Metabolomic signatures of aggressive prostate cancer

PSA, an outdated biomarker for prostate cancer: In search of a more specific biomarker, citrate takes the spotlight

The prevailing biomarker employed for prostate cancer (PCa) screening and diagnosis is the prostate-specific antigen (PSA). Despite excellent sensitivity, PSA lacks specificity, leading to false positives, unnecessary biopsies and overdiagnosis. Consequently, PSA is increasingly less used by clinicians, thus underscoring the imperative for the identification of new biomarkers. An emerging biomarker in this context is citrate, a molecule secreted by the normal prostate, which has been shown to be inversely correlated with PCa. Here, we discuss about PSA and its usage for PCa diagnosis, its lack of specificity, and the various conditions that can affect its levels. We then provide our vision about what we think would be a valuable addition to our PCa diagnosis toolkit, citrate. We describe the unique citrate metabolic program in the prostate and how this profile is reprogrammed during carcinogenesis. Finally, we summarize the evidence that supports the usage of citrate as a biomarker for PCa diagnosis, as it can be measured in various patient samples and be analyzed by several methods. The unique relationship between citrate and PCa, combined with the stability of citrate levels in other prostate-related conditions and the simplicity of its detection, further accentuates its potential as a biomarker.

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.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Plasma metabolomics profiling of 580 patients from an Early Detection Research Network prostate cancer cohort

Prostate cancer is the second most common cancer in men and affects 1 in 9 men in the United States. Early screening for prostate cancer often involves monitoring levels of prostate-specific antigen (PSA) and performing digital rectal exams. However, a prostate biopsy is always required for definitive cancer diagnosis. The Early Detection Research Network (EDRN) is a consortium within the National Cancer Institute aimed at improving screening approaches and early detection of cancers. As part of this effort, the Weill Cornell EDRN Prostate Cancer has collected and biobanked specimens from men undergoing a prostate biopsy between 2008 and 2017. In this report, we describe blood metabolomics measurements for a subset of this population. The dataset includes detailed clinical and prospective records for 580 patients who underwent prostate biopsy, 287 of which were subsequentially diagnosed with prostate cancer, combined with profiling of 1,482 metabolites from plasma samples collected at the time of biopsy. We expect this dataset to provide a valuable resource for scientists investigating prostate cancer metabolism.

Evaluation of prostate cancer tissue metabolomics: would clinics utilise it for diagnosis?

The difficulty of diagnosing prostate cancer (PC) with the available biomarkers frequently leads to over-diagnosis and overtreatment of PC, underscoring the need for novel molecular signatures. The purpose of this review is to provide a summary of the currently available cellular metabolomics for PC molecular signatures. A comprehensive search on PubMed was conducted to find studies published between January 2004 and August 2022 that reported biomarkers for PC detection, development, aggressiveness, recurrence and treatment response. Although potential studies have reported the presence of distinguishing molecules that can distinguish between benign and cancerous prostate tissue. However, there are few studies looking into signature molecules linked to disease development, therapy response or tumour recurrence. The majority of these studies use high-dimensional datasets, and the number of potential metabolites investigated frequently exceeds the size of the available samples. In light of this, pre-analytical, statistical, methodological and confounding factors such as antiandrogen therapy (NAT) may also be linked to the identified chemometric multivariate differences between PC and relevant control samples in the datasets. Despite the methodological and procedural challenges, a range of methodological groups and processes have consistently identified a number of signature metabolites and pathways that appear to imply a substantial involvement in the cellular metabolomics of PC for tumour formation and recurrence.

Underlying Features of Prostate Cancer-Statistics, Risk Factors, and Emerging Methods for Its Diagnosis

Prostate cancer (PCa) is the most frequently occurring type of malignant tumor and a leading cause of oncological death in men. PCa is very heterogeneous in terms of grade, phenotypes, and genetics, displaying complex features. This tumor often has indolent growth, not compromising the patient's quality of life, while its more aggressive forms can manifest rapid growth with progression to adjacent organs and spread to lymph nodes and bones. Nevertheless, the overtreatment of PCa patients leads to important physical, mental, and economic burdens, which can be avoided with careful monitoring. Early detection, even in the cases of locally advanced and metastatic tumors, provides a higher chance of cure, and patients can thus go through less aggressive treatments with fewer side effects. Furthermore, it is important to offer knowledge about how modifiable risk factors can be an effective method for reducing cancer risk. Innovations in PCa diagnostics and therapy are still required to overcome some of the limitations of the current screening techniques, in terms of specificity and sensitivity. In this context, this review provides a brief overview of PCa statistics, reporting its incidence and mortality rates worldwide, risk factors, and emerging screening strategies.

