Fig. 1

Overview of the study design: from gene expression profiling-based data sets to prognostic models for clinical outcome and biologically meaningful, disease-associated pathways. The proposed algorithm includes three major blocks. (1) The composition of the AID/APOBEC-associated multigene signature (n = 24) is assembled based on a knowledge-driven approach and applied for the real-time PCR-based gene expression profiling of a clinically well-characterized patient cohort with primary ovarian carcinoma (n = 186). (2) Twenty one profiling-derived variables are correlated with survival data. Univariate Cox regression analysis is applied to assess the prognostic effect of each individual gene and clinical variable. Multivariable Cox regression analysis is applied to build up the survival prognostic models accounting for mutual interconnections between the genes from the signature. Two different multivariable modeling algorithms are used. As outcome, three types of models are created: (i) Clinics – the model is based on the clinicopathological parameters only; (ii) AID/APOBEC – the model is based on the multigene profiling-derived data sets; and (iii) Combined – the model is based on the clinicopathological and gene profiling-derived variables in combination. In both algorithms the standardized coefficients (STDBETA) are used for ranking the individual variables in a model by their importance. The top-ranked genes are defined as target genes for the follow-up analyses. Important to note, parameters such as proportion of explained variation (PEV), c-index and p-value are calculated and used to compare the predictive accuracy and discriminative ability of the individual models. Alignment with patients’ survival data is illustrated by Kaplan-Meier estimates showing patient stratification into low, intermediate, and high risk groups. (3) Systems biology approach is used to assign the defined target genes with prognostic impact to disease-relevant biological pathways. Firstly, the web-based analysis platform for publically available microarray datasets (GENEVESTIGATOR) is used to extract the top genes co-regulated with the target genes in ovarian cancer tissues based on inclusion criteria specified in Methods. Secondly, the obtained gene lists are subjected to the Ingenuity-based core analysis. As input, in addition to the individual lists of co-regulated genes, the combined list (“mixed”) is used to mimic the mutual interconnections within the multigene signature. The core analysis includes alignment with Canonical Pathways, Functional Annotations & Diseases and Upstream Regulators. Thirdly, Spotfire, a data discovery and visualization software, is used for large-scale IPA-derived data processing and data mining. As final outcome, the 10-top Pathways/Functions/Regulators are defined