Additional file 1: of AID/APOBEC-network reconstruction identifies pathways associated with survival in ovarian cancer
Creators: name: Svoboda, Martin
name: Meshcheryakova, Anastasia
name: Heinze, Georg
name: Jaritz, Markus
name: Pils, Dietmar
name: Castillo-Tong, Dan
name: Hager, Gudrun
name: Thalhammer, Theresia
name: Jensen-Jarolim, Erika
name: Birner, Peter
name: Braicu, Ioana
name: Jalid Sehouli
name: Lambrechts, Sandrina
name: Vergote, Ignace
name: Mahner, Sven
name: Zimmermann, Philip
name: Zeillinger, Robert
name: Mechtcheriakova, Diana
Publisher: name: Figshare
Year: 2016
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Cancer
subject: 111714 Mental Health
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: 110309 Infectious Diseases
subject: Computational Biology

description: The following additional data are available with the online version of this paper. Figure S1. Graphical view of the expression range of target genes used in GENEVESTIGATOR-based analysis. Figure S2. Correlation analysis of reference HKGs expression values in microarray data sets. Figure S3. Figure shows the impact of individual variables on survival prediction of the multivariable model (LASSO) for OS if predictors are changed by +1 SD. Figure S4. Figure shows LASSO-based Kaplan/Meier estimates for OS and PFS. Figure S5. Figure shows expression profiles of the AID/APOBEC-based multigene signature in ovarian cancer cell lines. Figure S6. Figure shows the extended analysis of expression profiles of the AID/APOBEC signature genes in a wide range of ovarian cancer cell lines (n = 55). Figure S7. Shown is the result of hierarchical clustering for individual genes composing the AID/APOBEC signature across arrays/samples of 55 ovarian cancer cell lines. Figure S8. The heat maps show the distribution of the Canonical Pathways/Functional Annotations/Upstream Regulators between corresponding target genes. Figure S9. The pie charts indicate the overlap of Canonical Pathways/Functional Annotations/Upstream Regulators between output_mixed and output_individual. Table S1. Real-time PCR primer sequences. Table S2. Real-time PCR primers. Table S3. Genes composing the AID/APOBEC multigene signature. Table S4. Univariate Cox regression analysis of clinicopathological variables and gene profiling-derived data sets for OS and PFS. Table S5. Correlation analysis for the AID/APOBEC multigene-derived variables. Table S6. Multivariable models (ridge) for PFS. Table S7. Comparative analysis of multivariable models (LASSO) for prognostication of OS and PFS. Table S8. Multivariable models (LASSO) for OS. Table S9. Multivariable models (LASSO) for PFS. Table S10. Top 50 Affymetrix probe sets co-regulated with APOBEC3G. Table S11. Top 50 Affymetrix probe sets co-regulated with ESR1. Table S12. Top 50 Affymetrix probe sets co-regulated with ID2. Table S13. Top 50 Affymetrix probe sets co-regulated with ID3. Table S14. Top 50 Affymetrix probe sets co-regulated with PTPRC/CD45. Table S15. 10-top-AID/APOBEC signature-linked Canonical Pathways. Table S16. Top-AID/APOBEC signature-linked Upstream Regulators. Table S17. 10-top-AID/APOBEC signature-linked Upstream Regulators. (PDF 4761 kb)