Dataset Information

A molecular signature for the prediction of recurrence in colorectal cancer.

ABSTRACT:

Background

Several clinical and pathological factors have an impact on the prognosis of colorectal cancer (CRC), but they are not yet adequate for risk assessment. We aimed to identify a molecular signature that can reliably identify CRC patients at high risk for recurrence.

Results

Two hundred eighty-one CRC samples (stage II/III) were included in this study. A two-step gene expression profiling study was conducted. First, gene expression measurements from 81 fresh frozen CRC samples were obtained using Affymetrix Human Genome U133 Plus 2.0 Arrays. Second, a focused gene expression assay, including prognostic genes and genes of interest from literature reviews, was performed using 200 fresh frozen samples and a Taqman low-density array (TLDA) analysis. An optimal 31-gene expression classifier for the prediction of recurrence among patients with stage II/III CRC was developed using logistic regression analysis. This gene expression signature classified 58.5% of patients as low-risk and 41.5% as high-risk (P < 0.001). The signature was the strongest independent prognostic factor in the multivariate analysis. The five-year relapse-free survival (RFS) rates for the low-risk patients and the high-risk patients were 88.5% and 41.3% (P < 0.001), respectively.

Conclusion

We identified a 31-gene expression signature that is closely associated with the clinical outcome of stage II/III CRC patients.

SUBMITTER: Wang L

PROVIDER: S-EPMC4320628 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

A molecular signature for the prediction of recurrence in colorectal cancer.

Wang Lisha L Shen Xiaohan X Wang Zhimin Z Xiao Xiuying X Wei Ping P Wang Qifeng Q Ren Fei F Wang Yiqin Y Liu Zebing Z Sheng Weiqi W Huang Wei W Zhou Xiaoyan X Du Xiang X

Molecular cancer 20150203

<h4>Background</h4>Several clinical and pathological factors have an impact on the prognosis of colorectal cancer (CRC), but they are not yet adequate for risk assessment. We aimed to identify a molecular signature that can reliably identify CRC patients at high risk for recurrence.<h4>Results</h4>Two hundred eighty-one CRC samples (stage II/III) were included in this study. A two-step gene expression profiling study was conducted. First, gene expression measurements from 81 fresh frozen CRC sam ...[more]

PMID: 25645394

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Project description:BackgroundColorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for identifying high-risk patients with the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation, and their expression is frequently dysregulated in several cancers, including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored, which is the primary aim of this study.ResultsWe performed a systematic analysis of eRNA expression profiles in colon cancer (CC) and rectal cancer (RC) patients from the TCGA dataset. By using rigorous biomarker discovery approaches by splitting the entire dataset into a training and testing cohort, we identified a 22-eRNA panel in CC and a 19-eRNA panel in RC for predicting tumor recurrence. The Kaplan-Meier analysis showed that biomarker panels robustly stratified low and high-risk CC (p = 7.29 × 10-5) and RC (p = 6.81 × 10-3) patients with recurrence. Multivariate and LASSO Cox regression models indicated that both biomarker panels were independent predictors of recurrence and significantly superior to TNM staging in CC (HR = 11.89, p = 9.54 × 10-4) and RC (HR = 3.91, p = 3.52 × 10-2). Notably, the ROC curves demonstrated that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.833; 95% CI: 0.74-0.93) and RC (AUC = 0.834; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature that included the eRNA panels and TNM staging achieved an even greater predictive accuracy in patients with CC (AUC = 0.85).ConclusionsHerein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Further experimental validation in independent clinical cohorts, these biomarkers can potentially improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy.

