Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Metabolomic profile of secondary hyperparathyroidism in patients with chronic kidney disease stages 3-5 not receiving dialysis

Introduction

Secondary hyperparathyroidism (SHPT) is a common and serious complication of chronic kidney disease (CKD). Elucidating the metabolic characteristics of SHPT may provide a new theoretical basis for its prevention and treatment. This study aimed to perform a metabolomic analysis of SHPT in patients with CKD stages 3-5 not receiving dialysis.

Methods

A total of 76 patients with CKD, 85 patients with CKD-SHPT, and 67 healthy controls were enrolled in this study. CKD was diagnosed according to the criteria specified in the Kidney Disease Improving Global Outcomes 2012 guidelines. SHPT was diagnosed by experienced clinicians according to the Renal Disease Outcomes Quality Initiative Clinical Practice Guidelines. Serum renal function markers and the lipid profile were analyzed. Untargeted ultra performance liquid chromatography-tandem mass spectrometry was used to analyze the serum metabolites of patients with CKD and SHPT. Multivariate analysis of the data was performed using principal component analysis and partial least square discriminant analysis. Serum differential metabolites were identified and further characterized using databases. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes database. Correlations between differential metabolites and clinical parameters were determined using the Spearman correlation.

Results

The serum metabolomic profiles of patients with CKD with and without SHPT differed significantly. Differential metabolites were mainly enriched in the top four Kyoto Encyclopedia of Genes and Genomes pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; sphingolipid metabolism; glycerophospholipid metabolism; and phenylalanine metabolism. In total, 31 differential metabolites were identified; of these, L-tryptophan and (R)-(+)-1-phenylethylamine were decreased, while other amino acids and their derivatives, uremia toxins, carnitine, and lipids, were increased significantly in patients with SHPT compared to those without. The 14 lipid metabolites were positively correlated with levels of Urea, serum creatinine, cystatin C, and triglycerides and negatively correlated with the estimated glomerular filtration rate and levels of total and high- and low-density lipoprotein cholesterol.

Discussion

Disturbed amino acid and lipid metabolism were more apparent in patients with SHPT than in those without. This metabolomic profile of SHPT may provide a therapeutic foundation for its future clinical management.

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.

Dual-mode fluorescence and colorimetric smartphone-based sensing platform with oxidation-induced self-assembled nanoflowers for sarcosine detection

BACKGROUND

Early prostatic cancer (PCa) diagnosis significantly improves the chances of successful treatment and enhances patient survival rates. Traditional enzyme cascade-based early cancer detection methods offer efficiency and signal amplification but are limited by cost, complexity, and enzyme dependency, affecting stability and practicality. Meanwhile, sarcosine (Sar) is commonly considered a biomarker for PCa development. It is essential to develop a Sar detection method based on cascade reactions, which should be efficient, low skill requirement, and suitable for on-site testing.

RESULTS

To address this, our study introduces the synthesis of organic-inorganic self-assembled nanoflowers to optimize existing detection methods. The Sar oxidase (SOX)-inorganic hybrid nanoflowers (Cu3(PO4)2:Ce@SOX) possess inherent fluorescent properties and excellent peroxidase activity, coupled with efficient enzyme loading. Based on this, we have developed a dual-mode multi-enzyme cascade nanoplatform combining fluorescence and colorimetric methods for the detection of Sar. The encapsulation yield of Cu3(PO4)2:Ce@SOX reaches 84.5 %, exhibiting a remarkable enhancement in catalytic activity by 1.26-1.29 fold compared to free SOX. The present study employing a dual-signal mechanism encompasses 'turn-off' fluorescence signals ranging from 0.5 μM to 60 μM, with a detection limit of 0.226 μM, and 'turn-on' colorimetric signals ranging from 0.18 μM to 60 μM, with a detection limit of 0.120 μM.

SIGNIFICANCE

Furthermore, our study developed an intelligent smartphone sensor system utilizing cotton swabs for real-time analysis of Sar without additional instruments. The nano-platform exhibits exceptional repeatability and stability, rendering it well-suited for detecting Sar in authentic human urine samples. This innovation allows for immediate analysis, offering valuable insights for portable and efficient biosensors applicable to Sar and other analytes.

