Metabolomics: a novel approach to early and noninvasive prostate cancer detection

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Integrative analysis of transcriptomic and metabolomic profiles reveals enhanced arginine metabolism in androgen-independent prostate cancer cells

BACKGROUND

Prostate cancer is a common solid tumor that affects a significant number of men worldwide. Conventional androgen deprivation therapy (ADT) increases the risk of developing castration-resistant prostate cancer (CRPC). Effective clinical management of patients with CRPC is challenging due to the limited understanding.

METHODS

In this study, transcriptomic and metabolomic profiles of androgen-dependent prostate cancer cell line LNCaP and the androgen-independent cells developed from LNCaP cells (LNCaP-ADR) were investigated using RNA-sequencing and LC-MS/MS, respectively. The differentially expressed genes and metabolites were analyzed, and integrative analysis of transcriptomic and metabolomic data was further conducted to obtain a comprehensive understanding of the metabolic characteristics in LNCaP-ADR cells. Quantitative real-time PCR (QPCR) was employed to ascertain the mRNA expression levels of the selected differentially expressed genes.

RESULTS

The arginine and proline metabolism pathway was identified as a commonly altered pathway at both the transcriptional and metabolic levels. In the LNCaP-ADR cells, significant upregulation was observed for metabolites including 5-Aminopentanoic acid, L-Arginine, L-Glutamic acid, N-Acetyl-L-alanine, and Pyrrole-2-carboxylic acid at the metabolic level. At the transcriptional level, MAOA, ALDH3A2, ALDH2, ARG1, CKMT2, and CNDP1 were found to be significantly upregulated in the LNCaP-ADR cells. Gene set enrichment analysis (GSEA) identified various enriched gene sets in the LNCaP-ADR cells, encompassing inflammatory response, 9plus2 motile cilium, motile cilium, ciliary plasm, cilium or flagellum-dependent cell motility, cilium movement, cilium, response to endoplasmic reticulum stress, PTEN DN.V1 DN, SRC UP.V1 UP, IL15 UP.V1 DN, RB DN.V1 DN, AKT UP MTOR DN.V1 UP, VEGF A UP.V1 UP, and KRAS.LUNG.BREAST UP.V1 UP.

CONCLUSIONS

These findings highlight the substantial association between the arginine and proline metabolism pathway and CRPC, emphasizing the need to prioritize strategies that target dysregulated metabolites and differentially expressed genes as essential interventions in the clinical management of CRPC.

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.

Fibromodulin Gene Variants (FMOD) as Potential Biomarkers for Prostate Cancer and Benign Prostatic Hyperplasia

By the year 2050, the world's elderly population may increase exponentially, raising the rate of disease characteristic of this group, such as prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Prostate disorders have a multifactorial etiology, especially age and genetic factors. Currently, PCa is the second most frequent neoplasm in the male population worldwide. The fibromodulin gene encodes a small leucine-rich proteoglycan (SLRP) which acts in the collagen fibrillogenesis pathway, cell adhesion, and modulation of TGF-β signaling pathways, which has been recently associated with PCa. The present study sequenced the coding region of the FMOD in a sample of 44 PCa, 90 BPH, and 82 controls from a Brazilian population, and the results identified 6 variants: 2 missenses (p.(Tyr42Ser) and p.(Pro24Ala)); 3 synonymous (p.(His253=), p.(Asn353=), and p.(Glu79=)); and 1 intronic (c.980-114A>G). Of these, p.(Tyr42Ser), p.(Pro24Ala), and p.(Asn353=) are rare variants, and p.(Tyr42Ser) was predicted as potential pathogenic by the algorithms used here, in addition to not being observed in controls, suggesting that may be a potential biomarker for development of PCa and BPH. In conclusion, we identified for the first time, in Brazilian individuals with PCa and BPH, a potentially pathogenic variant in the analysis of FMOD gene. Further studies are needed to investigate the deleterious effect of this variant on the structure and/or function of the FMOD protein.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Noninvasive urine metabolomics of prostate cancer and its therapeutic approaches: a current scenario and future perspective

INTRODUCTION

The sensitive, specific, fast, robust and noninvasive biomarkers for the evaluation of prostate cancer (PC) remain elusive in medical research. However, efforts are in full sway to investigate and resolve these puzzles for clinical practice. Advances in modern analytical techniques, sample processing, and the emergence of multiple omics approaches have created a great hope for the development of better detection modalities for PC. The objective of the present review is to provide a concise overview of the PC metabolomics-based potential discriminating molecules in urine samples using nuclear magnetic resonance spectroscopy and mass spectrometry.

AREA COVERED

A literature search was executed to find the studies reporting the noninvasive urine-based biomarkers for the diagnosis and prognosis of underlying disease. Most studies have extensivelyreported PC discriminating molecules with their respective controls. Additionally, pathophysiology and the treatment paradigm of PC are summarized and related to the insights underpinning the therapeutic intervention of PC.

EXPERT OPINION

With multi-centric, global, comprehensive omics approaches via either a non- or least-invasive bio-matrix may open new avenues of research for PC biomarker discovery, backed by a molecular mechanistic outline.

