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Construction and validation of an autophagy-related long non-coding RNA signature to predict the prognosis of kidney renal papillary cell carcinoma


To identify the autophagy-related long non-coding RNAs (ARlncRNAs) associated with the prognosis of kidney renal papillary cell carcinoma (KIRP), thereby establishing a clinical prognostic model. The gene expression matrix and clinical survival information of patients with KIRP were downloaded from The Cancer Genome Atlas database, and were divided into the training and testing groups. ARlncRNAs associated with the KIRP prognosis were analyzed by univariate, Least Absolute Shrinkage and Selection Operator (LASSO(, and multivariate Cox regression to construct a signature. We combined clinical factors associated with the prognosis with ARlncRNAs to establish a prognostic model of patients with KIRP. A nomogram was established to predict 1-year, 3-year, and 5-year survival of patients with KIRP. Besides, we built the lncRNA-messenger RNA co-expression network and used Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis to detect the biological functions of ARlncRNAs. LEF1-AS1, CU634019.6, C2orf48, AC027228.2, and AC107464.3 were identified. A prognosis-related ARlncRNAs signature was constructed in the training group and validated in the testing group. Patients with KIRP with a low risk score had significantly longer survival time than those with a high risk score. The risk score significantly affected the prognosis of patients, thereby being used for modeling. The area under the receiver operating characteristic curve values of 1-year, 3-year, and 5-year overall survival were 0.80, 0.78, and 0.84 in the training group, respectively. The signature had high concordance index and good accuracy in predicting the prognosis, which were confirmed by the nomogram. The prognosis-related ARlncRNAs signature we identified had a more accurate prediction for the prognosis of patients with KIRP.

  • autophagy-related long noncoding RNAs
  • ARlncRNAs
  • kidney renal papillary cell carcinoma
  • KIRP
  • prognostic model
  • bioinformatics

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