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Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data
Adeboye O Osunkoya1, Qiqin Yin-Goen, John H Phan
1Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA.
Human Pathology
|August 22, 2009
Summary
This study identifies novel biomarkers to accurately classify renal cell carcinoma subtypes, aiding diagnosis when histology is unclear. These biomarkers improve differential diagnosis for clear cell, papillary, and chromophobe renal cell carcinoma.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Diagnostics
Background:
- Accurate subtyping of renal cell carcinoma (RCC) is crucial due to distinct pathobiology and clinical behaviors.
- Histopathological diagnosis can be equivocal, necessitating supplementary diagnostic tools.
- Existing biomarkers derived from microarray studies are insufficient due to expression heterogeneity.
Purpose of the Study:
- To develop novel bioinformatics systems for identifying candidate renal tumor biomarkers.
- To validate the differential expression of identified biomarkers in RCC subtypes.
- To assess the utility of these biomarkers for classifying RCC with equivocal histology.
Main Methods:
- Utilized bioinformatics systems (ArrayWiki, caCORRECT, omniBioMarker) to analyze merged microarray data from multiple RCC studies.
- Selected 6 novel candidate biomarkers based on gene-ranking algorithms.
- Verified differential expression using quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) on formalin-fixed paraffin-embedded tissues and tissue microarrays.
Main Results:
- Identified carbonic anhydrase IX and ceruloplasmin overexpressed in clear cell RCC.
- Found schwannomin-interacting protein 1 and E74-like factor 3 overexpressed in papillary RCC.
- Detected cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 overexpressed in chromophobe RCC. IHC confirmed associations for carbonic anhydrase IX, schwannomin-interacting protein 1, and cytochrome c oxidase subunit 5a.
Conclusions:
- Developed a novel bioinformatics pipeline for identifying and validating RCC biomarkers from microarray data.
- The identified biomarkers show potential as a multiplex panel for classifying RCC subtypes, especially with equivocal histology.
- Biomarker expression assays are increasingly vital for accurate RCC diagnosis, supporting needle core biopsies and targeted therapies.
