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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 cohort [hazard ratio (HR) = 5.27, 95% confidence interval (CI) 2.30-12.08, P < 0.0001). Analysis in two independent cohorts yielded consistent results (GSE14333: HR = 4.55, 95% CI 2.18-9.508, P < 0.0001; GSE33113: HR = 3.26, 95% CI 2.16-9.16, P = 0.0176). Further analysis revealed that the prognostic value of this signature was independent of tumor stage, postoperative chemotherapy and somatic mutation. Receiver operating characteristic (ROC) analysis showed that the area under ROC curve of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, and did well in the calibration plots. Furthermore, this 13-mRNA signature outperformed other known gene signatures, including oncotypeDX colon cancer assay. Single-sample gene-set enrichment analysis revealed that a group of pathways related to drug resistance, cancer metastasis and stemness were significantly enriched in the high-risk patients. In conclusion, this 13-mRNA signature may be a useful tool for prognostic evaluation and will facilitate personalized management of CRC patients.

journal_name

Mol Oncol

journal_title

Molecular oncology

authors

Tian X,Zhu X,Yan T,Yu C,Shen C,Hu Y,Hong J,Chen H,Fang JY

doi

10.1002/1878-0261.12117

subject

Has Abstract

pub_date

2017-11-01 00:00:00

pages

1544-1560

issue

11

eissn

1574-7891

issn

1878-0261

journal_volume

11

pub_type

杂志文章

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