Research Papers:

An integrated lncRNA, microRNA and mRNA signature to improve prognosis prediction of colorectal cancer

Yongfu Xiong, Rong Wang, Linglong Peng, Wenxian You, Jinlai Wei, Shouru Zhang, Xingye Wu, Jinbao Guo, Jun Xu, Zhenbing Lv and Zhongxue Fu _

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Oncotarget. 2017; 8:85463-85478. https://doi.org/10.18632/oncotarget.20013

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Yongfu Xiong1,*, Rong Wang1,*, Linglong Peng1,*, Wenxian You1, Jinlai Wei1, Shouru Zhang1, Xingye Wu1, Jinbao Guo1, Jun Xu1, Zhenbing Lv1 and Zhongxue Fu1

1Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

*These authors contributed equally to this work

Correspondence to:

Zhongxue Fu, email: [email protected]

Keywords: colon cancer, multi-RNA-based classifier, prognosis, TNM stage

Received: April 04, 2017    Accepted: June 28, 2017    Published: August 07, 2017


Although the outcome of patients with colorectal cancer (CRC) has improved significantly, prognosis evaluation still presents challenges due to the disease heterogeneity. Increasing evidences revealed the close correlation between aberrant expression of certain RNAs and the prognosis. We envisioned that combined multiple types of RNAs into a single classifier could improve postoperative risk classification and add prognostic value to the current stage system. Firstly, differentially expressed RNAs including mRNAs, miRNAs and lncRNAs were identified by two different algorithms. Then survival and LASSO analysis was conducted to screen survival-related DERs and build a multi-RNA-based classifier for CRC patient stratification. The prognostic value of the classifier was self-validated in the TCGA CRC cohort and further validated in an external independent set. Finally, survival receiver operating characteristic analysis was used to assess the performance of prognostic prediction. We found that the multi-RNA-based classifier consisted by 12 mRNAs, 1miRNA and 1 lncRNA, which could divide the patients into high and low risk groups with significantly different overall survival (training set: HR 2.54, 95%CI 1.67-3.87, p<0.0001; internal testing set: HR 2.54, 95%CI 1.67-3.87, p<0.0001; validation set: HR 5.02, 95% CI 2.2–11.6; p=0·0002). In addition, the classifier is not only independent of clinical features but also with a similar prognostic ability to the well-established TNM stage (AUC of ROC 0.83 versus 0.74, 95% CI = 0.608-0.824, P =0.0878). Furthermore, combination of the multi-RNA-based classifier with clinical features was a more powerful predictor of prognosis than either of the two parameters alone. In conclusion, the multi-RNA-based classifier may have important clinical implications in the selection of patients with CRC who are at high risk of mortality and add prognostic value to the current stage system.

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