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



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.


Skin 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


Consensus clustering


Similarity network fusion plus consensus clustering


Hierarchical 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.

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 (

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)


  1. Allen D, Lepple-Wienhues A, Cahalan M (1997) Ion channel phenotype of melanoma cell lines. J Membr Biol 155:27–34CrossRefPubMedGoogle Scholar
  2. Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C (2014) Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods 11:1138CrossRefPubMedPubMedCentralGoogle Scholar
  3. Austin PF, Cruse CW, Lyman G, Schroer K, Glass F, Reintgen DS (1994) Age as a prognostic factor in the malignant melanoma population. Ann Surg Oncol 1:487–494Google Scholar
  4. Azimi F et al (2012) Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol 30:2678–2683CrossRefPubMedGoogle Scholar
  5. Balch CM (1992) Cutaneous melanoma: prognosis and treatment results worldwide. Semin Surg Oncol 8:400–414Google Scholar
  6. Balch CM et al (2001) Prognostic factors analysis of 17,600 melanoma patients: validation of the American Joint Committee on Cancer melanoma staging system. J Clin Oncol 19:3622–3634CrossRefPubMedGoogle Scholar
  7. Balch CM et al (2009) Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol 27:6199–6206CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bhatia P, Friedlander P, Zakaria EA, Kandil E (2015) Impact of BRAF mutation status in the prognosis of cutaneous melanoma: an area of ongoing research. Ann Transl Med 3:24PubMedPubMedCentralGoogle Scholar
  9. Bonafede E (2015) Differential expression analysis for sequence count data via mixtures of negative binomials. AlmaGoogle Scholar
  10. Brunet J-P, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci 101:4164–4169Google Scholar
  11. Bryois J et al (2014) Cis and trans effects of human genomic variants on gene expression. PLoS Genet 10:e1004461CrossRefPubMedPubMedCentralGoogle Scholar
  12. ChantoôMe AL, Potier-Cartereau M, Roger SB, Vandier C, Soriani O, Joulin V (2013) Ion channels as promising therapeutic targets for melanoma. InTech 20:429–460Google Scholar
  13. Cin H et al (2011) Oncogenic FAM131B–BRAF fusion resulting from 7q34 deletion comprises an alternative mechanism of MAPK pathway activation in pilocytic astrocytoma. Acta Neuropathol 121:763–774CrossRefPubMedGoogle Scholar
  14. Clark WH, From L, Bernardino EA, Mihm MC (1969) The histogenesis and biologic behavior of primary human malignant melanomas of the skin. Cancer Res 29:705–727PubMedGoogle Scholar
  15. Conteduca G et al (2010) The role of AIRE polymorphisms in melanoma. Clin Immunol 136:96–104CrossRefPubMedGoogle Scholar
  16. Fecher LA, Cummings SD, Keefe MJ, Alani RM (2007) Toward a molecular classification of melanoma. J Clin Oncol 25:1606–1620CrossRefPubMedGoogle Scholar
  17. Fisher NM, Schaffer JV, Berwick M, Bolognia JL (2005) Breslow depth of cutaneous melanoma: impact of factors related to surveillance of the skin, including prior skin biopsies and family history of melanoma. J Am Acad Dermatol 53:393–406CrossRefPubMedGoogle Scholar
  18. Gao J et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1CrossRefPubMedPubMedCentralGoogle Scholar
  19. Garg K et al (2016) Tumor-associated B cells in cutaneous primary melanoma and improved clinical outcome. Human Pathol 54:157–164CrossRefGoogle Scholar
  20. Gomez-Lira M et al (2014) Association of promoter polymorphism – 765G> C in the PTGS2 gene with malignant melanoma in Italian patients and its correlation to gene expression in dermal fibroblasts. Exp Dermatol 23:766–768CrossRefPubMedGoogle Scholar
  21. Haluska F, Pemberton T, Ibrahim N, Kalinsky K (2007) The RTK/RAS/BRAF/PI3K pathways in melanoma: biology, small molecule inhibitors, and potential applications. Semin Oncol 34:546–554CrossRefPubMedGoogle Scholar
  22. Hawkes JE et al (2013) Report of a novel OCA2 gene mutation and an investigation of OCA2 variants on melanoma risk in a familial melanoma pedigree. J Dermatol Sci 69:30–37CrossRefPubMedGoogle Scholar
  23. Hinoue T et al (2012) Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res 22:271–282CrossRefPubMedPubMedCentralGoogle Scholar
  24. Holm K et al (2010) Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns. Breast Cancer Res 12(3):R36CrossRefPubMedPubMedCentralGoogle Scholar
  25. Inamdar GS, Madhunapantula SV, Robertson GP (2010) Targeting the MAPK pathway in melanoma: why some approaches succeed and other fail. Biochem Pharmacol 80:624–637CrossRefPubMedPubMedCentralGoogle Scholar
  26. Innominato PF, Libbrecht L, van den Oord JJ (2001) Expression of neurotrophins and their receptors in pigment cell lesions of the skin. J Pathol 194:95–100CrossRefPubMedGoogle Scholar
