Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma

Abstract

BackgroundConventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival.Methods and FindingsGene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status (p < 0.001).ConclusionscRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

Combining differential expression, chromosomal and pathway analyses for the molecular characterization of renal cell carcinoma

“…The results suggested that 2 molecularly distinct forms of clearcell RCC exist and that the integration of expression-profile data with clinical parameters could enhance the diagnosis and prognosis of clearcell RCC. More recently, a study of 177 clearcell RCC tumours 38 elicited a set of genes whose expression of mRNA correlated significantly with length of survival after surgery, even after controlling for conventional clinical parameters. Notably, overlapping genes were found in both studies, 37,38 further confirming the underlying molecular difference between nonaggressive and aggressive tumours and their clinical behaviour.…”

“…More recently, a study of 177 clearcell RCC tumours 38 elicited a set of genes whose expression of mRNA correlated significantly with length of survival after surgery, even after controlling for conventional clinical parameters. Notably, overlapping genes were found in both studies, 37,38 further confirming the underlying molecular difference between nonaggressive and aggressive tumours and their clinical behaviour. This finding has significant clinical implications for intervention strategies.…”

Molecular Portrait of Clear Cell Renal Cell Carcinoma: An Integrative Analysis of Gene Expression and Genomic Copy Number Profiling

“…Prolyl hydroxylase-3 (PHD3/ENGL3) is a member of the PHD family, which is involved in the degradation of HIF proteins in cooperation with VHL protein under normoxic conditions. PHD3 was found frequently over-expressed in RCC tissues, with high specificity to cancer samples (Zhao et al, 2006) and its usefulness as a novel tumor antigen for RCC immunotherapy has been recently demonstrated in clinical serum samples from RCC patients (Sato, 2008;Tanaka, 2011). Insulin-like growth factor binding protein 3 (IGFBP3) is one of the most over-expressed genes in ccRCC (Takahashi, 2005;Yao, 2005) and its increased protein expression has been demonstrated in 74% of ccRCCs by IHC and associated with higher Fuhrman nuclear grade (Chuang et al, 2008).…”

Genome-Wide Gene Expression Analysis to Identify Epistatic Gene-Pairs Associated with Prognosis of Breast Cancer

“…survival time) to certain explanatory variables. New methodologies were developed for calculating the survival probabilities using gene expression profiles when genome-wide expression data becomes increasingly available in the past two decades [38][39][40][41][42].…”