Molecular classification and subtype-specific characterization of skin cutaneous melanoma by aggregating multiple genomic platform data
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Traditional classification of melanoma is widely utilized with little apparent results making the development of robust classifiers that can guide therapies an urgency. Successful seminal research on classification has provided a wider understanding of cancer from multiple molecular profiles, respectively. However, it may ignore the complementary nature of the information provided by different types of data, which motivated us to subtype melanoma by aggregating multiple genomic platform data.
Aggregating three omics data of 328 melanoma samples, melanoma subtyping was performed by three clustering methods. Differences across subtypes were extracted by functional enrichment, epigenetically silencing, gene mutations and clinical features. Subtypes were further distinguished by putative biomarkers.
Functional enrichment of the subtype-specific differential expression genes endowed subtypes new designation: immune, melanin and ion, in which the first subtype was enriched for immune system, the second was characterized by melanin and pigmentation, and the third was enriched for ion-involved transmission process. Subtypes also differed in age, Breslow thickness, tumor site, mutation frequency of BRAF, PTGS2, CDKN2A, CDKN2B and incidence of epigenetically silencing for IL15RA, EPSTI1, LXN, CDKN1B genes.
Skin cutaneous melanoma can be robustly divided into three subtypes by SNFCC+. Compared with the TCGA classification derived from gene expression, the subtypes we presented share concordance, but new traits are excavated. Such a genomic classification offers insights to further personalize therapeutic decision-making and melanoma management.
KeywordsSkin cutaneous melanoma Molecular classification Gene mutation Epigenetically silencing Biomarker SNFCC+
Skin cutaneous melanoma
The Cancer Genome Atlas
Copy number variation
Differential expression gene
Similarity network fusion
Similarity network fusion plus consensus clustering
Non-negative matrix factorization
False discovery rate
Genomic data commons
Database for annotation, visualization and integrated discovery
This study was funded by the National Social Science Fund under award No. 16BTJ021.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
Availability of data and material
The data sets used and analyzed during the current study are available from the corresponding author on reasonable request and the data sets are also available in the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov).
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