The Application of Artificial Intelligence to Microarray Data: Identification of a Novel Gene Signature to Identify Bladder Cancer Progression

• Published in: European urology 2010-03, Vol.57 (3), p.398-406
• Main Authors: Catto, James W.F, Abbod, Maysam F, Wild, Peter J, Linkens, Derek A, Pilarsky, Christian, Rehman, Ishtiaq, Rosario, Derek J, Denzinger, Stefan, Burger, Maximilian, Stoehr, Robert, Knuechel, Ruth, Hartmann, Arndt, Hamdy, Freddie C
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biological and medical sciences; Carcinoma, Transitional Cell - genetics; Disease Progression; Female; Humans; Male; Medical sciences; Microarray Analysis; Nephrology. Urinary tract diseases; Tumors of the urinary system; Urinary Bladder Neoplasms - genetics; Urinary system involvement in other diseases. Miscellaneous; Urinary tract. Prostate gland; Urology
• ISSN: 0302-2838; 1873-7560
• DOI: 10.1016/j.eururo.2009.10.029

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.