Abstract
Simple SummaryProstate cancer (PCa) is a hormone-dependent tumor characterized by a highly heterogeneous clinical outcome. This neoplastic process has become a leading cause of cancer worldwide, with over 1.4 million new cases and a total of 375,000 deaths in 2020. Despite the efforts to improve the diagnosis, risk stratification, and treatment of PCa patients, a number of challenges still need to be addressed. In this context, integration of different multi-omics datasets may represent a powerful approach for the development of novel metabolic signatures that could contribute to the clinical management of PCa patients. This review aims to provide the most relevant findings of recently published multi-omics studies with a particular focus on describing the metabolic alterations associated with PCa.Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.
Highlights
Prostate cancer (PCa) is the second most frequent cancer and represents the fifth leading cause of cancer-related death in men worldwide [1]
Knock-down of glutamate oxaloacetate transaminase 1 (GOT1) in LNCaP and PC3 cells resulted in a significant decrease in cell viability, consistent with previous studies where GOT1 repression suppressed tumor growth in different tumors [91,92]. These results suggest that the metabolic alterations observed in urine liquid biopsies obtained from PCa patients could reflect the specific changes already observed in PCa cells and tumors
Metabolism is dysregulated to support the metabolic requirements of uncontrolled proliferation in cancer cells [151,152]. This rewiring of cellular metabolism leads to characteristic metabolic phenotypes that can be used for the development of effective screening methods for early cancer detection, patient selection strategies, or evaluation of treatment responses [153,154]
Summary
Prostate cancer (PCa) is the second most frequent cancer and represents the fifth leading cause of cancer-related death in men worldwide [1]. Several metabolomics analyses have been carried out on PCa samples (e.g., tissue, urine, serum, plasma, and seminal fluid) to characterize the specific metabolic profile associated with PCa progression and identify metabolic alterations that may potentially be used as clinical biomarkers (reviewed in [19,20,21,22]). Together, these studies have revealed a specific metabolic phenotype that could distinguish between healthy and PCa samples [23]. Nine of the stuNdiiensedoifscthuesssetdudinietshdisisrceuvsiseewdrienlitehdisornevthieewanrealliyesdisoonftbheenaignnalpyrsoisstoaftebenign prostate and PCa sampalensdfPorCiadseanmtipfyleinsgfosrpideceinfitcifyminetgasbpoeliccifiacltmereattaiboonlsicaaslstoecriaattioednswasitshoctihaetemd ewtait-h the metabolic bolic phenotyppehoefnPoCtyappeaotfiePnCtsa(pTaatbielent1s).(TInabtelegr1a)t.ioInnteogfrtartainonscorifpttroamnsiccrsipatnodmmicestaanbdolmomet-abolomics data ics data was thwe pasritmhearpyriampaprryoaapchprfooallcohwfeodlloinwtehdeisne tshtuesdeiesstu, adnieds,tiasnsduetissasumepslaems wpleersewtheere the biological biological specsimpeecnims penresfeprreenfetiraelnlytiaclollyleccotleldec. ted
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