Abstract
BackgroundRenal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach.MethodsWe integrated gene expression data from 97 primary RCC of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC.ResultsWe found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups.ConclusionWe propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance.
Highlights
Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals
We used the RNA extracted from 97 primary RCCs of different pathologic parameters, 15 RCC metastases and 34 cell lines, to identify any gene expression patterns in pathways containing more than 20 genes
The most prominent gene expression clusters are highlighted in Additional file 2: Figure S1 A-D and Additional file 3: Table S2. No such differentiating gene expression patterns were obtained through hierarchical clustering of the genes of the remaining pathways which, according to PANTHER, contained less than 150 genes
Summary
Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. Renal cell carcinoma (RCC) represents the most common malignancy arising in the adult kidney, with increasing incidence and poor prognosis [1]. RCC can be pathologically subdivided into different histological subtypes [2] based on the microscopic phenotype and the presence or absence of von Hippel-Lindau (VHL) gene. Multiple genes and signaling pathways have been implicated in renal cancer, VHL is the best characterized driver mutation, as it is mutated in the majority of sporadic ccRCC [10]. The low mutation frequencies reported for these genes in sporadic RCC subtypes [12,13,14], suggest other genes and pathways being relevant for the vast majority of RCC
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