The prostate-cancer metabolome

Urinary fatty acid biomarkers for prostate cancer detection

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Deep Mining from Omics Data

Since the advent of high-throughput omics technologies, various molecular data such as genes, transcripts, proteins, and metabolites have been made widely available to researchers. This has afforded clinicians, bioinformaticians, statisticians, and data scientists the opportunity to apply their innovations in feature mining and predictive modeling to a rich data resource to develop a wide range of generalizable prediction models. What has become apparent over the last 10 years is that researchers have adopted deep neural networks (or "deep nets") as their preferred paradigm of choice for complex data modeling due to the superiority of performance over more traditional statistical machine learning approaches, such as support vector machines. A key stumbling block, however, is that deep nets inherently lack transparency and are considered to be a "black box" approach. This naturally makes it very difficult for clinicians and other stakeholders to trust their deep learning models even though the model predictions appear to be highly accurate. In this chapter, we therefore provide a detailed summary of the deep net architectures typically used in omics research, together with a comprehensive summary of the notable "deep feature mining" techniques researchers have applied to open up this black box and provide some insights into the salient input features and why these models behave as they do. We group these techniques into the following three categories: (a) hidden layer visualization and interpretation; (b) input feature importance and impact evaluation; and (c) output layer gradient analysis. While we find that omics researchers have made some considerable gains in opening up the black box through interpretation of the hidden layer weights and node activations to identify salient input features, we highlight other approaches for omics researchers, such as employing deconvolutional network-based approaches and development of bespoke attribute impact measures to enable researchers to better understand the relationships between the input data and hidden layer representations formed and thus the output behavior of their deep nets.

1H NMR metabolomic and transcriptomic analyses reveal urinary metabolites as biomarker candidates in response to protein undernutrition in adult rats

Protein undernutrition contributes to the development of various diseases in broad generations. Urinary metabolites may serve as non-invasive biomarkers of protein undernutrition; however, this requires further investigation. We aimed to identify novel urinary metabolites as biomarker candidates responsive to protein undernutrition. Adult rats were fed control (CT; 14 % casein) or isoenergetic low-protein (LP; 5 % casein) diets for 4 weeks. 1H NMR metabolomics was applied to urine, plasma and liver samples to identify metabolites responsive to protein undernutrition. Liver samples were subjected to mRNA microarray and quantitative PCR analyses to elucidate the mechanisms causing fluctuations in identified metabolites. Urinary taurine levels were significantly lower in the LP group than in the CT group at week 1 and remained constant until week 4. Hepatic taurine level and gene expression level of cysteine dioxygenase type 1 were also significantly lower in the LP group than in the CT group. Urinary trimethylamine N-oxide (TMAO) levels were significantly higher in the LP group than in the CT group at week 2 and remained constant until week 4. Hepatic TMAO level and gene expression levels of flavin-containing mono-oxygenase 1 and 5 were also significantly higher in the LP group than in the CT group. In conclusion, urinary taurine and TMAO levels substantially responded to protein undernutrition. Furthermore, changes in hepatic levels of these metabolites and gene expressions associated with their metabolic pathways were also reflected in their fluctuating urinary levels. Thus, taurine and TMAO could act as non-invasive urinary biomarker candidates to detect protein undernutrition.

Vibrational Spectroscopy Saliva Profiling as Biometric Tool for Disease Diagnostics: A Systematic Literature

Saliva is a biofluid that can be considered as a “mirror” reflecting our body’s health status. Vibrational spectroscopy, Raman and infrared, can provide a detailed salivary fingerprint that can be used for disease biomarker discovery. We propose a systematic literature review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to evaluate the potential of vibrational spectroscopy to diagnose oral and general diseases using saliva as a biological specimen. Literature searches were recently conducted in May 2020 through MEDLINE-PubMed and Scopus databases, without date limitation. Finally, over a period of 10 years, 18 publications were included reporting on 10 diseases (three oral and seven general diseases), with very high diagnostic performance rates in terms of sensitivity, specificity, and accuracy. Thirteen articles were related to six different cancers of the following anatomical sites: mouth, nasopharynx, lung, esophagus, stomach, and breast. The other diseases investigated and included in this review were periodontitis, Sjögren’s syndrome, diabetes, and myocardial infarction. Moreover, most articles focused on Raman spectroscopy (n = 16/18) and more specifically surface-enhanced Raman spectroscopy (n = 12/18). Interestingly, vibrational spectroscopy appears promising as a rapid, label-free, and non-invasive diagnostic salivary biometric tool. Furthermore, it could be adapted to investigate subclinical diseases—even if developmental studies are required.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis

BACKGROUND

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues.

