An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma

Background Bladder cancer is one of the most common cancers, and its histopathological type is mainly bladder urothelial carcinoma, accounting for about 90%. The prognostic biomarkers of bladder cancer are classified into clinical features biomarkers and molecular biomarkers. Nevertheless, due to th...

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Main Authors: Rui Zhu, Xin Yang, Wenna Guo, Xin-Jian Xu, Liucun Zhu
Format: Article
Language:English
Published: PeerJ Inc. 2019-10-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/7836.pdf
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author Rui Zhu
Xin Yang
Wenna Guo
Xin-Jian Xu
Liucun Zhu
author_facet Rui Zhu
Xin Yang
Wenna Guo
Xin-Jian Xu
Liucun Zhu
author_sort Rui Zhu
collection DOAJ
description Background Bladder cancer is one of the most common cancers, and its histopathological type is mainly bladder urothelial carcinoma, accounting for about 90%. The prognostic biomarkers of bladder cancer are classified into clinical features biomarkers and molecular biomarkers. Nevertheless, due to the existence of individual specificity, patients with similar pathological characteristics still have great differences in the risk of disease recurrence. Therefore, it is often inaccurate to predict the survival status of patients based on clinical characteristic biomarkers, and a prognostic molecular biomarker that can grade the risk of bladder cancer patients is needed. Methods A total of three bladder urothelial carcinoma datasets were used in this study from the Cancer Genome Atlas database and Gene Expression Omnibus database. In order to avoid overfitting, all samples were randomly divided into one training set and three validation sets, which were used to construct and test the prognostic biomarker model of bladder urothelial carcinoma. Univariate and multivariate Cox regression were used to screen candidate mRNAs and construct prognostic biomarkers model. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the model. Results A prognostic biomarker model of bladder urothelial carcinoma combining with eight mRNA was constructed. Kaplan–Meier analyses indicated that a significant difference in the survival time of patients between the high-risk and the low-risk group. The area under the ROC curve were 0.632 (95% confidence interval (CI) [0.541–0.723]), 0.693 (95% CI [0.601–0.784]) and 0.686 (95% CI [0.540–0.831]) when the model was used to predict the patient’s survival time in three validation datasets. The model showed high accuracy and applicability.
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spelling doaj.art-21e00361d2bc469ebbc74575b9af72aa2023-12-03T10:33:37ZengPeerJ Inc.PeerJ2167-83592019-10-017e783610.7717/peerj.7836An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinomaRui Zhu0Xin Yang1Wenna Guo2Xin-Jian Xu3Liucun Zhu4Department of Mathematics, Shanghai University, Shanghai, ChinaSchool of Life Sciences, Shanghai University, Shanghai, ChinaSchool of Life Sciences, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Mathematics, Shanghai University, Shanghai, ChinaSchool of Life Sciences, Shanghai University, Shanghai, ChinaBackground Bladder cancer is one of the most common cancers, and its histopathological type is mainly bladder urothelial carcinoma, accounting for about 90%. The prognostic biomarkers of bladder cancer are classified into clinical features biomarkers and molecular biomarkers. Nevertheless, due to the existence of individual specificity, patients with similar pathological characteristics still have great differences in the risk of disease recurrence. Therefore, it is often inaccurate to predict the survival status of patients based on clinical characteristic biomarkers, and a prognostic molecular biomarker that can grade the risk of bladder cancer patients is needed. Methods A total of three bladder urothelial carcinoma datasets were used in this study from the Cancer Genome Atlas database and Gene Expression Omnibus database. In order to avoid overfitting, all samples were randomly divided into one training set and three validation sets, which were used to construct and test the prognostic biomarker model of bladder urothelial carcinoma. Univariate and multivariate Cox regression were used to screen candidate mRNAs and construct prognostic biomarkers model. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the model. Results A prognostic biomarker model of bladder urothelial carcinoma combining with eight mRNA was constructed. Kaplan–Meier analyses indicated that a significant difference in the survival time of patients between the high-risk and the low-risk group. The area under the ROC curve were 0.632 (95% confidence interval (CI) [0.541–0.723]), 0.693 (95% CI [0.601–0.784]) and 0.686 (95% CI [0.540–0.831]) when the model was used to predict the patient’s survival time in three validation datasets. The model showed high accuracy and applicability.https://peerj.com/articles/7836.pdfBladder urothelial carcinomaPrognosisBiomarkerRisk-stratification
spellingShingle Rui Zhu
Xin Yang
Wenna Guo
Xin-Jian Xu
Liucun Zhu
An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma
PeerJ
Bladder urothelial carcinoma
Prognosis
Biomarker
Risk-stratification
title An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma
title_full An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma
title_fullStr An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma
title_full_unstemmed An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma
title_short An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma
title_sort eight mrna signature predicts the prognosis of patients with bladder urothelial carcinoma
topic Bladder urothelial carcinoma
Prognosis
Biomarker
Risk-stratification
url https://peerj.com/articles/7836.pdf
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