Affordable Access

deepdyve-link
Publisher Website

Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.

Authors
  • Wang, Chao1
  • Pécot, Thierry
  • Zynger, Debra L
  • Machiraju, Raghu
  • Shapiro, Charles L
  • Huang, Kun
  • 1 Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
Type
Published Article
Journal
Journal of the American Medical Informatics Association
Publisher
Oxford University Press
Publication Date
2013
Volume
20
Issue
4
Pages
680–687
Identifiers
DOI: 10.1136/amiajnl-2012-001538
PMID: 23585272
Source
Medline
Keywords
License
Unknown

Abstract

Using TCGA data, we identified 48 pairs of significantly correlated morphological features and gene clusters; four morphological features were able to separate the local cohort with significantly different survival outcomes. Gene clusters correlated with these four morphological features further proved to be effective in predicting patient survival using multiple public gene expression datasets. These results suggest the efficacy of our workflow and demonstrate that integrative analysis holds promise for discovering biomarkers of complex diseases.

Report this publication

Statistics

Seen <100 times