Semen as a rich source of diagnostic biomarkers for prostate cancer: latest evidence and implications

Prostate cancer (PCa) is one of the most common cancers in men and the cause of numerous cancer deaths in the world. Nowadays, based on diagnostic criteria, prostate-specific antigen (PSA) evaluation and rectal examination are used to diagnose prostate-related malignancies. However, due to the different types of PCa, there are several doubts about the diagnostic value of PSA. On the other hand, semen is considered an appropriate source and contains various biomarkers in non-invasive diagnosing several autoimmune disorders and malignancies. Evidence suggests that analysis of semen biomarkers could be helpful in PCa diagnosis. Therefore, due to the invasiveness of most diagnostic methods in PCa, the use of semen as a biologic sample containing various biomarkers can lead to the emergence of novel and non-invasive diagnostic approaches. This review summarized recent studies on the use of various seminal biomarkers for diagnosis, prognosis and prediction of PCa.

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.

Serum profiling of anorexia nervosa: A 1H NMR-based metabolomics study

Our understanding of pathophysiological mechanisms underlying anorexia nervosa (AN) is incomplete. The aim was to conduct a metabolomics profiling of serum samples from women with AN (n = 65), women who have recovered from AN (AN-REC, n = 65), and age-matched healthy female controls (HC, n = 65). Serum concentrations of 21 metabolites were measured using proton nuclear magnetic resonance (¹H NMR). We used orthogonal partial least-squares discriminant analysis (OPLS-DA) modeling to assign group classification based on the metabolites. Analysis of variance (ANOVA) was used to test for metabolite concentration differences across groups. The OPLS-DA model could distinguish between the AN and HC groups (p = 9.05 × 10-11 R2Y = 0.36, Q2 = 0.37) and between the AN-REC and HC groups (p = 8.47 × 10-6, R2Y = 0.36, Q2 = 0.24,), but not between the AN and AN-REC groups (p = 0.63). Lower methanol concentration in the AN and AN-REC group explained most of the variance. Likewise, the strongest finding in the univariate analyses was lower serum methanol concentration in both AN and AN-REC compared with HC, which withstood adjustment for body mass index (BMI). We report for the first time lower serum concentrations of methanol in AN. The fact that low methanol was also found in recovered AN suggests that low serum concentration of methanol could either be trait marker or a scar effect of AN.

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.

Metabolism of prostate cancer by magnetic resonance spectroscopy (MRS)

Understanding the metabolism of prostate cancer (PCa) is important for developing better diagnostic approaches and also for exploring new therapeutic targets. Magnetic resonance spectroscopy (MRS) techniques have been shown to be useful in the detection and quantification of metabolites. PCa illustrates metabolic phenotype, showing lower levels of citrate (Cit), a key metabolite of oxidative phosphorylation and alteration in several metabolic pathways to sustain tumor growth. Recently, dynamic nuclear polarization (DNP) studies have documented high rates of glycolysis (Warburg phenomenon) in PCa. High-throughput metabolic profiling strategies using MRS on variety of samples including intact tissues, biofluids like prostatic fluid, seminal fluid, blood plasma/sera, and urine have also played a vital role in understanding the abnormal metabolic activity of PCa patients. The enhanced analytical potential of these techniques in the detection and quantification of a large number of metabolites provides an in-depth understanding of metabolic rewiring associated with the tumorigenesis. Metabolomics analysis offers dual advantages of identification of diagnostic and predictive biomarkers as well as in understanding the altered metabolic pathways which can be targeted for inhibiting the cancer progression. This review briefly describes the potential applications of in vivo ¹H MRS, high-resolution magic angle spinning spectroscopy (HRMAS) and in vitro MRS methods in understanding the metabolic changes of PCa and its usefulness in the management of PCa patients.

Discussion on Relevance and Studies of Prescription Compatibility in Chinese Medicine

With Chinese medicine (CM) gaining popularity in recent years, researchers and clinicians have put in much interest and effort into the makings and effects of it, especially after the recent announcement of World Health Orgnitation's incorporation of CM into mainstream medical compendium. Individual herb has complex properties, coming from its pharmacological properties and the Chinese medical principles of organ-directed, taste and dynamic orientational behaviours. The use of individual herb in CM is rare, where various herbs/ingredients are mostly found in a prescribed formula. To fully reveal the effects of CM is a great challenge. The complexity of various herbs in combined effect, the absorption and utility rate by the body, uniqueness of individual physique, sub-types of pathological behaviors and time-line progression of the healing process add on to the complication of understanding the full effect of CM. Various theories such as pathophysiology guidance, pharmacokinetic-pharmacodynamic compatibility method, and Global Systems Biology for Integrative Genomics, Proteomics and Metabolomics, which interactively provide a wider scope, more details, with the consideration of development timeline, may shed more light to revealing the full picture of the effects of compatibility prescription.

Challenges, applications and future directions of precision medicine in prostate cancer - the role of organoids and patient-derived xenografts

OBJECTIVE

To provide a clinically relevant outline of various current precision medicine principles and available evidence on the application and potential for a precision medicine approach in prostate cancer.

METHODS

Narrative review of the current literature in the field.

