Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis

• Type of Material: Article
• Authors: Adeboye O. Osunkoya, Emory University; Qiqin Yin-Goen, Emory University; John H. Phan, Emory University; Richard A Moffitt, Emory University; Todd H Stokes, Emory University; May D Wang, Emory University; Andrew N Young, Emory University
• Language: English
• Date: 2009-12
• Publisher: WB Saunders
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2009, Elsevier
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.humpath.2009.05.006
• Title of Journal or Parent Work: Human Pathology
• ISSN: 0046-8177
• Volume: 40
• Issue: 12
• Start Page: 1671
• End Page: 1678
• Grant/Funding Information: This work was supported by a grant from the National Cancer Institute Centers for Cancer Nanotechnology Excellence Program (U54CA119338).
• Keywords: Immunohistochemistry; Renal Cell Carcinoma; Microarrays; Bioinformatics; Quantitative RT-PCR
• Research Categories: Health Sciences, Pathology; Engineering, Biomedical

Abstract

Summary The differential diagnosis of clear cell, papillary and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative RT-PCR can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary and 10 chromophobe renal cell carcinoma; the microarray data was derived from two independent published studies. The ArrayWiki biocomputing system merged the microarray datasets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms, in order to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected six novel candidate biomakers from the omniBioMarker analysis, and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative RT-PCR and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1. Quantitative RT-PCR was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were overexpressed in clear cell renal cell carcinoma; schwannomin-interacting protein 1 and E74-like factor 3 were overexpressed in papillary renal cell carcinoma; and cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 were overexpressed in chromophobe renal cell carcinoma. Immunohistochemistry was performed on tissue microarrays containing 66 clear cell, 16 papillary and 12 chromophobe renal cell carcinoma. Cytoplasmic carbonic anhydrase IX staining was significantly associated with clear cell renal cell carcinoma. Strong cytoplasmic schwannomin-interacting protein 1 and cytochrome c oxidase subunit 5a staining were significantly more frequent in papillary and chromophobe renal cell carcinoma, respectively. In summary, we developed a novel process for identifying candidate renal tumor biomarkers from microarray data, and verifying differential expression in independent assays. The tumor biomarkers have potential utility as a multiplex expression panel for classifying renal cell carcinoma with equivocal histology. Biomarker expression assays are increasingly important for renal cell carcinoma diagnosis, as needle core biopsies become more common and different therapies for tumor subtypes continue to be developed.