METHODS

Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided.

RESULTS

The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites.

CONCLUSIONS

The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.

An electrochemiluminescence-supramolecular approach to sarcosine detection for early diagnosis of prostate cancer

Monitoring Prostate Cancer (PCa) biomarkers is an efficient way to diagnosis this disease early, since it improves the therapeutic success rate and suppresses PCa patient mortality: for this reason a powerful analytical technique such as electrochemiluminescence (ECL) is already used for this application, but its widespread usability is still hampered by the high cost of commercial ECL equipment. We describe an innovative approach for the selective and sensitive detection of the PCa biomarker sarcosine, obtained by a synergistic ECL-supramolecular approach, in which the free base form of sarcosine acts as co-reagent in a Ru(bpy)3(2+)-ECL process. We used magnetic micro-beads decorated with a supramolecular tetraphosphonate cavitand (Tiiii) for the selective capture of sarcosine hydrochloride in a complex matrix like urine. Sarcosine determination was then obtained with ECL measurements thanks to the complexation properties of Tiiii, with a protocol involving simple pH changes - to drive the capture-release process of sarcosine from the receptor - and magnetic micro-bead technology. With this approach we were able to measure sarcosine in the μM to mM window, a concentration range that encompasses the diagnostic urinary value of sarcosine in healthy subjects and PCa patients, respectively. These results indicate how this ECL-supramolecular approach is extremely promising for the detection of sarcosine and for PCa diagnosis and monitoring, and for the development of portable and more affordable devices.

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.

Controlled Release of Nor-β-lapachone by PLGA Microparticles: A Strategy for Improving Cytotoxicity against Prostate Cancer Cells

Prostate cancer is one of the most common malignant tumors in males and it has become a major worldwide public health problem. This study characterizes the encapsulation of Nor-β-lapachone (NβL) in poly(d,l-lactide-co-glycolide) (PLGA) microcapsules and evaluates the cytotoxicity of the resulting drug-loaded system against metastatic prostate cancer cells. The microcapsules presented appropriate morphological features and the presence of drug molecules in the microcapsules was confirmed by different methods. Spherical microcapsules with a size range of 1.03 ± 0.46 μm were produced with an encapsulation efficiency of approximately 19%. Classical molecular dynamics calculations provided an estimate of the typical adsorption energies of NβL on PLGA. Finally, the cytotoxic activity of NβL against PC3M human prostate cancer cells was demonstrated to be significantly enhanced when delivered by PLGA microcapsules in comparison with the free drug.

Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study

Serum metabolite profiling in Duchenne muscular dystrophy (DMD) may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS) for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate) in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future.

Accurate, fully-automated NMR spectral profiling for metabolomics

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person’s biofluids, which means such diseases can often be readily detected from a person’s “metabolic profile"—i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person’s metabolic profile. Given a 1D ¹HNMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the “signatures” of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively—with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments

We present here an innovative hypothesis and report preliminary evidence that the sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The NMR spectra of the urine samples provided by four healthy donors were converted into audio signals that were analyzed in two audio experiments by listeners with both musical and non-musical training. The listeners were first asked to cluster the audio signals of two donors on the basis of perceived similarity and then to classify unknown samples after having listened to a set of reference signals. In the clustering experiment, the probability of obtaining the same results by pure chance was 7.04% and 0.05% for non-musicians and musicians, respectively. In the classification experiment, musicians scored 84% accuracy which compared favorably with the 100% accuracy attained by sophisticated pattern recognition methods. The results were further validated and confirmed by analyzing the NMR metabolic profiles belonging to two other different donors. These findings support our hypothesis that the uniqueness of the metabolic phenotype is preserved even when reproduced as audio signal and warrants further consideration and testing in larger study samples.

