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A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis
Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays often uncover significant inconsistencies that hinder advances in clinical practice. Here we present an integrated microarr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628745/ https://www.ncbi.nlm.nih.gov/pubmed/23638386 http://dx.doi.org/10.7717/peerj.49 |
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author | Liu, Wanting Peng, Yonghong Tobin, Desmond J. |
author_facet | Liu, Wanting Peng, Yonghong Tobin, Desmond J. |
author_sort | Liu, Wanting |
collection | PubMed |
description | Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays often uncover significant inconsistencies that hinder advances in clinical practice. Here we present an integrated microarray analysis framework, based on a genome-wide relative significance (GWRS) and genome-wide global significance (GWGS) model. When applied to five microarray datasets on melanoma published between 2000 and 2011, this method revealed a new signature of 200 genes. When these were linked to so-called ‘melanoma driver’ genes involved in MAPK, Ca(2+), and WNT signaling pathways we were able to produce a new 12-gene diagnostic biomarker signature for melanoma (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). We have begun to experimentally validate a subset of these genes involved in MAPK signaling at the protein level, including CXCL13, COL11A1, PTPRF and SHC4 and found these to be over-expressed in metastatic and primary melanoma cells in vitro and in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. While SHC4 has been reported previously to be associated to melanoma, this is the first time CXCL13, COL11A1, and PTPRF have been associated with melanoma on experimental validation. Our computational evaluation indicates that this 12-gene biomarker signature achieves excellent diagnostic power in distinguishing metastatic melanoma from normal skin and benign nevus. Further experimental validation of the role of these 12 genes in a new signaling network may provide new insights into the underlying biological mechanisms driving the progression of melanoma. |
format | Online Article Text |
id | pubmed-3628745 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-36287452013-05-01 A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis Liu, Wanting Peng, Yonghong Tobin, Desmond J. Peerj Bioinformatics Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays often uncover significant inconsistencies that hinder advances in clinical practice. Here we present an integrated microarray analysis framework, based on a genome-wide relative significance (GWRS) and genome-wide global significance (GWGS) model. When applied to five microarray datasets on melanoma published between 2000 and 2011, this method revealed a new signature of 200 genes. When these were linked to so-called ‘melanoma driver’ genes involved in MAPK, Ca(2+), and WNT signaling pathways we were able to produce a new 12-gene diagnostic biomarker signature for melanoma (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). We have begun to experimentally validate a subset of these genes involved in MAPK signaling at the protein level, including CXCL13, COL11A1, PTPRF and SHC4 and found these to be over-expressed in metastatic and primary melanoma cells in vitro and in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. While SHC4 has been reported previously to be associated to melanoma, this is the first time CXCL13, COL11A1, and PTPRF have been associated with melanoma on experimental validation. Our computational evaluation indicates that this 12-gene biomarker signature achieves excellent diagnostic power in distinguishing metastatic melanoma from normal skin and benign nevus. Further experimental validation of the role of these 12 genes in a new signaling network may provide new insights into the underlying biological mechanisms driving the progression of melanoma. PeerJ Inc. 2013-03-05 /pmc/articles/PMC3628745/ /pubmed/23638386 http://dx.doi.org/10.7717/peerj.49 Text en © 2013 Liu et al. http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Bioinformatics Liu, Wanting Peng, Yonghong Tobin, Desmond J. A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
title | A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
title_full | A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
title_fullStr | A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
title_full_unstemmed | A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
title_short | A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
title_sort | new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628745/ https://www.ncbi.nlm.nih.gov/pubmed/23638386 http://dx.doi.org/10.7717/peerj.49 |
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