Extensive metabolic consequences of human glycosyltransferase gene knockouts in prostate cancer

BACKGROUND

Naturally occurring germline gene deletions (KO) represent a unique setting to interrogate gene functions. Complete deletions and differential expression of the human glycosyltransferase UGT2B17 and UGT2B28 genes are linked to prostate cancer (PCa) risk and progression, leukaemia, autoimmune and other diseases.

METHODS

The systemic metabolic consequences of UGT deficiencies were examined using untargeted and targeted mass spectrometry-based metabolomics profiling of carefully matched, treatment-naive PCa cases.

RESULTS

Each UGT KO differentially affected over 5% of the 1545 measured metabolites, with divergent metabolic perturbations influencing the same pathways. Several of the perturbed metabolites are known to promote PCa growth, invasion and metastasis, including steroids, ceramides and kynurenine. In UGT2B17 KO, reduced levels of inactive steroid-glucuronides were compensated by sulfated derivatives that constitute circulating steroid reservoirs. UGT2B28 KO presented remarkably lower levels of oxylipins paralleled by reduced inflammatory mediators, but higher ceramides unveiled as substrates of the enzyme in PCa cells.

CONCLUSION

The distinctive and broad metabolic rewiring caused by UGT KO reinforces the need to examine their unique and divergent functions in PCa biology.

Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management

BACKGROUND

Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients.

OBJECTIVE

The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour.

CONCLUSION

This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.

Ubiquitin-related lncRNAs: The new tool for prognosis prediction in prostate cancer

Objective

To establish a ubiquitin-related long noncoding ribonucleic acids (lncRNAs) prognosis prediction model for prostate cancer (Pca).

Methods

Data were acquired through The Cancer Genome Atlas (TCGA) database. Ubiquitin-related differentially expressed genes (DEGs) and lncRNAs in Pca were filtered out. UBE2S was selected as the representative gene and validated in vitro. Progression-free survival (PFS) predictive signature was established with ubiquitin-related lncRNAs screened by Cox regression analyses and internally validated. A nomogram was constructed to assess the prognosis of Pca patients. Gene enrichment analysis was performed to explore functional differences based on risk stratification. Between different risk groups, immune status and drug sensitivity were contrasted.

Results

A total of 254 ubiquitin-related genes were screened. UBE2S was shown to promote the proliferation of Pca cells in vitro. The predictive signature was established based on six ubiquitin-related lncRNAs and validated. The prognosis of Pca patients was worse with an increasing risk score. The area under the curve (AUC) of the signature was higher than that of clinicopathological variables (0.806 vs 0.504–0.701). The AUC was 0.811 for 1-year PFS, 0.807 for 3-year PFS, and 0.790 for 5-year PFS. The calibration curves of risk score-based nomogram demonstrated high consistency. By contrasting the expression of immune function, cells, and checkpoints, we found that the signature was closely related to immunity. The high-risk patients were more sensitive to gemcitabine, cisplatin, bortezomib, etc. and resistant to bicalutamide.

Conclusion

The ubiquitin-related lncRNAs can effectively predict the prognosis of Pca and may provide new treatment options for Pca.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

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.

Recent advances in nanomaterials for prostate cancer detection and diagnosis

Despite the significant progress in the discovery of biomarkers and the exploitation of technologies for prostate cancer (PCa) detection and diagnosis, the initial screening of these PCa-related biomarkers using current...

Development of simultaneous quantitative analysis of tricarboxylic acid cycle metabolites to identify specific metabolites in cancer cells by targeted metabolomic approach

The tricarboxylic acid (TCA) cycle is one of the most important pathways of energy metabolism, and the profiles of its components are influenced by factors such as diseases and diets. Therefore, the differences in metabolic profile of TCA cycle between healthy and cancer cells have been the focus of studies to understand pathological conditions. In this study, we developed a quantitative method to measure TCA cycle metabolites using LC-MS/MS to obtain useful metabolic profiles for development of diagnostic and therapeutic methods for cancer. We successfully analyzed 11 TCA cycle metabolites by LC MS/MS with high reproducibility by using a PFP column with 0.5% formic acid as a mobile phase. Next, we analyzed the concentration of TCA cycle metabolites in human cell lines (HaCaT: normal skin keratinocytes; A431: skin squamous carcinoma cells; SW480: colorectal cancer cells). We observed reduced concentration of succinate and increased concentration of citrate, 2-hydroxyglutarate, and glutamine in A431 cells as compared with HaCaT cells. On the other hand, decreased concentration of isocitrate, fumarate, and α-ketoglutarate and increased concentration of malate, glutamine, and glutamate in A431 cells were observed in comparison with SW480 cells. These findings suggested the possibility of identifying disease-specific metabolites and/or organ-specific metabolites by using this targeted metabolomic analysis.