Paramagnetic nanoparticles as a platform for FRET-based sarcosine picomolar detection

Herein, we describe an ultrasensitive specific biosensing system for detection of sarcosine as a potential biomarker of prostate carcinoma based on Förster resonance energy transfer (FRET). The FRET biosensor employs anti-sarcosine antibodies immobilized on paramagnetic nanoparticles surface for specific antigen binding. Successful binding of sarcosine leads to assembly of a sandwich construct composed of anti-sarcosine antibodies keeping the Förster distance (Ro) of FRET pair in required proximity. The detection is based on spectral overlap between gold-functionalized green fluorescent protein and antibodies@quantum dots bioconjugate (λex 400 nm). The saturation curve of sarcosine based on FRET efficiency (F₆₀₄/F₅₁₀ ratio) was tested within linear dynamic range from 5 to 50 nM with detection limit down to 50 pM. Assembled biosensor was then successfully employed for sarcosine quantification in prostatic cell lines (PC3, 22Rv1, PNT1A), and urinary samples of prostate adenocarcinoma patients.

A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

Highlights

Developed a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites.

A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement.

Applying the above strategy, several low abundant masked differential metabolites were rescued.

Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.

The role of sarcosine metabolism in prostate cancer progression

Metabolomic profiling of prostate cancer (PCa) progression identified markedly elevated levels of sarcosine (N-methyl glycine) in metastatic PCa and modest but significant elevation of the metabolite in PCa urine. Here, we examine the role of key enzymes associated with sarcosine metabolism in PCa progression. Consistent with our earlier report, sarcosine levels were significantly elevated in PCa urine sediments compared to controls, with a modest area under the receiver operating characteristic curve of 0.71. In addition, the expression of sarcosine biosynthetic enzyme, glycine N-methyltransferase (GNMT), was elevated in PCa tissues, while sarcosine dehydrogenase (SARDH) and pipecolic acid oxidase (PIPOX), which metabolize sarcosine, were reduced in prostate tumors. Consistent with this, GNMT promoted the oncogenic potential of prostate cells by facilitating sarcosine production, while SARDH and PIPOX reduced the oncogenic potential of prostate cells by metabolizing sarcosine. Accordingly, addition of sarcosine, but not glycine or alanine, induced invasion and intravasation in an in vivo PCa model. In contrast, GNMT knockdown or SARDH overexpression in PCa xenografts inhibited tumor growth. Taken together, these studies substantiate the role of sarcosine in PCa progression.

Metabolomics in epidemiology: sources of variability in metabolite measurements and implications

BACKGROUND

Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies.

METHODS

Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study.

RESULTS

Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of "usual" levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability.

CONCLUSION

Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies.

IMPACT

We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.

Metabolomics as a key integrator for "omic" advancement of personalized medicine and future therapies

Investigation into biological complexity, whether for a better understanding of disease or drug process, is a monumental task plaguing investigators. The lure of "omic" technologies for circumventing much of these challenges has led to widespread efforts and adoption. It is becoming clearer that a single "omic" approach (e.g., genomics) is often insufficient for completely defining the complexity in these biological systems. Hence, there is an increasing awareness that a "systems" approach will serve to increase resolution and confidence and provide a strong foundation for further hypothesis-driven investigation. Although certain metabolites are already considered clinically important, the profiling of metabolites via metabolomics (the profiling of metabolites to fully characterize metabolic pathways) is the most recent to mature of these "omic" technologies and has been only recently adopted as compared to genomic or proteomic approaches in systems inquiries. Recent reports suggest that this "omic" may well be a key data stream in systems investigations for endeavors in personalized medicine and biomarker identification, as it seems most closely relevant to the phenotype.

