A robust gene signature for the prediction of early relapse in stage I–III colon cancer

Dai, Weixing; Li, Yaqi; Mo, Shaobo; Feng, Yang; Zhang, Long; Xu, Ye; Li, Qingguo; Cai, Guoxiang

doi:10.1002/1878-0261.12175

Cited by 48 publications

(49 citation statements)

References 48 publications

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“…The risk score in the 13-gene signature and 15-gene signature was calculated according to the coefficients provided by the primary studies. 8 , 9 In ROC curve analysis, our signature showed a better predictive ability (AUC at 5 years: 0.724) than the 13-gene signature, 15-signature, and TNM stage (AUC at 5 years: 0.575, 0.509 and 0.703, respectively) ( Figure 5 ). Then, we calculated the C-indices according to minimum follow-up duration.…”

Section: Resultsmentioning

confidence: 89%

“…Several studies have also proposed gene-expression-based prognostic signatures in CRC. 8 – 11 However, those signatures are limited in sample size and sufficient cross-validation. In this study, we adopted a method based on relative ranking of gene expression levels, which could integrate samples from different datasets without the need for eliminating batch effects.…”

Section: Introductionmentioning

confidence: 99%

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A novel gene-pair signature for relapse-free survival prediction in colon cancer

Chen

Wang

Zhang

et al. 2018

CMAR

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BackgroundColon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC.MethodsFour microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients.ResultsThe risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615–0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870–4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627–0.766; HR: 2.926, 95% CI: 1.892–4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P<0.0001; P=0.0004; P=0.0125), especially for the patients with a longer follow-up (R2=0.988, P<0.0001). When the follow-up was >5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816–0.922; HR: 13.55, 95% CI: 7.409–24.78).ConclusionOur study identified a novel gene-pair signature for prediction of RFS in CC.

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Section: Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A novel gene-pair signature for relapse-free survival prediction in colon cancer

Chen

Wang

Zhang

et al. 2018

CMAR

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“…In recent years, research on combined biomarkers has also been emerging. Zhang Z et al [21] indicated a set of circRNAs that may serve as a candidate diagnostic biomarker, Dai W et al [22] revealed an integrated mRNA-lncRNA signature with predictive value of early relapse in colon cancer, Dai W et al [23] discovered a gene signature for the prediction of early relapse in stage I-III colon cancer and researcher identified a 14-lncRNA prognostic signature for patients with colon adenocarcinoma [24].…”

Section: Discussionmentioning

confidence: 99%

A Combined four-mRNA Signature Associated with Lymphatic Metastasis for Prognosis of Colorectal Cancer

Qiang

Zhao

et al. 2020

J. Cancer

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Background: Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Lymph node metastasis (LNM) is a common mode of metastasis of CRC. However, the combined mRNA biomarkers associated with LNM of CRC that can effectively predict CRC prognosis have not been reported yet. Methods: To identify biomarkers that are associated with LNM, we collected data from the The Cancer Genome Atlas (TCGA) database. The edgeR package was searched to seek LNM-related genes by comparisons between cancer samples and normal colorectal tissues and between LNM and non-LNM (NLNM) of CRC. Univariate and multivariate regression analysis of genes in the intersection to build gene signature associated with independent prognosis of CRC, and then verified by Kaplan-Meier curve and log-rank test, receiver operating characteristic (ROC) curve was used to determine the efficiency of survival prediction of our four-mRNA signature. Finally, the potential molecular mechanisms and properties of these gene signature were also explored with functional and pathway enrichment analysis. Results: 329 mRNAs were up-regulated in CRC tissues with LNM, and 8461 mRNAs were up-regulated in CRC tissues, the intersection is 100 mRNAs. After univariate and multivariate Cox regression analysis of 100 mRNAs, a novel four LNM related mRNAs (EPHA8, KRT85, GABRA3, and CLPSL1) were screened as independent prognostic indicators of CRC. Surprisingly, the four-mRNA signature can predict the prognosis of CRC patients independently of clinical factors andthe area under the curve (AUC) of the ROC is 0.730. The novel four-mRNA signature was used to identify high and low-risk groups. Stratified analysis indicated the risk score based on four-mRNA signature was an independent prognostic indicator for female, T3+T4, N1+N2 ,stage III+IV and patients with no new tumor event. Functional annotation of this risk model in high-risk patients revealed that pathways associated with neuroactive ligand-receptor interaction, estrogen signaling pathway, and steroid hormone biosynthesis. Conclusions: By conducting TCGA data mining, our study demonstrated that a four-mRNA signature associated with LNM can be used as a combined biomarker for independent prognosis of CRC.

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“…An array‐based database to identify survival‐associated mRNAs for prognostic prediction is significant and urgently needed to guide tailored therapy for patients with BC . In our study, we used GEO array data to screen differential mRNAs in a discovery set and selected 56 significant mRNAs according to the likelihood of patients with distant metastasis.…”

Section: Discussionmentioning

confidence: 99%

“…An array-based database to identify survival-associated mRNAs for prognostic prediction is significant and urgently needed to guide tailored therapy for patients with BC. 20,21,23 In our study, we used GEO array data to screen differential mRNAs in a discovery set and selected 56 significant mRNAs according to the likelihood of patients Our study results show that the evaluation of four mRNAs expression could be a vital tool for the management of BC patients, which can guide the stratification of patients that may recur or metastasis, aiding in decision making for tailored therapy, and then ultimately contributing to an improvement in survival rates.…”

Section: Discussionmentioning

confidence: 99%

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Xie

Wang

Shi

et al. 2018

J Cellular Molecular Medi

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Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.

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A robust gene signature for the prediction of early relapse in stage I–III colon cancer

Cited by 48 publications

References 48 publications

A novel gene-pair signature for relapse-free survival prediction in colon cancer

A novel gene-pair signature for relapse-free survival prediction in colon cancer

A Combined four-mRNA Signature Associated with Lymphatic Metastasis for Prognosis of Colorectal Cancer

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

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