CONCLUSION

Precision medicine is the concept of individualising patient management based on specific tumour characteristics and biology, rather than traditional histological subtypes. The overall aim is to personalise management to individual patients, to provide the right cancer treatment, to the right patient, at the right time. While the approach aims to improve clinical outcomes, decrease morbidity and improve survival in men with advanced prostate cancer, its clinical application is in its infancy. It does however show great promise in this and other cancers, and will continue to be an area of active research and clinical investigation.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

Semen miRNAs Contained in Exosomes as Non-Invasive Biomarkers for Prostate Cancer Diagnosis

Although it is specific for prostatic tissue, serum prostate-specific antigen (PSA) screening has resulted in an over-diagnosis of prostate cancer (PCa) and many unnecessary biopsies of benign disease due to a well-documented low cancer specificity, thus improvement is required. We profiled the expression level of miRNAs contained in semen exosomes from men with moderately increased PSA levels to assess their usefulness, either alone or in addition to PSA marker, as non-invasive biomarkers, for the early efficient diagnosis and prognosis of PCa. An altered miRNA expression pattern was found by a high throughput profiling analysis in PCa when compared with healthy individuals (HCt) exosomal semen samples. The presence of vasectomy was taken into account for the interpretation of results. Fourteen miRNAs were selected for miRNA validation as PCa biomarkers in a subsequent set of semen samples. In this explorative study, we describe miRNA-based models, which included miRNA expression values together with PSA levels, that increased the classification function of the PSA screening test with diagnostic and/or prognostic potential: [PSA + miR-142-3p + miR-142-5p + miR-223-3p] model (AUC:0,821) to discriminate PCa from BPH (Sn:91,7% Sp:42,9% vs Sn:100% Sp:14,3%); and [PSA + miR-342-3p + miR-374b-5p] model (AUC: 0,891) to discriminate between GS ≥ 7 tumours and men presenting PSA ≥ 4 ng/ml with no cancer or GS6 tumours (Sn:81,8% Sp:95% vs Sn:54,5% Sp:90%). The pathway analysis of predicted miRNA target genes supports a role for these miRNAs in PCa aetiology and/or progression. Our study shows semen exosome miRNA-based models as molecular biomarkers with the potential to improve PCa diagnosis/prognosis efficiency. As the next step, further prospective studies on larger cohorts of patients are required to validate the diagnostic and/or prognostic role of the miRNA panel before it could be adopted into clinical practice.

NMR-Based Metabolomic Profiling of Urine: Evaluation for Application in Prostate Cancer Detection

Detection of prostate cancer (PCa) and distinguishing indolent versus aggressive forms of the disease is a critical clinical challenge. The current clinical test is circulating prostate-specific antigen levels, which faces particular challenges in cancer diagnosis in the range of 4 to 10 ng/mL. Thus, a concerted effort toward building a noninvasive biomarker panel has developed. In this report, the hypothesis that nuclear magnetic resonance (NMR)-derived metabolomic profiles measured in the urine of biopsy-negative versus biopsy-positive individuals would nominate a selection of potential biomarker signals was investigated. 1H NMR spectra of urine samples from 317 individuals (111 biopsy-negative, 206 biopsy-positive) were analyzed. A double cross-validation partial least squares-discriminant analysis modeling technique was utilized to nominate signals capable of distinguishing the two classes. It was observed that after variable selection protocols were applied, a subset of 29 variables produced an area under the curve (AUC) value of 0.94 after logistic regression analysis, whereas a “master list” of 18 variables produced a receiver operating characteristic ROC) AUC of 0.80. As proof of principle, this study demonstrates the utility of NMR-based metabolomic profiling of urine biospecimens in the nomination of PCa-specific biomarker signals and suggests that further investigation is certainly warranted.

Association between bivariate expression of key oncogenes and metabolic phenotypes of patients with prostate cancer

BACKGROUND

AKT and MYC are two of the most prevalent oncogenes associated with prostate cancer. The precise effects of overexpression of these two key oncogenes on the regulation of metabolic pathways in prostate cancer are under active investigation; however, few studies have investigated their bivariate oncogene-pair expressions in metabolic prostate cancer phenotypes. This is primarily due to the lack of a suitable statistical method to analyze the data in the presence of oncogene interactions and within-metabolite-set correlations.

METHODS

We analyzed data on the expressions of phosphorylated AKT1 and MYC and the concentrations of 228 metabolites from 60 human prostate tumor samples and 16 normal tissue samples. The metabolomic data allowed us to study not only the measurement of individual metabolites, which can exhibit a dynamic range, but the enriched phenotypes in terms of "metabolite sets" that come from known metabolic pathways. We studied 71 metabolite sets defined by KEGG annotation. We used a modification of linear combination test (LCT) for multiple continuous outcomes to find associations between metabolite sets and oncogenic expressions, after accounting for the correlation between AKT1 and MYC expressions and the correlation between metabolites in a metabolite set. The LCT performance was evaluated using a simulation study.

RESULTS

Through a comprehensive analytical method, our study linked oncogenomics and metabolomics data from patients to improve our understanding of the interconnected mechanisms underlying prostate cancer. This study showed that dysregulations of AKT1 and MYC significantly alter the metabolic pathways activated by nonglucose nutrient sources and their downstream targets. Our findings highlighted the role of MYC as the leading, but not the only, oncogene in prostate oncogenesis. In our simulation study, the LCT performed better than the known alternative method, gene-set enrichment analysis (GSEA).

