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Background Over 90% of cancer-related deaths in clear cell renal cell carcinoma (RCC) are caused by tumor relapse and metastasis. Thus, there is an urgent need for new molecular markers that can potentiate the efficacy of the current clinical-based models of prognosis assessment. The objective of this study is to evaluate the potential significance of lactate dehydrogenase A (LDHA), assessed by immunohistochemical staining, as a prognostic marker in clear cell renal cell carcinoma in relation to clinicopathological features and clinical outcome. Methods We assessed the expression of LDHA at the protein level, by immunohistochemistry, and correlated its expression with multiple clinicopathological features including tumor size, clinical stage, histological grade, disease-free and overall survival in 385 patients with primary clear cell renal cell carcinoma. We also correlated the LDHA expression with overall survival, at mRNA level, in an independent data set of 170 clear cell renal cell carcinoma cases from The Cancer Genome Atlas databases. Cox proportional hazards models adjusted for the potential clinicopathological factors were used to test for associations between the LDHA expression and both disease-free survival and overall survival. Results There is statistically significant positive correlation between LDHA level of expression and tumor size, clinical stage and histological grade. Moreover, LDHA expression shows significantly inverse correlation with both disease-free survival and overall survival in patients with clear cell renal cell carcinoma. Our results are validated by examining LDHA expression, at the mRNA level, in the independent data set of clear cell renal cell carcinoma cases from The Cancer Genome Atlas databases which also shows that higher lactate dehydrogenase A expression is associated with significantly shorter overall survival. Conclusion Our results indicate that LDHA up-regulation can be a predictor of poor prognosis in clear cell renal cell carcinoma. Thus, it represents a potential prognostic biomarker that can boost the accuracy of other prognostic models in patients with clear cell renal cell carcinoma. [ABSTRACT FROM AUTHOR]

Abstract: Background: Most early stage kidney cancers are renal cell carcinomas (RCCs), and most are diagnosed incidentally by imaging as small renal masses (SRMs). Indirect evidence suggests that most small RCCs grow slowly and rarely metastasize. Objective: To determine the progression and growth rates for newly diagnosed SRMs stratified by needle core biopsy pathology. Design, setting, and participants: A multicenter prospective phase 2 clinical trial of active surveillance of 209 SRMs in 178 elderly and/or infirm patients was conducted from 2004 until 2009 with treatment delayed until progression. Intervention: Patients underwent serial imaging and needle core biopsies. Measurements: We measured rates of change in tumor diameter (growth measured by imaging) and progression to ≥4cm, doubling of tumor volume, or metastasis with histology on biopsy. Results and limitations: Local progression occurred in 25 patients (12%), plus 2 progressed with metastases (1.1%). Of the 178 subjects with 209 SRMs, 127 with 151 SRMs had>12 mo of follow-up with two or more images, with a mean follow-up of 28 mo. Their tumor diameters increased by an average of 0.13cm/yr. Needle core biopsy in 101 SRMs demonstrated that the presence of RCC did not significantly change growth rate. Limitations included no central review of imaging and pathology and a short follow-up. Conclusions: This is the first SRM active surveillance study to correlate growth with histology prospectively. In the first 2 yr, the rate of local progression to higher stage is low, and metastases are rare. SRMs appear to grow very slowly, even if biopsy proven to be RCC. Many patients with SRMs can therefore be initially managed conservatively with serial imaging, avoiding the morbidity of surgical or ablative treatment. [Copyright &y& Elsevier]

Abstract: Background: Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. Objective: Our aim was to identify microRNA (miRNA) signatures that can distinguish the different RCC subtypes accurately. Design, setting, and participants: A total of 94 different subtype cases were analysed. miRNA microarray analysis was performed on fresh frozen tissues of three common RCC subtypes (clear cell, chromophobe, and papillary) and on oncocytoma. Results were validated on the original as well as on an independent set of tumours, using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis with miRNA-specific primers. Measurements: Microarray data were analysed by standard approaches. Relative expression for qRT-PCR was determined using the ΔΔCT method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44). Experiments were done in triplicate, and an average was calculated. Fold change was expressed as a log2 value. The top-scoring pairs classifier identified operational decision rules for distinguishing between different RCC subtypes and was robust under cross-validation. Results and limitations: We developed a classification system that can distinguish the different RCC subtypes using unique miRNA signatures in a maximum of four steps. The system has a sensitivity of 97% in distinguishing normal from RCC, 100% for clear cell RCC (ccRCC) subtype, 97% for papillary RCC (pRCC) subtype, and 100% accuracy in distinguishing oncocytoma from chromophobe RCC (chRCC) subtype. This system was cross-validated and showed an accuracy of about 90%. The oncogenesis of ccRCC is more closely related to pRCC, whereas chRCC is comparable with oncocytoma. We also developed a binary classification system that can distinguish between two individual subtypes. Conclusions: MiRNA expression patterns can distinguish between RCC subtypes. [Copyright &y& Elsevier]