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

Lookup NU author(s): Dr Jérémie Nsengimana

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2017, The Author(s).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 × 10−4) alone remained predictive after adjusting for clinical predictors.


Publication metadata

Author(s): Metri R, Mohan A, Nsengimana J, Pozniak J, Molina-Paris C, Newton-Bishop J, Bishop D, Chandra N

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2017

Volume: 7

Online publication date: 11/12/2017

Acceptance date: 10/11/2017

Date deposited: 12/06/2020

ISSN (electronic): 2045-2322

Publisher: Nature Research

URL: https://doi.org/10.1038/s41598-017-17330-0

DOI: 10.1038/s41598-017-17330-0

PubMed id: 29229936


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