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 in situ hybridization. Additionally, the protein expression was 
detected by Western blot.
RESULTS: A prognostic prediction model of CRC was built based on nine ARlncRNAs 
(NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, 
LINC00957, and CD27-AS1). 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. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal 
colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. 
Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with 
NCM460. After autophagy activation, LINCC00174 expression was significantly 
downregulated both in NCM460 and HT29, while NKILA 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.