[Preliminary results of metabolite in serum and urine of lung cancer patients detected by metabolomics]

背景与目的

肺癌是当今世界各国最常见的恶性肿瘤之一。目前尚没有寻找到理想的用于肺癌诊断的肿瘤标志物，因而尝试用各种新方法来探索新的生物学标志物已成为肺癌研究的热点。本研究采用代谢组学技术对肺癌患者和其它肺部疾病患者血清及尿液中的小分子代谢物质进行分析，以寻求潜在的肺癌肿瘤标志物。

方法

运用气相色谱/质谱法（gas chromatography/mass spectrometry, GC/MS）对19例肺癌与15例其它肺部疾病患者的血清及尿液样本进行代谢组学分析，采用正交偏最小二乘判别分析法（orthogonal to partial least squares discriminant analysis, OPLS-DA）进行建模，运用两样本的t检验寻找两组间差异性代谢产物。

结果

检测到血清中代谢产物共57种，尿液中代谢产物共38种，多变量统计结果显示肺癌患者与其它肺部疾病患者的代谢谱有明显差异，根据t检验结果寻找到血清相关的差异代谢产物13种，尿液相关的差异代谢产物7种。

结论

利用代谢组学方法能区分肺癌与其它肺部疾病患者，其结果在分子水平辅助肺癌的诊断、未来作为新技术应用于肺癌的诊断有一定的前景。

An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer

OBJECTIVE

The present study was designed to search for potential diagnostic biomarkers in the serum of colorectal cancer (CRC).

BACKGROUND

CRC is the third most common cancer worldwide, and its prognosis is poor at early stages. A panel of novel biomarkers is urgently needed for early diagnosis of CRC.

METHODS

An integrated proteomics and metabolomics approach was performed to define oncofetal biomarkers in CRC by protein and metabolite profiling of serum samples from CRC patients, healthy control adults, and fetus. The differentially expressed proteins were identified by a 2-D DIGE (2-Dimensional Difference Gel Electrophoresis) coupled with a Finnigan LTQ-based proteomics approach. Meanwhile, the serum metabolome was analyzed using gas chromatography-mass spectrometry integrated with a commercial mass spectral library for peak identification.

RESULTS

Of the 28 identified proteins and the 34 analyzed metabolites, only 5 protein spots and 6 metabolites were significantly increased or decreased in both CRC and fetal serum groups compared with the healthy adult group. Data from supervised predictive models allowed a separation of 93.5% of CRC patients from the healthy controls using the 6 metabolites. Finally, correlation analysis was applied to establish quantitative linkages between the 5 individual metabolite 3-hydroxybutyric acid, L-valine, L-threonine, 1-deoxyglucose, and glycine and the 5 individual proteins MACF1, APOH, A2M, IGL@, and VDB. Furthermore, 10 potential oncofetal biomarkers were characterized and their potential for CRC diagnosis was validated.

CONCLUSION

The integrated approach we developed will promote the translation of biomarkers with clinical value into routine clinical practice.

Transcriptional and metabolic data integration and modeling for identification of active pathways

With the growing availability of omics data generated to describe different cells and tissues, the modeling and interpretation of such data has become increasingly important. Pathways are sets of reactions involving genes, metabolites, and proteins highlighting functional modules in the cell. Therefore, to discover activated or perturbed pathways when comparing two conditions, for example two different tissues, it is beneficial to use several types of omics data. We present a model that integrates transcriptomic and metabolomic data in order to make an informed pathway-level decision. Since metabolites can be seen as end-points of perturbations happening at the gene level, the gene expression data constitute the explanatory variables in a sparse regression model for the metabolite data. Sophisticated model selection procedures are developed to determine an appropriate model. We demonstrate that the transcript profiles can be used to informatively explain the metabolite data from cancer cell lines. Simulation studies further show that the proposed model offers a better performance in identifying active pathways than, for example, enrichment methods performed separately on the transcript and metabolite data.

