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Journal of Cancer Research and Clinical Oncology

, Volume 144, Issue 9, pp 1635–1647 | Cite as

Molecular classification and subtype-specific characterization of skin cutaneous melanoma by aggregating multiple genomic platform data

  • Xiaofan Lu
  • Qianyuan Zhang
  • Yue Wang
  • Liya Zhang
  • Huiling Zhao
  • Chen Chen
  • Yaoyan Wang
  • Shengjie Liu
  • Tao Lu
  • Fei Wang
  • Fangrong Yan
Original Article – Cancer Research
  • 229 Downloads

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Skin cutaneous melanoma Molecular classification Gene mutation Epigenetically silencing Biomarker SNFCC+ 

Abbreviations

SKCM

Skin cutaneous melanoma

TCGA

The Cancer Genome Atlas

CNV

Copy number variation

DEG

Differential expression gene

SNF

Similarity network fusion

CC

Consensus clustering

SNFCC+

Similarity network fusion plus consensus clustering

HC

Hierarchical clustering

NMF

Non-negative matrix factorization

FDR

False discovery rate

GDC

Genomic data commons

DAVID

Database for annotation, visualization and integrated discovery

Notes

Funding

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.

Ethical approval

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).

Supplementary material

432_2018_2684_MOESM1_ESM.docx (1.9 mb)
Supplementary material 1 (DOCX 1898 KB)
432_2018_2684_MOESM2_ESM.docx (21 kb)
Supplementary material 2 (DOCX 21 KB)
432_2018_2684_MOESM3_ESM.xlsx (35 kb)
Supplementary material 3 (XLSX 35 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Center of Biostatistics and Computational PharmacyChina Pharmaceutical UniversityNanjingPeople’s Republic of China
  2. 2.State Key Laboratory of Natural MedicineChina Pharmaceutical UniversityNanjingPeople’s Republic of China

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