CONCLUSIONS

Our study offers a solution for linking genomics and metabolomics, working directly with multiple continuous and correlated measurements.

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.

Discrimination between the human prostate normal and cancer cell exometabolome by GC-MS

Serum prostate-specific antigen (PSA) is currently the most used biomarker in clinical practice for prostate cancer (PCa) detection. However, this biomarker has several drawbacks. In this work, an untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling of PCa cells was performed to prove the concept that metabolic alterations might differentiate PCa cell lines from normal prostate cell line. For that, we assessed the differences in volatile organic compounds (VOCs) profile in the extracellular medium (exometabolome) of four PCa cell lines and one normal prostate cell line at two pH values (pH 2 and 7) by GC-MS. Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal prostate cells. The most altered metabolites included ketones, aldehydes and organic acids. Among these, we highlight pentadecane-2-one and decanoic acid, which were significantly increased in PCa compared to normal cells, and cyclohexanone, 4-methylheptan-2-one, 2-methylpentane-1,3-diol, 4-methylbenzaldehyde, 1-(3,5-dimethylfuran-2-yl)ethanone, methyl benzoate and nonanoic acid, which were significantly decreased in PCa cells. The PCa volatilome was markedly influenced by the VOCs extraction pH, though the discriminant capability was similar. Overall, our data suggest that VOCs monitoring has the potential to be used as a PCa screening methodology.

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.

Seminal plasma enables selection and monitoring of active surveillance candidates using nuclear magnetic resonance-based metabolomics: A preliminary investigation

Background

Diagnosis and monitoring of localized prostate cancer requires discovery and validation of noninvasive biomarkers. Nuclear magnetic resonance (NMR)-based metabolomics of seminal plasma reportedly improves diagnostic accuracy, but requires validation in a high-risk clinical cohort.

Materials and methods

Seminal plasma samples of 151 men being investigated for prostate cancer were analyzed with ¹H-NMR spectroscopy. After adjustment for buffer (add-to-subtract) and endogenous enzyme influence on metabolites, metabolite profiling was performed with multivariate statistical analysis (principal components analysis, partial least squares) and targeted quantitation.

Results

Seminal plasma metabolites best predicted low- and intermediate-risk prostate cancer with differences observed between these groups and benign samples. Lipids/lipoproteins dominated spectra of high grade samples with less metabolite contributions. Overall prostate cancer prediction using previously described metabolites was not validated.

Conclusion

Metabolomics of seminal plasma in vitro may assist urologists with diagnosis and monitoring of either low or intermediate grade prostate cancer. Less clinical benefit may be observed for high-risk patients. Further investigation in active surveillance cohorts, and/or in combination with in vivo magnetic resonance spectroscopic imaging may further optimize localized prostate cancer outcomes.

NMR spectroscopy of filtered serum of prostate cancer: A new frontier in metabolomics

BACKGROUND

To address the shortcomings of digital rectal examinations (DRE), serum prostate-specific antigen (PSA), and trans-rectal ultrasound (TRUS) for precise determination of prostate cancer (PC) and differentiation from benign prostatic hyperplasia (BPH), we applied (1) H-nuclear magnetic resonance (NMR) spectroscopy as a surrogate tactic for probing and prediction of PC and BPH.

METHODS

The study comprises 210 filtered sera from suspected PC, BPH, and a healthy subjects' cohort (HC). The filtered serum approach delineates to identify and quantify 52 metabolites using (1) H NMR spectroscopy. All subjects had undergone clinical evaluations (DRE, PSA, and TRUS) followed by biopsy for Gleason score, if needed. NMR-measured metabolites and clinical evaluation data were examined separately using linear multivariate discriminant function analysis (DFA) to probe the signature descriptors for each cohort.

RESULTS

DFA indicated that glycine, sarcosine, alanine, creatine, xanthine, and hypoxanthine were able to determine abnormal prostate (BPH + PC). DFA-based classification presented high precision (86.2% by NMR and 68.1% by clinical laboratory method) in discriminating HC from BPH + PC. DFA reveals that alanine, sarcosine, creatinine, glycine, and citrate were able to discriminate PC from BPH. DFA-based categorization exhibited high accuracy (88.3% by NMR and 75.2% by clinical laboratory method) to differentiate PC from BPH.

CONCLUSIONS

(1) H NMR-based metabolic profiling of filtered-serum sample appears to be assuring, swift, and least-invasive for probing and prediction of PC and BPH with its signature metabolic profile. This novel technique is not only on a par with histopathological evaluation of PC determination but is also comparable to liquid chromatography-based mass spectrometry to identify the metabolites. Prostate 76:1106-1119, 2016. © 2016 Wiley Periodicals, Inc.

Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.

Detection of prostate cancer using a voltammetric electronic tongue

A simple method based on the multivariate analysis of data from urine using an electronic voltammetric tongue is used to detect patients with prostate cancer. A sensitivity of 91% and a specificity of 73% were obtained to distinguish the urine from cancer patients and the urine from non-cancer patients.