Unveiling Disrupted Lipid Metabolism in Benign Prostate Hyperplasia, Prostate Cancer, and Metastatic Patients: Insights from a Colombian Nested Case-Control Study

Prostate cancer is a significant global health concern, and its prevalence is increasing worldwide. Despite extensive research efforts, the complexity of the disease remains challenging with respect to fully understanding it. Metabolomics has emerged as a powerful approach to understanding prostate cancer by assessing comprehensive metabolite profiles in biological samples. In this study, metabolic profiles of patients with benign prostatic hyperplasia (BPH), prostate cancer (PCa), and metastatic prostate cancer (Met) were characterized using an untargeted approach that included metabolomics and lipidomics via liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry. Comparative analysis among these groups revealed distinct metabolic profiles, primarily associated with lipid biosynthetic pathways, such as biosynthesis of unsaturated fatty acids, fatty acid degradation and elongation, and sphingolipid and linoleic acid metabolism. PCa patients showed lower levels of amino acids, glycerolipids, glycerophospholipids, sphingolipids, and carnitines compared to BPH patients. Compared to Met patients, PCa patients had reduced metabolites in the glycerolipid, glycerophospholipid, and sphingolipid groups, along with increased amino acids and carbohydrates. These altered metabolic profiles provide insights into the underlying pathways of prostate cancer's progression, potentially aiding the development of new diagnostic, and therapeutic strategies.

Current advances of liquid biopsies in prostate cancer: Molecular biomarkers

Prostate cancer (PCa) incidence is increasing and endangers men's lives. Early detection of PCa could improve overall survival (OS) by preventing metastasis. The prostate-specific antigen (PSA) test is a popular screening method. Several advisory groups, however, warn against using the PSA test due to its high false positive rate, unsupported outcome, and limited benefit. The number of disease-related biopsies performed annually far outweighs the number of diagnoses. Thus, there is an urgent need to develop accurate diagnostic biomarkers to detect PCa and distinguish between aggressive and indolent cancers. Recently, non-coding RNA (ncRNA), circulating tumor DNA (ctDNA)/ctRNA, exosomes, and metabolomic biomarkers in the liquid biopsies (LBs) of patients with PCa showed significant differences and clinical benefits in diagnosis, prognosis, and monitoring response to therapy. The analysis of urinary exosomal ncRNA presented a substantial correlation among Exos-miR-375 downregulation, clinical T stage, and bone metastases of PCa. Furthermore, the expression of miR-532-5p in urine samples was a vital predictive biomarker of PCa progression. Thus, this review focuses on promising molecular and metabolomic biomarkers in LBs from patients with PCa. We thoroughly addressed the most recent clinical findings of LB biomarker use in diagnosing and monitoring PCa in early and advanced stages.

The metabolomic plasma profile of patients with Duchenne muscular dystrophy: providing new evidence for its pathogenesis

BACKGROUND

Duchenne muscular dystrophy (DMD) is a fatal genetic muscle-wasting disease that affects 1 in 5000 male births with no current cure. Despite great progress has been made in the research of DMD, its underlying pathological mechanism based on the metabolomics is still worthy of further study. Therefore, it is necessary to gain a deeper understanding of the mechanisms or pathogenesis underlying DMD, which may reveal potential therapeutic targets and/or biomarkers.

RESULTS

Plasma samples from 42 patients with DMD from a natural history study and 40 age-matched healthy volunteers were subjected to a liquid chromatography-mass spectrometry-based non-targeted metabolomics approach. Acquired metabolic data were evaluated by principal component analysis, partial least squares-discriminant analysis, and metabolic pathway analysis to explore distinctive metabolic patterns in patients with DMD. Differentially expressed metabolites were identified using publicly available and integrated databases. By comparing the DMD and healthy control groups, 25 differential metabolites were detected, including amino acids, unsaturated fatty acids, carnitine, lipids, and metabolites related to the gut microbiota. Correspondingly, linoleic acid metabolism, D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, and alanine, aspartate, and glutamate metabolism were significantly altered in patients with DMD, compared with those of healthy volunteers.

CONCLUSIONS

Our study demonstrated the abnormal metabolism of amino acids, energy, and lipids in patients with DMD, consistent with pathological features, such as recurrent muscle necrosis and regeneration, interstitial fibrosis, and fat replacement. Additionally, we found that metabolites of intestinal flora were disordered in DMD patients, providing support for treatment of intestinal microbia disturbance in DMD diseases. Our study provides a new research strategy for understanding the pathogenesis of DMD.

