Metabolomics Approaches and Applications in Prostate Cancer Research

Prognostic marker VPS72 could promote the malignant progression of prostate cancer

BACKGROUND

This paper attempted to clarify the role and mechanism of vacuolar protein sorting-associated protein 72 homolog (VPS72) in the progression of prostate cancer (PCa).

METHODS

Clinical information and gene expression profiles of patients with prostate cancer were obtained from The Cancer Genome Atlas (TCGA). VPS72 expression in PCa and the potential mechanism by which VPS72 affects PCa progression was investigated. Next, we performed COX regression analysis to identify the independent prognostic factors of PCa, and constructed a nomogram. The sensitivity of chemotherapeutic medications was anticipated using "pRRophetic". Subsequently, in vitro assays to validate the effect of VPS72 on PCa cell proliferation, migration and susceptibility to anti-androgen therapy.

RESULTS

The expression of VPS72 was considerably higher in PCa tissues compared to normal tissues. Significant correlations were found between high VPS72 expression and a poor prognosis and adverse clinicopathological factors. The nomogram model constructed based on VPS72 expression has good predictive performance. According to GSEA, VPS72-related genes were enriched in the NF-kB pathways, cytokine-cytokine receptor interaction and chemokine signaling pathway in PCa. Although PCa with low VPS72 expression was more adaptable to chemotherapeutic medications, our in vitro experiment showed that VPS72 knockdown significantly decreased the PCa cell migration, proliferation, and resistance to anti-androgen therapy.

CONCLUSIONS

In summary our findings suggests that VPS72 could play a crucial role in the malignant progression of PCa, and its expression level can be employed as a possible biomarker of PCa prognosis.

PLA inhibits TNF-α-induced PANoptosis of prostate cancer cells through metabolic reprogramming

Previous studies have shown that phenyllactic acid (alpha-Hydroxyhydrocinnamic acid, 2-Hydroxy-3-phenylpropionic acid, PLA), a type of organic acid metabolite, has excellent diagnostic efficacy when used to differentiate between prostate cancer, benign prostatic hyperplasia, and prostatitis. This research aims to explore the molecular mechanism by which PLA influences the PANoptosis of prostate cancer (PCa) cell lines. First, we found that PLA was detected in all prostate cancer cell lines (PC-3, PC-3 M, DU145, LNCAP). Further experiments showed that the addition of PLA to prostate cancer cells could promote ATP generation, enhance cysteine desulfurase (NFS1) expression, and reduce tumor necrosis factor alpha (TNF-α) levels, thereby inhibiting apoptosis in prostate cancer cells. Notably, overexpression of NFS1 can inhibit the binding of TNF-α to serpin mRNA binding protein 1 (SERBP1), suggesting that NFS1 competes with TNF-α for binding to SERBP1. Knockdown of SERBP1 significantly reduced the level of small ubiquity-related modifier (SUMO) modification of TNF-α. This suggests that NFS1 reduces the SUMO modification of TNF-α by competing with SERBP1, thereby reducing the expression and stability of TNF-α and ultimately inhibiting apoptosis in prostate cancer cell lines. In conclusion, PLA inhibits TNF-α induced panapoptosis of prostate cancer cells through metabolic reprogramming, providing a new idea for targeted treatment of prostate cancer.

Overexpression of DDX49 in prostate cancer is associated with poor prognosis

BACKGROUND

There is increasing evidence that DEAD-box helicases (DDX) can act either as promoters or suppressors in various cancer types. Nevertheless, the function of DDX49 in prostate cancer (PCa) is unknown. This study reveals the prognostic and predictive value of DDX49 in PCa.

METHODS

First, we evaluated the expression of DDX49 between PCa and normal tissues based on TCGA and GEO databases. Univariate and multivariate regression analyses were conducted to reveal the risk factors for PCa recurrence. A K-M curve was employed to assess the relationship between DDX49 and recurrence-free survival. In vitro, DDX49 expression was evaluated in PCa and normal prostate cell lines. Furthermore, we constructed a shDDX49 lentivirus to knock down the expression of DDX49. Celigo^® Image Cytometer and MTT assay were performed to analyse cell proliferation in PC-3 cells. Cell cycle distribution was detected with flow cytometry analysis. Apoptosis affected by the lack of DDX49 was metred with the PathScan^® Stress and Apoptosis Signalling Antibody Array Kit.

