Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

BMC Medicine

Eleftherios P Diamandis¹  Shalini Makawita²  Caitlin C Chrystoja³  Ioannis Prassas² 

[1] Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada;Department of Clinical Biochemistry, University Health Network, Toronto, ON, Canada;Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada

关键词: tissue-specific proteins;    biomarkers;    bioinformatics;   

Others  :  1126263
DOI  :  10.1186/1741-7015-10-39

 received in 2011-12-16, accepted in 2012-04-19,  发布年份 2012

【 摘 要 】

Background

There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.

Methods

Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.

Results

Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.

Conclusions

We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.

【 授权许可】

   
2012 Prassas et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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