A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs.

INTRODUCTION

Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor-node-metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment.

METHODS

CRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence hybridization. Additionally, the protein expression was detected by Western blot.

RESULTS

A prognostic prediction model of CRC was built based on nine ARlncRNAs (, , , , , , , , and ). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. and were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, and were overexpressed in HT29 compared with NCM460. After autophagy activation, expression was significantly downregulated both in NCM460 and HT29, while expression was significantly increased.

CONCLUSION

The new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.