RESULTS

This study shows a high increase in DDX49 in PCa tissues in comparison with normal tissues and that increased DDX49 indicates a poor prognosis among PCa patients. Meanwhile, DDX49 knockdown suppressed the proliferation and migration of PC-3 cells, causing cell cycle arrest in the G1 phase. Stress and apoptosis pathway analysis revealed that the phosphorylation of HSP27, p53, and SAPK/JNK was reduced in the DDX49 knockdown group compared with the control group.

CONCLUSIONS

In summary, these results suggest that high expression of DDX49 predicts a poor prognosis among PCa patients. Downregulation of DDX49 can suppress cell proliferation, block the cell cycle, and facilitate cell apoptosis. Therefore, knockdown of DDX49 is a promising novel therapy for treating patients with PCa.

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

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

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Urinary Metabolic Biomarkers in Cancer Patients: An Overview

The pathogenesis of cancer involves multiple molecular alterations at the level of genome, epigenome, and stromal environment, resulting in several deregulated signal transduction pathways. Metabolites are not only end products of gene and protein expression but also a consequence of the mutual relationship between the genome and the internal environment. Considering that metabolites serve as a comprehensive chemical fingerprint of cell metabolism, metabolomics is emerging as the method able to discover metabolite biomarkers that can be developed for early cancer detection, prognosis, and response to treatment. Urine represents a noninvasive source, available and rich in metabolites, useful for cancer diagnosis, prognosis, and treatment monitoring. In this chapter, we reported the main published evidences on urinary metabolic biomarkers in the studied cancers related to hepatopancreatic and urinary tract with the aim at discussing their promising role in clinical practice.

Precision Medicine Approach in Prostate Cancer

Prostate cancer is the most prevalent type of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-grade or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patients' survival and to improve their quality of life. Precision medicine is a novel emerging field that plays an essential role in identifying new sub-classifications of diseases and in providing guidance in treatment that is based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with highthroughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.

Patient-derived tumour models for personalized therapeutics in urological cancers

Preclinical knowledge of dysregulated pathways and potential biomarkers for urological cancers has undergone limited translation into the clinic. Moreover, the low approval rate of new anticancer drugs and the heterogeneous drug responses in patients indicate that current preclinical models do not always reflect the complexity of malignant disease. Patient-derived tumour models used in preclinical uro-oncology research include 3D culture systems, organotypic tissue slices and patient-derived xenograft models. Technological innovations have enabled major improvements in the capacity of these tumour models to reproduce the clinical complexity of urological cancers. Each type of patient-derived model has inherent advantages and limitations that can be exploited, either alone or in combination, to gather specific knowledge on clinical challenges and address unmet clinical needs. Nevertheless, few opportunities exist for patients with urological cancers to benefit from personalized therapeutic approaches. Clinical validation of experimental data is needed to facilitate the translation and implementation of preclinical knowledge into treatment decision making.

High-Throughput Metabolomics for Identification of Metabolic Pathways and Deciphering the Effect Mechanism of Dioscin on Rectal Cancer From Cell Metabolic Profiles Coupled With Chemometrics Analysis

High-throughput liquid chromatography-mass spectrometry (LC-MS)-based metabolomics can provide the holistic analysis of the low molecular weight endogenous metabolites in cells and reflect the changes of cellular regulation and metabolic pathways. Our study designed to reveal the potentially pharmacological effects of dioscin on SW480 rectal cancer cells using nontargeted metabolomics method to probe into small molecular metabolites and pathway changes. After the cell assay of proliferation, apoptosis, migration, and invasion, the dioscin-treated cell samples were prepared for nontargeted metabolomics analysis based on LC-MS tool to describe the metabolic profiles. Dioscin has prevented cell proliferation and accelerated cell apoptosis, and it also inhibited the SW480 rectal cancer cells' migration and invasion. A total of 22 metabolites were selected as promising biomarkers of pharmacological reaction of dioscin to rectal cancer, and eight highly correlated pathways including D-glutamine and D-glutamate metabolism, pyruvate metabolism, arachidonic acid metabolism, phenylalanine metabolism, tryptophan metabolism, glycolysis or gluconeogenesis, citrate cycle (TCA cycle), and butanoate metabolism were identified. It showed that strategies based on cell metabolomics are helpful tools to discover the small molecular metabolites to elucidate the action mechanism of drug.

