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 ...
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. (C) 2009 European Association of Urology. Published by Elsevier B. V. All rights reserved.
Altmetric