Targeting Sphingosine 1-Phosphate Metabolism as a Therapeutic Avenue for Prostate Cancer

Prostate cancer (PC) is the second most common cancer in men worldwide. More than 65% of men diagnosed with PC are above 65. Patients with localized PC show high long-term survival, however with the disease progression into a metastatic form, it becomes incurable, even after strong radio- and/or chemotherapy. Sphingosine 1-phosphate (S1P) is a bioactive lipid that participates in all the steps of oncogenesis including tumor cell proliferation, survival, migration, invasion, and metastatic spread. The S1P-producing enzymes sphingosine kinases 1 and 2 (SK1 and SK2), and the S1P degrading enzyme S1P lyase (SPL), have been shown to be highly implicated in the onset, development, and therapy resistance of PC during the last 20 years. In this review, the most important studies demonstrating the role of S1P and S1P metabolic partners in PC are discussed. The different in vitro, ex vivo, and in vivo models of PC that were used to demonstrate the implication of S1P metabolism are especially highlighted. Furthermore, the most efficient molecules targeting S1P metabolism that are under preclinical and clinical development for curing PC are summarized. Finally, the possibility of targeting S1P metabolism alone or combined with other therapies in the foreseeable future as an alternative option for PC patients is discussed. Research Strategy: PubMed from INSB was used for article research. First, key words "prostate & sphingosine" were used and 144 articles were found. We also realized other combinations of key words as "prostate cancer bone metastasis" and "prostate cancer treatment". We used the most recent reviews to illustrate prostate cancer topic and sphingolipid metabolism overview topic.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. “Classical” tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Relationship between 4-Hydroxynonenal (4-HNE) as Systemic Biomarker of Lipid Peroxidation and Metabolomic Profiling of Patients with Prostate Cancer

An oxidative degradation product of the polyunsaturated fatty acids, 4-hydroxynonenal (4-HNE), is of particular interest in cancer research due to its concentration-dependent pleiotropic activities affecting cellular antioxidants, metabolism, and growth control. Although an increase in oxidative stress and lipid peroxidation was already associated with prostate cancer progression a few decades ago, the knowledge of the involvement of 4-HNE in prostate cancer tumorigenesis is limited. This study investigated the appearance of 4-HNE-protein adducts in prostate cancer tissue by immunohistochemistry using a genuine 4-HNE monoclonal antibody. Plasma samples of the same patients and samples of the healthy controls were also analyzed for the presence of 4-HNE-protein adducts, followed by metabolic profiling using LC-ESI-QTOF-MS and GC-EI-Q-MS. Finally, the analysis of the metabolic pathways affected by 4-HNE was performed. The obtained results revealed the absence of 4-HNE-protein adducts in prostate carcinoma tissue but increased 4-HNE-protein levels in the plasma of these patients. Metabolomics revealed a positive association of different long-chain and medium-chain fatty acids with the presence of prostate cancer. Furthermore, while linoleic acid positively correlated with the levels of 4-HNE-protein adducts in the blood of healthy men, no correlation was obtained for cancer patients indicating altered lipid metabolism in this case. The metabolic pathway of unsaturated fatty acids biosynthesis emerged as significantly affected by 4-HNE. Overall, this is the first study linking 4-HNE adduction to plasma proteins with specific alterations in the plasma metabolome of prostate cancer patients. This study revealed that increased 4-HNE plasma protein adducts could modulate the unsaturated fatty acids biosynthesis pathway. It is yet to be determined if this is a direct result of 4-HNE or whether they are produced by the same underlying mechanisms. Further mechanistic studies are needed to grasp the biological significance of the observed changes in prostate cancer tumorigenesis.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

Identification of biomarkers associated with the feed efficiency by metabolomics profiling: results from the broiler lines divergent for high or low abdominal fat content

Background

Improving feed efficiency (FE) is one of the main objectives in broiler breeding. It is difficult to directly measure FE traits, and breeders hence have been trying to identify biomarkers for the indirect selection and improvement of FE traits. Metabolome is the "bridge" between genome and phenome. The metabolites may potentially account for more of the phenotypic variation and can suitably serve as biomarkers for selecting FE traits. This study aimed to identify plasma metabolite markers for selecting high-FE broilers. A total of 441 birds from Northeast Agricultural University broiler lines divergently selected for abdominal fat content were used to analyze plasma metabolome and estimate the genetic parameters of differentially expressed metabolites.

Results

The results identified 124 differentially expressed plasma metabolites (P < 0.05) between the lean line (high-FE birds) and the fat line (low-FE birds). Among these differentially expressed plasma metabolites, 44 were found to have higher positive or negative genetic correlations with FE traits (|r_g| ≥ 0.30). Of these 44 metabolites, 14 were found to display moderate to high heritability estimates (h² ≥ 0.20). However, among the 14 metabolites, 4 metabolites whose physiological functions have not been reported were excluded. Ultimately, 10 metabolites were suggested to serve as the potential biomarkers for breeding the high-FE broilers. Based on the physiological functions of these metabolites, reducing inflammatory and improving immunity were proposed to improve FE and increase production efficiency.

