Evidence map›Paper›PMID 40775071›Full record

ArticleMolecular biology reports2025

Transcriptomic profiling of canine mammarygland tumors reveals subtype-specific and shared differentially expressed mRNAs.

Shaohsu Wang, Nobuhiro Nozaki, Nazmul Hasan, Mohammad Arif, Silvia Guil-Luna, Osamu Yamato, Naoki Miura

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Article in Molecular biology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Shaohsu WangJoint Graduate School of Veterinary Medicine, Kagoshima University, 21-24, Korimoto 1-Chome, Kagoshima Shi, Kagoshima Ken, 890-0065, Japan.
Nobuhiro NozakiJoint Graduate School of Veterinary Medicine, Kagoshima University, 21-24, Korimoto 1-Chome, Kagoshima Shi, Kagoshima Ken, 890-0065, Japan.
Nazmul HasanJoint Graduate School of Veterinary Medicine, Kagoshima University, 21-24, Korimoto 1-Chome, Kagoshima Shi, Kagoshima Ken, 890-0065, Japan.
Mohammad ArifJoint Graduate School of Veterinary Medicine, Kagoshima University, 21-24, Korimoto 1-Chome, Kagoshima Shi, Kagoshima Ken, 890-0065, Japan.
Silvia Guil-LunaDepartment of Anatomy and Comparative Pathology and Toxicology, University of Cordoba, Cordoba, Spain.
Osamu YamatoJoint Graduate School of Veterinary Medicine, Kagoshima University, 21-24, Korimoto 1-Chome, Kagoshima Shi, Kagoshima Ken, 890-0065, Japan.
Naoki MiuraJoint Graduate School of Veterinary Medicine, Kagoshima University, 21-24, Korimoto 1-Chome, Kagoshima Shi, Kagoshima Ken, 890-0065, Japan. k9236024@kadai.jp.ORCID http://orcid.org/0000-0002-4625-5231

Funding

Japan Society for the Promotion of Science 21H02366
6 · The paper itself

Abstract

backgroundCanine mammary gland tumors (MGTs) are among the most frequently diagnosed neoplasms in intact female dogs and are increasingly regarded as a potential model of human breast cancer. However, the transcriptomic landscape across distinct histological subtypes remains poorly characterized. To explore the molecular alterations underlying MGT subtypes and to identify potential biomarkers, we performed transcriptomic profiling using next-generation sequencing (NGS). METHOD AND

resultA total of 18 canine MGT samples were analyzed using NGS. mRNA expression levels were compared between three groups: dogs with benign mixed tumor, adenoma, or carcinoma, and healthy controls. DEGs were identified from NGS reads with the Empirical Analysis of DGE tool (CLC genomic workbench) and subjected to functional enrichment analysis. A total of 47 DEGs were identified across MGT subtypes. Among these, two genes, overexpressed gene ENSCAFG00845029941 and underexpressed gene FGL2, were selected for validation via quantitative RT-PCR based on statistical significance and biological relevance. DEG clustering was observed on chromosomes 20 (carcinoma group) and 26 (benign mixed tumor and adenoma groups). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed enrichment in pathways related to muscle development and calcium ion binding.

conclusionWe potentially implicated FGL2 and ENSCAFG00845029941 as DEGs involved in multi-subtype oncogenesis for canine MGTs, as novel findings. DEGs on chromosome 26 may be implicated in benign tumor oncogenesis, and those on chromosome 20 may be similarly implicated in carcinoma.

Indexed as

Dog DiseasesMammary Neoplasms, AnimalAdenomaAnimalsBiomarkers, TumorDogsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene OntologyHigh-Throughput Nucleotide SequencingRNA, MessengerTranscriptomeBiomarkers, TumorRNA, MessengerCanineDifferentially expressed genesFGL2Mammary gland tumorNext generation sequencingTranscriptome profile

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.