Renal cell carcinoma (RCC) incidence accounts for about 3 to 10 cases per 100,000 individuals with a predilection for adult males over 60 year old (1.6:1 male/female ratio) (Chow, 2010; Nese, 2009). In Europe, about 60,000 individuals are affected by RCC every year, with a mortality rate of about 18,000 subjects and an incidence rate for all stages steadily rising over the last three decades. Although inherited forms occur in a number of familial cancer syndromes, as the well-known von Hippel-Lindau (VHL) syndrome, RCC is commonly sporadic (Cohen & McGovern, 2005; Kaelin, 2007) and, as recently highlighted by the National Cancer Institute (NCI), influenced by the interplay between exposure to environmental risk factors and genetic susceptibility of exposed individuals (Chow et al., 2010). Being poorly symptomatic in early phases, many cases become clinically detectable only when already advanced and, as such, therapy-resistant (Motzer, 2011). Based on histology, RCC can be classified into several subtypes, i.e., clear cell (80% of cases), papillary (10%), chromophobe (5%) and oncocytoma (5%), each one characterized by specific histo- pathological features, malignant potential and clinical outcome (Cohen & McGovern, 2005). Patient stratification is normally achieved using prognostic algorithms and nomograms based on multiple clinico-pathological factors such as TNM stage, Fuhrman nuclear grade, tumor size, performance status, necrosis and other hematological indices (Flanigan et al., 2011), although the most efficient predictors of survival and recurrence are based on nuclear grade alone (Nese et al., 2009). As recently reviewed by Brannon et al. (Brannon & Rathmell, 2010), a finer RCC subtype classification could be obtained exploiting the vast amount of genomic and transcriptional data that have been presented in numerous studies. For instance, several authors proposed a molecular classification of RCC based on differential gene expression profiles, with any subtype characterized by the activation of distinct gene sets (Brannon, 2010; Furge, 2004; Skubitz, 2006; Sültmann, 2005; Zhang, 2008), while others identified RCC-specific biomarkers (e.g. CA9, ki67, VEGF proteins, phosphorylated AKT, PTEN, HIF-1). Lately, it has been reported that microRNAs, a small class of non coding RNA molecules, could contribute to RCC development at different levels and may represent a new group of potential tumor biomarkers (Redova et al., 2011). Despite the numerous efforts in dissecting the molecular features of RCC through functional genomics, not a single transcriptional signature or biomarker has gained approval for clinical application yet (Arsanious, 2009; Eichelberg, 2009; Lam, 2007; Yin-Goen, 2006), so that the identification of novel molecular markers to improve early diagnosis and prognostic prediction and of candidate targets to develop new therapeutic approaches remains of primary importance for this pathology.
Molecular Portrait of Clear Cell Renal Cell Carcinoma: An Integrative Analysis of Gene Expression and Genomic Copy Number Profiling / Battaglia, C.; Mangano, E.; Bicciato, Silvio; Frascati, F.; Nuzzo, Simona; Tinaglia, V.; Bianchi, C.; Perego, R. A.; Cifola, I.. - STAMPA. - (2011), pp. 23-56.
|Data di pubblicazione:||2011|
|Titolo:||Molecular Portrait of Clear Cell Renal Cell Carcinoma: An Integrative Analysis of Gene Expression and Genomic Copy Number Profiling|
|Autore/i:||Battaglia, C.; Mangano, E.; Bicciato, Silvio; Frascati, F.; Nuzzo, Simona; Tinaglia, V.; Bianchi, C.; Perego, R. A.; Cifola, I.|
|Titolo del libro:||Robert J. Amato|
|Citazione:||Molecular Portrait of Clear Cell Renal Cell Carcinoma: An Integrative Analysis of Gene Expression and Genomic Copy Number Profiling / Battaglia, C.; Mangano, E.; Bicciato, Silvio; Frascati, F.; Nuzzo, Simona; Tinaglia, V.; Bianchi, C.; Perego, R. A.; Cifola, I.. - STAMPA. - (2011), pp. 23-56.|
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