Construction of a prognostic model of bladder cancer using cuproptosis-associated long non-coding RNA based on The Cancer Genome Atlas database
10.3760/cma.j.cn115355-20230423-00206
- VernacularTitle:基于癌症基因组图谱数据库构建膀胱癌铜死亡相关长链非编码RNA预后模型
- Author:
Ming ZHOU
1
;
Yifan LI
;
Yuyong SHEN
Author Information
1. 扬州大学附属医院泌尿外科，扬州 225000
- Keywords:
Urinary bladder neoplasms;
Cuproptosis;
RNA, long non-coding;
Prognosis
- From:
Cancer Research and Clinic
2023;35(11):808-814
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a prognostic risk model of bladder cancer using cuproptosis-associated long non-coding RNA (lncRNA) and test its predictive efficacy.Methods:RNA expression sequencing data and clinical data of corresponding samples were downloaded from The Cancer Gene Atlas (TCGA) database. The 17 key genes associated with cuproptosis was obtained from the published literature, and then lncRNA of the key genes associated with cuproptosis was screened by correlation analysis based on the lncRNA data from TCGA database. The cuproptosis lncRNA associated with the prognosis of bladder cancer patients were screened by using Cox regression and Lasso regression. A total of 403 bladder cancer patients with complete clinical information screened from TCGA database were divided into a training set (203 cases) and a test set (200 cases), and the prognostic risk prediction model was constructed based on the samples in the training set and the above key independent prognosis-related cuproptosis lncRNA. According to the median value of the risk score, patients in all the datasets, the test set and the training set of bladder cancer screened from TCGA database were divided into high-risk group and low-risk group, and R language survival package was applied to compare the differences in overall survival between the two groups in each dataset. The predictive effect of the model was verified using principal component analysis (PCA) and receiver operating characteristic (ROC) curve. Univariate and multivariate Cox regression analysis were used to analyze the factors affecting overall survival of 403 bladder cancer patients, and ROC curve was used to analyze the efficacy of each factor for predicting the prognosis of bladder cancer.Results:After screening, a total of 4 cuproptosis lncRNA with independent prognostic significance were included (AC104564.3, LINC00649, AL136084.3 and AL136295.2), and the prognostic model constructed based on these 4 lncRNA was as follows: risk score = -0.713 42×AC104564.3-0.744 94×LINC00649+0.410 93×AL136084.3-0.736 89×AL136295.2. Survival analysis showed that the overall survival of the high-risk group in all datasets, the test set and the training set was poorer than that of the low-risk group (all P < 0.05), suggesting that a high risk score predicted poor prognosis. ROC curve analysis showed that the areas under the curve of applying the risk prediction model to predict 1-, 3- and 5-year overall survival of all 403 patients in TCGA database were 0.665, 0.629 and 0.692. Multivariate Cox regression analysis showed that age (≥ 65 years old vs. < 65 years old: OR = 1.027, 95% CI 1.011-1.044, P < 0.001), stage (stage Ⅳ vs. stage Ⅲ vs. stage Ⅱ vs. stage Ⅰ vs. unknown stage: OR = 1.593, 95% CI 1.308-1.939, P < 0.001) and risk score (high vs. low: OR = 1.258, 95% CI 1.126-1.406, P < 0.001) were the independent influencing factors of patients' overall survival. ROC curve analysis showed that the areas under the curve of age, stage and risk score for predicting the patients' 5-year overall survival were 0.614, 0.685 and 0.692, suggesting that the risk prediction model had better predictive efficacy. Conclusions:A prognosis risk prediction model for bladder cancer patients is constructed based on 4 lncRNA associated with cuproptosis, and the model is internally validated to have a high predictive efficacy.
