For the random control sample, we generated a 20-gene signature where the signature was populated with randomly selected genes selected by a random number generator http://www.random.org. Analysis of survival differences between good-prognosis and poor-prognosis groups Unless otherwise indicated, GraphPad Prism 5™ software was used to www.selleckchem.com/products/wnt-c59-c59.html complete survival analysis, BIBF-1120 linear regression, and comparison of survival means, as well as all associated statistical tests, and ROC analysis, to measure the predictive ability of the prognosis gene signature in both the training
and validation data sets. Additional details available as supplementary methods. Comparison of models We calculated the predictive accuracy (Cases correctly predicted Vs All cases), specificity (Cases of correctly predicted good overall survival Vs Cases of actual good overall survival), and positive predictive value (PPV) (Cases
VX-680 research buy correctly predicted of poor survival Vs All cases predicted poor survival) for our 20-gene signature, the Aurora kinase A, and 70-gene signature models. Patients were divided into good and poor survival groups based on Aurora kinase A expression, where the average expression of Aurora kinase A for all patients was used as the cut-off separating the two groups. The 70-gene signature classification for the patients was included in the original clinical data file. Gene ontology Gene names were uploaded to the gene ontology website http://www.geneontology.org, and the biological processes associated with the human form of the gene were recorded. Results Generation and validation of a gene signature that predicts human breast cancer patient survival To establish a gene signature that could accurately predict the survival outcome of human breast cancer patients we used a 295 patient database containing both clinical data relating to patient survival and occurrence triclocarban of metastases, as well as the patient’s individual tumor gene expression profiles. We divided this database into training and validation groups, containing 144 and 151 patients, respectively. We then identified genes whose expression
levels correlated with patient survival as described in Methods. The 10 most highly ranked genes predictive of poor-prognosis and those 10 genes most highly predictive of good-prognosis established a 20-gene expression based predictor (Table 1). Table 1 Genes comprising the 20-gene signature 95% CI interval Gene ID# Systemic_name Gene name/symbol Average Upper Lower 10855 D43950 KIAA0098 -0.004 0.027 -0.035 19769 U96131 TRIP13 -0.039 -0.001 -0.077 14841 NM_014865 KIAA0159 -0.007 0.029 -0.044 15318 Contig55725_RC -0.219 -0.150 -0.289 12548 AF047002 ALY -0.040 -0.008 -0.072 3342 NM_004111 FEN1 -0.028 0.003 -0.058 3493 NM_004153 ORC1L 0.037 0.057 0.017 8204 NM_004631 LRP8 0.038 0.067 0.009 3838 NM_002794 PSMB2 -0.024 0.004 -0.051 3938 Contig55771_RC -0.047 -0.005 -0.088 6615 NM_004496 HNF3A -0.216 -0.120 -0.