Project description:BackgroundA major challenge in stage II colorectal carcinoma is to identify patients with increased risk of recurrence. Biomarkers that distinguish patients with poor prognosis from patients without recurrence are currently lacking. This study aims to develop a robust DNA methylation classifier that allows the prediction of recurrence and chemotherapy benefit in patients with stage II colorectal cancer. We performed a genome-wide DNA methylation capture sequencing in 243 stage II colorectal carcinoma samples and identified a relapse-specific DNA methylation signature consisting of eight CpG sites.MethodsTwo hundred and forty-three patients with stage II CRC were enrolled in this study. In order to select differential methylation sites among recurrence and non-recurrence stage II CRC samples, DNA methylation profiles of 62 tumour samples including 31 recurrence and 31 nonrecurrence samples were analysed using the Agilent SureSelectXT Human Methyl-Seq, a comprehensive target enrichment system to analyse CpG methylation. Pyrosequencing was applied to quantify the methylation level of candidate DNA methylation sites in 243 patients. Least absolute shrinkage and selection operator (LASSO) method was employed to build the disease recurrence prediction classifier.ResultsWe identified a relapse-related DNA methylation signature consisting of eight CpG sites in stage II CRC by DNA methylation capture sequencing. The classifier showed significantly higher prognostic accuracy than any clinicopathological risk factors. The Kaplan-Meier survival curve showed an association of high-risk score with poor prognosis. In multivariate analysis, the signature was the most significant prognosis factor, with an HR of 2.80 (95% CI, 1.71-4.58, P < 0.001). The signature could identify patients who are suitable candidates for adjuvant chemotherapy.ConclusionsAn eight-CpG DNA methylation signature is a reliable prognostic and predictive tool for disease recurrence in patients with stage II CRC.

Project description:Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

Project description:Purpose: The current tumor-node-metastasis (TNM) staging system is inadequate at identifying patients with high-risk colorectal cancer. Using a systematic and comprehensive biomarker discovery and validation approach, we aimed to identify an miRNA recurrence classifier (MRC) that can improve upon the current TNM staging as well as is superior to currently offered molecular assays.Experimental Design: Three independent genome-wide miRNA expression profiling datasets were used for biomarker discovery (N = 158) and in silico validation (N = 109 and N = 40) to identify an miRNA signature for predicting tumor recurrence in patients with colorectal cancer. Subsequently, this signature was analytically trained and validated in retrospectively collected independent patient cohorts of fresh-frozen (N = 127, cohort 1) and formalin-fixed paraffin-embedded (FFPE; N = 165, cohort 2 and N = 139, cohort 3) specimens.Results: We identified an 8-miRNA signature that significantly predicted recurrence-free interval (RFI) in the discovery (P = 0.002) and two independent publicly available datasets (P = 0.00006 and P = 0.002). The RT-PCR-based validation in independent clinical cohorts revealed that MRC-derived high-risk patients succumb to significantly poor RFI in patients with stage II and III colorectal cancer [cohort 1: hazard ratio (HR), 3.44 (1.56-7.45), P = 0.001; cohort 2: HR, 6.15 (3.33-11.35), P = 0.001; and cohort 3: HR, 4.23 (2.26-7.92), P = 0.0003]. In multivariate analyses, MRC emerged as an independent predictor of tumor recurrence and achieved superior predictive accuracy over the currently available molecular assays. The RT-PCR-based MRC risk score = (-0.1218 × miR-744) + (-3.7142 × miR-429) + (-2.2051 × miR-362) + (3.0564 × miR-200b) + (2.4997 × miR-191) + (-0.0065 × miR-30c2) + (2.2224 × miR-30b) + (-1.1162 × miR-33a).Conclusions: This novel MRC is superior to currently used clinicopathologic features, as well as National Comprehensive Cancer Network (NCCN) criteria, and works regardless of adjuvant chemotherapy status in identifying patients with high-risk stage II and III colorectal cancer. This can be readily deployed in clinical practice with FFPE specimens for decision-making pending further model testing and validation. Clin Cancer Res; 24(16); 3867-77. ©2018 AACRSee related commentary by Rodriguez et al., p. 3787.

Dataset Information

A molecular signature for the prediction of recurrence in colorectal cancer.

Background

Results

Conclusion

Publications

A molecular signature for the prediction of recurrence in colorectal cancer.

Similar Datasets

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