Volatile organic compounds as new biomarkers for colorectal cancer: a review

Analysis of the volatile part of the metabolome (volatile organic compounds, VOC) present in the gas phase of excreted materials is a promising new screening tool for several cancers, including colorectal cancer (CRC). The VOC signature can reflect health status, like a 'fingerprint', and can be modified in several diseases. Technical difficulties still limit the widespread use of VOC analysis in the clinical setting, but this approach has already been applied successfully in the diagnosis of CRC. The present study reviews the available data on VOC present in the headspace (the gaseous constituents of a closed space above a liquid or solid) of blood, urine, faeces and breath as a potential screening tool for CRC. A systematic electronic literature search was conducted in PubMed, Scirus and Google using the following keywords: Metabolomic, Volatile Organic Compounds (VOC), Electronic-nose and Colorectal Cancer. Only articles published in English between 2000 and 2015 were selected and these were independently checked by two of the authors. Ten papers describing the reliability of VOC analysis in breath and faeces, blood and urine were selected; all indicated good reliability in detecting CRC. The use of different substrates and different analytical platforms has led to the identification of different patterns of VOC. The reliability of a metabolomic approach as a noninvasive biomarker for use in CRC screening is supported by this review despite several limitations due to the number of patients included in each study, the different analytical platforms and biological materials used and different VOC identified.

Prostate-based biofluids for the detection of prostate cancer: A comparative study of the diagnostic performance of cell-sourced RNA biomarkers

BACKGROUND

Prostate cancer (PCa) diagnosis requires improvement with the aid of more accurate biomarkers. Postejaculate urethral washings (PEUW) could be a physiological equivalent to urine obtained following rectal prostatic massage, the current basis for the prostate cancer antigen 3 (PCA3) test. The aim of this study was to investigate if PEUW contained prostate-based material, evidenced by the presence of prostate specific antigen (PSA), and to evaluate the diagnostic performance of PEUW-based biomarkers.

METHODS

Male patients referred for elevated serum PSA or abnormal digital rectal examination provided ejaculate and PEUW samples. PSA, PCA3, and β2-microglobulin (β2M) were quantified in ejaculate and PEUW and compared with absolute and clinically significant (according to D'Amico criteria) PCa presence, as determined by biopsies. Diagnostic performance was determined and compared with serum PSA using receiver operating characteristic analysis.

RESULTS

From 83 patients who provided PEUW samples, paired analysis with ejaculate samples was possible for 38 patients, while analysis in an unpaired, extended cohort was possible for 62 patients. PSA and PCA3 were detected in PEUW, normalized to β2M, and PCA3:PSA was calculated. In predicting absolute PCa status, PCA3:β2M in ejaculate [area under the curve (AUC) 0.717] and PEUW (AUC 0.569) were insignificantly better than PCA3:PSA (AUC 0.668 and 0.431, respectively) and comparable with serum PSA (AUC 0.617) with similar trends observed for the extended cohort. When considering clinically significant PCa presence, serum PSA in the comparison (AUC 0.640) and extended cohorts (AUC 0.665) was comparable with PCA3: β2M (AUC 0.667) and PCA3:PSA (AUC 0.605) in ejaculate, with lower estimates for PEUW in the comparison (PCA3: β2M AUC 0.496; PCA3:PSA AUC 0.342) and extended (PCA3: β2M AUC 0.497; PCA3:PSA AUC 0.469) cohorts. The statistical analysis was limited by sample size.

CONCLUSION

PEUW contains prostatic material, but has limited diagnostic accuracy when considering cell-derived DNA analysis. PCA3-based markers in ejaculate are comparable to serum PSA and digital rectal examination-urine markers.

NMR-based metabolomics of prostate cancer: a protagonist in clinical diagnostics

Advances in the application of NMR spectroscopy-based metabolomic profiling of prostate cancer comprises a potential tactic for understanding the impaired biochemical pathways arising due to a disease evolvement and progression. This technique involves qualitative and quantitative estimation of plethora of small molecular weight metabolites of body fluids or tissues using state-of-the-art chemometric methods delivering an important platform for translational research from basic to clinical, to reveal the pathophysiological snapshot in a single step. This review summarizes the present arrays and recent advancements in NMR-based metabolomics and a glimpse of currently used medical imaging tactics, with their role in clinical diagnosis of prostate cancer.

Effects of Curative Colorectal Cancer Surgery on Exhaled Volatile Organic Compounds and Potential Implications in Clinical Follow-up

OBJECTIVE

The aim of this study was to determine whether the volatile organic compounds (VOCs) pattern in colorectal cancer (CRC) patients is modified by curative surgery for a potential application in the oncologic follow-up.

BACKGROUND

CRC has been proved to induce metabolic derangements detectable by high through-output techniques in exhaled breath showing a specific pattern of VOCs.

METHODS

Forty-eight CRC patients and 55 healthy controls (HC) entered the study. Thirty-two patients (M/F: 1.4; mean age 63 years) attended the oncologic follow-up (mean 24 months) and were found disease-free. Breath samples were collected under similar environmental conditions into a Tedlar bags and processed offline by thermal-desorption gas chromatography-mass spectrometry (TD-GC-MS). VOCs were selected by U test to build a Probabilistic Neural Network (PNN) model to set-up a training phase, which was cross-validated using the leave-one out method.

