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Micro RNA differential expression profile in canine mammary gland tumor by next generation sequencing
- Source :
- Gene. 818:146237
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Canine mammary gland tumors are very common and represent a potential model of human breast cancer, and microRNA (miRNAs) are promising biomarkers and therapeutic targets for these tumors. Accordingly, we aimed to identify miRNAs differentially expressed in canine mammary gland tumors using next generation sequencing (NGS), with subsequent confirmatory qPCR and target gene analyses. Mammary gland tissue was collected from healthy dogs (n=7) and dogs with suspected tumors (n=80). A subset of samples was analyzed with NGS to identify differentially expressed miRNAs with CLC Genome Workbench. Normal (n=10), tumor-adjacent (n=6), and tumor-bearing (n=76) mammary gland tissue samples were analyzed for the identified miRNAs using qPCR. An in silico analysis (TargetScan) was performed to predict the miRNAs' target genes using gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (DAVID). We identified four miRNAs (cfa-miR-1-3p, cfa-miR-133a-3p, cfa-miR-133b-3p, and cfa-miR-133c-3p) as down regulated in canine mammary gland tumor tissues relative to normal and tumor adjacent tissues. KEGG analysis revealed the potential target genes of cfa-miR-1-3p are related to the Rap1 signaling pathway, adherens junction, and Ras signaling pathway, and those of the miR-133 family are related to the TGF-beta signaling pathway, synaptic vesicle cycle, and sphingolipid signaling pathway. In combination, these target genes are related to the regulation of transcription and DNA binding transcription (GO analysis), and the Hippo signaling pathway, adherens junction, and endocytosis (KEGG analysis). Accordingly, we suggest these four miRNAs are promising potential biomarker candidates for canine mammary gland tumors warranting further investigation.
Details
- ISSN :
- 03781119
- Volume :
- 818
- Database :
- OpenAIRE
- Journal :
- Gene
- Accession number :
- edsair.doi.dedup.....a1f71a5239bef5c36f5abb48043855d1
- Full Text :
- https://doi.org/10.1016/j.gene.2022.146237