Eight-gene risk score predicts progression and prognosis in bladder cancer

Abstract Background: 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).Methods: Using RNA-Sequence data of 49 BC patients in our center 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 score 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 score was constructed for prognostic prediction. The mRNA and protein expression levels of eight genes in cell lines and tissues were further investigated.Results: A novel eight-gene risk score was closely related to the malignant clinical features of BC and could predict the prognosis of patients in the training dataset (TCGA) and three validation sets (GSE3289 , GSE13507 and IMvigor210 trial). The nomogram showed good prognostic prediction and calibration. The mRNA and protein expression level of the eight genes were differentially expressed in cell lines and tissues.Conclusions: In our study, we established a novel eight-gene risk score which could predict the progression and prognoses of BC patients.

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

Frontiers in Oncology ◽

10.3389/fonc.2021.632459 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ruiliang Wang ◽

Zongtai Zheng ◽

Shiyu Mao ◽

Wentao Zhang ◽

Ji Liu ◽

...

Keyword(s):

Bladder Cancer ◽

Protein Expression ◽

Cell Lines ◽

Invasive Bladder Cancer ◽

Muscle Invasive Bladder Cancer ◽

Expression Levels ◽

Risk Of Death ◽

Mrna And Protein Expression ◽

Muscle Invasive ◽

Prognostic Prediction

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.

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Construction and validation of a novel eight-gene risk score to predict the progression and prognosis of bladder cancer

10.21203/rs.3.rs-120263/v2 ◽

2021 ◽

Author(s):

Ruiliang Wang ◽

Zongtai Zheng ◽

Shiyu Mao ◽

Wentao Zhang ◽

Ji Liu ◽

...

Keyword(s):

Bladder Cancer ◽

Risk Score ◽

Cox Regression ◽

Cox Model ◽

The Cancer Genome Atlas ◽

Invasive Bladder Cancer ◽

Training Dataset ◽

Muscle Invasive Bladder Cancer ◽

Risk Of Death ◽

Muscle Invasive

Abstract Background: 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 of bladder cancer (BC).Methods: Using RNA-Sequence data of 49 BC patients in our center and weighted gene co-expression network analysis methods, a co-expression network of genes was developed from which three key modules with prognostic value were selected using Univariate Cox regression in The Cancer Genome Atlas Program (TCGA). Subsequently, an eight-gene risk score was established using the Least absolute shrinkage and selection operator Cox model. Results: A novel eight-gene risk score was closely related to the malignant clinical features of BC and could predict the prognosis of patients in the training dataset (TCGA) and two validation sets (GSE3289 and GSE13507). Further, a nomogram for predicting the overall survival of patients was designed. The nomogram showed good calibration with clinical value through decision curve analysis. Lastly, we found that the mRNA and protein expression level of the eight genes were found to be differentially expressed in cell lines and tissue.Conclusions: In our study, we established a novel eight-gene risk score which could predict the progression and prognoses of BC patients.

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A stemness-related seven-gene signature for predicting prognosis and treatment response in muscle invasive bladder cancer

10.21203/rs.3.rs-25781/v1 ◽

2020 ◽

Author(s):

Maolang Tian ◽

Jinlan He ◽

Jiaqi Han ◽

Hong Zhu

Keyword(s):

Bladder Cancer ◽

High Risk ◽

Clinical Outcome ◽

Treatment Response ◽

Risk Score ◽

Gene Signature ◽

Invasive Bladder Cancer ◽

Cancer Stemness ◽

Muscle Invasive Bladder Cancer ◽

Muscle Invasive

Abstract Background: Muscle invasive bladder cancer (MIBC) is an aggressive cancer characterized by therapeutic resistance and poor prognosis, which are possibly due to the existence of cancer stem cells (CSCs). In this study, we aimed to characterize the expression of cancer stemness-related genes and develop a multi-gene risk signature to predict clinical outcome and treatment response in MIBC.Methods: The mRNA expression data and clinical data of MIBC patients were collected from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, which included the TCGA training cohort (n = 333) and three GEO validation cohorts, GSE13507 (n = 165), GSE32548 (n = 127), and GSE48075 (n = 72). A list of 166 stemness-related genes were obtained from the Cancer Single Cell State Atlas (CancerSEA) database and prognostic genes for overall survival (OS) were identified by univariate Cox analysis. Then, the least absolute shrinkage and selection operator (LASSO) regression and stepwise multivariate Cox regression were performed to generate a multi-gene risk signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, multivariate analysis, and stratification analysis were used to evaluate the performance of the gene signature. We also explored the relationship between risk score and response to chemotherapy and radiotherapy in MIBC patients. Moreover, independent prognostic factors for OS were combined together into a nomogram to improve predictive performance.Results: Firstly, a total of 25 prognostic genes were identified. Then, a seven-gene risk signature (EGFR, FOXA2, HES1, MME, RBM6, SMOC2, and TFRC) was constructed and it could robustly classify MIBC patients into high -risk and low-risk groups with different clinical outcomes. ROC curves showed that the seven-gene signature had a robust predictive accuracy in four cohorts. Besides, high risk score was significantly associated with advanced clinical stage and treatment failure. As an independent risk factor for OS, the stemness-related seven-gene signature could achieve better prognostic accuracy when integrated with clinical factors. Conclusions: We developed and validated a robust stemness-related gene signature which could robustly predicate clinical outcome and shed light on the cancer stemness in bladder cancer.

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