A new frontier in personalized cancer therapy: mapping molecular changes

Mutations in the genome of a normal cell can affect the function of its many genes and pathways. These alterations could eventually transform the cell from a normal to a malignant state by allowing an uncontrolled proliferation of the cell and formation of a cancer tumor. Each tumor in an individual patient can have hundreds of mutated genes and perturbed pathways. Cancers clinically presenting as the same type or subtype could potentially be very different at the molecular level and thus behave differently in response to therapy. The challenge is to distinguish the key mutations driving the cancer from the background of mutational noise and find ways to effectively target them. The promise is that such a molecular approach to classifying cancer will lead to better diagnostic, prognostic and personalized treatment strategies. This article provides an overview of advances in the molecular characterization of cancers and their applications in therapy.

Plasma free amino acid profiling of five types of cancer patients and its application for early detection

Background

Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection.

Methods and Findings

Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling.

Conclusions

These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.

The important role of glycine N-methyltransferase in the carcinogenesis and progression of prostate cancer

Glycine N-methyltransferase (GNMT) has a role in the metabolism of methionine as well as in gluconeogenesis. It has recently been reported that the GNMT gene acts as a tumor-susceptible gene. However, little is known about the specific function of GNMT in carcinogenesis and malignant progression. To better our understanding of the function of GNMT in prostate cancer, we used siRNAs to examine the effects of GNMT knockdown on cell proliferation and the cell cycle. In addition, the relation between immunohistochemical GNMT expression and clinicopathologic parameters was investigated in 148 prostate cancer tissues. Here, we show that siRNA-mediated GNMT knockdown results in an inhibition of proliferation, and induces G1 arrest and apoptosis in prostate cancer cell lines. Moreover, high cytoplasmic GNMT expression was also correlated with a higher Gleason score (P<0.001) and higher pT stage (P=0.027). The patients with high GNMT cytoplasmic expression showed significantly lower disease-free survival rates than patients with low expression (P<0.001). High GNMT cytoplasmic expression had a significant impact on patient disease-free survival in multivariate analysis (P=0.005). This is the first investigation to reveal the novel finding that GNMT may have an important role in promoting prostate cancer cell growth via the regulation of apoptosis and contribute to the progression of prostate cancer. The modulation of GNMT expression or function may be a strategy for developing novel therapeutics for prostate cancer. GNMT may represent a novel marker of malignant progression and poor prognosis in prostate cancer.

LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery

A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.

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

Prostate cancer (PCa) is the most commonly diagnosed visceral cancer in men and is responsible for the second highest cancer-related male mortality rate in Western countries, with increasing rates being reported in Korea, Japan, and China. Considering the low sensitivity of prostate-specific antigen (PSA) testing, it is widely agreed that reliable, age-independent markers of the presence, nature, and progression of PCa are required to facilitate diagnosis and timely treatment. Metabolomics or metabonomics has recently emerged as a novel method of PCa detection owing to its ability to monitor changes in the metabolic signature, within biofluids or tissue, that reflect changes in phenotype and function. This review outlines the physiology of prostate tissue and prostatic fluid in health and in malignancy in relation to metabolomics as well as the principles underlying the methods of metabolomic quantification. Promising metabolites, metabolic profiles, and their correlation with the presence and stage of PCa are summarized. Application of metabolomics to biofluids and in vivo quantification as well as the direction of current research in supplementing and improving current methods of detection are discussed. The current debate in the urology literature on sarcosine as a potential biomarker for PCa is reviewed and discussed. Metabolomics promises to be a valuable tool in the early detection of PCa that may enable earlier treatment and improved clinical outcomes.

Possibility of multivariate function composed of plasma amino acid profiles as a novel screening index for non-small cell lung cancer: a case control study

Background

The amino-acid balance in cancer patients often differs from that in healthy individuals, because of metabolic changes. This study investigated the use of plasma amino-acid profiles as a novel marker for screening non-small-cell lung cancer (NSCLC) patients.

Methods

The amino-acid concentrations in venous blood samples from pre-treatment NSCLC patients (n = 141), and age-matched, gender-matched, and smoking status-matched controls (n = 423), were measured using liquid chromatography and mass spectrometry. The resultant study data set was subjected to multiple logistic regression analysis to identify amino acids related with NSCLC and construct the criteria for discriminating NSCLC patients from controls. A test data set derived from 162 patients and 3,917 controls was used to validate the stability of the constructed criteria.

