A novel 6-gene signature derived from tumor-infiltrating T cells and neutrophils predicts survival of bladder urothelial carcinoma

Abstract Background: Bladder urothelial carcinoma (BLCA) is the most common malignant tumor of the urinary system. Ferroptosis is a new type of programmed cell death that is iron-dependent and different from apoptosis, cell necrosis, and autophagy. Studies have indicated that many genes are involved in regulating ferroptosis or markers of ferroptosis. However, the value of genes related to ferroptosis in BLCA remains unclear.Methods: We comprehensively evaluated the differences in ferroptosis genes in patients with BLCA and control samples based on public databases, including mRNA expression, mutations, and copy number variations. The ferroptosis gene expression profile was used for consistent clustering to obtain ferr.clusterA and ferrclusterB. An analysis of differences between groups was performed to obtain ferroptosis-related gene, and then consistent clustering was performed to obtain ferr.gene.cluster A and ferr.gene.clusterB. Subsequently, the random forest algorithm was used to reduce dimensionality, Cox analysis was used to screen characteristic genes, principal component analysis was performed, and the ferroptosis score was constructed to quantify the ferroptosis expression of a single sample.Results: According to the ferroptosis score, the samples could be divided into two groups with significant differences in prognosis, which proves that the ferroptosis expression pattern in a single tumor can predict the prognostic response of patients.Conclusion: In summary, ferroptosis-related genes are significantly related to the progression of BLCA.

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Prognostic value of hypoxia gene expression in bladder cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.6_suppl.548 ◽

2020 ◽

Vol 38 (6_suppl) ◽

pp. 548-548

Author(s):

Hyun Chang ◽

Seung-Hyun Lee ◽

Taeryool Koo ◽

Moon Ho Kim ◽

Soo-Yoon Sung

Keyword(s):

Bladder Cancer ◽

Urothelial Carcinoma ◽

Cancer Patients ◽

Prognostic Value ◽

Cox Regression ◽

Gene Signature ◽

Survival Curves ◽

Kaplan Meier ◽

Bladder Urothelial Carcinoma

548 Background: The prognostic value of hypoxia in bladder cancer remains unknown. We aimed to evaluate the potential role of hypoxia gene signature as prognostic factors in bladder cancer patients. Methods: We investigated the hypoxia gene signature and clinicopathologic features of The Cancer Genome Atlas (TCGA) bladder urothelial carcinoma (n = 408) using the Kaplan-Meier survival curves and multivariate Cox regression analyses. The clinicopathologic data and the processed data of hypoxia gene signature were obtained from TCGA Bladder urothelial carcinoma database. Results: Hypoxia gene signature score was significantly associated with overall survival (OS) and progression-free survival (PFS). Higher score resulted in shorter OS and PFS in Kaplan-Meier survival curves with Log-rank test ( P < 0.01 and P <0.05, respectively). In multivariate analysis containing clinical prognostic variables, higher hypoxia gene signature score predicted poor OS (adjusted HR= 1.58, 95% CI 1.15 - 2.19; P <0.01). Conclusions: Hypoxia gene signature was an independent prognostic factor in bladder cancer. Prospective studies are needed to evaluate the prognostic role of hypoxia in bladder cancer patients.

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Identifying novel cell glycolysis related gene signature predictive of overall survival in bladder urothelial carcinoma

10.21203/rs.3.rs-30639/v1 ◽

2020 ◽

Author(s):

Xin Zhao ◽

Jia Li ◽

Jiafeng Li ◽

Wenjun Xiong ◽

Rui Jiang

Keyword(s):

Data Mining ◽

Overall Survival ◽

High Risk ◽

Urothelial Carcinoma ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Bladder Urothelial Carcinoma

Abstract Background: Bladder urothelial carcinoma (BLCA) is the most common pathological type of bladder cancer and featured by a high risk for relapse and metastasis. Although many biomarkers have been developed by data mining and experimental studies to predict the prognosis of BLCA, a single-gene biomarker usually has poor specificity and sensitivity, leading to unsatisfactory prediction. Therefore, novel gene signatures are needed to more accurately predict the prognosis of BLCA.Methods: Data mining was performed for expression profile analysis of 433 mRNA expression data from the TCGA BLCA patients (n=412). Gene Set Enrichment Analysis (GSEA) was used to interpret the glycolysis-related gene sets. Gene signature related to the prognosis of BLCA was identified by univariate and multivariate Cox regression. A risk score was computed based on three genes by linear regression model and its relation with overall survival was investigated by Kaplan-Meier analysis.Results: Three genes (CHPF, AK3, NUP188) were found to be significantly correlated to the overall survival of BLCA patients. Based on the signature composed of these three genes, 412 BLCA patients were divided into high-risk and low-risk groups. The survival time of the high-risk group was significantly shorter than that of the low-risk group, indicating a worse prognosis.Conclusion: A signature composed of three glycolysis-related genes was developed as biomarkers to predict the prognosis of BLCA and to provide a meaningful reference for the clinical treatment of BLCA.

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Three Prognostic Biomarkers Correlate With Immunotherapy Response in Bladder Urothelial Carcinoma

10.21203/rs.3.rs-125356/v1 ◽

2020 ◽

Author(s):

ya guo ◽

Yin Bin Zhang ◽

Yi Li ◽

Wang Hui Su ◽

Shan He ◽

...