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

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

Beyond liquid biopsy: Toward non-invasive assays for distanced cancer diagnostics in pandemics

Liquid biopsy technologies have seen a significant improvement in the last decade, offering the possibility of reliable analysis and diagnosis from several biological fluids. The use of these technologies can overcome the limits of standard clinical methods, related to invasiveness and poor patient compliance. Along with this there are now mature examples of lab-on-chips (LOC) which are available and could be an emerging and breakthrough technology for the present and near-future clinical demands that provide sample treatment, reagent addition and analysis in a sample-in/answer-out approach. The possibility of combining non-invasive liquid biopsy and LOC technologies could greatly assist in the current need for minimizing exposure and transmission risks. The recent and ongoing pandemic outbreak of SARS-CoV-2, indeed, has heavily influenced all aspects of life worldwide. Ordinary tasks have been forced to switch from “in presence” to “distanced”, limiting the possibilities for a large number of activities in all fields of life outside of the home. Unfortunately, one of the settings in which physical distancing has assumed noteworthy consequences is the screening, diagnosis and follow-up of diseases.

In this review, we analyse biological fluids that are easily collected without the intervention of specialized personnel and the possibility that they may be used -or not-for innovative diagnostic assays. We consider their advantages and limitations, mainly due to stability and storage and their integration into Point-of-Care diagnostics, demonstrating that technologies in some cases are mature enough to meet current clinical needs.

Fatty Acid Synthesis in Prostate Cancer: Vulnerability or Epiphenomenon?

Tumor metabolism supports the energetic and biosynthetic needs of rapidly proliferating cancer cells and modifies intra- and intercellular signaling to enhance cancer cell invasion, metastasis, and immune evasion. Prostate cancer exhibits unique metabolism with high rates of de novo fatty acid synthesis driven by activation of the androgen receptor (AR). Increasing evidence suggests that activation of this pathway is functionally important to promote prostate cancer aggressiveness. However, the mechanisms by which fatty acid synthesis are beneficial to prostate cancer have not been well defined. In this review, we summarize evidence indicating that fatty acid synthesis drives progression of prostate cancer. We also explore explanations for this phenomenon and discuss future directions for targeting this pathway for patient benefit.

Plasma Sarcosine Measured by Gas Chromatography-Mass Spectrometry Distinguishes Prostatic Intraepithelial Neoplasia and Prostate Cancer from Benign Prostate Hyperplasia

OBJECTIVE

Sarcosine was postulated in 2009 as a biomarker for prostate cancer (PCa). Here, we assess plasma sarcosine as a biomarker that is complementary to prostate-specific antigen (PSA).

METHODS

Plasma sarcosine was measured using gas chromatography-mass spectrometry (GC-MS) in adults classified as noncancerous controls (with benign prostate hyperplasia [BPH], n = 36), with prostatic intraepithelial neoplasia (PIN, n = 16), or with PCa (n = 27). Diagnostic accuracy was assessed using receiver operating characteristic curve analysis.

RESULTS

Plasma sarcosine levels were higher in the PCa (2.0 µM [1.3-3.3 µM], P <.01) and the PIN (1.9 µM [1.2-6.5 µM], P <.001) groups than in the BPH (0.9 µM [0.6-1.4 µM]) group. Plasma sarcosine had "good" and "very good" discriminative capability to detect PIN (area under the curve [AUC], 0.734) and PCa (AUC, 0.833) versus BPH, respectively. The use of PSA and sarcosine together improved the overall diagnostic accuracy to detect PIN and PCa versus BPH.

CONCLUSION

Plasma sarcosine measured by GC-MS had "good" and "very good" classification performance for distinguishing PIN and PCa, respectively, relative to noncancerous patients diagnosed with BPH.

A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer

OBJECTIVE

Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH).

METHODS

We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy.

RESULTS

We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1-2 and 4-5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3).

CONCLUSIONS

A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.

Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI

Background

Prostate cancer tissues are inherently heterogeneous, which presents a challenge for metabolic profiling using traditional bulk analysis methods that produce an averaged profile. The aim of this study was therefore to spatially detect metabolites and lipids on prostate tissue sections by using mass spectrometry imaging (MSI), a method that facilitates molecular imaging of heterogeneous tissue sections, which can subsequently be related to the histology of the same section.

Methods

Here, we simultaneously obtained metabolic and lipidomic profiles in different prostate tissue types using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MSI. Both positive and negative ion mode were applied to analyze consecutive sections from 45 fresh-frozen human prostate tissue samples (N = 15 patients). Mass identification was performed with tandem MS.

