Literature DB >> 22238264

Interactome-transcriptome integration for predicting distant metastasis in breast cancer.

Maxime Garcia1, Raphaelle Millat-Carus, François Bertucci, Pascal Finetti, Daniel Birnbaum, Ghislain Bidaut.   

Abstract

MOTIVATION: High-throughput gene expression profiling yields genomic signatures that allow the prediction of clinical conditions including patient outcome. However, these signatures have limitations, such as dependency on the training set, and worse, lack of generalization.
RESULTS: We propose a novel algorithm called ITI (interactome-transcriptome integration), to extract a genomic signature predicting distant metastasis in breast cancer by superimposition of large-scale protein-protein interaction data over a compendium of several gene expression datasets. Training on two different compendia showed that the estrogen receptor-specific signatures obtained are more stable (11-35% stability), can be generalized on independent data and performs better than previously published methods (53-74% accuracy). AVAILABILITY: The ITI algorithm source code from analysis are available under CeCILL from the ITI companion website: http://bioinformatique.marseille.inserm.fr/iti. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2012        PMID: 22238264     DOI: 10.1093/bioinformatics/bts025

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

Review 1.  Systems Biology of Cancer Metastasis.

Authors:  Yasir Suhail; Margo P Cain; Kiran Vanaja; Paul A Kurywchak; Andre Levchenko; Raghu Kalluri
Journal:  Cell Syst       Date:  2019-08-28       Impact factor: 10.304

2.  Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression.

Authors:  Chun-Pei Cheng; I-Ying Kuo; Hakan Alakus; Kelly A Frazer; Olivier Harismendy; Yi-Ching Wang; Vincent S Tseng
Journal:  Bioinformatics       Date:  2014-07-10       Impact factor: 6.937

3.  RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes.

Authors:  Ashish Saini; Jingyu Hou; Wanlei Zhou
Journal:  ScientificWorldJournal       Date:  2014-01-19

4.  HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings.

Authors:  Claire Rioualen; Quentin Da Costa; Bernard Chetrit; Emmanuelle Charafe-Jauffret; Christophe Ginestier; Ghislain Bidaut
Journal:  PLoS One       Date:  2017-09-26       Impact factor: 3.240

5.  Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes.

Authors:  Mark Burton; Mads Thomassen; Qihua Tan; Torben A Kruse
Journal:  Cancer Inform       Date:  2012-12-10

6.  Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods.

Authors:  Mark Burton; Mads Thomassen; Qihua Tan; Torben A Kruse
Journal:  ScientificWorldJournal       Date:  2012-11-28

7.  Breast cancer prognosis risk estimation using integrated gene expression and clinical data.

Authors:  Ashish Saini; Jingyu Hou; Wanlei Zhou
Journal:  Biomed Res Int       Date:  2014-05-14       Impact factor: 3.411

8.  Identification of TUBB2A by quantitative proteomic analysis as a novel biomarker for the prediction of distant metastatic breast cancer.

Authors:  Dongyoon Shin; Joonho Park; Dohyun Han; Ji Hye Moon; Han Suk Ryu; Youngsoo Kim
Journal:  Clin Proteomics       Date:  2020-05-24       Impact factor: 3.988

  8 in total

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