Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

Abstract Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially re...

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Autores principales:	Frederick S. Vizeacoumar, Hongyu Guo, Lynn Dwernychuk, Adnan Zaidi, Andrew Freywald, Fang-Xiang Wu, Franco J. Vizeacoumar, Shahid Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/41d22c54bca4422d97431dd9a80790c4
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spelling	oai:doaj.org-article:41d22c54bca4422d97431dd9a80790c42021-12-02T14:22:43ZMining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery10.1038/s41598-021-87037-w2045-2322https://doaj.org/article/41d22c54bca4422d97431dd9a80790c42021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87037-whttps://doaj.org/toc/2045-2322Abstract Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.Frederick S. VizeacoumarHongyu GuoLynn DwernychukAdnan ZaidiAndrew FreywaldFang-Xiang WuFranco J. VizeacoumarShahid AhmedNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Frederick S. Vizeacoumar Hongyu Guo Lynn Dwernychuk Adnan Zaidi Andrew Freywald Fang-Xiang Wu Franco J. Vizeacoumar Shahid Ahmed Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
description	Abstract Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.
format	article
author	Frederick S. Vizeacoumar Hongyu Guo Lynn Dwernychuk Adnan Zaidi Andrew Freywald Fang-Xiang Wu Franco J. Vizeacoumar Shahid Ahmed
author_facet	Frederick S. Vizeacoumar Hongyu Guo Lynn Dwernychuk Adnan Zaidi Andrew Freywald Fang-Xiang Wu Franco J. Vizeacoumar Shahid Ahmed
author_sort	Frederick S. Vizeacoumar
title	Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
title_short	Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
title_full	Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
title_fullStr	Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
title_full_unstemmed	Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
title_sort	mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/41d22c54bca4422d97431dd9a80790c4
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Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

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