Results

Pairwise comparisons of cancer, non-cancer epithelium, and stroma revealed several metabolic differences between the tissue types. We detected increased levels of metabolites crucial for lipid metabolism in cancer, including metabolites involved in the carnitine shuttle, which facilitates fatty acid oxidation, and building blocks needed for lipid synthesis. Metabolites associated with healthy prostate functions, including citrate, aspartate, zinc, and spermine had lower levels in cancer compared to non-cancer epithelium. Profiling of stroma revealed higher levels of important energy metabolites, such as ADP, ATP, and glucose, and higher levels of the antioxidant taurine compared to cancer and non-cancer epithelium.

Conclusions

This study shows that specific tissue compartments within prostate cancer samples have distinct metabolic profiles and pinpoint the advantage of methodology providing spatial information compared to bulk analysis. We identified several differential metabolites and lipids that have potential to be developed further as diagnostic and prognostic biomarkers for prostate cancer. Spatial and rapid detection of cancer-related analytes showcases MALDI-TOF MSI as a promising and innovative diagnostic tool for the clinic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00242-z.

Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine

Prostate cancer (PC) is one of the most common male cancers worldwide. Until now, there is no consensus about using urinary metabolomic profiling as novel biomarkers to identify PC. In this study, urine samples from 50 PC patients and 50 non-cancerous individuals (control group) were collected. Based on 1H nuclear magnetic resonance (1H-NMR) analysis, 20 metabolites were identified. Subsequently, principal component analysis (PCA), partial least squares-differential analysis (PLS-DA) and ortho-PLS-DA (OPLS-DA) were applied to find metabolites to distinguish PC from the control group. Furthermore, Wilcoxon test was used to find significant differences between the two groups in metabolite urine levels. Guanidinoacetate, phenylacetylglycine, and glycine were significantly increased in PC, while L-lactate and L-alanine were significantly decreased. The receiver operating characteristics (ROC) analysis revealed that the combination of guanidinoacetate, phenylacetylglycine, and glycine was able to accurately differentiate 77% of the PC patients with sensitivity = 80% and a specificity = 64%. In addition, those three metabolites showed significant differences in patients stratified for Gleason score 6 and Gleason score ≥7, indicating potential use to detect significant prostate cancer. Pathway enrichment analysis using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and the SMPDB (The Small Molecule Pathway Database) revealed potential involvement of KEGG "Glycine, Serine, and Threonine metabolism" in PC. The present study highlights that guanidinoacetate, phenylacetylglycine, and glycine are potential candidate biomarkers of PC. To the best knowledge of the authors, this is the first study identifying guanidinoacetate, and phenylacetylglycine as potential novel biomarkers in PC.

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (P _adjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (P _adjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. IMPLICATIONS: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients

Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.

Human Prostate Cancer is Characterized by an Increase in Urea Cycle Metabolites

Despite it being the most common incident of cancer among men, the pathophysiological mechanisms contributing to prostate cancer (PCa) are still poorly understood. Altered mitochondrial metabolism is postulated to play a role in the development of PCa. To determine the key metabolites (which included mitochondrial oncometabolites), benign prostatic and cancer tissues of patients with PCa were analyzed using capillary electrophoresis and liquid chromatography coupled with mass spectrometry. Gene expression was studied using real-time PCR. In PCa tissues, we found reduced levels of early tricarboxylic acid cycle metabolites, whereas the contents of urea cycle metabolites including aspartate, argininosuccinate, arginine, proline, and the oncometabolite fumarate were higher than that in benign controls. Fumarate content correlated positively with the gene expression of oncogenic HIF1α and NFκB pathways, which were significantly higher in the PCa samples than in the benign controls. Furthermore, data from the TCGA database demonstrated that prostate cancer patients with activated NFκB pathway had a lower survival rate. In summary, our data showed that fumarate content was positively associated with carcinogenic genes.

Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status

Background

Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore between TMPRSS2-ERG rearrangement-positive and -negative PC subclasses.

Methods

Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (¹H HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by ¹H/³¹P NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort.

Results

The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and α-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in β-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress.

Conclusions

Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.

Rapid Prostate Cancer Noninvasive Biomarker Screening Using Segmented Flow Mass Spectrometry-Based Untargeted Metabolomics

Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.

The ternary complex factor protein ELK1 is an independent prognosticator of disease recurrence in prostate cancer

BACKGROUND

Both hormone-sensitive and castration- and enzalutamide-resistant prostate cancers (PCa) depend on the ternary complex factor (TCF) protein ELK1 to serve as a tethering protein for the androgen receptor (AR) to activate a critical set of growth genes. The two sites in ELK1 required for AR binding are conserved in other members of the TCF subfamily, ELK3 and ELK4. Here we examine the potential utility of the three proteins as prognosticators of disease recurrence in PCa.

METHODS

Transcriptional activity assays; Retrospective analysis of PCa recurrence using data on 501 patients in The Cancer Genome Atlas (TCGA) database; Unpaired Wilcoxon rank-sum test and multiple comparison correction using the Holm's method; Spearman's correlations; Kaplan-Meier methods; Univariable and multivariable Cox regression analyses; LASSO-based penalized Cox regression models; Time-dependent area under the receiver operating characteristic (ROC) curve.

