Altered amino and fatty acids metabolism in Sudanese prostate cancer patients: insights from metabolic analysis

Introduction

Prostate cancer (PCa) poses a significant global health challenge, characterized by its increasing incidence and substantial impact on patient morbidity and mortality (1). With advancements in anticancer therapies leading to improved survival rates, the emergence of cardiovascular toxicities and hypertension (HTN) presents clinicians with intricate management dilemmas (2). Understanding the complex biochemical alterations associated with PCa is vital for advancing diagnostic and therapeutic strategies (3). Metabolomics offers a comprehensive snapshot of the metabolome, the complete set of small molecule metabolites within a biological system, providing insights into dynamic changes occurring in cellular metabolism (4,5). It has emerged as a pivotal tool in elucidating the intricate metabolic changes underlying PCa development and progression (6–9). Particularly, the investigation of altered metabolic pathways involving lipids, fatty acids, and free amino acids holds immense importance in understanding the molecular mechanisms driving PCa pathogenesis (10,11).

Among the plethora of molecular factors implicated in PCa pathogenesis, lipids have emerged as pivotal players, governing diverse cellular processes crucial for tumor progression (12). Emerging evidence suggests a complex interplay between lipid metabolism, HTN, and androgen deprivation therapies (ADTs) in PCa patients, adding further layers of complexity to patient care (13,14). Dysregulated lipid metabolism not only fuels the energy demands of proliferating cancer cells but also contributes to the structural integrity of cellular membranes and facilitates signaling pathways crucial for PCa progression (15). Fatty acids, the building blocks of complex lipids, are intricately involved in various cellular processes, including energy production, membrane synthesis, and signaling modulation (16). Perturbations in fatty acid metabolism have been implicated in PCa pathophysiology, influencing tumor aggressiveness, therapeutic resistance, and disease prognosis (17). Metabolomic studies have unveiled alterations in fatty acid composition and metabolism associated with PCa, highlighting their potential as biomarkers for disease diagnosis and therapeutic targets for intervention (10,18). Moreover, free amino acids play pivotal roles in cellular metabolism, serving as precursors for protein synthesis, energy production, and signaling molecules (19). Alteration in amino acid metabolism has been implicated in PCa progression, influencing cell proliferation, invasion, and metastasis (20,21). Metabolomic profiling has uncovered variations in amino acid levels and metabolism in PCa, offering insights into the relation between metabolic rewiring and oncogenic signaling pathways (8,11).

Most metabolomic PCa studies are conducted predominantly in European and Asian populations. The generalization of findings to other ethnic groups may be limited due to inherent genetic, environmental, and lifestyle differences (22). Therefore, investigating altered metabolic pathways in diverse populations, including those from African regions like Sudan, is crucial for elucidating population-specific variations in PCa biology. Sudan presents a unique demographic landscape characterized by its distinct genetic ancestry, dietary habits, and environmental exposures (23). The region is characterized by a rich diversity highlighted by the extensive presence of major continental African language families, including Niger-Congo (NC), Nilo-Saharan (NS), and Afro-Asiatic (AA) (24). Our study aims to reveal underlying metabolic phenotypes across Sudanese PCa patients to better understand the triggering mechanisms and corresponding tumor heterogeneity.

Material and methods

Results

Association of hypertension with patient characteristics

Our findings indicate that despite a higher proportion of non-hypertensive patients falling under metabotype B, their lipid metabolite levels remain elevated compared to other groups. To uncover potential metabolic patterns associated with hypertension, a thorough examination of demographic, clinical (Tab. S3), and metabolic (Tab. S4) data comparing patients with and without HTN has been performed. Our analysis unveiled significantly elevated levels of PSA in hypertensive patients compared to the non-hypertensive group (p-value = 0.037), remarking the association between hypertension and prostate health. A discrepancy in the T2DM prevalence was observed between hypertensive and non-hypertensive groups (p-value = 0.021). Most hypertensive patients (81.8%) were found to be non-diabetic while 94.1% of non-hypertensive patients presented with T2DM. Moreover, hypertensive patients exhibited significantly higher concentrations of various lipid metabolites compared to their non-hypertensive counterparts. These included unsaturated lipid -CH = CH- (p-value = 0.021), lipid alpha-CH₂ (p-value = 0.007), lipid CH₂ (p-value = 0.002), lipid beta-CH₂ (p-value = 0.02), and glycerol phospholipid (p-value = 0.005), as evidenced by our statistical analysis (Fig. 3, Tab. S4). These findings underscore the intricate metabolic alterations associated with hypertension and the need for further explorations in understanding the pathophysiology of hypertension and its complications in PCa patients.

Feature	Metabotype A (n = 21)	Metabotype B (n = 9)	Metabotype C (n = 10)	Metabotype D (n = 10)	p-Value
Hospital					0.073
KSH, n (%)	8 (38.1)	7 (77.8)	5 (50.0)	9 (90.0)
MC, n (%)	4 (19.0)	0 (0.0)	3 (30.0)	1 (10.0)
OTH, n (%)	5 (23.8)	2 (22.2)	0 (0.0)	0 (0.0)
SUH, n (%)	4 (19.0)	0 (0.0)	2 (20.0)	0 (0.0)
Ethnicity					0.342
AA, n (%)	13 (61.9)	4 (44.4)	2 (20.0)	5 (50.0)
NC, n (%)	4 (19.0)	4 (44.4)	5 (50.0)	4 (40.0)
NS, n (%)	4 (19.0)	1 (11.1)	3 (30.0)	1 (10.0)
Age (year), median [IQR]	75 [65–80]	70 [63–72]	71.5 [68-75]	61 [57.5-74]	0.237
HTN					0.027
No, n (%)	5 (23.8)	7 (77.8)	3 (30.0)	2 (20.0)
Yes, n (%)	16 (76.2)	2 (22.2)	7 (70.0)	8 (80.0)
T2DM					0.408
No, n (%)	14 (66.7)	3 (33.3)	6 (60.0)	5 (50.0)
Yes, n (%)	7 (33.3)	6 (66.7)	4 (40.0)	5 (50.0)
PSA (ng/mL), median [IQR]	42.7[31-61.7]	35.8[24.9-43.7]	49.0[44-57.5]	72.8[46.6-91.6]	0.231
Biopsy type					0.611
Needle biopsy, n (%)	4 (19.0)	2 (22.2)	4 (40.0)	3 (30.0)
…Prostatectomy, n (%)	1 (4.8)	0 (0.0)	1 (10.0)	0 (0.0)
TVP, n (%)	0 (0.0)	0 (0.0)	0 (0.0)	1 (10.0)
TURP, n (%)	16 (76.2)	7 (77.8)	5 (50.0)	6 (60.0)
PNI					0.521
No, n (%)	17 (81.0)	8 (88.9)	8 (80.0)	6 (60.0)
Yes, n (%)	4 (19.0)	1 (11.1)	2 (20.0)	4 (40.0)
Grade					0.056
1, n (%)	5 (23.8)	0 (0.0)	0 (0.0)	0 (0.0)
2, n (%)	5 (23.8)	0 (0.0)	1 (10.0)	0 (0.0)
3, n (%)	2 (9.5)	1 (11.1)	1 (10.0)	1 (10.0)
4, n (%)	2 (9.5)	6 (66.7)	5 (50.0)	4 (40.0)
5, n (%)	7 (33.3)	2 (22.2)	3 (30.0)	5 (50.0)

AA = Afro-Asiatic; HTN = hypertension; IQR = interquartile range; KSH = Kuwaiti Specialized Hospital; MC = Military Crop; NC = Niger-Congo; NS = Nilo-Saharan; OTH = Omdurman Teaching Hospital; PNI = perineural invasion; PSA = prostate-specific antigen; SUH = Soba University Hospital; T2DM = type2 diabetes mellitus; TURP = transurethral resection of prostate; TVP = transurethral laser vaporization of prostate.

Discussion

Despite the lack of specific metabolomic studies of PCa in Sudanese patients, this research tries to investigate the metabolic profile within the Sudanese context. Mostly, Sudanese PCa patients frequently present with late-stage diagnoses characterized by elevated PSA levels and Gleason scores (34). The diverse ethnic composition of our study, with a predominant representation of ethnicities such as AA, NC, and NS, mirrors the demographic diversity typical of this African region (25). Metabolic stratification of PCa patients using machine learning techniques offers a promising approach to elucidate the disease’s heterogeneity and enhance personalized treatment strategies. By analyzing metabolomic profiles from plasma samples, machine learning algorithms can categorize patients into distinct metabolic subtypes based on their unique metabolic signatures. Several studies have showcased the feasibility and potential of this approach. For instance, Cacciatore et al (2021) utilized KODAMA, a clustering algorithm to stratify PCa patients into different metabotypes using metabolomic profiling data (33). Their findings highlighted distinct inflammatory metabolic patterns associated with PCa aggressiveness in South African patients.

In our study, we applied KODAMA to categorize Sudanese PCa patients based on their NMR-measured metabolic data, identifying four distinct metabolic groups. These groups differed in PCa grade distribution and exhibited significant variability in hypertension prevalence. An increased level of lipid metabolites, including fatty acids, cholesterol, and glycerol phospholipids, was observed in patients within the second metabotype (metabotype B). This metabolic signature revealed a potential link between lipid metabolism and the clinical characteristics of PCa patients grouped within this group. Unlike other metabotypes, this group is dominated by non-hypertensive patients, which implies another factor that can interfere with the recorded raised lipid metabolites. Previous research implicated dysregulated lipid metabolism in cancer progression, including PCa (35–37). Accumulation of phospholipids and cholesterol in the circulation interacts with androgens and the immune system and triggers PCa development (38). The extended utilization of ADT is recognized for inducing alterations in the lipid profile both during and following treatment (39,40). It may be potentially contributing to the enhanced lipid biosynthesis in metabotype B group. Furthermore, our analysis also revealed a positive correlation between estimated lipid metabolites and certain amino acids, particularly valine, leucine, and isoleucine, which are classified as branched-chain amino acids (BCAAs). These BCAAs play pivotal roles in crucial cellular processes such as protein synthesis, energy production, and cellular signaling (41). The dysregulation of BCAA metabolism has been implicated in the pathogenesis of various cancers, including PCa (42). The genes encoding these amino acids have been reported to be expressed at higher levels in PCa tumor tissue compared to non-transformed tissue (43). Previous studies have illuminated the relation between lipid and amino acid metabolism in various cancer types (44,45). Specifically, cancer cells, including those of PCa, often exhibit heightened demands for lipid synthesis and fatty acid oxidation to fuel their growth and meet their energy requirements, while simultaneously orchestrating the transport and metabolic pathways of amino acids to sustain proliferation and ensure survival (46). Aside from BCAAs, our results also highlighted variations in the concentrations of other metabolites such as glutamine, glutamate, histidine, and pyruvate across the detected metabotype groups. The abnormal metabolism of amino acids in PCa cells often manifests as a disruption in glutamic acid metabolism, which is synthesized from glutamine to fulfill the demands of growth and proliferation (47). Plasma samples from PCa patients have been shown to exhibit elevated levels of valine, glutamine, creatine, tyrosine, phenylalanine, histidine, and 3-methylhistidine (48). Increased levels of BCAAs, glutamate, and a reduction in the levels of glycine, dimethylglycine, fumarate, and 4-imidazole-acetate have been identified as discriminative factors between PCa patients and those with benign prostatic hyperplasia (49). Lipid oxidation might be initiated by glycosylation of phospholipids leading to formation of advanced glycation end products. This pathway might increase the severity of inflammatory diseases, and the initiation and progression of malignant tumors (50). GlycA and GlycB are identifiable as two distinct signals in NMR spectroscopy that indicate the glycation status of the most abundant acute-phase inflammatory proteins, reflecting the patient inflammatory status (51). From our results, there was increased concentrations of GlycA and GlycB in the fourth metabotype (metabotype D) associated with low levels of lipid metabolites. Consistent with our results, a previous study reported a similar PCa metabotype, characterized by a high level of GlycB and reduced concentration of lipids (33). The possible origin of this metabotype is still under investigation.

Conclusion

This study sheds light on the metabolic landscape of PCa in Sudanese patients, emphasizing the potential of metabolomics in enhancing personalized treatment strategies. The identified metabolic subtypes using machine learning techniques provide valuable insights into disease heterogeneity, guiding targeted interventions. The associations between lipid metabolites and PCa characteristics underscore the importance of metabolic dysregulation in disease progression. Further research into the interplay between lipid and amino acid metabolism, informed by our findings and previous studies, promises to deepen our understanding of PCa pathogenesis and identify novel therapeutic targets.

Limitations and future directions

While the study provided valuable insights into the metabolic variations among PCa patients with comorbidities like hypertension, this study has some limitations that should be considered. The relatively small sample size (50 patients) limits the generalizability of the findings, and the focus on a specific population in Sudan may not fully capture metabolic variations across different regions or ethnic groups. Finally, while NMR spectroscopy was useful for metabolomic profiling, it may have missed other relevant metabolites that more sensitive techniques like mass spectrometry could detect. Despite these limitations, the study offers practical insights identifying distinct metabolic phenotypes in PCa patients, particularly concerning hypertension. The findings may also contribute to the development of biomarkers for early detection and risk stratification, particularly in patients with metabolic comorbidities like hypertension. Further research could explore these metabolic pathways to inform targeted therapies.

Acknowledgments

We would like to express our deep and sincere gratitude and appreciation to all urology and histopathology surgeons, clinicians, and technical staff for their efforts in performing this study.

Disclosures

Conflicts of Interest: The authors declare no conflicts of interest.

Financial support: The research was funded by the International Centre for Genetic Engineering and Biotechnology (L.F.Z. and S.C.), and the UN Technology Bank for Least Developed Countries, TWAS and ICGEB under South-North and South-South Fellowships (D.A.).

Informed consent statement: Informed consent was taken from all the participants.

Author contributions: Conceptualization, D.A., S.C., and L.F.Z.; methodology, S.C.; formal analysis, D.A., E.A.A.S., S.C.; resources, D.A., E.A.A.M., H.E.A.B.A., A.M.I., S.C., and L.F.Z.; clinical data curation, D.A., E.A.A.M., H.E.A.B.A., A.M.I.; writing – original draft preparation, D.A. and E.A.A.S.; writing – review and editing, S.C. and L.F.Z.; visualization, E.A.A.S.; supervision, S.C. and L.F.Z.; project administration, S.C. and L.F.Z.; funding acquisition, D.A., S.C., and L.F.Z. All authors have read and agreed to the published version of the manuscript.

Institutional review board statement: Specimen collection and research contents were examined and approved by the National Health Research Ethics Committee of the Sudanese Federal Ministry of Health (6-9-2021). The study was performed in accordance with the Declaration of Helsinki.

Data availability statement: All data produced in the present study are available upon reasonable request to the authors. NMR spectra were deposited at the MetaboLight database.