High-throughput liquid chromatography mass-spectrometry-driven lipidomics discover metabolic biomarkers and pathways as promising targets to reveal the therapeutic effects of the Shenqi pill

Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy.

Metabolomics-driven identification of perturbations in amino acid and sphingolipid metabolism as therapeutic targets in a rat model of anorexia nervosa disease using chemometric analysis and a multivariate analysis platform

It is important to explore novel therapeutic targets and develop an effective strategy for the treatment of anorexia nervosa. In this work, serum samples were analyzed using ultra-performance liquid chromatography coupled with quadrupole time-of flight mass spectrometry (UPLC/Q-TOF MS) coupled with chemometric analysis and multivariate analysis to obtain the metabolites and their corresponding pathways. In addition, knock-in and knock-down of the key enzyme in vivo was performed to verify the reliability of the obtained metabolic pathway, which is closely associated with the anorexia nervosa pathomechanism and the potential targets. There were significant differences in the biochemical parameters between the model group and the control group. A total of 26 potential biomarkers were identified to resolve the difference between the control and model rats, which were closely related to amino acid metabolism, sphingolipid metabolism, arachidonic acid metabolism, the citrate cycle, and so forth. According to the ingenuity pathway analysis, we further elucidated the relationship between the gene, protein, and metabolite alteration in anorexia nervosa, which are involved in cellular compromise, lipid metabolism, small molecule biochemistry, cell signaling, molecular transport, nucleic acid metabolism, cell morphology, cellular function and maintenance. Arginosuccinate synthetase (ASS) deficiency was accompanied by a significant downregulation of the β-endorphin and ghrelin in the animal models. The metabolites and pathways obtained using the metabolomics strategy may provide valuable information for the early treatment for anorexia nervosa.

It is important to explore novel therapeutic targets and develop an effective strategy for the treatment of anorexia nervosa.

Functional metabolomics using UPLC-Q/TOF-MS combined with ingenuity pathway analysis as a promising strategy for evaluating the efficacy and discovering amino acid metabolism as a potential therapeutic mechanism-related target for geniposide against alcoholic liver disease

Metabolomics has been used as a strategy to evaluate the efficacy of and potential targets for natural products.

Identification of the perturbed metabolic pathways associating with prostate cancer cells and anticancer affects of obacunone

Functional metabolomics could bring correlative information about specific cell types under different conditions for exploring cell properties and functions. In this study, we adopt a non-targeted cell metabolomics strategy to reveal the proliferation inhibition mechanism of obacunone on 22RV1 prostate cancer cells. Using high-throughput liquid chromatography-high definition mass spectrometry combined with pattern recognition methods was performed to analyze the cell metabolic profiles and pathway of obacunone on prostate cancer. A total of twenty one proposed metabolites in prostate cancer cell and nine vital metabolic pathways such as nicotinate and nicotinamide metabolism, phenylalanine metabolism as well as tryptophan metabolism were identified from large amounts of data. Then, we have built an overall metabolic description network of obacunone to defense prostate cancer. In addition, morphological observation, cell proliferation and apoptosis analysis of 22RV1 human prostate cancer cells were performed to better understand physiopathologic changes after obacunone treatment. Functional metabolomics is a valuable tool that insight into the natural product mechanisms and contributes to new drug discovery. SIGNIFICANCE: In this study, we probe into the proliferation inhibition effect of obacunone on 22RV1 prostate cancer cells by differentiating metabolic changes of cell sample in control and obacunone administration. Using the non-targeted and targeted cell metabolomics approaches, our findings were manifested that obacunone effectually control proliferation and promote apoptosis in 22RV1 prostate cancer cells, which were related to twenty one proposed metabolites, and nicotinate and nicotinamide metabolism, phenylalanine metabolism, tryptophan metabolism as well as ascorbate metabolism. These data were suggested that functional metabolomics analysis have potential to explore the pharmacodynamic mechanism through resolving metabolic changes in cancer cells that possesses higher clinical application value. The advances in the molecular understanding of the roles of metabolomic pathway concerned with particular metabolites in obacunone administration attract more attention in favor of burgeoning therapeutic measures resisting prostate cancer.