RESULTS

ELK4 but not ELK3 was coactivated by AR similar to ELK1. Tumor expression of neither ELK3 nor ELK4 was associated with disease-free survival (DFS). ELK1 was associated with higher clinical T-stage, pathology T-stage, Gleason score, prognostic grade, and positive lymph node status. ELK1 was a negative prognosticator of DFS, independent of ELK3, ELK4, clinical T-stage, pathology T-stage, prognostic grade, lymph node status, age, and race. Inclusion of ELK1 increased the abilities of the Oncotype DX and Prolaris gene panels to predict disease recurrence, correctly predicting disease recurrence in a unique subset of patients.

CONCLUSIONS

ELK1 is a strong, independent prognosticator of disease recurrence in PCa, underscoring its unique role in PCa growth. Inclusion of ELK1 may enhance the utility of currently used prognosticators for clinical decision making in prostate cancer.

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Downregulation of CENPF Remodels Prostate Cancer Cells and Alters Cellular Metabolism

Metabolic alterations in prostate cancer (PC) are associated with progression and aggressiveness. However, the underlying mechanisms behind PC metabolic functions are unknown. The authors' group recently reported on the important role of centromere protein F (CENPF), a protein associated with the centromere-kinetochore complex and chromosomal segregation during mitosis, in PC MRI visibility. This study focuses on discerning the role of CENPF in metabolic perturbation in human PC3 cells. A series of bioinformatics analyses shows that CENPF is one gene that is strongly associated with aggressive PC and that its expression is positively correlated with metastasis. By identifying and reconstructing the CENPF network, additional associations with lipid regulation are found. Further untargeted metabolomics analysis using gas chromatography-time-of-flight-mass spectrometry reveals that silencing of CENPF alters the global metabolic profiles of PC cells and inhibits cell proliferation, which suggests that CENPF may be a critical regulator of PC metabolism. These findings provide useful scientific insights that can be applied in future studies investigating potential targets for PC treatment.

Metabolomics Contributions to the Discovery of Prostate Cancer Biomarkers

Prostate cancer (PCa) is one of the most frequently diagnosed cancers and a leading cause of death among men worldwide. Despite extensive efforts in biomarker discovery during the last years, currently used clinical biomarkers are still lacking enough specificity and sensitivity for PCa early detection, patient prognosis, and monitoring. Therefore, more precise biomarkers are required to improve the clinical management of PCa patients. In this context, metabolomics has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids. Thus, changes in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism have been reported in different studies analyzing PCa patients' biofluids. The review provides an up-to-date summary of the main metabolic alterations that have been described in biofluid-based studies of PCa patients, as well as a discussion regarding their potential to improve clinical PCa diagnosis and prognosis. Furthermore, a summary of the most significant findings reported in these studies and the connections and interactions between the different metabolic changes described has also been included, aiming to better describe the specific metabolic signature associated to PCa.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

Metabolomics Biomarkers of Prostate Cancer: A Systematic Review

Prostate cancer (PCa) diagnosis with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment, highlighting the need for novel biomarkers. The aim of this review is to provide a summary of available metabolomics PCa biomarkers, particularly for clinically significant disease. A systematic search was conducted on PubMed for publications from July 2008 to July 2018 in accordance with PRISMA guidelines to report biomarkers with respect to their application in PCa diagnosis, progression, aggressiveness, recurrence, and treatment response. The vast majority of studies report biomarkers with the ability to distinguish malignant from benign prostate tissue with a few studies investigating biomarkers associated with disease progression, treatment response or tumour recurrence. In general, these studies report high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially also be associated with pre-analytical, technical, statistical and confounding factors. Giving the technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence.

Approaches to urinary detection of prostate cancer

Background:

Prostate cancer is the most common cancer in American men that ranges from low risk states amenable to active surveillance to high risk states that can be lethal especially if untreated. There is a critical need to develop relatively non-invasive and clinically useful methods for screening, detection, prognosis, disease monitoring, and prediction of treatment efficacy. In this review, we focus on important advances as well as future efforts needed to drive clinical innovation in this area of urine biomarker research for prostate cancer detection and prognostication.

Methods:

We provide a review of current literature on urinary biomarkers for prostate cancer. We evaluate the strengths and limitations of a variety of approaches that vary in sampling strategies and targets measured; discuss reported urine tests for prostate cancer with respect to their technical, analytical, and clinical parameters; and provide our perspectives on critical considerations in approaches to developing a urine-based test for prostate cancer.

