Abstract
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. In discovery phase, 11 lncRNAs were found to be associated with CRC recurrence in tissues using high-throughput lncRNAs microarray and reverse transcription quantitative real-time PCR. 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, COX model showed it was an independent factor for CRC prognosis. Moreover, there was a significant relationship in detection of 5-exolncRNAs between plasma samples and paired serum samples. In summary, the 5-exolncRNAs panel robustly stratifies CRC patients' risk of recurrence, enabling more accurate prediction of prognosis.