An integrated metabolomics and 16S rRNA gene sequencing approach exploring the molecular pathways and potential targets behind the effects of Radix Scrophulariae

To assess the impact of the caecal microbiota on faecal metabolic phenotypes in the presence of Radix Scrophulariae (Chinese name: Xuanshen), an integrated approach involving 16S rRNA gene sequencing combined with ultrahigh-performance liquid chromatography/time-of-flight mass spectrometry (UHPLC/TOF-MS)-based faecal metabolomics was applied to Radix Scrophulariae-treated rats. Interestingly, Radix Scrophulariae led to significant gut microbiota changes at the phylum and genus levels in treated rats compared to control rats. Additionally, distinct changes in faecal metabolites, including linoleic acid (LA), guanosine, inosine, hypoxanthine, xanthine, 4-hydroxycinnamic acid, cholic acid, N-acetyl-d-glucosamine, l-urobilinogen and uridine, were observed in Radix Scrophulariae-treated rats. Of these, seven metabolites were up-regulated, and the remaining three metabolites were down-regulated. Moreover, there were substantial associations between altered levels of gut microbiota genera and discrepant levels of faecal metabolites, particularly for compounds involved in LA and purine metabolism. These results demonstrated that the gut microbiota is altered in association with faecal metabolism following treatment with Radix Scrophulariae. Our findings suggest that further application of this 16S rRNA gene sequencing and UHPLC/TOF-MS-based metabolomics approach will facilitate the assessment of the pharmacological action of Radix Scrophulariae and thus expand the scope of this herb.

An integrated approach to assess impact of caecal microbiota on faecal metabolic phenotypes in the presence of Radix Scrophulariae involving 16S rRNA gene sequencing combined with UHPLC/TOF-MS-based faecal metabolomics was applied to Radix Scrophulariae-treated rats.

Targeting regulation of tryptophan metabolism for colorectal cancer therapy: a systematic review

Colorectal cancer (CRC) is one of the most malignant cancers resulting from abnormal metabolism alterations. As one of the essential amino acids, tryptophan has a variety of physiological functions, closely related to regulation of immune system, central nervous system, gastrointestinal nervous system and intestinal microflora. Colorectal cancer, a type of high-grade malignancy disease, stems from a variety of factors and often accompanies inflammatory reactions, dysbacteriosis, and metabolic disorders. Colorectal cancer accompanies inflammation and imbalance of intestinal microbiota and affects tryptophan metabolism. It is known that metabolites, rate-limiting enzymes, and ARH in tryptophan metabolism are associated with the development of CRC. Specifically, IDO1 may be a potential therapeutic target in colorectal cancer treatment. Furthermore, the reduction of tryptophan amount is proportional to the poor quality of life for colorectal cancer patients. This paper aims to discuss the role of tryptophan metabolism in a normal organism and investigate the relationship between this amino acid and colorectal cancer. This study is expected to provide theoretical support for research related to targeted therapy for colorectal cancer. Furthermore, strategies that modify tryptophan metabolism, effectively inhibiting tumor progression, may be more effective for CRC treatment.

Colorectal cancer (CRC) is one of the most malignant cancers resulting from abnormal metabolism alterations.

Applications and potential mechanisms of herbal medicines for rheumatoid arthritis treatment: a systematic review

In this review, we systematically discuss the role of traditional Chinese medicine (TCM) in rheumatoid arthritis (RA) disease treatment. TCM classifies the subtypes of RA through its own theoretical method, which is beneficial for more accurate diagnosis and treatment with Chinese herbal medicines (CHMs) that are more suitable for different syndromes. TCM mainly uses a flexible combination of CHMs to play an important role in RA treatment. The main components of these extracts can be subdivided into alkaloids, flavonoids, triterpenes, saponins and other compounds. Using a platform of transgenic and induced arthritis models, we explore the potential mechanisms of TCM against RA with the help of omics analysis techniques and methods. These mechanisms are mainly CHM and its extracts can inhibit RA patients and experimental animal models, including synovitis, vascular proliferation and bone injury; this involves many biological signal exchange targets and pathways. In conclusion, the role of TCM in RA treatment mainly involves reducing the expression and secretion of pro-inflammatory factors, thus decreasing the degree of abnormal immune response.

In this review, we systematically discuss the role of traditional Chinese medicine (TCM) in rheumatoid arthritis (RA) disease treatment.

