA demonstrated that there is no straightforward mapping between molecular signatures and histological classification of canine MGTs at the miRNA level.
BACKGROUND/AIM: Canine mammary gland tumors (MGTs), as a potential model of human breast cancer, have a well-defined histological classification system. MicroRNA (miRNA) expression is a key part of the molecular signatures of both MGTs and human breast cancer, although the signatures alone do not yet provide a sufficient basis for definitive diagnosis. In this study, we investigated the association between miRNA expression patterns and histological classification. MATERIALS AND METHODS: Mammary gland tissue was collected from healthy dogs (n=7) and dog patients (n=80). Further samples (n=5) were obtained from established MGT cell lines. We targeted miRNAs differentially expressed in metastatic tumor tissue versus non-metastatic and normal tissue. A subset of samples was analyzed using small RNA next generation sequencing (NGS) with subsequent qPCR. RESULTS: Six differentially expressed miRNAs were selected from the NGS analysis and submitted for large-scale qPCR. The large-scale qPCR analysis revealed greater alternations in miRNA expression. Large-scale analysis, based on 79 samples, revealed a hierarchical clustering based on selected miRNAs that did not strikingly match the histopathological subtype classification. CONCLUSION: We successfully investigated the large-scale miRNA expression pattern in canine MGT and provided the whole miRNA expression. The selected miRNA demonstrated that there is no straightforward mapping between molecular signatures and histological classification of canine MGTs at the miRNA level.
Authors: R Michelle Boggs; Zachary M Wright; Mark J Stickney; Weston W Porter; Keith E Murphy Journal: Mamm Genome Date: 2008-07-30 Impact factor: 2.957
Authors: Lara Lusa; Lisa M McShane; James F Reid; Loris De Cecco; Federico Ambrogi; Elia Biganzoli; Manuela Gariboldi; Marco A Pierotti Journal: J Natl Cancer Inst Date: 2007-11-13 Impact factor: 13.506
Authors: Britta Weigelt; Alan Mackay; Roger A'hern; Rachael Natrajan; David S P Tan; Mitch Dowsett; Alan Ashworth; Jorge S Reis-Filho Journal: Lancet Oncol Date: 2010-02-22 Impact factor: 41.316
Authors: Andrew D Yates; Premanand Achuthan; Wasiu Akanni; James Allen; Jamie Allen; Jorge Alvarez-Jarreta; M Ridwan Amode; Irina M Armean; Andrey G Azov; Ruth Bennett; Jyothish Bhai; Konstantinos Billis; Sanjay Boddu; José Carlos Marugán; Carla Cummins; Claire Davidson; Kamalkumar Dodiya; Reham Fatima; Astrid Gall; Carlos Garcia Giron; Laurent Gil; Tiago Grego; Leanne Haggerty; Erin Haskell; Thibaut Hourlier; Osagie G Izuogu; Sophie H Janacek; Thomas Juettemann; Mike Kay; Ilias Lavidas; Tuan Le; Diana Lemos; Jose Gonzalez Martinez; Thomas Maurel; Mark McDowall; Aoife McMahon; Shamika Mohanan; Benjamin Moore; Michael Nuhn; Denye N Oheh; Anne Parker; Andrew Parton; Mateus Patricio; Manoj Pandian Sakthivel; Ahamed Imran Abdul Salam; Bianca M Schmitt; Helen Schuilenburg; Dan Sheppard; Mira Sycheva; Marek Szuba; Kieron Taylor; Anja Thormann; Glen Threadgold; Alessandro Vullo; Brandon Walts; Andrea Winterbottom; Amonida Zadissa; Marc Chakiachvili; Bethany Flint; Adam Frankish; Sarah E Hunt; Garth IIsley; Myrto Kostadima; Nick Langridge; Jane E Loveland; Fergal J Martin; Joannella Morales; Jonathan M Mudge; Matthieu Muffato; Emily Perry; Magali Ruffier; Stephen J Trevanion; Fiona Cunningham; Kevin L Howe; Daniel R Zerbino; Paul Flicek Journal: Nucleic Acids Res Date: 2020-01-08 Impact factor: 16.971