Pathway-based biomarkers for breast cancer in proteomics

Fan Zhang; Youping Deng; Mu Wang; Li Cui; Renee Drabier

doi:10.4137/CIN.S14069

Pathway-based biomarkers for breast cancer in proteomics

Fan Zhang, Youping Deng, Mu Wang, Li Cui, Renee Drabier

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Genes do not function alone but through complex biological pathways. Pathway-based biomarkers may be a reliable diagnostic tool for early detection of breast cancer due to the fact that breast cancer is not a single homogeneous disease. We applied Integrated Pathway Analysis Database (IPAD) and Gene Set Enrichment Analysis (GSEA) approaches to the study of pathway-based biomarker discovery problem in breast cancer proteomics. Our strategy for identifying and analyzing pathway-based biomarkers are threefold. Firstly, we performed pathway analysis with IPAD to build the gene set database. Secondly, we ran GSEA to identify 16 pathway-based biomarkers. Lastly, we built a Support Vector Machine model with three-way data split and fivefold cross-validation to validate the biomarkers. The approach-unraveling the intricate pathways, networks, and functional contexts in which genes or proteins function-is essential to the understanding molecular mechanisms of pathway-based biomarkers in breast cancer.

Original language	English
Pages (from-to)	101-108
Number of pages	8
Journal	Cancer Informatics
Volume	13
Issue number	Suppl 5
DOIs	https://doi.org/10.4137/CIN.S14069
State	Published - 2014

Keywords

Breast cancer
Machine learning
Pathway analysis
Proteomics

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title = "Pathway-based biomarkers for breast cancer in proteomics",

abstract = "Genes do not function alone but through complex biological pathways. Pathway-based biomarkers may be a reliable diagnostic tool for early detection of breast cancer due to the fact that breast cancer is not a single homogeneous disease. We applied Integrated Pathway Analysis Database (IPAD) and Gene Set Enrichment Analysis (GSEA) approaches to the study of pathway-based biomarker discovery problem in breast cancer proteomics. Our strategy for identifying and analyzing pathway-based biomarkers are threefold. Firstly, we performed pathway analysis with IPAD to build the gene set database. Secondly, we ran GSEA to identify 16 pathway-based biomarkers. Lastly, we built a Support Vector Machine model with three-way data split and fivefold cross-validation to validate the biomarkers. The approach-unraveling the intricate pathways, networks, and functional contexts in which genes or proteins function-is essential to the understanding molecular mechanisms of pathway-based biomarkers in breast cancer.",

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TY - JOUR

T1 - Pathway-based biomarkers for breast cancer in proteomics

AU - Zhang, Fan

AU - Deng, Youping

AU - Wang, Mu

AU - Cui, Li

AU - Drabier, Renee

N1 - Publisher Copyright: © the authors, publisher and licensee Libertas Academica Limited.

PY - 2014

Y1 - 2014

N2 - Genes do not function alone but through complex biological pathways. Pathway-based biomarkers may be a reliable diagnostic tool for early detection of breast cancer due to the fact that breast cancer is not a single homogeneous disease. We applied Integrated Pathway Analysis Database (IPAD) and Gene Set Enrichment Analysis (GSEA) approaches to the study of pathway-based biomarker discovery problem in breast cancer proteomics. Our strategy for identifying and analyzing pathway-based biomarkers are threefold. Firstly, we performed pathway analysis with IPAD to build the gene set database. Secondly, we ran GSEA to identify 16 pathway-based biomarkers. Lastly, we built a Support Vector Machine model with three-way data split and fivefold cross-validation to validate the biomarkers. The approach-unraveling the intricate pathways, networks, and functional contexts in which genes or proteins function-is essential to the understanding molecular mechanisms of pathway-based biomarkers in breast cancer.

AB - Genes do not function alone but through complex biological pathways. Pathway-based biomarkers may be a reliable diagnostic tool for early detection of breast cancer due to the fact that breast cancer is not a single homogeneous disease. We applied Integrated Pathway Analysis Database (IPAD) and Gene Set Enrichment Analysis (GSEA) approaches to the study of pathway-based biomarker discovery problem in breast cancer proteomics. Our strategy for identifying and analyzing pathway-based biomarkers are threefold. Firstly, we performed pathway analysis with IPAD to build the gene set database. Secondly, we ran GSEA to identify 16 pathway-based biomarkers. Lastly, we built a Support Vector Machine model with three-way data split and fivefold cross-validation to validate the biomarkers. The approach-unraveling the intricate pathways, networks, and functional contexts in which genes or proteins function-is essential to the understanding molecular mechanisms of pathway-based biomarkers in breast cancer.

KW - Breast cancer

KW - Machine learning

KW - Pathway analysis

KW - Proteomics

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JO - Cancer Informatics

JF - Cancer Informatics

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Pathway-based biomarkers for breast cancer in proteomics

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Keywords

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