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.

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.

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.

scholarly journals Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230536
Author(s):  
Guillermo López-García ◽  
José M. Jerez ◽  
Leonardo Franco ◽  
Francisco J. Veredas
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Gene Expression Data ◽  
Cancer Survival ◽  
Survival Prediction ◽  
Expression Data

scholarly journals The Immune Cell Infiltration Patterns and Characterization Score in Bladder Cancer to Identify Prognostic.

2022 ◽  
Author(s):  
Yongsheng Zhang ◽  
Yunlong Wang ◽  
Jichuang Wang ◽  
Kaixiang Zhang
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
T Cells ◽  
Gene Expression Data ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Expression Data ◽  
Common Gene

Abstract Bladder cancer (BLCA) is among the most frequent types of cancer. Patients with BLCA have a significant recurrence rate and a poor post-surgery survival rate. Recent research has found a link between tumor immune cell infiltration (ICI) and the prognosis of BLCA patients. However, the ICI picture of BLCA remains unclear. Common gene expression data was obtained by combining the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) expression databases. Two computational algorithms were proposed to unravel the ICI landscape of BLCA patients. The R package "limma" was applied to find differentially expressed genes (DEGs). Principal-component analysis (PCA) was used to calculate the ICI score. A total of 569 common gene expression data were retrieved from TCGA and GEO cohorts. CD8+ T cells were found to have a substantial positive connection with activated memory CD4+ T cells and immune score. On the contrary, CD8+ T cells were found to have a substantial negative connection with Macrophages M0. Thirty-eight DEGs were selected. Two ICI patterns were defined by unsupervised clustering method. Patients of BLCA were separated into two groups. The high ICI score group exhibits better outcome than the low one (p < 0.001). Finally, the group with a high tumor mutation burden (TMB) as well as a high ICI score had the best outcome. (p <0.001). Combining TMB and ICI score resulted in a more accurate survival prediction, suggesting that ICI score could be used as a prognostic marker for BLCA patients.

Sign in / Sign up

Export Citation Format

Share Document