Results:

There has been an extensive history of exploring urine as a source of biomarkers for prostate cancer that has resulted in a variety of urine tests that are in current clinical use. Importantly, at least three tests have demonstrated high sensitivity (~90%) and negative predictive value (~95%) for clinically significant tumors; however, there has not been widespread adoption of these tests.

Conclusions:

Conceptual and methodological advances in the field will help to drive the development of novel urinary tests that in turn may lead to a shift in the clinical paradigm for prostate cancer diagnosis and management.

Surface-enhanced Raman spectroscopy before radical prostatectomy predicts biochemical recurrence better than CAPRA-S

Objective

The objective of this study was to evaluate the performance of surface-enhanced Raman spectroscopy (SERS) in the prediction of early biochemical recurrence after radical prostatectomy (RP).

Patients and methods

We synthesized monodisperse gold nanoparticles as SERS-enhanced substrates and analyzed preoperative plasma samples of patients who underwent RP. The roles of clinical risk model (Cancer of the Prostate Risk Assessment [CAPRA] score) and distinctive SERS spectra on prediction of early biochemical recurrence were evaluated. The principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage the spectral data and develop diagnostic algorithm.

Results

A total of 306 preoperative plasma Raman spectra from 102 patients were collected. SERS spectrum from those who developed early biochemical recurrence were compared to those who remained biochemical recurrence-free. The SERS detected more abundant circulating free nucleic acid bases in biochemical recurrence population, presenting significant stronger intensities at SERS spectral bands 725 and 1,328 cm⁻¹. The addition of Raman spectral peak 1,328 cm⁻¹ to CAPRA postsurgical (CAPRA-S) score significantly improved the predictive power of logistic regression model compared to simple CAPRA score (P<0.001). Meanwhile, the leave-one-out cross-validation method was used to validate the PCA-LDA model and revealed the sensitivity, specificity, and accuracy of 65.8%, 87.5%, and 79.4%, respectively. The receiver operating characteristic (ROC) curve was used to evaluate the performance of different models. Area under the ROC curve of the CAPRA-S score model alone was 0.77, however, when combined with Raman spectral peak 1,328 cm⁻¹, it improved to 0.81.

Conclusion

Our primary results suggested that SERS could be a meaningful technique for prediction of early biochemical recurrence in prostate cancer.

Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways

Prostate cancer treatment is based on the stratification of disease as low-, intermediate- or high-risk. This stratification has been largely based on anatomic pathology of the disease, as well as through the use of prostate specific antigen (PSA). However, despite this stratification, there remains heterogeneity within the current classification schema. Utilizing a metabolic approach may help to further establish novel biomolecular markers of disease aggressiveness. These markers may eventually be useful in not only the diagnosis of disease but in creating tumor specific targeted therapy for improved clinical outcomes.

Metabolic Vulnerabilities of Prostate Cancer: Diagnostic and Therapeutic Opportunities

Cancer cells hijack metabolic pathways to support bioenergetics and biosynthetic requirements for their uncontrolled growth. Thus, cancer can be considered as a metabolic disease. In this review, we discuss the main metabolic features of prostate cancer with a particular focus on the link between oncogene-directed cancer metabolic regulation, metabolism rewiring, and epigenetic regulation. The potential of using metabolic profiling as a means to predict disease behavior and to identify novel therapeutic targets and new diagnostic markers will be addressed as well as the current challenges in metabolomics analyses. Finally, diagnostic and prognostic metabolic imaging approaches, including positron emission tomography, mass spectrometry, nuclear magnetic resonance, and their translational applications, will be discussed. Here, we emphasize how targeting metabolic vulnerabilities in prostate cancer may pave the way for novel personalized diagnostic and therapeutic interventions.

Liquid Biopsy Potential Biomarkers in Prostate Cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide with an incidence of 14.8% and a mortality of 6.6%. Shortcomings in comprehensive medical check-ups in low- and middle-income countries lead to delayed detection of PCa and are causative of high numbers of advanced PCa cases at first diagnosis. The performance of available biomarkers is still insufficient and limited applicability, including logistical and financial burdens, impedes comprehensive implementation into health care systems. There is broad agreement on the need of new biomarkers to improve (i) early detection of PCa, (ii) risk stratification, (iii) prognosis, and (iv) treatment monitoring. This review focuses on liquid biopsy tests distinguishing high-grade significant (Gleason score (GS) ≥ 7) from low-grade indolent PCa. Available biomarkers still lack performance in risk stratification of biopsy naïve patients. However, biomarkers with highly negative predictive values may help to reduce unnecessary biopsies. Risk calculators using integrative scoring systems clearly improve decision-making for invasive prostate biopsy. Emerging biomarkers have the potential to substitute PSA and improve the overall performance of risk calculators. Until then, PSA should be used and may be replaced whenever enough evidence has accumulated for better performance of a new biomarker.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

NMR-based metabolomics studies of human prostate cancer tissue

INTRODUCTION

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. Serum prostate-specific antigen (PSA) remains the most used biomarker in the detection and management of patients with PCa, in spite of the problems related with its low specificity, false positive rate and overdiagnosis. Furthermore, PSA is unable to discriminate indolent from aggressive PCa, which can lead to overtreatment. Early diagnosed and treated PCa can have a good prognosis and is potentially curable. Therefore, the discovery of new biomarkers able to detect clinically significant aggressive PCa is urgently needed.

