Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Metri, Rahul and Mohan, Abhilash and Nsengimana, Jeremie and Pozniak, Joanna and Molina-Paris, Carmen and Newton-Bishop, Julia and Bishop, David and Chandra, Nagasuma (2017) Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach. In: SCIENTIFIC REPORTS, 7 .

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Abstract

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 x 10(-4)) alone remained predictive after adjusting for clinical predictors.

Item Type:	Journal Article
Publication:	SCIENTIFIC REPORTS
Publisher:	10.1038/s41598-017-17330-0
Additional Information:	Copy right for this article belongs to the NATURE PUBLISHING GROUP, MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
Department/Centre:	Division of Physical & Mathematical Sciences > Mathematics
Date Deposited:	13 Jan 2018 07:09
Last Modified:	13 Jan 2018 07:09
URI:	http://eprints.iisc.ac.in/id/eprint/58581

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