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.

Cell metabolomics identify regulatory pathways and targets of magnoline against prostate cancer

Prostate cancer is known as a common malignant tumor in clinics and moreover, traditional chemotherapeutic drugs have great toxic side effects and drug resistance. Therefore, the searching the highly efficient and low toxicity antitumor drugs from natural drugs has become an important approach for the treatment of prostate cancer. Many studies showed that Cortex Phellodendri has important therapeutic significance for prostate cancer. Magnoline is the main component of Cortex Phellodendri Amurensis, and it is of great significance to evaluate the effect of magnoline on prostate cancer. By using metabolomics, we established a comprehensive analysis strategy based on cell metabolic analysis to study the inhibitory effect of magnoline on the proliferation of prostate cancer cell line 22RV1, and finally conducted an analysis on the cell metabolism footprint samples. Results showed that magnoline had a significant inhibitory effect on the proliferation of the prostate cancer cell line 22RV1. According to the established cell metabolomics methods, we found that 12 metabolic biomarkers of the cell metabolic footprint samples, respectively, could inhibit the proliferation of prostate cancer cells. Magnoline could significantly affect these metabolic biomarkers to disrupt the growth and proliferation of the prostate cancer cell line 22RV1. Additionally, through MetPA analysis indicated that these biomarkers were closely correlated with a variety of metabolic pathways in tumor cells, including the energy metabolism, amino acid metabolism and fatty acid metabolism, most of which were associated with nutrition and energy metabolism. Therefore, we speculated that because of the disturbance of nutrition metabolism and energy metabolism of the prostate cancer cell line 22RV1, cells could not provide the material basis for rapid proliferation, eventually resulting in the inhibition effect.

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

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

Side effects of methotrexate therapy for rheumatoid arthritis: A systematic review

Methotrexate (MTX) is used as an anchor disease-modifying anti-rheumatic drugs (DMARDs) in treating rheumatoid arthritis (RA) because of its potent efficacy and tolerability. MTX benefits a large number of RA patients but partially suffered from side effects. A variety of side effects can be associated with MTX when treating RA patients, from mild to severe or discontinuation of the treatment. In this report, we reviewed the possible side effects that MTX might cause from the most common gastrointestinal toxicity effects to less frequent malignant diseases. In order to achieve regimen with less side effects, the administration of MTX with appropriate dose and a careful pretreatment inspection is necessary. Further investigations are required when combining MTX with other drugs so as to enhance the efficacy and reduce side effects at the same time. The management of MTX treatment is also discussed to provide strategies for occurred side effects. Thus, this review will provide scholars with a comprehensive understanding the side effects of MTX administration by RA patients.

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.

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

INTRODUCTION

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

OBJECTIVES

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

METHODS

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1H nuclear magnetic resonance (1H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

RESULTS

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

CONCLUSION

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

Metabolomic Biomarkers in Urine of Cushing's Syndrome Patients

Cushing’s syndrome (CS) is a disease which results from excessive levels of cortisol in the human body. The disorder is associated with various signs and symptoms which are also common for the general population not suffering from compound hypersecretion. Thus, more sensitive and selective methods are required for the diagnosis of CS. This follow-up study was conducted to determine which steroid metabolites could serve as potential indicators of CS and possible subclinical hypercortisolism in patients diagnosed with so called non-functioning adrenal incidentalomas (AIs). Urine samples from negative controls (n = 37), patients with CS characterized by hypercortisolism and excluding iatrogenic CS (n = 16), and patients with non-functioning AIs with possible subclinical Cushing’s syndrome (n = 25) were analyzed using gas chromatography-mass spectrometry (GC/MS) and gas chromatograph equipped with flame ionization detector (GC/FID). Statistical and multivariate methods were applied to investigate the profile differences between examined individuals. The analyses revealed hormonal differences between patients with CS and the rest of examined individuals. The concentrations of selected metabolites of cortisol, androgens, and pregnenetriol were elevated whereas the levels of tetrahydrocortisone were decreased for CS when opposed to the rest of the study population. Moreover, after analysis of potential confounding factors, it was also possible to distinguish six steroid hormones which discriminated CS patients from other study subjects. The obtained discriminant functions enabled classification of CS patients and AI group characterized by mild hypersecretion of cortisol metabolites. It can be concluded that steroid hormones selected by applying urinary profiling may serve the role of potential biomarkers of CS and can aid in its early diagnosis.

