Recurrence-associated gene signature optimizes recurrence-free survival prediction of colorectal cancer.

ABSTRACT: High throughput gene expression profiling has showed great promise in providing insight into molecular mechanisms. Metastasis-related mRNAs may potentially enrich genes with the ability to predict cancer recurrence, therefore we attempted to build a recurrence-associated gene signature to improve prognostic prediction of colorectal cancer (CRC). We identified 2848 differentially expressed mRNAs by analyzing CRC tissues with or without metastasis. For the selection of prognostic genes, a LASSO Cox regression model (least absolute shrinkage and selection operator method) was employed. Using this method, a 13-mRNA signature was identified and then validated in two independent Gene Expression Omnibus cohorts. This classifier could successfully discriminate the high-risk patients in discovery c

SUBMITTER: Tian X 

PROVIDER: S-EPMC5664005 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature