Plasma metabolomic profile in prostatic intraepithelial neoplasia and prostate cancer and associations with the prostate-specific antigen and the Gleason score

Pharmacological targets of SGLT2 inhibition on prostate cancer mediated by circulating metabolites: a drug-target Mendelian randomization study

Background

The relationship between sodium-glucose cotransporter 2 (SGLT2) inhibitors and prostate cancer is still unknown. Although these inhibitors can influence tumor glycolysis, the underlying mechanism requires further exploration.

Methods

A two-sample two-step MR was used to determine 1) causal effects of SGLT2 inhibition on prostate cancer; 2) causal effects of 1,400 circulating metabolites or metabolite ratios on prostate cancer; and 3) mediation effects of these circulating metabolites. Genetic proxies for SGLT2 inhibition were identified as variants in the SLC5A2 gene and glycated hemoglobin level (HbA1c). Additionally, positive control analysis on type 2 diabetes mellitus (T2DM) was conducted to test the selection of genetic proxies. Phenome Wide Association Study (PheWAS) and MR-PheWAS analysis were used to explore potential treatable diseases and adverse outcomes of SGLT2 inhibitors.

Results

Genetically predicted SGLT2 inhibition (per 1 SD decrement in HbA1c) was associated with reduced risk of T2DM [odds ratio (OR) = 0.66 (95% CI 0.53, 0.82), P = 1.57 × 10-4]; prostate cancer [0.34 (0.23, 0.49), P = 2.21 × 10-8] and prostate-specific antigen [0.26 (0.08, 0.81), P = 2.07 × 10-2]. The effect of SGLT2 inhibition on prostate cancer was mediated by uridine level, with a mediated proportion of 9.34% of the total effect. In MR-PheWAS, 65 traits were found to be associated with SLGT2 inhibitors (P < 1.78 × 10-5), and among them, 13 were related to diabetes.

Conclusion

Our study suggested that SGLT2 inhibition could lower prostate cancer risk through uridine mediation. More mechanistic and clinical research is necessary to explore how uridine mediates the link between SGLT2 inhibition and prostate cancer.

Causality of genetically determined metabolites on susceptibility to prevalent urological cancers: a two-sample Mendelian randomization study and meta-analysis

Background

Prevalent urological cancers, including kidney, prostate, bladder, and testicular cancers, contribute significantly to global cancer incidence and mortality. Metabolomics, focusing on small-molecule intermediates, has emerged as a tool to understand cancer etiology. Given the knowledge gap in this field, we employ a two-sample Mendelian randomization (MR) analysis to investigate the causal relationships between genetically determined metabolites (GDMs) and the susceptibility to four common urological cancers.

Methods

The study employs genome-wide association studies (GWAS) data from European populations, featuring the most extensive case count available for both blood metabolites and four prevalent urological cancers. Preliminary and secondary MR analyses were separately conducted, employing inverse variance weighted (IVW) as the primary method. Multiple statistical analyses, including the MR-Steiger test, Cochran's Q test, leave-one-out analysis, MR-Egger intercept analysis, and MR-PRESSO analysis, were executed to ensure robustness. Additionally, a meta-analysis was carried out to consolidate findings. The weighted median (WM) method was utilized for a relatively lenient correction (PWM < 0.05).

Results

After rigorous genetic variation filtering, 645 out of 1,400 metabolites were included in both preliminary and secondary MR analyses. Preliminary MR analysis identified 96 potential causal associations between 94 distinct metabolites and four urological cancers. Secondary analysis based on Finnish outcome data revealed 93 potential causal associations. Cross-database meta-analysis identified 68 blood metabolites associated with four urological cancers. Notably, 31 metabolites remained significant after using WM for correction, with additional 37 suggestive causal relationships. Reverse MR analysis revealed a significant causal association between genetically predicted prostate cancer and elevated 4-hydroxychlorothalonil levels (IVW, combined OR: 1.039, 95% CI 1.014-1.064, p = 0.002; WM, combined OR: 1.052, 95% CI 1.010-1.095, p = 0.014).

Conclusion

This comprehensive MR study provides insights into the causal relationships between blood metabolites and urological cancers, revealing potential biomarkers and therapeutic targets, thereby addressing gaps in understanding and laying the foundation for targeted interventions in urological cancer research and treatment.

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

1H-NMR-based urine metabolomics of prostate cancer and benign prostatic hyperplasia

Background

Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are prevalent conditions affecting a significant portion of the male population, particularly with advancing age. Traditional diagnostic methods, such as digital rectal examination (DRE) and prostate-specific antigen (PSA) tests, have limitations in specificity and sensitivity, leading to potential overdiagnosis and unnecessary biopsies.

Significance

This study explores the effectiveness of 1H NMR urine metabolomics in distinguishing PCa from BPH and in differentiating various PCa grades, presenting a non-invasive diagnostic alternative with the potential to enhance early detection and patient-specific treatment strategies.

Results

The study demonstrated the capability of 1H NMR urine metabolomics in detecting distinct metabolic profiles between PCa and BPH, as well as among different Gleason grade groups. Notably, this method surpassed the PSA test in distinguishing PCa from BPH. Untargeted metabolomics analysis also revealed several metabolites with varying relative concentrations between PCa and BPH cases, suggesting potential biomarkers for these conditions.

Circulating metabolite biomarkers: a game changer in the human prostate cancer diagnosis

PURPOSE

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men in Western and Asian countries. Serum prostate-specific antigen (PSA) test has been the routine diagnostic method despite the tremendous research in diagnostic markers for early detection of PCa. A shift towards a promising and potential biomarker for PCa detection is through metabolomic profiling of biofluids, particularly the blood and urine samples. Finding reliable, routinely usable circulating metabolite biomarkers may not be a distant reality.

METHODS

We performed a PubMed-based literature search of metabolite biomarkers in blood and urine for the early detection of prostate cancer. The timeline of these searches was limited between 2007 and 2022 and the following keywords were used: 'metabolomics', 'liquid biopsy', 'circulating metabolites', 'serum metabolite', 'plasma metabolite', and 'urine metabolite' with respect to 'prostate cancer'. We focussed only on diagnosis-based studies with only the subject-relevant articles published in the English language and excluded all of the other irrelevant publications that included prostate tissue biomarkers and cell line biomarkers.

RESULTS

We have consolidated all the blood and urine-based potential metabolite candidates in individual as well as panels, including lipid classes, fatty acids, amino acids, and volatile organic compounds which may become useful for PCa diagnosis.

CONCLUSION

All these metabolome findings unveil the impact of different dimensions of PCa development, giving a promising strategy to diagnose the disease since suspected individuals can be subjected to repeated and largescale blood and urine testing.

Target Metabolome Profiling-Based Machine Learning as a Diagnostic Approach for Cardiovascular Diseases in Adults

Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p < 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling.

Alteration in Plasma Metabolome in High-Fat Diet-Fed Monocyte Chemotactic Protein-1 Knockout Mice Bearing Pulmonary Metastases of Lewis Lung Carcinoma

Both clinical and laboratory studies have shown that monocyte chemotactic protein-1 (MCP-1) is involved in cancer spread. To understand the role of MCP-1 in metabolism in the presence of metastasis, we conducted an untargeted metabolomic analysis of primary metabolism on plasma collected from a study showing that MCP-1 deficiency reduces spontaneous metastasis of Lewis lung carcinoma (LLC) to the lungs in mice fed a high-fat diet (HFD). In a 2 × 2 design, wild-type (WT) or Mcp-1 knockout (Mcp-1 ^-/-) mice maintained on the AIN93G standard diet or HFD were subcutaneously injected with LLC cells to induce lung metastasis. We identified 87 metabolites for metabolomic analysis from this study. Amino acid metabolism was altered considerably in the presence of LLC metastases with the aminoacyl-tRNA biosynthesis pathways as the leading pathway altered. The HFD modified lipid and energy metabolism, evidenced by lower contents of arachidonic acid, cholesterol, and long-chain saturated fatty acids and higher contents of glucose and pyruvic acid in mice fed the HFD. These findings were supported by network analysis showing alterations in fatty acid synthesis and glycolysis/gluconeogenesis pathways between the 2 diets. Furthermore, elevations of the citrate cycle intermediates (citric acid, fumaric acid, isocitric acid, and succinic acid) and glyceric acid in Mcp-1 ^-/- mice, regardless of diet, suggest the involvement of MCP-1 in mitochondrial energy metabolism during LLC metastasis. The present study demonstrates that MCP-1 deficiency and the HFD altered plasma metabolome in mice bearing LLC metastases. These findings can be useful in understanding the impact of obesity on prevention and treatment of cancer metastasis.

Neural regulations of the tumor microenvironment

The identification of nerves in the tumor microenvironment has ushered in a new area of research in cancer biology. Numerous studies demonstrate the presence of various types of peripheral nerves (sympathetic, parasympathetic, sensory) within the tumor microenvironment; moreover, an increased density of nerves in the tumor microenvironment correlates with worse prognosis. In this review, we address the current understanding of nerve-mediated alterations of the tumor microenvironment and how they impact disease through a variety of processes, including direct nerve-cancer cell communication, alteration of the infiltrative immune population, and alteration of stromal components.

Carcinogenic effect of adenylosuccinate lyase (ADSL) in prostate cancer development and progression through the cell cycle pathway

Background

Prostate cancer (PCa) is still a serious male malignant disease across the world. However, no exact pathogenesis had been explained. Although adenylosuccinate lyase (ADSL) gene was identified to be important in PCa early in 1987, its comprehensive functions for PCa have not been presented.

Methods

The cBioPortal for Cancer Genomics, Oncomine and GEO database were retrieved to investigate the associations between of the ADSL gene and PCa. Then, the PC-3, DU145 and C4-2B cell lines were applied in vitro experiments. RNA sequencing and further western blot (WB) were applied to explore the potential mechanisms of ADSL gene in PCa.

Results

Based on PCa clinical datasets, we firstly found ADSL gene highly expressed in PCa tissues. Moreover, its transcript level increased in the metastatic PCa further. Elevated ADSL gene expression indicated a poor prognosis of PCa. While inhibiting the expression of ADSL with siRNA, the ability of cell proliferation and migration all declined markedly, with increased cell apoptosis inversely. Most of cells were blocked in the G0/G1 phase. Additionally, RNA sequencing also discovered the inactivity of cell cycle pathway after ADSL knockdown, which had also confirmed on the proteins levels.

Conclusions

Our study identified the ADSL as an oncogene of PCa through regulating the cell cycle pathway firstly, with explicit cell and clinical phenotypes. Further mechanisms were needed to confirm its carcinogenic effect.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02174-6.

A Unique Urinary Metabolic Feature for the Determination of Bladder Cancer, Prostate Cancer, and Renal Cell Carcinoma

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most prevalent cancer among urological cancers. However, there are no cancer-specific symptoms that can differentiate them as well as early clinical signs of urological malignancy. Furthermore, many metabolic studies have been conducted to discover their biomarkers, but the metabolic profiling study to discriminate between these cancers have not yet been described. Therefore, in this study, we aimed to investigate the urinary metabolic differences in male patients with PCa (n = 24), BCa (n = 29), and RCC (n = 12) to find the prominent combination of metabolites between cancers. Based on ¹H NMR analysis, orthogonal partial least-squares discriminant analysis was applied to find distinct metabolites among cancers. Moreover, the ranked analysis of covariance by adjusting a potential confounding as age revealed that 4-hydroxybenzoate, N-methylhydantoin, creatinine, glutamine, and acetate had significantly different metabolite levels among groups. The receiver operating characteristic analysis created by prominent five metabolites showed the great discriminatory accuracy with area under the curve (AUC) > 0.7 for BCa vs. RCC, PCa vs. BCa, and RCC vs. PCa. This preliminary study compares the metabolic profiles of BCa, PCa, and RCC, and reinforces the exploratory role of metabolomics in the investigation of human urine.

Liquid chromatography-mass spectrometry based metabolic characterization of pleural effusion in patients with acquired EGFR-TKI resistance

Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) acquired resistance remains a major barrier in the clinical treatment of lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutations. Despite extensive efforts, mechanism of acquired resistance has not yet been elucidated clearly. The subject of this study was to characterize the metabolic signatures relevant to acquired EGFR-TKI resistance in pleural effusion (PE), and identify potential biomarkers in PE of patients with acquired EGFR-TKI resistance. PE from EGFR-TKI untreated group (n = 30) and EGFR-TKI resistant group (n = 18) was analyzed using liquid chromatography-mass spectrometry (LCMS) based metabolomic. Multivariate statistical analysis revealed distinctive diff ;erences between the groups. A total of 34 significantly differential metabolites in PE were identified, among which, the acquired EGFR-TKI resistant group had higher levels of l-lysine, taurine, ornithine and citrulline, and lower levels of l-tryptophan, kynurenine, l-phenylalanine, l-leucine, N-formyl-l-methionine, 3-hydroxyphenylacetic acid and N-acetyl-d-phenylalanine in PE than that of the EGFR-TKI untreated group. These metabolites are mainly involved in six amino acid metabolic pathways. In addition, 3-hydroxyphenylacetic acid and N-acetyl-d-phenylalanine showed the highest AUC values of 0.934 and 0.929 in receiver operating characteristic analysis. Through LCMS metabolomics, our study identified potential biomarkers in PE, differentiating EGFR-TKI resistant patients from untreated patients, as well as the mechanisms underlying acquired EGFR-TKI resistance; thus, providing novel insights into acquired EGFR-TKI resistance.