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Abstract
The molecular heterogeneity of feline mammary carcinomas (FMCs) represents a prognostic and therapeutic challenge. RNA-Seq-based comparative transcriptomic profiling serves to identify recurrent and exclusive differentially expressed genes (DEGs) across sample types and molecular subtypes. Using mass-parallel RNA-Seq, we identified DEGs and performed comparative function-based analysis across 15 tumours (four basal-like triple-negative [TN], eight normal-like TN, and three luminal B fHER2 negative [LB fHER2−]), two cell lines (CL, TiHo-0906, and TiHo-1403) isolated from the primary tumours (LB fHER2−) of two cats included in this study, and 13 healthy mammary tissue controls. DEGs in tumours were predominantly upregulated; dysregulation of CLs transcriptome was more extensive, including mostly downregulated genes. Cell-cycle and metabolic-related DEGs were upregulated in both tumours and CLs, including therapeutically-targetable cell cycle regulators (e.g. CCNB1, CCNB2, CDK1, CDK4, GTSE1, MCM4, and MCM5), metabolic-related genes (e.g. FADS2 and SLC16A3), heat-shock proteins (e.g. HSPH1, HSP90B1, and HSPA5), genes controlling centrosome disjunction (e.g. RACGAP1 and NEK2), and collagen molecules (e.g. COL2A1). DEGs specifically upregulated in basal-like TN tumours were involved in antigen processing and presentation, in normal-like TN tumours encoded G protein-coupled receptors (GPCRs), and in LB fHER2− tumours were associated with lysosomes, phagosomes, and endosomes formation. Downregulated DEGs in CLs were associated with structural and signalling cell surface components. Hence, our results suggest that upregulation of genes enhancing proliferation and metabolism is a common feature among FMCs and derived CLs. In contrast, the dissimilarities observed in dysregulation of membrane components highlight CLs’ disconnection with the tumour microenvironment. Furthermore, recurrent and exclusive DEGs associated with dysregulated pathways might be useful for the development of prognostically and therapeutically-relevant targeted panels.
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Details
1 The University of Queensland, School of Veterinary Science, Gatton, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537)
2 Graz University of Technology, Institute of Biomedical Informatics, Graz, Austria (GRID:grid.410413.3) (ISNI:0000 0001 2294 748X); Rostock University Medical Center, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock, Germany (GRID:grid.413108.f) (ISNI:0000 0000 9737 0454)
3 Chronix Biomedical, Göttingen, Germany (GRID:grid.413108.f)
4 University of Göttingen, Institute of Veterinary Medicine, Göttingen, Germany (GRID:grid.7450.6) (ISNI:0000 0001 2364 4210)
5 University of Veterinary Medicine Hannover Foundation, Small Animal Clinic, Hannover, Germany (GRID:grid.412970.9) (ISNI:0000 0001 0126 6191)
6 University of Lisbon, Faculty of Veterinary Medicine, CIISA – Centre for Interdisciplinary Research in Animal Health, Lisbon, Portugal (GRID:grid.9983.b) (ISNI:0000 0001 2181 4263); Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), Lisbon, Portugal (GRID:grid.9983.b)
7 University of Veterinary Medicine Hannover Foundation, Department of Pathology, Hannover, Germany (GRID:grid.412970.9) (ISNI:0000 0001 0126 6191)
8 University of Rostock, Haematology, Oncology and Palliative Medicine, Clinic III, Rostock, Germany (GRID:grid.10493.3f) (ISNI:0000000121858338)