Conclusions

According to the pipeline for the selection of the metabolite markers established in this study, it was suggested that 10 metabolites including 7-ketocholesterol, dimethyl sulfone, epsilon-(gamma-glutamyl)-lysine, gamma-glutamyltyrosine, 2-oxoadipic acid, L-homoarginine, testosterone, adenosine 5'-monophosphate, adrenic acid, and calcitriol could be used as the potential biomarkers for breeding the "food-saving broilers".

Sánchez B, León C, Nozal L, Mora-Rodríguez JM, Castro F, Crego AL, Díaz-Laviada I. Metabolic fingerprinting of chemotherapy-resistant prostate cancer stem cells. An untargeted metabolomic approach by liquid chromatography-mass spectrometry

Chemoresistance is one of the most important challenges in cancer therapy. The presence of cancer stem cells within the tumor may contribute to chemotherapy resistance since these cells express high levels of extrusion pumps and xenobiotic metabolizing enzymes that inactivate the therapeutic drug. Despite the recent advances in cancer cell metabolism adaptations, little is known about the metabolic adaptations of the cancer stem cells resistant to chemotherapy. In this study, we have undertaken an untargeted metabolomic analysis by liquid chromatography–high-resolution spectrometry combined with cytotoxicity assay, western blot, quantitative real-time polymerase chain reaction (qPCR), and fatty acid oxidation in a prostate cancer cell line resistant to the antiandrogen 2-hydroxiflutamide with features of cancer stem cells, compared to its parental androgen-sensitive cell line. Metabolic fingerprinting revealed 106 out of the 850 metabolites in ESI+ and 67 out of 446 in ESI- with significant differences between the sensitive and the resistant cell lines. Pathway analysis performed with the unequivocally identified metabolites, revealed changes in pathways involved in energy metabolism as well as posttranscriptional regulation. Validation by enzyme expression analysis indicated that the chemotherapy-resistant prostate cancer stem cells were metabolically dormant with decreased fatty acid oxidation, methionine metabolism and ADP-ribosylation. Our results shed light on the pathways underlying the entry of cancer cells into dormancy that might contribute to the mechanisms of drug resistance.

Significant metabolic alterations in non-small cell lung cancer patients by epidermal growth factor receptor-targeted therapy and PD-1/PD-L1 immunotherapy

Background: Cancer-related deaths are primarily attributable to lung cancer, of which non-small cell lung cancer (NSCLC) is the most common type. Molecular targeting therapy and antitumor immunotherapy have both made great strides in the treatment of NSCLC, but their underlying mechanisms remain unclear, especially from a metabolic perspective.

Methods: Herein, we used a nontargeted metabolomics approach based on liquid chromatography-mass spectrometry to analyze the metabolic response of NSCLC patients to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) or PD-1/PD-L1 inhibitors. Multiple analyses, including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA) and pathway analysis, were used for metabolic data analysis. Additionally, differential metabolites were analysed and identified by publically available and integrated databases.

Results: After treatment with EGFR-TKIs or PD-1/PD-L1 inhibitors, glutamate/glutamine, phenylalanine, n-acetyl-l-leucine, n-acetyl-d-tryptophan, D-n-valine, arachidonic acid, and linoleic acid levels were significantly increased in patients with NSCLC, whereas carnitine, stearyl carnitine, palmitoyl carnitine, linoleic carnitine, and palmitic acid levels were markedly decreased. Compared with newly diagnosed, untreated patients, there were three shared metabolic pathways (phenylalanine metabolism, glycerophospholipid metabolism, and D-glutamine and D-glutamate metabolism) in the EGFR-TKIs or PD-1/PD-L1 inhibitor-treated groups, all of which were related to lipid and amino acid metabolism. Moreover, there were significant differences in lipid metabolism (glycerophospholipid metabolism and phosphatidylinositol signaling) and amino acid metabolism (tryptophan metabolism) between the EGFR-TKI and PD-1/PD-L1 inhibitor groups.

Conclusion: Our results show that EGFR-TKIs and PD-1/PD-L1 inhibitors induce changes in carnitine, amino acids, fatty acids, and lipids and alter related metabolic pathways in NSCLC patients. Endogenous metabolism changes occur due to drug action and might be indicative of antitumor therapeutic effect. These findings will provide new clues for identifying the antitumor mechanism of these two treatments from the perspective of metabolism.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.