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

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

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.

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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

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Funding

This study was funded by the National Social Science Fund under award No. 16BTJ021.

Author information

Author notes

  1. Xiaofan Lu, Qianyuan Zhang and Yue Wang contributed equally to this work.

Authors and Affiliations

  1. Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, 210009, People’s Republic of China

    Xiaofan Lu, Qianyuan Zhang, Yue Wang, Liya Zhang, Huiling Zhao, Chen Chen, Yaoyan Wang, Shengjie Liu, Fei Wang & Fangrong Yan

  2. State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing, 210009, People’s Republic of China

    Tao Lu

Authors
  1. Xiaofan Lu
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  2. Qianyuan Zhang
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  11. Fangrong Yan
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Corresponding authors

Correspondence to Tao Lu, Fei Wang or Fangrong Yan.

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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).

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Lu, X., Zhang, Q., Wang, Y. et al. Molecular classification and subtype-specific characterization of skin cutaneous melanoma by aggregating multiple genomic platform data. J Cancer Res Clin Oncol 144, 1635–1647 (2018). https://doi.org/10.1007/s00432-018-2684-7

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  • DOI: https://doi.org/10.1007/s00432-018-2684-7

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