LAUSR

Canine mammary gland tumors (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n=4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed 4 molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumor grades, histopathology diagnoses, short survival, and high TP53 mutation rates (Ps<0.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (Ps<0.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases, and short survival.