Gene signatures of pulmonary metastases of renal cell carcinoma reflect the disease-free interval and the number of metastases per patient

Department of Urology, Dresden University of Technology, Dresden, Germany.
International Journal of Cancer (Impact Factor: 5.09). 07/2009; 125(2):474-82. DOI: 10.1002/ijc.24353
Source: PubMed


Our understanding of metastatic spread is limited and molecular mechanisms causing particular characteristics of metastasis are largely unknown. Herein, transcriptome-wide expression profiles of a unique cohort of 20 laser-resected pulmonary metastases (Mets) of 18 patients with clear-cell renal cell carcinoma (RCC) were analyzed to identify expression patterns associated with two important prognostic factors in RCC: the disease-free interval (DFI) after nephrectomy and the number of Mets per patient. Differentially expressed genes were identified by comparing early (DFI < or = 9 months) and late (DFI > or = 5 years) Mets, and Mets derived from patients with few (< or =8) and multiple (> or =16) Mets. Early and late Mets could be separated by the expression of genes involved in metastasis-associated processes, such as angiogenesis, cell migration and adhesion (e.g., PECAM1, KDR). Samples from patients with multiple Mets showed an elevated expression of genes associated with cell division and cell cycle (e.g., PBK, BIRC5, PTTG1) which indicates that a high number of Mets might result from an increased growth potential. Minimal sets of genes for the prediction of the DFI and the number of Mets per patient were identified. Microarray results were confirmed by quantitative PCR by including nine further pulmonary Mets of RCC. In summary, we showed that subgroups of Mets are distinguishable based on their expression profiles, which reflect the DFI and the number of Mets of a patient. To what extent the identified molecular factors contribute to the development of these characteristics of metastatic spread needs to be analyzed in further studies.

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Available from: Matthias Meinhardt, Jan 02, 2015
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    • "Gene expression datasets utilized were the public pre-processed datasets that included six cancer types and 10 datasets in total: colorectal cancer (Lin07 [25], Barrier06 [44]), breast cancer (Wang05 [36], Van02 [28]), clear-cell renal cell carcinoma (ccRCC, Jones05 [45], Wuttig09 [46]), non-small cell lung cancer(NSCLC, Sanchez10 [38], Beer02 [39]), melanoma (Riker08 [47]), gliomas(Freije04 [48]) (details were provided in Table 1). Missing values for each probe were filled by applied R package ‘impute’ [49]. "
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