Genome-Wide Analysis of Long Non-Coding RNA Profiles in Canine Oral Melanomas

Abstract

Mucosal melanomas (MM) are rare aggressive cancers in humans, and one of the most common forms of oral cancers in dogs. Similar biological and histological features are shared between MM in both species, making dogs a powerful model for comparative oncology studies of melanomas. Although exome sequencing recently identified recurrent coding mutations in canine MM, little is known about changes in non-coding gene expression, and more particularly, in canine long non-coding RNAs (lncRNAs), which are commonly dysregulated in human cancers. Here, we sampled a large cohort (n = 52) of canine normal/tumor oral MM from three predisposed breeds (poodles, Labrador retrievers, and golden retrievers), and used deep transcriptome sequencing to identify more than 400 differentially expressed (DE) lncRNAs. We further prioritized candidate lncRNAs by comparative genomic analysis to pinpoint 26 dog-human conserved DE lncRNAs, including SOX21-AS, ZEB2-AS, and CASC15 lncRNAs. Using unsupervised co-expression network analysis with coding genes, we inferred the potential functions of the DE lncRNAs, suggesting associations with cancer-related genes, cell cycle, and carbohydrate metabolism Gene Ontology (GO) terms. Finally, we exploited our multi-breed design to identify DE lncRNAs within breeds. This study provides a unique transcriptomic resource for studying oral melanoma in dogs, and highlights lncRNAs that may potentially be diagnostic or therapeutic targets for human and veterinary medicine.

Keywords: dogs; long non-coding RNAs (lncRNAs); mucosal melanoma; transcriptome sequencing.

Figure 1

Expression analysis of the 52 oral melanoma samples. (a) Principal component analysis (PCA) of the 52 samples, based on gene-normalized counts, with control and tumor samples in blue and orange, respectively; (b) M-A plot representing log2-fold gene changes between tumors and controls over the mean of the normalized counts, with red points corresponding to significantly DE genes, with an adjusted p-value < 0.05, and without a log-fold change threshold; genes falling outside of the window are plotted as open triangles.

Figure 2

Differential expression of dog–human-conserved lncRNAs. (a) Down-regulation of the SOX21-AS1 lncRNA between control samples (blue) versus tumor samples (orange), with the log2 of normalized counts on the y-axis; lines connect matched samples from the same individuals. (b) Same representation for the up-regulation of the lncRNA CASC15.

Figure 3

Clustering dendrogram. A total of 59 coexpression modules were constructed with assigned module colors at the bottom. The number of lncRNAs in the 59 modules is listed in Supplementary Materials Table S4.

Figure 4

Module–trait associations. (a) Each row corresponds to a ME (module eigengene), and the column to the mucosal melanoma trait. Each cell contains the corresponding correlation and p-value with melanoma. Each correlation is color-coded according to the strength of the correlation, with a red gradient for positive correlations (red bar in 4.a). Modules Yellow and Tan are positively correlated (p < 5 × 10⁻⁵). (b) Modules with negative correlations according to the strength of the correlation; (blue bar in 4.b). Module Brown and Medium-orchid are the most significantly negatively correlated (p < 1 × 10⁻¹⁶). (c) Scatterplot of gene significance for mucosal melanoma status vs module membership for the Brown module. It shows a highly significant correlation between gene significance and Module membership in this module.

Figure 5

GO terms (Biological Process) enriched for (a) positively correlated and (b) negatively correlated modules with oral melanoma: the top ten enriched GO items are represented.