RESULTS

A total of 11 VOCs were finally selected for their excellent discriminant performance in identifying disease-free patients in follow-up from CRC patients before surgery, (sensitivity 100%, specificity 97.92%, accuracy 98.75%, and AUC: 1). The same VOCs pattern discriminated follow-up patients from HC, with a sensitivity of 100%, specificity of 90.91%, accuracy of 94.25%, and AUC 0.959.

CONCLUSIONS

Exhaled VOCs pattern from CRC patients is modified by cancer removal confirming the tight relationship between tumor metabolism and exhaled VOCs. PNN analysis provides a high discriminatory tool to identify patients disease-free after curative surgery suggesting potential implications in CRC screening and secondary prevention.

Metabolomics and its Application to the Development of Clinical Laboratory Tests for Prostate Cancer

INTRODUCTION

There is a critical need to develop clinical laboratory assays that provide risk assessment for men at elevated risk for prostate cancer, and once diagnosed, could further identify those men with clinically significant disease.

METHODS

Recent advancements in analytical instrumentation have enabled mass spectrometry-based metabolomics methodologies. Further advancements in chromatographic techniques have facilitated high throughput, quantitative assays for a broad spectrum of biochemicals.

RESULTS

Screening metabolomics techniques have been applied to biospecimens from large cohorts of men comparing those individuals with prostate cancer to those with no evidence of malignancy. Work beginning in tissues has identified biochemical profiles that correlate with disease and disease severity, including tumor grade and stage. Some of these metabolic abnormalities, such as dramatic elevations in sarcosine, have been found to translate into biological fluids, especially blood and urine, which can be sampled in a minimally invasive manner.

DISCUSSION

The differential abundances of these tumor-associated metabolites have been found to improve the performance of clinical prognostic/diagnostic tools.

CONCLUSION

The outlook is bright for metabolomic technology to address clinical diagnostic needs for prostate cancer patient management. Early validation of specific clinical tests provides a preview of further successes in this area. Metabolomics has shown its utility to complement and augment traditional clinical approaches as well as emerging genomic, transcriptomic and proteomic methodologies, and is expected to play a key role in the precision medicine-based management of the prostate cancer patient.

Novel tools for prostate cancer prognosis, diagnosis, and follow-up

Prostate-specific antigen (PSA) is the main diagnostic tool when it comes to prostate cancer but it possesses serious limitations. Therefore, there is an urgent need for more sensitive and specific biomarkers for prostate cancer prognosis and patient follow-up. Recent advances led to the discovery of many novel diagnostic/prognostic techniques and provided us with many worthwhile candidates. This paper briefly reviews the most promising biomarkers with respect to their implementation in screening, early detection, diagnostic confirmation, prognosis, and prediction of therapeutic response or monitoring disease and recurrence; and their use as possible therapeutic targets. This review also examines the possible future directions in the field of prostate cancer marker research.

Tumor-stroma metabolic relationship based on lactate shuttle can sustain prostate cancer progression

Background

Cancer cell adopts peculiar metabolic strategies aimed to sustain the continuous proliferation in an environment characterized by relevant fluctuations in oxygen and nutrient levels. Monocarboxylate transporters MCT1 and MCT4 can drive such adaptation permitting the transport across plasma membrane of different monocarboxylic acids involved in energy metabolism.

Methods

Role of MCTs in tumor-stroma metabolic relationship was investigated in vitro and in vivo using transformed prostate epithelial cells, carcinoma cell lines and normal fibroblasts. Moreover prostate tissues from carcinoma and benign hypertrophy cases were analyzed for individuating clinical-pathological implications of MCT1 and MCT4 expression.

Results

Transformed prostate epithelial (TPE) and prostate cancer (PCa) cells express both MCT1 and MCT4 and demonstrated variable dependence on aerobic glycolysis for maintaining their proliferative rate. In glucose-restriction the presence of L-lactate determined, after 24 h of treatment, in PCa cells the up-regulation of MCT1 and of cytochrome c oxidase subunit I (COX1), and reduced the activation of AMP-activated protein kinase respect to untreated cells. The blockade of MCT1 function, performed by si RNA silencing, determined an appreciable antiproliferative effect when L-lactate was utilized as energetic fuel. Accordingly L-lactate released by high glycolytic human diploid fibroblasts WI-38 sustained survival and growth of TPE and PCa cells in low glucose culture medium. In parallel, the treatment with conditioned medium from PCa cells was sufficient to induce glycolytic metabolism in WI-38 cells, with upregulation of HIF-1a and MCT4. Co-injection of PCa cells with high glycolytic WI-38 fibroblasts determined an impressive increase in tumor growth rate in a xenograft model that was abrogated by MCT1 silencing in PCa cells. The possible interplay based on L-lactate shuttle between tumor and stroma was confirmed also in human PCa tissue where we observed a positive correlation between stromal MCT4 and tumor MCT1 expression.

Conclusions

Our data demonstrated that PCa progression may benefit of MCT1 expression in tumor cells and of MCT4 in tumor-associated stromal cells. Therefore, MCTs may result promising therapeutic targets in different phases of neoplastic transformation according to a strategy aimed to contrast the energy metabolic adaptation of PCa cells to stressful environments.

