A risk score staging system based on the expression of seven genes predicts the outcome of bladder cancer

Chu, Jianfeng; Li, Ning; Li, Fengguang

doi:10.3892/ol.2018.8904

Cited by 18 publications

(15 citation statements)

References 27 publications

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“…Expression of PDGFRA was reported in BLCA specimens; however, such report lacked sufficient experimental study (Terada, 2009;Terada, 2013). AHNAK was identified as a unique intracellular protein with different expression level and subcellular localization between BLCA sample and control (Lee et al, 2018); whereas, its prognostic value was observed in other studies (Chu et al, 2018;Wu et al, 2019). High expression of EDNRA was found to be associated with poor outcome in patients with advanced BLCA in a bioinformatics report (Laurberg et al, 2014).…”

Section: Discussionmentioning

confidence: 93%

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

Qiu

et al. 2020

Front. Genet.

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Background: There has been no report of prognostic signature based on immunerelated genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA). Methods: RNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified via Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n = 284) and test datasets (n = 119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset, and external dataset GSE13507 (n = 165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BLCA. Results: A set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes [hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6-19, P < 0.001]. The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754, and 0.772 at 1-, 3-, and 5year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4 + T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit.

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Section: Discussionmentioning

confidence: 93%

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

Qiu

et al. 2020

Front. Genet.

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“…Liu et al (2018)[39]50.83/ n = 119;0.68/ n = 1206ACADS; C1QTNF9B; RP11-60 L3.1; CTD-3195I5.3; has-miR-3913-1; has-miR-891aJ. Chu et al (2018)[40]30.615/ n = 407NA/ NA7ZNF230; BCL2L14; AHNAK; TMEM109; APOL2; AGER; AOC2Z. Xu et al (2019)[41]NA0.735/ n = 412NA/ NA11MXRA7; EMP1; TNFA1P8L3; SERPINB12; SAPCD1; GABRG1; PLEKHG4B; ABCA4; PTPRR; XAGE2; PEX5LNote: NA represents “not available”, Ref.…”

Section: Resultsmentioning

confidence: 99%

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival

et al. 2019

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Background Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Methods Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. Results WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. Conclusion The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA. Electronic supplementary material The online version of this article (10.1186/s41065-019-0100-1) contains supplementary material, which is available to authorized users.

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“…Multiple genes, processes and stages are involved in the development of bladder cancer [20]. Abnormalities in apoptosis and cell EMT changes have been closely correlated with bladder cancer progression [21].…”

Section: Discussionmentioning

confidence: 99%

Cryptotanshinone Inhibites Bladder Cancer Cell Proliferation and Promotes Apoptosis via the PTEN/PI3K/AKT Pathway

Liu¹,

Lin²,

Chen³

et al. 2020

J. Cancer

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Cryptotanshinone (CTT), extracted from the root of Salvia miltiorrhiza Bunge (Danshen), exhibits activities against a variety of human cancers in vitro and in vivo. The purpose of this study was to investigate the potential inhibitory effect of CTT on bladder cancer. In this study, we found that CTT inhibited bladder cancer cell proliferation, migration, and invasion and promoted apoptosis. In addition, CTT modulated the expression of proteins via the PI3K/AKT pathway, and the inhibition of PI3K/AKT signalling was due to induction of PTEN expression. Taken together, the results of the present study demonstrated the anticancer effect of CTT on bladder cancer cells, which might be associated with the downregulation of PI3K/AKT/mTOR and NF-κB signalling pathway proteins, and this inhibition was mediated by the induction of PTEN.

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A risk score staging system based on the expression of seven genes predicts the outcome of bladder cancer

Cited by 18 publications

References 27 publications

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival

Cryptotanshinone Inhibites Bladder Cancer Cell Proliferation and Promotes Apoptosis via the PTEN/PI3K/AKT Pathway

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