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SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

Author

Listed:
  • Raul Aguirre-Gamboa
  • Hugo Gomez-Rueda
  • Emmanuel Martínez-Ledesma
  • Antonio Martínez-Torteya
  • Rafael Chacolla-Huaringa
  • Alberto Rodriguez-Barrientos
  • José G Tamez-Peña
  • Victor Treviño

Abstract

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

Suggested Citation

  • Raul Aguirre-Gamboa & Hugo Gomez-Rueda & Emmanuel Martínez-Ledesma & Antonio Martínez-Torteya & Rafael Chacolla-Huaringa & Alberto Rodriguez-Barrientos & José G Tamez-Peña & Victor Treviño, 2013. "SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-9, September.
    • Handle: RePEc:plo:pone00:0074250
    DOI: 10.1371/journal.pone.0074250
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    References listed on IDEAS

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    1. Patrick J. Heagerty & Yingye Zheng, 2005. "Survival Model Predictive Accuracy and ROC Curves," Biometrics, The International Biometric Society, vol. 61(1), pages 92-105, March.
    2. Andrea H. Bild & Guang Yao & Jeffrey T. Chang & Quanli Wang & Anil Potti & Dawn Chasse & Mary-Beth Joshi & David Harpole & Johnathan M. Lancaster & Andrew Berchuck & John A. Olson & Jeffrey R. Marks &, 2006. "Oncogenic pathway signatures in human cancers as a guide to targeted therapies," Nature, Nature, vol. 439(7074), pages 353-357, January.
    3. Markus Ringnér & Erik Fredlund & Jari Häkkinen & Åke Borg & Johan Staaf, 2011. "GOBO: Gene Expression-Based Outcome for Breast Cancer Online," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    4. Jan Budczies & Frederick Klauschen & Bruno V Sinn & Balázs Győrffy & Wolfgang D Schmitt & Silvia Darb-Esfahani & Carsten Denkert, 2012. "Cutoff Finder: A Comprehensive and Straightforward Web Application Enabling Rapid Biomarker Cutoff Optimization," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-7, December.
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    1. Jose-Gerardo Tamez-Peña & Juan-Andrés Rodriguez-Rojas & Hugo Gomez-Rueda & Jose-Maria Celaya-Padilla & Roxana-Alicia Rivera-Prieto & Rebeca Palacios-Corona & Margarita Garza-Montemayor & Servando Card, 2018. "Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.

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