Results

The plasma amino-acid profiles significantly differed between the NSCLC patients and the controls. The obtained model (including alanine, valine, isoleucine, histidine, tryptophan and ornithine concentrations) performed well, with an area under the curve of the receiver-operator characteristic curve (ROC_AUC) of >0.8, and allowed NSCLC patients and controls to be discriminated regardless of disease stage or histological type.

Conclusions

This study shows that plasma amino acid profiling will be a potential screening tool for NSCLC.

Advances in NMR-based biofluid analysis and metabolite profiling

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

Glycine transport inhibitors for the treatment of schizophrenia

Multiple lines of evidence indicate that hypofunction of glutamatergic neurotransmission via N-methyl-D-aspartate (NMDA) receptors might be implicated in the pathophysiology of schizophrenia, suggesting that increasing NMDA receptor function via pharmacological manipulation could provide a new strategy for the management of schizophrenia. Currently, the glycine modulatory sites on NMDA receptors present the most attractive therapeutic targets for the treatment of schizophrenia. One means of enhancing NMDA receptor neurotransmission is to increase the availability of the obligatory co-agonist glycine at modulatory sites on the NMDA receptors through the inhibition of glycine transporter-1 (GlyT-1) on glial cells. Clinical studies have demonstrated that the GlyT-1 inhibitor sarcosine (N-methyl glycine) shows antipsychotic activity in patients with schizophrenia. Accordingly, a number of pharmaceutical companies have developed novel and selective GlyT-1 inhibitors for the treatment of schizophrenia. This paper provides an overview of the various GlyT-1 inhibitors and their therapeutic potential.

Advances in proteomic prostate cancer biomarker discovery

Prostate cancer is the most common non-cutaneous cancer in men in the United States. For reasons largely unknown, the incidence of prostate cancer has increased in the last two decades, in spite or perhaps because of a concomitant increase in serum prostate-specific antigen (PSA) screening. While PSA is acknowledged not to be an ideal biomarker for prostate cancer detection, it is however widely used by physicians due to lack of an alternative. Thus, the identification of a biomarker(s) that can complement or replace PSA represents a major goal for prostate cancer research. Screening complex biological specimens such as blood, urine, and tissue to identify protein biomarkers has become increasingly popular over the last decade thanks to advances in proteomic discovery methods. The completion of human genome sequence together with new development in mass spectrometry instrumentation and bioinformatics has been a major driving force in biomarker discovery research. Here we review the current state of proteomic applications as applied to various sample sources including blood, urine, tissue, and "secretome" for the purpose of prostate cancer biomarker discovery. Additionally, we review recent developments in validation of putative markers, efforts at systems biology approach, and current challenges of proteomics in biomarker discovery.

Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches

BACKGROUND

In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed.

METHODOLOGY/PRINCIPAL FINDINGS

The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.

CONCLUSIONS/SIGNIFICANCE

High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.

Prostate-specific antigen-based screening for prostate cancer in the third millennium: useful or hype?

Prostate cancer is the most prevalent malignancy in men and the third leading cause of cancer deaths worldwide. Although the wide-spread introduction of total prostate-specific antigen (tPSA) testing has revolutionized the approach to the managed care of this disease, there are some biological, analytical, clinical, and economical issues that argue against the cost-effectiveness of tPSA-based population screening for early identification of cancer. The on-going standardization/harmonization efforts, along with the outcomes of recent epidemiological investigations, demonstrate that the current tPSA thresholds might be revised and possibly recalculated according to several demographical variables, such as age, ethnicity, genotype, family history, and body mass index. A major shortcoming of tPSA screening is the lack of reliable evidences of reduction in prostate cancer-associated mortality, due to the large lead-time because of the indolent growth rate, the impossibility to differentiate high-grade from indolent cancers, and the treatment-associated morbidity. Since no single tPSA cut-off was proven able to efficiently identify men at higher risk of death, the jeopardy of over-diagnosis and over-treatment is also tangible. The large expenditure is an additional source of concern. Finally, a wide-spread population screening also carries several ethical, social, and psychological implications, which might overwhelm the potential benefits.