Regressions of Clustered Gene Expression Data Manifest Tumor-Specific Genes in Urinary Bladder Cancer

2019 ◽  
Author(s):  
Michail Sarafidis ◽  
Apostolos Zaravinos ◽  
Dimitra Iliopoulou ◽  
Dimitrios Koutsouris ◽  
George I. Lambrou
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Urinary Bladder ◽  
Gene Expression Data ◽  
Urinary Bladder Cancer ◽  
Expression Data

Influence of Cigarette Smoking on Prostaglandin Synthesis and Cyclooxygenase-2 Gene Expression in Human Urinary Bladder Cancer

2002 ◽  
Vol 20 (5-6) ◽  
pp. 651-656 ◽  
Author(s):  
Alaa F. Badawi ◽  
Samy L. Habib ◽  
Mohammed A. Mohammed ◽  
Ahmed A. Abadi ◽  
Michael S. Michael
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Urinary Bladder ◽  
Cigarette Smoking ◽  
Urinary Bladder Cancer ◽  
Prostaglandin Synthesis ◽  
Cyclooxygenase 2 ◽  
Human Urinary Bladder

Expression Signature of E2F1 and Its Associated Genes Predict Superficial to Invasive Progression of Bladder Tumors

2010 ◽  
Vol 28 (16) ◽  
pp. 2660-2667 ◽  
Author(s):  
Ju-Seog Lee ◽  
Sun-Hee Leem ◽  
Sang-Yeop Lee ◽  
Sang-Cheol Kim ◽  
Eun-Sung Park ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cancer Progression ◽  
Gene Expression Data ◽  
Gene Expression Signature ◽  
Molecular Signature ◽  
Expression Data ◽  
Bladder Tumors ◽  
Expression Signature ◽  
Leave One Out

Purpose In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. Patients and Methods Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. Results Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. Conclusion We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.

scholarly journals Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

2021 ◽  
Vol 12 (1) ◽  
pp. 29-47
Author(s):  
Mauro Nascimben ◽  
Manolo Venturin ◽  
Lia Rimondini
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Data ◽  
Cancer Survival ◽  
Survival Rates ◽  
Sample Population ◽  
Expression Data ◽  
Discrete Interval ◽  
Classic Approach ◽  
Genetic Profiles

Abstract Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization.

UP-01.018 Linear Correlations in Chromosomal-Based Gene Expression in Urinary Bladder Cancer

Urology ◽  
2011 ◽  
Vol 78 (3) ◽  
pp. S190 ◽  
Author(s):  
A. Zaravinos ◽  
G. Lambrou ◽  
I. Boulalas ◽  
D. Volanis ◽  
D. Delakas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Urinary Bladder ◽  
Urinary Bladder Cancer ◽  
Linear Correlations

scholarly journals Gene expression is highly correlated on the chromosome level in urinary bladder cancer

Cell Cycle ◽  
2013 ◽  
Vol 12 (10) ◽  
pp. 1544-1559 ◽  
Author(s):  
GEORGE LAMBROU ◽  
Maria Adamaki ◽  
Dimitris Delakas ◽  
Demetrios A. Spandidos ◽  
Spiros Vlahopoulos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Urinary Bladder ◽  
Urinary Bladder Cancer ◽  
Highly Correlated ◽  
Chromosome Level

COXEN prediction of antineoplastic drug sensitivity in bladder cancer patients.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 365-365
Author(s):  
Shalin Kothari ◽  
Daniel Gustafson ◽  
Keith Killian ◽  
James Costello ◽  
Daniel C. Edelman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Data ◽  
Drug Sensitivity ◽  
Model Building ◽  
Response To Therapy ◽  
Antineoplastic Drug ◽  
In Vitro Assays ◽  
Expression Data

365 Background: COXEN (Co-eXpression ExtrapolatioN) uses molecular profiles as a “rosetta stone” for translating drug sensitivities of one set of cancers into predictions for another completely independent set of cell lines or human tumors. The ability of COXEN to predict drug effectiveness in pts using tumor samples from in vitro assays is unique. Methods: We tested the predictive value of COXEN for standard chemotherapies in a cohort of bladder cancer pts. Total RNA was extracted from formalin fixed paraffin embedded (FFPE) tissue and converted to cDNA, amplified with Ovation FFPE WTA, and hybridized to a GeneChip Human Genome U133 Plus 2.0 array. Using gene expression data from 278 independent bladder tumors, COXEN scores were generated using bioinformatics models originally built using the NCI-60 cell line panel and a model building algorithm (MiPP). Gene expression data was processed to score 76 FDA approved antineoplastic drugs. Results: A total of 24 samples were tested (15 tumors with 1 sample and 9 tumors with 2 biological replicas (2 samples from the same tumor)) from 15 pts who received chemotherapy (median age 64 (41-74); 73% male; with muscle invasive bladder cancer (MIBC) (12/15, 80%) or metastatic bladder cancer (mBC) (3/15, 20%)). Response to therapy was confirmed by pathologic response in MIBC pts and radiologic response in mBC pts. Chemotherapies evaluated included: methotrexate/vinblastine/doxorubicin/cisplatin; gemcitabine/cisplatin; gemcitabine/carboplatin; and cisplatin/etoposide. COXEN accurately predicted antineoplastic drug sensitivity in 11/15 (73%) pts (75% MIBC and 67% mBC), of which 7/11 pts had 2 biological samples. However, only 3/7 (43%) biological replicas confirmed COXEN prediction. COXEN accurately predicted drug sensitivity in 9/10 (90%) pts with response and 2/5 (40%) pts with resistance to therapy. Conclusions: COXEN did well in predicting antineoplastic drug response for the majority of bladder cancer pts in this cohort. However, predictions from 2 samples within the same tumor were not always consistent, likely due to the expected tumor heterogeneity found in bladder cancer tumors. A prospective clinical trial in patients with mBC using COXEN to select next best therapy is in development.

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