  27. Inozume T et al (2005) Novel melanoma antigen, FCRL/FREB, identified by cDNA profile comparison using DNA chip are immunogenic in multiple melanoma patients. Int J Cancer 114:283–290CrossRefPubMedGoogle Scholar
  28. Jacquelot N et al (2016) Chemokine receptor patterns in lymphocytes mirror metastatic spreading in melanoma. J Clin Investig 126:921–937CrossRefPubMedGoogle Scholar
  29. Jayakumar A, Rauvolfova J, Bao H, Fokt I, Skora S, Heimberger A, Priebe W (2013) Abstract 3251: Blockade of HIF-1 with a small molecule inhibitor WP1066 in melanoma. Cancer Res 73:3251–3251Google Scholar
  30. Jemal A, Siegel R, Xu J, Ward E (2010) Cancer statistics, 2010 CA: a cancer. J Clin 60:277–300Google Scholar
  31. Jonsson G et al (2010) Gene expression profiling-based identification of molecular subtypes in stage IV Melanomas with different clinical outcome clinical cancer research an official. J Am Assoc Cancer Res 16:3356–3367Google Scholar
  32. Journe F et al (2011) TYRP1 mRNA expression in melanoma metastases correlates with clinical outcome. Br J Cancer 105:1726–1732CrossRefPubMedPubMedCentralGoogle Scholar
  33. Kawaguchi M, Hearing VJ (2011) The roles of ADAMs family proteinases in skin diseases. Enzyme Res 2011:482498CrossRefPubMedPubMedCentralGoogle Scholar
  34. Koh SS et al (2012) Differential gene expression profiling of primary cutaneous melanoma and sentinel lymph node metastases. Mod Pathol 25:828–837CrossRefPubMedGoogle Scholar
  35. Li M, Xiong Z-G (2011) Ion channels as targets for cancer therapy. Int J Physiol Pathophysiol Pharmacol 3:156–166PubMedPubMedCentralGoogle Scholar
  36. Li T et al (2013) Identification of epithelial stromal interaction 1 as a novel effector downstream of Krüppel-like factor 8 in breast cancer invasion and metastasis. Oncogene 33:4746–4755CrossRefPubMedPubMedCentralGoogle Scholar
  37. Li FJ et al (2014) Emerging Roles for the FCRL Family Members in Lymphocyte Biology and Disease. Curr Top Microbiol Immunol 382:29–50PubMedPubMedCentralGoogle Scholar
  38. Liu W, Peng Y, Tobin DJ (2013) A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis PeerJ 1:e49Google Scholar
  39. Lodolce J et al (2002) Interleukin-15 and the regulation of lymphoid homeostasis. Mol Immunol 39:537–544CrossRefPubMedGoogle Scholar
  40. Macià A, Herreros J, Martí RM, Cantí C (2015) Calcium channel expression and applicability as targeted therapies in melanoma. Biomed Res Int 2015:587135CrossRefPubMedPubMedCentralGoogle Scholar
  41. Maurer M, Somasundaram R, Herlyn M, Wagner SN (2012) Immunotargeting of tumor subpopulations in melanoma patients: A paradigm shift in therapy approaches. Oncoimmunology 1:1454–1456CrossRefPubMedPubMedCentralGoogle Scholar
  42. McGovern VJ et al (1973) The classification of malignant melanoma and its histologic reporting. Cancer 32:1446–1457Google Scholar
  43. Monti S, Tamayo P, Mesirov J, Golub T (2003) Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 52:91–118CrossRefGoogle Scholar
  44. Palmieri G (2012) Molecular classification of patients with cutaneous melanoma: a reality. J Mol Biomark Diagn 3:e110Google Scholar
  45. Payne AS, Cornelius LA (2002) The role of chemokines in melanoma tumor growth and metastasis. J Investig Dermatol 118:915–922CrossRefPubMedGoogle Scholar
  46. Rogers HW, Weinstock MA, Feldman SR, Coldiron BM (2015) Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US Population, 2012. Jama Dermatol 151:1081–1086CrossRefPubMedGoogle Scholar
  47. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefGoogle Scholar
  48. Scolyer RA, Long GV, Thompson JF (2011) Evolving concepts in melanoma classification and their relevance to multidisciplinary melanoma patient care. Mol Oncol 5:124–136CrossRefPubMedPubMedCentralGoogle Scholar
  49. Slominski R, Zmijewski M, Slominski AT (2015) On the role of melanin pigment in melanoma. Exp Dermatol 24:258–259CrossRefPubMedPubMedCentralGoogle Scholar
  50. Stern RS (2010) Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol 146:279–282CrossRefPubMedGoogle Scholar
  51. TCGA (2015) Genomic classification of cutaneous melanoma. Cell 161:1681–1696CrossRefGoogle Scholar
  52. Verreault M, Webb MS, Ramsay EC, Bally MB (2006) Gene silencing in the development of personalized cancer treatment: the targets, the agents and the delivery systems. Curr Gene Ther 6:505–533CrossRefPubMedGoogle Scholar
  53. Wang B et al (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11:333–337CrossRefPubMedGoogle Scholar
  54. Xu T et al (2017) CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation, and visualization. Bioinformatics 33:3131–3133CrossRefPubMedGoogle Scholar
  55. Zhao Q, Shi X, Xie Y, Huang J, Shia B, Ma S (2015) Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA. Brief Bioinform 16:291–303CrossRefPubMedGoogle Scholar

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

Personalised recommendations