Impact of TGF-b on breast cancer from a quantitative proteomic analysis

Jaegyoon Ahn, Youngmi Yoon, Yunku Yeu, Hookuen Lee, Sanghyun Park

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

There has been much active research in bioinformatics to support our understanding of oncogenesis and tumor progression. Most research relies on mRNA gene expression data to identify marker genes or cancer specific gene networks. However, considering that proteins are functional molecules that carry out the biological tasks of genes, they can be direct markers of biological functions. Protein abundance data on a genome scale have not been investigated in depth due to the limited availability of high throughput protein assays. This hindrance is chiefly caused by a lack of robust techniques such as RT-PCR (real-time polymerase chain reaction). In this study, we quantified phospho-proteomes of breast cancer cell lines treated with TGF-beta (transforming growth factor beta). To discover biomarkers and observe changes in the signaling pathways related to breast cancer, we applied a protein network-based approach to generate a classifier of subnet markers. The accuracy of that classifier outperformed other network-based classification algorithms, and current feature selection and classification algorithms. Moreover, many cancer-related proteins were identified in those sub-networks. Each sub-network provides functional insights and can serve as a potential marker for TGF-beta treatments. After interpreting the roles of proteins in sub-networks with various signaling pathways, we found strong candidate proteins and various related interactions that are expected to affect breast cancer outcomes. These results demonstrate the high quality of the quantified phospho-proteomes data and show that our network construction and classification method is appropriate for an analysis of this type of data.

Original languageEnglish
Pages (from-to)2096-2102
Number of pages7
JournalComputers in Biology and Medicine
Volume43
Issue number12
DOIs
Publication statusPublished - 2013 Dec 1

Fingerprint

Proteomics
Breast Neoplasms
Proteins
Genes
Proteome
Transforming Growth Factor beta
Biomarkers
Classifiers
Gene Regulatory Networks
Neoplasm Genes
Computational Biology
Research
Polymerase chain reaction
Real-Time Polymerase Chain Reaction
Neoplasms
Carcinogenesis
Bioinformatics
Gene expression
Genome
Feature extraction

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Cite this

Ahn, Jaegyoon ; Yoon, Youngmi ; Yeu, Yunku ; Lee, Hookuen ; Park, Sanghyun. / Impact of TGF-b on breast cancer from a quantitative proteomic analysis. In: Computers in Biology and Medicine. 2013 ; Vol. 43, No. 12. pp. 2096-2102.
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Impact of TGF-b on breast cancer from a quantitative proteomic analysis. / Ahn, Jaegyoon; Yoon, Youngmi; Yeu, Yunku; Lee, Hookuen; Park, Sanghyun.

In: Computers in Biology and Medicine, Vol. 43, No. 12, 01.12.2013, p. 2096-2102.

Research output: Contribution to journalArticle

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