Abstract

Background: Recurrence is a major cause of cancer-related deaths in colorectal cancer (CRC) patients, but the current strategies are limited to predict this clinical behavior. Our aim is to develop a recurrence prediction model based on long non-coding RNAs (lncRNAs) in exosomes of serum to improve the prediction accuracy. Methods: High-throughput lncRNAs microarray and reverse transcription quantitative real-time PCR were used to identify the recurrence-associated lncRNAs in 30 matched CRC tissues and sera. The recurrence prediction model was built with multivariable Cox analysis in 150-patient training set, and evaluated in an independent 203-patient test set using ROC, Kaplan-Meier, and COX analysis. Findings: In discovery phase, 11 lncRNAs were found to be associated with CRC recurrence in tissues, and 9 of them were correlated with their expression levels of serum exosomes. In training phase, a model based on 5-exosomal lncRNAs (exolncRNAs) panel was constructed, and showed high distinguish capability for recurrent CRC patients. ROC showed the panel was superior to serum CEA and CA19-9 in prediction of CRC recurrence. In both training and test sets, high-risk patients defined by the 5-exolncRNAs panel had poor recurrence free and overall survival. And, it was an independent factor for CRC prognosis. Interpretation: The 5-exolncRNAs panel robustly stratifies CRC patients' risk of recurrence, enabling more accurate prediction of prognosis. Funding Statement: Jinan Science and Technology Development Plan (201805003, 201805061), Shandong Medical and Health Technology Development Project (2018WSB20002), Shandong Key Research and Development Program (2018GSF118104), and National Natural Science Foundation of China (81301506). Declaration of Interests: The authors declared: There are no relevant conflicts of interest. Ethics Approval Statement: This study was approved by the Ethics Committee of Shandong Provincial Third Hospital and Qilu Hospital of Shandong University, and written informed consent was obtained from each patient.

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