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Construction and Validation of a Novel Eight-Gene Risk Signature to Predict the Progression and Prognosis of Bladder Cancer

Authors :
Ji Liu
Ruiliang Wang
Shiyu Mao
Cheng Li
Shenghua Liu
Wentao Zhang
Zongtai Zheng
Xudong Yao
Source :
Frontiers in Oncology, Frontiers in Oncology, Vol 11 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

The progression from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) increases the risk of death. It is therefore important to find new relevant molecular models that will allow for effective prediction of the progression and prognosis of bladder cancer (BC). Using RNA-Sequence data of 49 BC patients in Shanghai tenth people’s hospital (STPH) and weighted gene co-expression network analysis methods, a co-expression network of genes was developed and three key modules associated with malignant progression were selected. Based on the genes in three key modules, an eight-gene risk signature was established using univariate Cox regression and the Least absolute shrinkage and selection operator Cox model in The Cancer Genome Atlas Program (TCGA) and validated in validation sets. Subsequently, a nomogram based on the risk signature was constructed for prognostic prediction. The mRNA and protein expression levels of eight genes in cell lines and tissues were further investigated. The novel eight-gene risk signature was closely related to the malignant clinical features of BC and could predict the prognosis of patients in the training dataset (TCGA) and four validation sets (GSE32894, GSE13507, IMvigor210 trial, and STPH). The nomogram showed good prognostic prediction and calibration. The mRNA and protein expression levels of the eight genes were differentially expressed in cell lines and tissues. In our study, we established a novel eight-gene risk signature that could predict the progression and prognoses of BC patients.

Details

Language :
English
ISSN :
2234943X
Volume :
11
Database :
OpenAIRE
Journal :
Frontiers in Oncology
Accession number :
edsair.doi.dedup.....10cb6ff5af1a160e6ba5155855d12b4a