Plasma metabolic profiling and novel metabolite biomarkers for diagnosing prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer death among men and associated with profound metabolic changes.

High-throughput metabolomics enables biomarker discovery in prostate cancer

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in the world.

The Karlsruhe Metabolomics and Nutrition (KarMeN) Study: Protocol and Methods of a Cross-Sectional Study to Characterize the Metabolome of Healthy Men and Women

Background

The human metabolome is influenced by various intrinsic and extrinsic factors. A precondition to identify such biomarkers is the comprehensive understanding of the composition and variability of the metabolome of healthy humans. Sample handling aspects have an important impact on the composition of the metabolome; therefore, it is crucial for any metabolomics study to standardize protocols on sample collection, preanalytical sample handling, storage, and analytics to keep the nonbiological variability as low as possible.

Objective

The main objective of the KarMeN study is to analyze the human metabolome in blood and urine by targeted and untargeted metabolite profiling (gas chromatography-mass spectrometry [GC-MS], GC×GC-MS, liquid chromatography-mass spectrometry [LC-MS/MS], and¹H nuclear magnetic resonance [NMR] spectroscopy) and to determine the impact of sex, age, body composition, diet, and physical activity on metabolite profiles of healthy women and men. Here, we report the outline of the study protocol with special regard to all aspects that should be considered in studies applying metabolomics.

Methods

Healthy men and women, aged 18 years or older, were recruited. In addition to a number of anthropometric (height, weight, body mass index, waist circumference, body composition), clinical (blood pressure, electrocardiogram, blood and urine clinical chemistry) and functional parameters (lung function, arterial stiffness), resting metabolic rate, physical activity, fitness, and dietary intake were assessed, and 24-hour urine, fasting spot urine, and plasma samples were collected. Standard operating procedures were established for all steps of the study design. Using different analytical techniques (LC-MS, GC×GC-MS,¹H NMR spectroscopy), metabolite profiles of urine and plasma were determined. Data will be analyzed using univariate and multivariate as well as predictive modeling methods.

Results

The project was funded in 2011 and enrollment was carried out between March 2012 and July 2013. A total of 301 volunteers were eligible to participate in the study. Metabolite profiling of plasma and urine samples has been completed and data analysis is currently underway.

Conclusions

We established the KarMeN study applying a broad set of clinical and physiological examinations with a high degree of standardization. Our experimental approach of combining scheduled timing of examinations and sampling with the multiplatform approach (GC×GC-MS, GC-MS, LC-MS/MS, and¹H NMR spectroscopy) will enable us to differentiate between current and long-term effects of diet and physical activity on metabolite profiles, while enabling us at the same time to consider confounders such as age and sex in the KarMeN study.

Trial Registration

German Clinical Trials Register DRKS00004890; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00004890 (Archived by WebCite at http://www.webcitation.org/6iyM8dMtx)

Altered plasma levels of decanoic acid in colorectal cancer as a new diagnostic biomarker

Colorectal cancer (CRC) is one of the most common tumors in developed countries. The five-year survival rate decreases depending on how advanced the CRC is when first diagnosed. Screening has been proven to greatly reduce mortality from colorectal cancer, but an ideal screening tool is far from being established. Here, we aimed to discover and validate early CRC biomarkers by means of an untargeted/targeted metabolomic approach. A preliminary untargeted analysis of plasma lipids performed on a small patient cohort (30 plasma samples) revealed some alterations that occurred in the presence of this tumor. In particular, medium-chain fatty acids with between six and twelve carbon atoms (C6-C12) were found to be the lipid class that showed the most marked changes upon the development of CRC. In order to evaluate the utility of this lipid class as diagnostic CRC biomarkers, a further study based on a wider cohort of patients (117 plasma samples) was performed. Using a targeted approach, these fatty acids were quantified in plasma samples by means of fast gas chromatography coupled to a time-of-flight analyzer. Plasma samples from patients with CRCs at different tumor stages were analyzed and compared to those from healthy subjects, ulcerative colitis patients, high-grade dysplasia adenoma patients, and breast cancer patients in order to test the specificity and sensitivity of these possible biomarkers. Results revealed significant differences among the considered groups in terms of their C6, C8, C10, and C12 fatty acid plasma concentrations. In particular, receiver operating characteristic (ROC) curves obtained for the C10 fatty acid gave an area under the curve of 0.8195 along with a sensitivity of 87.8 % and a specificity of 80 %, strongly suggesting that it could be a valuable early diagnostic biomarker of CRC.