Keyword(s):

T Cells ◽

Urothelial Carcinoma ◽

Cd8 T Cells ◽

Clinical Characteristics ◽

Venn Diagram ◽

Hub Genes ◽

New Targets ◽

Network Analyses ◽

Bladder Urothelial Carcinoma ◽

Key Genes

Abstract Background: There are currently no satisfactory biomarkers to predict prognosis and evaluate the benefit of immunotherapy in bladder urothelial carcinoma (BLCA) patients. This study aimed to develop a predictive signature that could accurately predict prognosis and evaluate the response to immunotherapy in BLCA. Methods: Differentially expressed genes (DEGs) were identified using the GEPIA and Oncomine databases, and the common genes between the two database were selected using a Venn diagram. In addition, gene ontology enrichment and protein–protein interaction (PPI) network analyses were performed. We further identified the prognosis-related hub genes using the survival R package and confirmed in three online databases (PROGgenesV2, PrognoScan, and OSblca). Moreover, the correlation between prognosis-related hub genes and clinical characteristics was analyzed. Finally, comprehensive bioinformatics analysis was carried out to investigate the association between the three genes and immunity. Results: A total of 750 and 1881 DEGs were identified from GEPIA and Oncomine, respectively, and 69 common DEGs were selected. The most significantly enriched term among the 69 common DEGs was “mitotic cell cycle”, and 11 hub genes were detected by PPI analysis. Moreover, three prognosis-related hub genes, AURKA, BIRC5, and CKS1B, were identified, and which were associated with clinical characteristics, in particular, histological subtypes and TP53 mutation status. Furthermore, our results showed that the expression levels of the three genes were positively correlated with CD8+ T cells and tumor mutation burden (TMB), and with PD-L1, which had higher expression in responders to immunotherapy and in the C2 (IFN-gamma dominant) subtype. Drug–gene interaction network analysis demonstrated that these genes and related drugs could be used to help develop new targets for BLCA immunotherapy. Conclusions: Our study suggested that three key genes in BLCA were correlated with poor prognosis and immune cell infiltration, especially that of CD8+ T cells. The responses of these prognosis-related genes to immunotherapy in BLCA may be associated with CD8+ T cells, TMB, and PD-L1 expression. These key genes and their related drugs may help to develop new targets for BLCA immunotherapy.

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1569: BCG Resistant Bladder Urothelial Carcinoma Under-Expresses Ligands for Natural Cytotoxicity Receptors of Natural Killer Cells

The Journal of Urology ◽

10.1016/s0022-5347(18)31757-9 ◽

2007 ◽

Vol 177 (4S) ◽

pp. 518-518

Author(s):

Vladimir Yutkin ◽

Dov Pode ◽

Tal I. Arnon ◽

Eli Pikarsky ◽

Ofer Mandelboim

Keyword(s):

Natural Killer Cells ◽

Urothelial Carcinoma ◽

Natural Killer ◽

Natural Cytotoxicity ◽

Killer Cells ◽

Bladder Urothelial Carcinoma ◽

Natural Cytotoxicity Receptors

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Bladder Urothelial Carcinoma

10.32388/353tri ◽

2020 ◽

Author(s):

Keyword(s):

Urothelial Carcinoma ◽

Bladder Urothelial Carcinoma

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Quantifying subepithelial connective tissue infiltration in bladder urothelial carcinoma in pT1 staging as risk factor of progression and/or relapse

10.26226/morressier.578f37ffd462b8028d89063d ◽

2016 ◽

Author(s):

Miguel Angel Trigo Cebrián

Keyword(s):

Risk Factor ◽

Connective Tissue ◽

Urothelial Carcinoma ◽

Bladder Urothelial Carcinoma

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Identification and development of a novel invasion-related gene signature for prognosis prediction in colon adenocarcinoma

Cancer Cell International ◽

10.1186/s12935-021-01795-1 ◽

2021 ◽

Vol 21 (1) ◽

Cited By ~ 1

Author(s):

Jiahua Liu ◽

Chunhui Jiang ◽

Chunjie Xu ◽

Dongyang Wang ◽

Yuguang Shen ◽

...

Keyword(s):

T Cells ◽

External Validation ◽

Colon Adenocarcinoma ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Immune Infiltration ◽

Prognosis Prediction ◽

Protein Levels ◽

Limma Package ◽

Cox Analysis

AbstractThe overall survival of metastatic colon adenocarcinoma (COAD) remains poor, so it is important to explore the mechanisms of metastasis and invasion. This study aimed to identify invasion-related genetic markers for prognosis prediction in patients with COAD. Three molecular subtypes (C1, C2, and C3) were obtained based on 97 metastasis-related genes in 365 COAD samples from The Cancer Genome Atlas (TCGA). A total of 983 differentially expressed genes (DEGs) were identified among the different subtypes by using the limma package. A 6-gene signature (ITLN1, HOXD9, TSPAN11, GPRC5B, TIMP1, and CXCL13) was constructed via Lasso-Cox analysis. The signature showed strong robustness and could be used in the training, testing, and external validation (GSE17537) cohorts with stable predictive efficiency. Compared with other published signatures, our model showed better performance in predicting outcomes. Pan-cancer expression analysis results showed that ITLN1, TSPAN11, CXCL13, and GPRC5B were downregulated and TIMP1 was upregulated in most tumor samples, including COAD, which was consistent with the results of the TCGA and GEO cohorts. Western blot analysis and immunohistochemistry were performed to validate protein expression. Tumor immune infiltration analysis results showed that TSPAN11, GPRC5B, TIMP1, and CXCL13 protein levels were significantly positively correlated with CD4+ T cells, macrophages, neutrophils, and dendritic cells. Further, the TIMP1 and CXCL13 proteins were significantly related to the tumor immune infiltration of CD8+ T cells. We recommend using our signature as a molecular prognostic classifier to assess the prognostic risk of patients with COAD.

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