A novel methodology is presented for detecting and quantifying indirect effects on cancer survival mediated through several target genes of transcription factors in cancer microarray data

Ferkingstad, Egil

Frigessi, Arnoldo

Lyng, Heidi

English

PubMed

Egil Ferkingstad

Arnoldo Frigessi

Heidi Lyng

Springer - Publisher Connector

Indirect genomic effects on survival from gene expression data

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In cancer, genes may have indirect effects on patient survival, mediated through interactions with other genes. Methods to study the indirect effects that contribute significantly to survival are not available. We propose a novel methodology to detect and quantify indirect effects from gene expression data. We discover indirect effects through several target genes of transcription factors in cancer microarray data, pointing to genetic interactions that play a significant role in tumor progression

NORA - Norwegian Open Research Archives

A gene-expression signature as a predictor of survival in breast cancer.

A linear regression model for the analysis of life times. Stat Med

A model for non-parametric regression analysis of counting processes.

A PPAR response element regulates transcription of the gene for human adipose differentiationrelated protein.

Aalen OO: Dynamic path analysis - a new approach to analyzing time-dependent covariates. Lifetime Data Anal

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA

Baranova A: PPARgamma activation by thiazolidinediones (TZDs) may modulate breast carcinoma outcome: the importance of interplay with TGFbeta signalling.

BS: Quantitative inference of dynamic regulatory pathways via microarray data.

Califano A: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.

Califano A: Reverse engineering of regulatory networks in human B cells. Nat Genet

Censored data and the bootstrap.

Characterizing disease states from topological properties of transcriptional regulatory networks.

Core Team: R: A Language and Environment for Statistical Computing.

DG: Distinct and overlapping roles for E2F family members in transcription, proliferation and apoptosis. Curr Mol Med

Differential regulation and properties of angiopoietin-like proteins

E2F1 death pathways as targets for cancer therapy.

Elstner E: Peroxisome proliferator-activated receptor-gamma ligands for the treatment of breast cancer. Expert Opin Investig Drugs

Gene expression in cancer: the application of microarrays. Expert Rev Mol Diagn

Genomic approach to biomarker identification and its recent applications. Cancer Biomark

Gerds T: Assessment of survival prediction models based on microarray data. Bioinformatics

Glucose regulates the transcription of human genes relevant to HDL metabolism: responsive elements for peroxisome proliferator-activated receptor are involved in the regulation of phospholipid transfer protein. Diabetes

Graphical Models.

Hann SR: Myc-mediated transformation: the repression connection. Oncogene

Hiebert SW: Role of RUNX family members in transcriptional repression and gene silencing. Oncogene

I: Pathway studio - the analysis and navigation of molecular networks.

Kawecka-Jaszcz K: The selected pathophysiological aspects of PPARs activation.

Lingjærde OC: Predicting survival from microarray data - a comparative study. Bioinformatics

López-Guillermo A, et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.

LZ: Cancer therapeutics: targeting the dark side of Myc.

LZ: The myc oncogene: MarvelouslY complex. Adv Cancer Res

Methods and biomarkers for the diagnosis and prognosis of cancer and other diseases: towards personalized medicine. Drug Resist Updat

Microarray tools for deciphering complex diseases. Front Biosci

Package addreg for Additive Hazard Regression Analysis [http://www.med.uio.no/imb/stat/addreg]

Pollack JR: Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia.

PPARs and their metabolic modulation: new mechanisms for transcriptional regulation?

R package for indirect genomic effects on survival

Regression models and life-tables.

RUNX expression and function in human B cells. Crit Rev Eukaryot Gene Expr

Signal transducer and activator of transcription 5 (STAT5).

Supervised harvesting of expression trees. Genome Biol

SW: Enriching for direct regulatory targets in perturbed gene-expression profiles. Genome Biol

Tibshirani R: Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol

To die or not to die: how does p53 decide? Oncogene

Toge T: Inducing cancer cell death by targeting transcription factors. Anticancer Drugs

Transforming growth factor alpha and mouse models of human breast cancer. Oncogene

Wessels LF: Cross-validated Cox regression on microarray gene expression data. Stat Med

Y: E2F1 and c-Myc in cell growth and death. Cell Cycle

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Indirect genomic effects on survival from gene expression data

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