Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array

Abstract

We investigated the changes in gene expression accompanying the development and progression of kidney cancer by use of 31,500-element complementary DNA arrays. We measured expression profiles for paired neoplastic and noncancerous renal epithelium samples from 37 individuals. Using an experimental design optimized for factoring out technological and biological noise, and an adapted statistical test, we found 1738 differentially expressed cDNAs with an expected number of six false positives. Functional annotation of these genes provided views of the changes in the activities of specific biological pathways in renal cancer. Cell adhesion, signal transduction, and nucleotide metabolism were among the biological processes with a large proportion of genes overexpressed in renal cell carcinoma. Down-regulated pathways in the kidney tumor cells included small molecule transport, ion homeostasis, and oxygen and radical metabolism. Our expression profiling data uncovered gene expression changes shared with other epithelial tumors, as well as a unique signature for renal cell carcinoma. [Expression data for the differentially expressed cDNAs are available as a Web supplement at http://www.dkfz-heidelberg.de/abt0840/whuber/rcc.]

Figure 1

Histograms with tumor-normal ratios (logarithm to base 10) for two genes. (A) fibronectin 1, (B) metallothionein 1G. The bars in the histograms are colored by patient. In most cases, four ratios were measured for each patient. The four subpanels correspond to different tumor stages, stage I, III, and IV primary tumors, and metastases (M). The histograms show systematic trends. (A) Up-regulation, (B) down-regulation, interindividual variations (different-color patches within a histogram), and experimental noise (distribution of same-color patches). The vertical bars indicate ratios of 1/3.5 and 3.5. Histograms for all genes discussed in this article can be found in the Web supplement.

Figure 2

(Top) Histogram of the S-statistic for the set of 31 patients with tumor stages I, III, or IV. (Bottom) Distribution of the S-statistic under the null hypothesis of no differential expression (see Methods). The red bars show the rejection regions at a two-sided significance level of α = 2×10⁻⁴.

Figure 3

The number of differentially expressed genes identified depends on the data set size. For each sample size between 5 and 36, 32 random subsamples of patients were drawn from the total 37 patients data set. Box plots of the number of selected genes are plotted against the patient sample size. (A) Adapted sign test at significance level 2 × 10⁻⁴, (B) ratio-voting criterion with R₀ = 3.5 and Φ = 0.3. The horizontal lines in the boxes represent lower and upper quartiles, and median. The lines extending from each end of the box show the extent of the rest of the data. (+) Outliers.

Figure 4

Biological processes involved in RCC. A total of 321 differentially expressed genes were annotated for biological process according to the Gene Ontology proposed by Ashburner et al. (2000). The frequency of up-regulated (light bars) and down-regulated (dark bars) genes in RCC are plotted for the 23 biological processes scored.

Figure 5

Gene expression patterns for genes associated with different biological processes. The colors represent the median tumor-normal ratio for stages I, III, IV, and M patients, respectively. The color scale is shown. More detailed tables and more biological processes are available as on-line supplementary material.