The melanoma transformation rate of an individual nevus is very low despite the detection of oncogenic BRAF or NRAS mutations in 100% of nevi. Acquired melanocytic nevi do, however, mimic melanoma, and approximately 30% of all melanomas arise within pre-existing nevi. Using whole-exome sequencing of 30 matched nevi, adjacent normal skin, and saliva we sought to identify the underlying genetic mechanisms for nevus development. All nevi were clinically, dermoscopically, and histopathologically documented. In addition to identifying somatic mutations, we found mutational signatures relating to UVR mirroring those found in cutaneous melanoma. In nevi we frequently observed the presence of the UVR mutation signature compared with adjacent normal skin (97% vs. 10%, respectively). Copy number aberration analysis showed that for nevi with copy number loss of tumor suppressor genes, this loss was balanced by loss of potent oncogenes. Moreover, reticular and nonspecific patterned nevi showed an increased (P < 0.0001) number of copy number aberrations compared with globular nevi. The mutation signature data generated in this study confirms that UVR strongly contributes to nevogenesis. Copy number changes reflect at a genomic level the dermoscopic differences of acquired melanocytic nevi. Finally, we propose that the balanced loss of tumor suppressor genes and oncogenes is a protective mechanism of acquired melanocytic nevi.
Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, we have utilised next generation RNA sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes that are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as qPCR RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.