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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.

SUBMITTER: Tian X

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

REPOSITORIES: biostudies-literature

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

Tian Xianglong X Zhu Xiaoqiang X Yan Tingting T Yu Chenyang C Shen Chaoqin C Hu Ye Y Hong Jie J Chen Haoyan H Fang Jing-Yuan JY

Molecular oncology 20170923 11

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 Co ...[more]

PMID: 28796930

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Project description:BackgroundRadical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis.MethodsMethylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis.ResultsFive co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6-7 or 8-10) could be further divided into the high-risk group and the low-risk group.ConclusionThese results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.

Dataset Information

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

Publications

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

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