Abstract
PURPOSE: The purpose of this study was to identify autophagy-associated long noncoding RNAs (ARlncRNAs) using the kidney renal clear cell carcinoma (KIRC) patient data from The Cancer Genome Atlas (TCGA) database and to construct a prognostic risk-related ARlncRNAs signature to accurately predict the prognosis of KIRC patients. METHODS: The KIRC patient data were originated from TCGA database and were classified into a training set and testing set. Seven prognostic risk-related ARlncRNAs, identified using univariate, lasso, and multivariate Cox regression analysis, were used to construct prognostic risk-related signatures. Kaplan-Meier curves and receiver operating characteristic (ROC) curves as well as independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate and verify the specificity and sensitivity of the signature in training set and testing set, respectively. Two nomograms were established to predict the probable 1-, 3-, and 5-year survival of the KIRC patients. In addition, the lncRNA-mRNA co-expression network was constructed and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify biological functions of ARlncRNAs. RESULTS: We constructed and verified a prognostic risk-related ARlncRNAs signature in training set and testing set, respectively. We found the survival time of KIRC patients with low-risk scores was significantly better than those with high-risk scores in training set and testing set. ROC curves suggested that the area under the ROC (AUC) value for prognostic risk score signature was 0.81 in training set and 0.705 in testing set. And AUC values corresponding to 1-, 3-, and 5 years of OS were 0.809, 0.753, and 0.794 in training set and 0.698, 0.682, and 0.754 in testing set, respectively. We established the two nomograms that confirmed high C-index and accomplished good prediction accuracy. CONCLUSIONS: We constructed a prognostic risk-related ARlncRNAs signature that could accurately predict the prognosis of KIRC patients.