Introduction: Clear cell renal cell carcinoma (ccRCC) is a metabolic disease, of which the incidence rate is increasing worldwide. Renal carcinoma is characterized by mutations in target genes involved in metabolic pathways. Metabolic reprogramming covers different processes such as aerobic glycolysis, fatty acid metabolism, and the utilization of tryptophan, glutamine, and arginine. In the era of the multi-omics approach (with integrated transcriptomics, proteomics, and metabolomics), discovering biomarkers for early diagnosis is gaining renewed importance. Areas covered: In this review, we discuss the pathophysiological mechanisms underlying ccRCC metabolic reprogramming. In addition, we describe the emerging metabolomics-based biomarkers differentially expressed in ccRCC and the rationale for the recently developed drugs specifically targeting the ccRCC metabolome. Expert opinion: A number of metabolic pathways will be explored in future years, and many of these pathways are potential therapeutic targets and may serve as diagnostic and prognostic biomarkers of ccRCC.

An altered metabolism is involved in the development of clear cell - renal cell carcinoma (ccRCC), and in this tumor many altered genes play a fundamental role in controlling cell metabolic activities. We delineated a large-scale metabolomic profile of human ccRCC, and integrated it with transcriptomic data to connect the variations in cancer metabolism with gene expression changes. Moreover, to better analyze the specific contribution of metabolic gene alterations potentially associated with tumorigenesis and tumor progression, we evaluated the transcription profile of primary renal tumor cells. Untargeted metabolomic analysis revealed a signature of an increased glucose uptake and utilization in ccRCC. In addition, metabolites related to pentose phosphate pathway were also altered in the tumor samples in association with changes in Krebs cycle intermediates and related metabolites. We identified NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 4-like 2 (NDUFA4L2) as the most highly expressed gene in renal cancer cells and evaluated its role in sustaining angiogenesis, chemoresistance, and mitochondrial dysfunction. Finally, we showed that silencing of NDUFA4L2 affects cell viability, increases mitochondrial mass, and induces ROS generation in hypoxia.

Laparoscopic and robotic simple prostatectomy (SP) have been introduced with the aim of reducing the morbidity of the standard open technique. To report a large multi-institutional series of minimally invasive SP (MISP). Consecutive cases of MISP done for the treatment of bladder outlet obstruction (BOO) due to benign prostatic enlargement (BPE) between 2000 and 2014 at 23 participating institutions in the Americas and Europe were included in this retrospective analysis. Laparoscopic or robotic SP. Demographic data and main perioperative outcomes were gathered and analyzed. A multivariable analysis was conducted to identify factors associated with a favorable trifecta outcome, arbitrarily defined as a combination of the following postoperative events: International Prostate Symptom Score 15ml/s, and no perioperative complications. Overall, 1330 consecutive cases were analyzed, including 487 robotic (36.6%) and 843 laparoscopic (63.4%) SP cases. Median overall prostate volume was 100ml (range: 89-128). Median estimated blood loss was 200ml (range: 150-300). An intraoperative transfusion was required in 3.5% of cases, an intraoperative complication was recorded in 2.2% of cases, and the conversion rate was 3%. Median length of stay was 4 d (range: 3-5). On pathology, prostate cancer was found in 4% of cases. Overall postoperative complication rate was 10.6%, mostly of low grade. At a median follow-up of 12 mo, a significant improvement was observed for subjective and objective indicators of BOO. Trifecta outcome was not significantly influenced by the type of procedure (robotic vs laparoscopic; p=0.136; odds ratio [OR]: 1.6; 95% confidence interval [CI], 0.8-2.9), whereas operative time (p=0.01; OR: 0.9; 95% CI, 0.9-1.0) and estimated blood loss (p=0.03; OR: 0.9; 95% CI, 0.9-1.0) were the only two significant factors. Retrospective study design, lack of a control arm, and limited follow-up represent major limitations of the present analysis. This study provides the largest outcome analysis reported for MISP for BOO/BPE. These findings confirm that SP can be safely and effectively performed in a minimally invasive fashion in a variety of healthcare settings in which specific surgical expertise and technology is available. MISP can be considered a viable surgical treatment in cases of large prostatic adenomas. The use of robotic technology for this indication can be considered in centers that have a robotic program in place for other urologic indications. Analysis of a large data set from multiple institutions shows that surgical removal of symptomatic large prostatic adenomas can be carried out with good outcomes by using robot-assisted laparoscopy.

Clear cell renal cell carcinoma (ccRCC) is the most frequent histological kidney cancer subtype. Over the last decade, significant progress has been made in identifying the genetic and metabolic alterations driving ccRCC development. In particular, an integrated approach using transcriptomics, metabolomics, and lipidomics has led to a better understanding of ccRCC as a metabolic disease. The metabolic profiling of this cancer could help define and predict its behavior in terms of aggressiveness, prognosis, and therapeutic responsiveness, and would be an innovative strategy for choosing the optimal therapy for a specific patient. This review article describes the current state-of-the-art in research on ccRCC metabolic pathways and potential therapeutic applications. In addition, the clinical implication of pharmacometabolomic intervention is analyzed, which represents a new field for novel stage-related and patient-tailored strategies according to the specific susceptibility to new classes of drugs.

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.