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27 September 2019 : Clinical Research  

Novel Multiple miRNA-Based Signatures for Predicting Overall Survival and Recurrence-Free Survival of Colorectal Cancer Patients

Jinrong Qian1ABEF, Lifeng Zeng2ABC*, Xiaohua Jiang3ADF, Zhiyong Zhang2DF, Xiaojiang Luo3AEF

DOI: 10.12659/MSM.916948

Med Sci Monit 2019; 25:7258-7271


BACKGROUND: Colorectal cancer (CRC) has become a heavy health burden around the world, accounting for about 10% of newly diagnosed cancer cases. In the present study, we aimed to establish the miRNA-based prediction signature to assess the prognosis of CRC patients.

MATERIAL AND METHODS: A total of 451 CRC patients’ expression profiles and clinical information were download from the TCGA database. LASSO Cox regression was conducted to construct the overall survival (OS)- and recurrence-free survival (RFS)-associated prediction signatures, by which CRC patients were divided into low- and high-risk groups. Kaplan-Meier (K-M) curve and receiver operating characteristic (ROC) curves were used to explore the discriminatory ability and stability of the signatures. Functional enrichment analyses were performed to identify the probable mechanisms.

RESULTS: miRNA-216a, miRNA-887, miRNA-376b, and miRNA-891a were used to build the prediction formula associated with OS, while miR-1343, miR-149, miR-181a-1, miR-217, miR-3130-1, miR-378a, miR-542, miR-6716, miR-7-3, miR-7702, miR-677, and miR-891a were obtained to construct the formula related to RFS. K-M curve and ROC curve revealed the good discrimination and efficiency of OS in the training (P<0.001, AUC=0.712) and validation cohorts (P=0.019, AUC=0.657), as well as the results of RFS in the training (P<0.001, AUC=0.714) and validation cohorts (P=0.042, AUC=0.651). The function annotations for the targeted genes of these miRNAs show the potential mechanisms of CRC.

CONCLUSIONS: We established 2 novel miRNA-based prediction signatures of OS and RFS, which are reliable tools to assess the prognosis of CRC patients.

Keywords: Colorectal Neoplasms

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750