Towards the potential use of1H NMR spectroscopy in urine samples for prostate cancer detection

An multivariate approach based on¹H NMR spectra profiles of urine samples to detect patients with prostate cancer.

High-resolution NMR spectroscopy of human body fluids and tissues in relation to prostate cancer

High-resolution NMR spectroscopic studies of prostate tissue extracts, prostatic fluid, seminal fluid, serum and urine can be used for the detection of prostate cancer, based on the differences in their metabolic profiles. Useful diagnostic information is obtained by the detection or quantification of as many metabolites as possible and comparison with normal samples. Only a few studies have shown the potential of high-resolution in vitro NMR of prostate tissues. A survey of the literature has revealed that studies on body fluids, such as urine and serum, in relation to prostate cancer are rare. In addition, the potential of NMR of nuclei other than (1)H, such as (13)C and (31)P, has not been exploited fully. The metabolomic analysis of metabolites, detected by high-resolution NMR, may help to identify metabolites which could serve as useful biomarkers for prostate cancer detection. Such NMR-derived biomarkers would not only help in prostate cancer detection and in understanding the in vivo MRS metabolic profile, but also to investigate the biochemical and metabolic changes associated with cancer. Here, we review the published research work on body fluids in relation to prostate and prostate tissue extracts, and highlight the potential of such studies for future work.

Correlation of serum β2-microglobulin levels with prostate-specific antigen, Gleason score, clinical stage, tumor metastasis and therapy efficacy in prostate cancer

BACKGROUND AND AIMS

Despite previous reports implying a role of β2-microglobulin (β2M) in the development of prostate cancer (PCa), the correlation of serum β2M with the clinicopathological features, therapy efficacy and prognosis of patients with PCa have not been fully clarified. The present study aims to investigate the serum levels of β2M in patients with PCa and explore the potential use of β2M as a tumor marker for diagnosis, treatment and prognosis of PCa.

METHODS

Serum β2M levels in 120 patients with PCa, 50 patients with benign prostate hyperplasia (BPH) and 85 healthy age-matched controls were measured by enzyme immunoassay. The correlation of serum β2M with the clinicopathological features, therapy efficacy and the prognosis of PCa were subsequently assessed.

RESULTS

Our results showed that: (i) PCa patients had significantly higher levels of β2M compared to those of patients with BPH or those of healthy controls. (ii) Serum β2M were markedly elevated in patients with high stage or grade PCa as compared to patients with low stage or grade PCa. (iii) We measured significantly higher levels of β2M in patients with metastasis as compared to patients lacking metastasis. (iv) During follow-up, serum β2M showed a marked decrease after successful therapy and a significant further increase in recurrent disease.

CONCLUSIONS

Our results demonstrate that serum β2M is correlated closely with the clinical stage, Gleason grade, PSA, distant metastasis and therapy efficacy in patients with PCa. Serum β2M may be a useful biomarker for clinical diagnosis, follow-up and prognosis of PCa.

Selection of candidate radiation biomarkers in the serum of rats exposed to gamma-rays by GC/TOFMS-based metabolomics

In the study, gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) techniques coupled with principal components analysis (PCA) were used to investigate metabolite perturbations in the serum of the rats exposed to 0.75, 3 or 8 Gy gamma rays. Male standard deviation rats were gamma-irradiated at doses of 0.75, 3 and 8 Gy (1.9 Gy min(-1)) or sham-irradiated. Serum samples were collected over the first 24 h under the exposure to irradiation in order to analyse the samples by GC/TOFMS. And multivariate data were analysed by PCA. The composition of metabolites in serum yielded distinct metabolomic phenotypes for 0.75, 3 and 8 Gy at 24 h after irradiation. Nine serum metabolites were significantly altered as a result of radiation exposure. Up-regulated metabolites included inositol, serine, lysine, glycine, threonine and glycerol; down-regulated metabolites included isocitrate, gluconic acid and stearic acid. The nine metabolites were significantly altered after ionising radiation for they may be the potential biomarkers for the diagnosis of radiation injury.

A network-based feature selection approach to identify metabolic signatures in disease

The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.

Metabolomics and bladder cancer

Diagnosis of bladder cancer is primarily made based on clinical presentation and then by direct visualization with cystoscopy. Despite the massive investments recently made to identify urinary-based assays that are able to diagnosis urothelial carcinoma, urine cytology and cystoscopy still remain the gold standard. Recently proof of principle studies have shown that noninvasive urine-based metabolomics, using high pressure liquid chromatography (HPLC) and nuclear magnetic resonance (NMR), may be able to accurately diagnosis bladder cancer. This review discusses the published studies investigating metabolomics and bladder cancer and the future potential of this developing field.

Prostate cancer - a biomarker perspective

Despite early detection and reduced risk of death, prostate cancer still remains the second leading cause of cancer death in American men. There is currently no cure for advanced prostate cancer. The multistage, stochastic and highly heterogeneous nature of prostate cancer, coupled with genetic and epigenetic alterations that occur during disease progression and response to therapy, represent fundamental challenges in our quest to understand and control this complex and prevalent disease. Recent advances in drug development and breakthroughs in omics technologies have renewed our efforts to identify novel biomarkers for prostate cancer prognosis, prediction, and therapeutic response monitoring. In this perspective article, we overview the current status and highlight future prospects of biomarkers for prostate cancer, a disease that affects millions of men worldwide.

