Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival.

Details

Serval ID
serval:BIB_F5A4A00843E4
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival.
Journal
Clinical Cancer Research : An Official Journal of the American Association For Cancer Research
Author(s)
Salas S., Brulard C., Terrier P., Ranchere-Vince D., Neuville A., Guillou L., Lae M., Leroux A., Verola O., Jean-Emmanuel K., Bonvalot S., Blay J.Y., Le Cesne A., Aurias A., Coindre J.M., Chibon F.
ISSN
1078-0432 (Print)
ISSN-L
1078-0432
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
21
Number
18
Pages
4194-4200
Language
english
Notes
Publication types: Journal ArticlePublication Status: ppublish
Abstract
PURPOSE: Because desmoid tumors exhibit an unpredictable clinical course, translational research is crucial to identify the predictive factors of progression in addition to the clinical parameters. The main issue is to detect patients who are at a higher risk of progression. The aim of this work was to identify molecular markers that can predict progression-free survival (PFS).
EXPERIMENTAL DESIGN: Gene-expression screening was conducted on 115 available independent untreated primary desmoid tumors using cDNA microarray. We established a prognostic gene-expression signature composed of 36 genes. To test robustness, we randomly generated 1,000 36-gene signatures and compared their outcome association to our define 36-genes molecular signature and we calculated positive predictive value (PPV) and negative predictive value (NPV).
RESULTS: Multivariate analysis showed that our molecular signature had a significant impact on PFS while no clinical factor had any prognostic value. Among the 1,000 random signatures generated, 56.7% were significant and none was more significant than our 36-gene molecular signature. PPV and NPV were high (75.58% and 81.82%, respectively). Finally, the top two genes downregulated in no-recurrence were FECH and STOML2 and the top gene upregulated in no-recurrence was TRIP6.
CONCLUSIONS: By analyzing expression profiles, we have identified a gene-expression signature that is able to predict PFS. This tool may be useful for prospective clinical studies. Clin Cancer Res; 21(18); 4194-200. ©2015 AACR.
Pubmed
Web of science
Open Access
Yes
Create date
17/11/2015 18:36
Last modification date
20/08/2019 17:22
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