METHODS

This revision was based on an electronic literature search, using Pubmed, with Nuclear Magnetic Resonance (NMR), tissue and prostate cancer as keywords. All metabolomic studies performed in PCa tissues by NMR spectroscopy, from 2007 until March 2018, were included in this review.

RESULTS

In the context of cancer, metabolomics allows the analysis of the entire metabolic profile of cancer cells. Several metabolic alterations occur in cancer cells to sustain their abnormal rates of proliferation. NMR proved to be a suitable methodology for the evaluation of these metabolic alterations in PCa tissues, allowing to unveil alterations in citrate, spermine, choline, choline-related compounds, lactate, alanine and glutamate.

CONCLUSION

The study of the metabolic alterations associated with PCa progression, accomplished by the analysis of PCa tissue by NMR, offers a promising approach for elucidating biochemical pathways affected by PCa and also for discovering new clinical biomarkers. The main metabolomic alterations associated with PCa development and promising biomarker metabolites for diagnosis of PCa were outlined.

Systems biology in hepatology: approaches and applications

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

A Testosterone Metabolite 19-Hydroxyandrostenedione Induces Neuroendocrine Trans-Differentiation of Prostate Cancer Cells via an Ectopic Olfactory Receptor

Olfactory receptor OR51E2, also known as a Prostate Specific G-Protein Receptor, is highly expressed in prostate cancer but its function is not well understood. Through in silico and in vitro analyses, we identified 24 agonists and 1 antagonist for this receptor. We detected that agonist 19-hydroxyandrostenedione, a product of the aromatase reaction, is endogenously produced upon receptor activation. We characterized the effects of receptor activation on metabolism using a prostate cancer cell line and demonstrated decreased intracellular anabolic signals and cell viability, induction of cell cycle arrest, and increased expression of neuronal markers. Furthermore, upregulation of neuron-specific enolase by agonist treatment was abolished in OR51E2-KO cells. The results of our study suggest that OR51E2 activation results in neuroendocrine trans-differentiation. These findings reveal a new role for OR51E2 and establish this G-protein coupled receptor as a novel therapeutic target in the treatment of prostate cancer.

Peiminine inhibits colorectal cancer cell proliferation by inducing apoptosis and autophagy and modulating key metabolic pathways

Peiminine, a compound extracted from the bulbs of Fritillaria thunbergii and traditionally used as a medication in China and other Asian countries, was reported to inhibit colorectal cancer cell proliferation and tumor growth by inducing autophagic cell death. However, its mechanism of anticancer action is not well understood, especially at the metabolic level, which was thought to primarily account for peiminine's efficacy against cancer. Using an established metabolomic profiling platform combining ultra-performance liquid chromatography/tandem mass spectrometry with gas chromatography/mass spectrometry, we identified metabolic alterations in colorectal cancer cell line HCT-116 after peiminine treatment. Among the identified 236 metabolites, the levels of 57 of them were significantly (p < 0.05) different between peiminine-treated and -untreated cells in which 45 metabolites were increased and the other 12 metabolites were decreased. Several of the affected metabolites, including glucose, glutamine, oleate (18:1n9), and lignocerate (24:0), may be involved in regulation of the phosphoinositide 3-kinase/Akt/mammalian target of rapamycin (mTOR) pathway and in the oxidative stress response upon peiminine exposure. Peiminine predominantly modulated the pathways responsible for metabolism of amino acids, carbohydrates, and lipids. Collectively, these results provide new insights into the mechanisms by which peiminine modulates metabolic pathways to inhibit colorectal cancer cell growth, supporting further exploration of peiminine as a potential new strategy for treating colorectal cancer.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

GC-MS-Based Endometabolome Analysis Differentiates Prostate Cancer from Normal Prostate Cells

Prostate cancer (PCa) is an important health problem worldwide. Diagnosis and management of PCa is very complex because the detection of serum prostate specific antigen (PSA) has several drawbacks. Metabolomics brings promise for cancer biomarker discovery and for better understanding PCa biochemistry. In this study, a gas chromatography–mass spectrometry (GC-MS) based metabolomic profiling of PCa cell lines was performed. The cell lines include 22RV1 and LNCaP from PCa with androgen receptor (AR) expression, DU145 and PC3 (which lack AR expression), and one normal prostate cell line (PNT2). Regarding the metastatic potential, PC3 is from an adenocarcinoma grade IV with high metastatic potential, DU145 has a moderate metastatic potential, and LNCaP has a low metastatic potential. Using multivariate analysis, alterations in levels of several intracellular metabolites were detected, disclosing the capability of the endometabolome to discriminate all PCa cell lines from the normal prostate cell line. Discriminant metabolites included amino acids, fatty acids, steroids, and sugars. Six stood out for the separation of all the studied PCa cell lines from the normal prostate cell line: ethanolamine, lactic acid, β-Alanine, L-valine, L-leucine, and L-tyrosine.

