CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery
- Authors
- Kwon, Min-Seok; Nam, Seungyoon; Lee, Sungyoung; Ahn, Young Zoo; Chang, Hae Ryung; Kim, Yon Hui; Park, Taesung
- Issue Date
- 19-Sep-2017
- Publisher
- IMPACT JOURNALS LLC
- Keywords
- biomarker performance; claudin; gastric cancer; transcriptome; data mining
- Citation
- ONCOTARGET, v.8, no.41, pp.69808 - 69822
- Journal Title
- ONCOTARGET
- Volume
- 8
- Number
- 41
- Start Page
- 69808
- End Page
- 69822
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/5701
- DOI
- 10.18632/oncotarget.19270
- ISSN
- 1949-2553
- Abstract
- The recent creation of enormous, cancer-related "Big Data" public depositories represents a powerful means for understanding tumorigenesis. However, a consistently accurate system for clinically evaluating single/multi-biomarkers remains lacking, and it has been asserted that oft-failed clinical advancement of biomarkers occurs within the very early stages of biomarker assessment. To address these challenges, we developed a clinically testable, web-based tool, CANcer-specific single/multi-biomarker Evaluation System (CANES), to evaluate biomarker effectiveness, across 2,134 whole transcriptome datasets, from 94,147 biological samples (from 18 tumor types). For user-provided single/multi-biomarkers, CANES evaluates the performance of single/multi-biomarker candidates, based on four classification methods, support vector machine, random forest, neural networks, and classification and regression trees. In addition, CANES offers several advantages over earlier analysis tools, including: 1) survival analysis; 2) evaluation of mature miRNAs as markers for user-defined diagnostic or prognostic purposes; and 3) provision of a "pan-cancer" summary view, based on each single marker. We believe that such "landscape" evaluation of single/multi-biomarkers, for diagnostic therapeutic/prognostic decision-making, will be highly valuable for the discovery and "repurposing" of existing biomarkers (and their specific targeted therapies), leading to improved patient therapeutic stratification, a key component of targeted therapy success for the avoidance of therapy resistance.
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