Inflammatory metabolic profile of South African patients with prostate cancer

Abstract

Background Men with African ancestry are more likely to develop aggressive prostate cancer (PCa) and to die from this disease. The study of PCa in the South African population represents an opportunity for biomedical research due to the high prevalence of aggressive PCa. While inflammation is known to play a significant role in PCa progression, its association with tumor stage in populations of African descent has not been explored in detail. Identification of new metabolic biomarkers of inflammation may improve diagnosis of patients with aggressive PCa. Methods Plasma samples were profiled from 41 South African men with PCa using nuclear magnetic resonance (NMR) spectroscopy. A total of 41 features, including metabolites, lipid classes, total protein, and the inflammatory NMR markers, GlycA, and GlycB, were quantified from each NMR spectrum. The Bruker’s B.I.-LISA protocols were used to characterize 114 parameters related to the lipoproteins. The unsupervised KODAMA method was used to stratify the patients of our cohort based on their metabolic profile. Results We found that the plasma of patients with very high risk, aggressive PCa and high level of C-reactive protein have a peculiar metabolic phenotype (metabotype) characterized by extremely high levels of GlycA and GlycB. The inflammatory processes linked to the higher level of GlycA and GlycB are characterized by a deep change of the plasma metabolome that may be used to improve the stratification of patients with PCa. We also identified a not previously known relationship between high values of VLDL and low level of GlycB in a different metabotype of patients characterized by lower-risk PCa. Conclusions For the first time, a portrait of the metabolic changes in African men with PCa has been delineated indicating a strong association between inflammation and metabolic profiles. Our findings indicate how the metabolic profile could be used to identify those patients with high level of inflammation, characterized by aggressive PCa and short life expectancy. Integrating a metabolomic analysis as a tool for patient stratification could be important for opening the door to the development of new therapies. Further investigations are needed to understand the prevalence of an inflammatory metabotype in patients with aggressive PCa.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

“…Several studies have revealed specific metabolic alterations in PCa patients by comparing prostate tumors of different grade. Furthermore, different systemic and local metabolic alterations have consistently been associated with PCa risk and progression [111][112][113][114][115][116]. In line with this, in a recent study by Gómez-Cebrián et al, the specific metabolomics profile of high-grade PCa patients was characterized on the basis of the alterations in metabolite levels identified in the serum and urine of PCa patients with different tumor grades [100].…”

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

“…Using untargeted metabolomics techniques, Mayerle demonstrated that a biomarker signature (nine metabolites and an additional CA19-9) could be identified as differential diagnoses of PDAC and CP (Mayerle et al, 2018). In addition, inflammatory metabolites identified by serum metabolomics can also stratify tumor and improve diagnosis of patients with aggressive tumor (Cacciatore et al, 2021). The discovery of metabolomics for tumor blood biomarkers can be extended to various aspects, which include the discovery of significantly different metabolites, changes in the tumor metabolic and direct identification of tumors and inflammation.…”

Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer

“…Thus, data from multiple research groups have demonstrated changes to circulating lipid levels in PCa patients. Lipid metabolites, sterols, steroids, and phospholipids in the serum or plasma might be good candidates for PCa detection, and findings correlating serum metabolomics to PCa may potentially be used for the development of new therapies for PCa [58].…”