Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster display session

3702 - A robust gene signature for the detection of early relapse in stage I-III colon cancer.


09 Sep 2017


Poster display session


Translational Research;  Colon and Rectal Cancer


Weixing Dai


Annals of Oncology (2017) 28 (suppl_5): v158-v208. 10.1093/annonc/mdx393


W. Dai, Y. Li, S. Mo, G. Cai

Author affiliations

  • Department Of Colorectal Surgery, Fudan University Shanghai Cancer Center, 200032 - Shanghai/CN


Abstract 3702


Almost 40% to 50% of colon cancer relapse emerged within the first year after initial primary resection. We hypothesized that differences in mRNA expression before treatment could identify patients at high risk of early relapse.


Public microarray datasets of stage I-III colon cancer samples were extracted from Gene Expression Omnibus database. Propensity score matching analysis was performed between patients in early relapse group and long-term survival group from GSE39582 discovery series (N = 386) and internal validation series (N = 111). Linear Models for Microarray data (LIMMA) method were then used to identify the differentially expressed genes (DEGs). We then built an eight-mRNA-signature using Cox regression model. Time-dependent ROC was used to analyze the predictive accuracy of this classifier in both the discovery and internal validation series. The prognostic value of the signature was further externally validated in GSE14333 and GSE33113 datasets.


After DEGs analysis, eight mRNAs were found with more than 1.5 fold changes and P value


We developed a robust mRNA signature consisting of both up- and down-regulated mRNAs that can effectively classify colon cancer patients into groups with low and high risks of early relapse. This mRNA signature may help select high-risk colon cancer patients who deserve more aggressive therapeutic intervention.

Clinical trial identification

Legal entity responsible for the study

Guoxiang Cai




All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.