Sequence-based analysis of healthy canine tissues and oral malignant melanoma identifies disease-associated small RNAs and candidate miRNAs.

Small RNA sequencing is a promising approach for exploring small RNA dynamics and discovering biomarkers in veterinary oncology. However, small RNA annotation in dogs remains limited and inconsistent across reference databases, which may hinder identification of biologically relevant molecules. Here, we applied a sequence-based analysis framework to characterize canine small RNAs using publicly available healthy beagle tissue datasets together with oral malignant melanoma (OMM) clinical samples from client-owned dogs collected under ethical approval and informed owner consent. OMM is an aggressive canine tumor and a comparative model of human mucosal melanoma. After preprocessing and unique-sequence counting, reproducibly detected sequences were annotated using miRBase, MirGeneDB, and RNAcentral in a multi-database, cross-species framework. Comparison of reference resources confirmed fewer canine entries than human and mouse entries in RNAcentral and miRBase, with limited concordance between canine miRBase and MirGeneDB entries. Across 20 healthy canine tissue groups, sequence-level analysis showed that stable small RNA expression extended beyond registered mature miRNAs to isomiRNAs, seed-shifted isomiRNAs, pre-miRNA-derived fragments, and non-miRNA small RNAs. From pre-miRNA-derived fragments, we identified six candidate miRNAs, including the first reported canine miRNA-offset RNAs (moRNAs), cfa-moR-326-5p and cfa-moR-8859-P2-5p. In OMM, dysregulated sequences included candidate miRNAs, isomiRNAs, fragmented non-miRNA small RNAs, and two moRNA candidates. These sequence-defined small RNAs may represent overlooked regulatory molecules and clinically relevant biomarker candidates for canine OMM. Overall, sequence-based analysis can reveal biologically plausible small RNAs missed or under-classified by reference-based approaches, improving molecular characterization of canine tumors and prioritizing candidates for future diagnostic, prognostic, and functional validation.