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.

Renal cell carcinoma (RCC) is the seventh most common cancer in men and the ninth most common cancer in women worldwide. There is plenty of evidence about the role of the immune system in surveillance against tumors. Thanks to a better understanding of immunosurveillance mechanisms, immunotherapy has been introduced as a promising cancer treatment in recent years. Renal cell carcinoma (RCC) has long been thought chemoresistant but highly immunogenic. Considering that up to 30% of the patients present metastatic disease at diagnosis, and around 20–30% of patients undergoing surgery will suffer recurrence, we need to identify novel therapeutic targets. The introduction of immune checkpoint inhibitors (ICIs) in the clinical management of RCC has revolutionized the therapeutic approach against this tumor. Several clinical trials have shown that therapy with ICIs in combination or ICIs and the tyrosine kinase inhibitor has a very good response rate. In this review article we summarize the mechanisms of immunity modulation and immune checkpoints in RCC and discuss the potential therapeutic strategies in renal cancer treatment.

An altered metabolism is involved in the development of clear cell renal carcinoma (ccRCC). MUC1 overexpression has been found to be associated with advanced disease and poor prognosis. In this study, we evaluated the metabolomic profile of human ccRCC, according to MUC1 expression, and integrated it with transcriptomic data. Moreover, we analyzed the role of MUC1 in sustaining ccRCC aggressiveness and the prognostic value of its soluble form CA15-3. Integrated metabolomic and transcriptomic analysis showed that MUC1-expressing ccRCC was characterized by metabolic reprogramming involving the glucose and lipid metabolism pathway. In addition, primary renal cancer cells treated with a small interfering RNA targeting MUC1 (siMUC1) migrated and proliferated at a slower rate than untreated cancer cells. After cisplatin treatment, the death rate of cancer cells treated with siMUC1 was significantly greater than that of untreated cells. Kaplan–Meier curves showed significant differences in CSS and PFS among groups of patients with high versus low levels of CA15-3. In a multivariate analysis, CA15-3 was an independent adverse prognostic factor for cancer-specific and progression-free survival. In conclusion, MUC1 expressing ccRCC is characterized by a particular metabolic reprogramming. The inhibition of MUC1 expression decreases cell motility and viability and improves cisplatin susceptibility, suggesting that this pathway can regulate de novo chemotherapy resistance in ccRCC.

Prostate cancer (PCa) is the most common male malignancy and the fifth leading cause of cancer death in men worldwide. Prostate cancer cells are characterized by a hybrid glycolytic/oxidative phosphorylation phenotype determined by androgen receptor signaling. An increased lipogenesis and cholesterogenesis have been described in PCa cells. Many studies have shown that enzymes involved in these pathways are overexpressed in PCa. Glutamine becomes an essential amino acid for PCa cells, and its metabolism is thought to become an attractive therapeutic target. A crosstalk between cancer and stromal cells occurs in the tumor microenvironment because of the release of different cytokines and growth factors and due to changes in the extracellular matrix. A deeper insight into the metabolic changes may be obtained by a multi-omic approach integrating genomics, transcriptomics, metabolomics, lipidomics, and radiomics data.

Autophagy is a complex process involved in several cell activities, including tissue growth, differentiation, metabolic modulation, and cancer development. In prostate cancer, autophagy has a pivotal role in the regulation of apoptosis and disease progression. Several molecular pathways are involved, including PI3K/AKT/mTOR. However, depending on the cellular context, autophagy may play either a detrimental or a protective role in prostate cancer. For this purpose, current evidence has investigated how autophagy interacts within these complex interactions. In this article, we discuss novel findings about autophagic machinery in order to better understand the therapeutic response and the chemotherapy resistance of prostate cancer. Autophagic-modulation drugs have been employed in clinical trials to regulate autophagy, aiming to improve the response to chemotherapy or to anti-cancer treatments. Furthermore, the genetic signature of autophagy has been found to have a potential means to stratify prostate cancer aggressiveness. Unfortunately, stronger evidence is needed to better understand this field, and the application of these findings in clinical practice still remains poorly feasible.

Metabolomic analysis has proven to be a useful tool in biomarker discovery and the molecular classification of cancers. In order to find new biomarkers, and to better understand its pathological behavior, bladder cancer also has been studied using a metabolomics approach. In this article, we review the literature on metabolomic studies of bladder cancer, focusing on the different available samples (urine, blood, tissue samples) used to perform the studies and their relative findings. Moreover, the multi-omic approach in bladder cancer research has found novel insights into its metabolic behavior, providing excellent start-points for new diagnostic and therapeutic strategies. Metabolomics data analysis can lead to the discovery of a “signature pathway” associated with the progression of bladder cancer; this aspect could be potentially valuable in predictions of clinical outcomes and the introduction of new treatments. However, further studies are needed to give stronger evidence and to make these tools feasible for use in clinical practice.