Novel breast cancer biomarkers identified by integrative proteomic and gene expression mapping.

Published

Journal Article

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.

Full Text

Duke Authors

Cited Authors

  • Ou, K; Yu, K; Kesuma, D; Hooi, M; Huang, N; Chen, W; Lee, SY; Goh, XP; Tan, LK; Liu, J; Soon, SY; Bin Abdul Rashid, S; Putti, TC; Jikuya, H; Ichikawa, T; Nishimura, O; Salto-Tellez, M; Tan, P

Published Date

  • April 2008

Published In

Volume / Issue

  • 7 / 4

Start / End Page

  • 1518 - 1528

PubMed ID

  • 18318472

Pubmed Central ID

  • 18318472

International Standard Serial Number (ISSN)

  • 1535-3893

Digital Object Identifier (DOI)

  • 10.1021/pr700820g

Language

  • eng

Conference Location

  • United States