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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-28865

Catto, J W F; Abbod, M F; Wild, P J; Linkens, D A; Pilarsky, C; Rehman, I; Rosario, D J; Denzinger, S; Burger, M; Stoehr, R; Knuechel, R; Hartmann, A; Hamdy, F C (2009). The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression. European Urology, 57(3):398-406.

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Abstract

BACKGROUND: New methods for identifying bladder cancer (BCa) progression are required. Gene expression microarrays can reveal insights into disease biology and identify novel biomarkers. However, these experiments produce large datasets that are difficult to interpret. OBJECTIVE: To develop a novel method of microarray analysis combining two forms of artificial intelligence (AI): neurofuzzy modelling (NFM) and artificial neural networks (ANN) and validate it in a BCa cohort. DESIGN, SETTING, AND PARTICIPANTS: We used AI and statistical analyses to identify progression-related genes in a microarray dataset (n=66 tumours, n=2800 genes). The AI-selected genes were then investigated in a second cohort (n=262 tumours) using immunohistochemistry. MEASUREMENTS: We compared the accuracy of AI and statistical approaches to identify tumour progression. RESULTS AND LIMITATIONS: AI identified 11 progression-associated genes (odds ratio [OR]: 0.70; 95% confidence interval [CI], 0.56-0.87; p=0.0004), and these were more discriminate than genes chosen using statistical analyses (OR: 1.24; 95% CI, 0.96-1.60; p=0.09). The expression of six AI-selected genes (LIG3, FAS, KRT18, ICAM1, DSG2, and BRCA2) was determined using commercial antibodies and successfully identified tumour progression (concordance index: 0.66; log-rank test: p=0.01). AI-selected genes were more discriminate than pathologic criteria at determining progression (Cox multivariate analysis: p=0.01). Limitations include the use of statistical correlation to identify 200 genes for AI analysis and that we did not compare regression identified genes with immunohistochemistry. CONCLUSIONS: AI and statistical analyses use different techniques of inference to determine gene-phenotype associations and identify distinct prognostic gene signatures that are equally valid. We have identified a prognostic gene signature whose members reflect a variety of carcinogenic pathways that could identify progression in non-muscle-invasive BCa.

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Surgical Pathology
DDC:610 Medicine & health
Language:English
Date:2009
Deposited On:29 Jan 2010 12:03
Last Modified:28 Nov 2013 03:09
Publisher:Elsevier
ISSN:0302-2838
Publisher DOI:10.1016/j.eururo.2009.10.029
PubMed ID:19913990
Citations:Web of Science®. Times Cited: 14
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