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The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of
Marc E Lenburg1, Louis S Liou, Norman P Gerry
1Department of Genetics & Genomics, Boston University School of Medicine 715 Albany Street, E613 Boston, Massachusetts 02118, USA. mlenburg@bu.edu
Background:
Renal cell carcinoma is a common malignancy that often presents as a metastatic-disease for which there are no effective treatments. To gain insights into the mechanism of renal cell carcinogenesis, a number of genome-wide expression profiling studies have been performed. Surprisingly, there is very poor agreement among these studies as to which genes are differentially regulated. To better understand this lack of agreement we profiled renal cell tumor gene expression using genome-wide microarrays (45,000 probe sets) and compare our analysis to previous microarray studies.
Methods:
We hybridized total RNA isolated from renal cell tumors and adjacent normal tissue to Affymetrix U133A and U133B arrays. We removed samples with technical defects and removed probesets that failed to exhibit sequence-specific hybridization in any of the samples. We detected differential gene expression in the resulting dataset with parametric methods and identified keywords that are overrepresented in the differentially expressed genes with the Fisher-exact test.
Results:
We identify 1,234 genes that are more than three-fold changed in renal tumors by t-test, 800 of which have not been previously reported to be altered in renal cell tumors. Of the only 37 genes that have been identified as being differentially expressed in three or more of five previous microarray studies of renal tumor gene expression, our analysis finds 33 of these genes (89%). A key to the sensitivity and power of our analysis is filtering out defective samples and genes that are not reliably detected.
Conclusions:
The widespread use of sample-wise voting schemes for detecting differential expression that do not control for false positives likely account for the poor overlap among previous studies. Among the many genes we identified using parametric methods that were not previously reported as being differentially expressed in renal cell tumors are several oncogenes and tumor suppressor genes that likely play important roles in renal cell carcinogenesis. This highlights the need for rigorous statistical approaches in microarray studies.
Insights
This study identifies new genes involved in renal cell carcinoma (kidney cancer) by analyzing gene expression data. Rigorous methods improve accuracy, revealing potential oncogenes and tumor suppressors for better cancer treatment strategies.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Renal cell carcinoma (RCC) is a prevalent cancer often presenting with metastatic disease and lacking effective treatments.
- Genome-wide expression profiling studies for RCC have shown poor agreement on differentially regulated genes.
- Understanding these discrepancies is crucial for advancing RCC research and therapeutic development.
Purpose of the Study:
- To profile renal cell tumor gene expression using genome-wide microarrays.
- To compare the current analysis with previous microarray studies to understand discrepancies.
- To identify novel differentially expressed genes in RCC.
Main Methods:
- Hybridized total RNA from renal cell tumors and adjacent normal tissue to Affymetrix U133A and U133B arrays.
- Removed technically defective samples and unreliable probesets.
- Detected differential gene expression using parametric methods and identified overrepresented keywords with the Fisher-exact test.
Main Results:
- Identified 1,234 genes with >3-fold change in renal tumors; 800 were newly reported.
- Found 33 of 37 (89%) differentially expressed genes common to at least three previous studies.
- Demonstrated that filtering defective samples and unreliable genes enhances analysis sensitivity and power.
Conclusions:
- Poor overlap in previous studies is likely due to sample-wise voting schemes without false positive control.
- Identified novel oncogenes and tumor suppressor genes crucial for renal cell carcinogenesis.
- Emphasizes the necessity of rigorous statistical approaches in microarray studies for reliable results.