Metabolomics and transcriptomics profiles reveal the dysregulation of the tricarboxylic acid cycle and related mechanisms in prostate cancer

Genetic alterations drive metabolic reprograming to meet increased biosynthetic precursor and energy demands for cancer cell proliferation and survival in unfavorable environments. A systematic study of gene-metabolite regulatory networks and metabolic dysregulation should reveal the molecular mechanisms underlying prostate cancer (PCa) pathogenesis. Herein, we performed gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq analyses in prostate tumors and matched adjacent normal tissues (ANTs) to elucidate the molecular alterations and potential underlying regulatory mechanisms in PCa. Significant accumulation of metabolic intermediates and enrichment of genes in the tricarboxylic acid (TCA) cycle were observed in tumor tissues, indicating TCA cycle hyperactivation in PCa tissues. In addition, the levels of fumarate and malate were highly correlated with the Gleason score, tumor stage and expression of genes encoding related enzymes and were significantly related to the expression of genes involved in branched chain amino acid degradation. Using an integrated omics approach, we further revealed the potential anaplerotic routes from pyruvate, glutamine catabolism and branched chain amino acid (BCAA) degradation contributing to replenishing metabolites for TCA cycle. Integrated omics techniques enable the performance of network-based analyses to gain a comprehensive and in-depth understanding of PCa pathophysiology and may facilitate the development of new and effective therapeutic strategies.

New biomarkers for diagnosis and prognosis of localized prostate cancer

The diagnostics and management of localized prostate cancer is complicated because of cancer heterogeneity and differentiated progression in various subgroups of patients. As a prostate cancer biomarker, FDA-approved detection assay for serum prostate specific antigen (PSA) and its derivatives are not potent enough to diagnose prostate cancer, especially high-grade disease (Gleason ≥7). To date, a collection of new biomarkers was developed. Some of these markers are superior for primary screening while others are particularly helpful for cancer risk stratification, detection of high-grade cancer, and prediction of adverse events. Two of those markers such as proPSA (a part of the Prostate Health Index (PHI)) and prostate specific antigen 3 (PCA3) (a part of the PCA3 Progensa test) were recently approved by FDA for clinical use. Other markers are not PDA-approved yet but are available from Clinical Laboratory Improvement Amendment (CLIA)-certified clinical laboratories. In this review, we characterize diagnostic performance of these markers and their diagnostic and prognostic utility for prostate cancer.

Everolimus exhibits anti-tumorigenic activity in obesity-induced ovarian cancer

Everolimus inhibits mTOR kinase activity and its downstream targets by acting on mTORC1 and has anti-tumorigenic activity in ovarian cancer. Clinical and epidemiologic data find that obesity is associated with worse outcomes in ovarian cancer. In addition, obesity leads to hyperactivation of the mTOR pathway in epithelial tissues, suggesting that mTOR inhibitors may be a logical choice for treatment in obesity-driven cancers. However, it remains unclear if obesity impacts the effect of everolimus on tumor growth in ovarian cancer. The present study was aimed at evaluating the effects of everolimus on cytotoxicity, cell metabolism, apoptosis, cell cycle, cell stress and invasion in human ovarian cancer cells. A genetically engineered mouse model of serous ovarian cancer fed a high fat diet or low fat diet allowed further investigation into the inter-relationship between everolimus and obesity in vivo. Everolimus significantly inhibited cellular proliferation, induced cell cycle G1 arrest and apoptosis, reduced invasion and caused cellular stress via inhibition of mTOR pathways in vitro. Hypoglycemic conditions enhanced the sensitivity of cells to everolimus through the disruption of glycolysis. Moreover, everolimus was found to inhibit ovarian tumor growth in both obese and lean mice. This reduction coincided with a decrease in expression of Ki-67 and phosphorylated-S6, as well as an increase in cleaved caspase 3 and phosphorylated-AKT. Metabolite profiling revealed that everolimus was able to alter tumor metabolism through different metabolic pathways in the obese and lean mice. Our findings support that everolimus may be a promising therapeutic agent for obesity-driven ovarian cancers.