Molecular markers for cancer prognosis and treatment: have we struck gold?

The last decade has witnessed an emerging role for molecular or biochemical markers indicating a specific cellular mechanism or tissue function, often called 'biomarkers'. Biomarkers such as altered DNA, proteins and inflammatory cytokines are critical in cancer research and strategizing treatment in the clinic. In this review we look at the application of biological indicators to cancer research and highlight their roles in cancer detection and treatment. With technological advances in gene expression, genomic and proteomic analysis, biomarker discovery is expanding fast. We focus on some of the predominantly used markers in different types of malignancies, their advantages, and their limitations. Finally we conclude by looking at the future of biomarkers, their utility in the tumorigenic studies, and the progress towards personalized treatment strategies.

Correlation between Chemokine Receptor CXCR4 Expression and Prognostic Factors in Patients with Prostate Cancer

PURPOSE

We evaluated the correlation between the expression of CXCR4 and prognostic factors in patients with prostate cancer.

MATERIALS AND METHODS

A total of 57 patients who had undergone surgery for prostate cancer were enrolled. Specimens were obtained before any treatment and were stained with antihuman CXCR4 antibody. The intensity of staining was graded as low or high. The age, pretreatment prostate-specific antigen (PSA) level, Gleason score, T stage, biochemical recurrence, local recurrence, and distant metastasis were compared according to the expression of CXCR4 in patients with prostate cancer.

RESULTS

Local recurrence was higher in the group with high expression, in 11 of 36 cases (30.6%), than in the group with low expression, in 1 of 21 cases (4.8%), with statistical significance (p=0.040). Distant metastasis was also associated with expression, occurring in 10 of 36 cases (27.8%) in the group with high expression and in 1 of 21 cases (4.8%) in the group with low expression (p=0.041). In the logistic regression test, CXCR4 expression was the only factor in determining local recurrence (p=0.016) and distant metastasis (0.022). Furthermore, the group with high CXCR4 expression showed significantly longer cancer-specific survival than did the low expression group (p=0.041). CXCR4 showed no association with age (p=0.881), pretreatment PSA level (p=0.584), Gleason score (p=0.640), T stage (p=0.967), or biochemical recurrence (p=0.081).

CONCLUSIONS

The high expression of CXCR4 was associated with local recurrence and distant metastasis. CXCR4 expression was shown to be a useful prognostic factor for patients with prostate cancer.

Application of metabolomics to prostate cancer

The prostate has long been known to exhibit unique metabolite profiles. In the last decade, advances in nuclear magnetic resonance spectroscopy and mass spectrometry have been applied toward identifying metabolic alterations in prostate cancer that may provide clinically useful biomarkers. As with genomics and proteomics, advances in technology and bioinformatics have led to the application of metabolomic profiling to prostate cancer-the high throughput evaluation of a large complement of metabolites in the prostate and how they are altered by disease perturbations. Recently, high profile publications have drawn attention to the potential of metabolomic analysis to identify biomarkers for early detection or disease progression from readily accessible body fluids as well as tissue specimens from biopsy and surgery. This review will examine applications of metabolomics to prostate cancer and highlight clinical associations and potential challenges.

Cyclooxygenase-2 overexpression in chronic inflammation associated with benign prostatic hyperplasia: is it related to apoptosis and angiogenesis of prostate cancer?

Purpose

This study was performed to investigate the relationship between cyclooxygenase-2 (COX-2) expression and apoptosis/angiogenesis in inflammatory and noninflammatory benign prostatic hyperplasia (BPH) and prostate cancer (PC).

Materials and Methods

This study involved 64 BPH and 57 PC patients. The BPH histopathologies were classified by the presence of chronic inflammation as follows: noninflammatory BPH (NI-BPH; n=23) and inflammatory BPH (I-BPH; n=41). The association between the expression of COX-2, expression of Bcl-2, the apoptotic index (AI), expression of vascular endothelial growth factor (VEGF), and microvascular density (MVD) in the prostate was investigated.

Results

An overexpression of COX-2, Bcl-2, and VEGF was observed in cases of PC compared with cases of BPH. In PC, the AI was lower and MVD was higher than in BPH. In NI-BPH, I-BPH, and PC, the overexpression of COX-2, Bcl-2, and VEGF gradually increased. The AI was high in I-BPH, but did not differ significantly between the NI-BPH and I-BPH groups or between the NI-BPH and PC groups. MVD was significantly high in PC, but no significant difference was found between NI-BPH and I-BPH. A significant correlation was shown between the overexpression of COX-2 and Bcl-2, and COX-2 and VEGF. However, the AI was not correlated with the overexpression of COX-2 or Bcl-2. MVD was correlated with the overexpression of COX-2 and VEGF.

Conclusions

COX-2 overexpression in PC is correlated with a decrease in apoptosis and an increase in angiogenesis. Chronic inflammation in BPH causes an overexpression of COX-2, which induces the increased expression of Bcl-2 and VEGF. It is likely that chronic inflammation plays a role in the intermediate step of carcinogenesis in the prostate.