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Differential expression in clear cell renal cell carcinoma identified by gene expression profiling.

ABSTRACT: Gene expression profiling has been shown to provide prognostic information on patients with solitary sporadic renal cell carcinoma. To our knowledge there is no reliable way to differentiate synchronous renal metastasis from bilateral primary tumors in patients with bilateral renal cell carcinoma. We present data using a custom kidney cancer cDNA array that can predict the outcome in patients with unilateral and bilateral renal cell carcinoma.Fresh frozen tissue from 38 clear cell renal cell carcinomas was analyzed using a cancer cDNA array containing 3,966 genes relevant to cancer or kidney development. Median followup was 5.3 years. Cancer recurred in 12 patients (43%) and 11 (39%) had died by the last followup.Using a training data set of 8 tumors a 44 gene expression profile distinguishing aggressive and indolent clear cell renal cell carcinoma was identified. Of 29 single clear cell renal cell carcinomas 16 and 13 were predicted to be indolent and aggressive, respectively, by the gene expression profile. Recurrence-free survival at 5 years was 68% and 42% in these 2 groups, respectively (p = 0.032). Clear cell renal cell carcinoma classified as indolent or aggressive according to SSIGN (stage, size, grade and necrosis) score showed a 5-year recurrence-free survival rate of 78% and 42%, respectively (p = 0.021). On Cox proportional hazards analysis the gene expression profile was not an independent predictor of recurrence-free survival after accounting for SSIGN score. Gene expression profile classification correlated with cancer specific survival at 5 years in 4 of 4 patients with metachronous clear cell renal cell carcinoma but in only 2 of 4 with bilateral synchronous clear cell renal cell carcinoma.Gene expression profiling using a kidney cancer relevant cDNA array can differentiate between aggressive and indolent clear cell renal cell carcinomas. Gene expression profile results may be most useful for unilateral clear cell renal cell carcinoma when results are discordant with predictions of tumor behavior based on standard clinicopathological features. In addition, gene expression profiling can provide prognostic information that may help characterize tumors of unknown clinical stage, such as bilateral metachronous clear cell renal cell carcinoma.

SUBMITTER: Lane BR

PROVIDER: S-EPMC2706314 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Differential expression in clear cell renal cell carcinoma identified by gene expression profiling.

Lane Brian R BR Li Jianbo J Zhou Ming M Babineau Denise D Faber Pieter P Novick Andrew C AC Williams Bryan R G BR Williams Bryan R G BR

The Journal of urology 20081217 2

<h4>Purpose</h4>Gene expression profiling has been shown to provide prognostic information on patients with solitary sporadic renal cell carcinoma. To our knowledge there is no reliable way to differentiate synchronous renal metastasis from bilateral primary tumors in patients with bilateral renal cell carcinoma. We present data using a custom kidney cancer cDNA array that can predict the outcome in patients with unilateral and bilateral renal cell carcinoma.<h4>Materials and methods</h4>Fresh f ...[more]

PMID: 19095258

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis.MethodsWe collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis.ResultsUsing this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC.ConclusionsThe integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.

Dataset Information

Differential expression in clear cell renal cell carcinoma identified by gene expression profiling.

Publications

Differential expression in clear cell renal cell carcinoma identified by gene expression profiling.

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