Metabolomic Approaches in Cancer Epidemiology

Metabolomics is the study of low molecular weight molecules or metabolites produced within cells and biological systems. It involves technologies such as mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) that can measure hundreds of thousands of unique chemical entities (UCEs). The metabolome provides one of the most accurate reflections of cellular activity at the functional level and can be leveraged to discern mechanistic information during normal and disease states. The advantages of metabolomics over other “omics” include its high sensitivity and ability to enable the analysis of relatively few metabolites compared with the number of genes and messenger RNAs (mRNAs). In clinical samples, metabolites are more stable than proteins or RNA. In fact, metabolomic profiling in basic, epidemiologic, clinical, and translational studies has revealed potential new biomarkers of disease and therapeutic outcome and has led to a novel mechanistic understanding of pathogenesis. These potential biomarkers include novel metabolites associated with cancer initiation, regression, and recurrence. Unlike genomics or even proteomics, however, the degree of metabolite complexity and heterogeneity within biological systems presents unique challenges that require specialized skills and resources to overcome. This article discusses epidemiologic studies of altered metabolite profiles in several cancers as well as challenges in the field and potential approaches to overcoming them.

Metabolic fingerprinting to understand therapeutic effects and mechanisms of silybin on acute liver damage in rat

BACKGROUND

Metabolic fingerprinting is a rapid and noninvasive analysis, representing a powerful approach for the characterization of phenotypes and the distinction of specific metabolic states due to environmental alterations. It has become a valuable analytical approach for the characterization of phenotypes and is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in bio-samples. Silybin has displayed bright prospects in the prevention and therapy of liver injury, and we had conducted a preliminary exploration on the molecular mechanism of the hepatoprotective effects of silybin. Because the knowledge on the metabolic responses of an acute liver damage rat to the silybin is still scarce, metabolic fingerprinting can provide relevant information on the intrinsic metabolic adjustments.

MATERIALS AND METHODS

Here, the physiological and metabolic changes in the acute liver damage rat were investigated by performing a metabolic analysis. The phenotypic response was assessed by liquid chromatography/mass spectrometry (LC/MS) combined with pattern recognition approaches such as principal components analysis and partial least squares projection to supervised latent structures and discriminant analysis. Multivariate analysis of the data showed trends in scores plots that were related to the concentration of the silybin.

RESULTS

Results indicate 10 ions (7 upregulated and 3 downregulated) as differentiating metabolites. Key observations include perturbations of metabolic pathways linked to glutathione metabolism, tryptophan metabolism, cysteine and methionine metabolism, etc., Overall, this investigation illustrates the power of the LC/MS combined with the pattern recognition methods that can engender new insights into silybin affecting on metabolism pathways of an acute liver damage rat.

CONCLUSION

The present study demonstrates that the combination of metabolic fingerprinting with appropriate chemometric analysis is a valuable approach for studying cellular responses to silybin drug and can provide additional insight into the mechanisms.

Organized metabolic crime in prostate cancer: The coexpression of MCT1 in tumor and MCT4 in stroma is an independent prognosticator for biochemical failure

BACKGROUND

Lactate import or export over cell membranes is facilitated by monocarboxylate transporters (MCTs) 1 and 4. Expression profiles can be markers of an oxidative or glycolytic phenotype. Descriptive studies and functional studies in neoplastic cells and fibroblasts in prostate cancer (PC) have suggested a distinct phenotype. We aimed to explore expression of MCT1 and MCT4 in PC cells and surrounding stroma in a large cohort. Additionally, we wanted to find out if distinct expression profiles were associated with biochemical failure-free survival (BFFS).

METHODS

Tissue microarrays were constructed from 535 patients with radical prostatectomies between January 1, 1995, and December 31, 2005. Immunohistochemistry was used to detect expression, and degrees of expression were evaluated semiquantitatively by 2 pathologists using light microscopy.

RESULTS

For MCT1, there was only epithelial expression, whereas there was a low level of expression of MCT4 in tumor and stroma. A total of 172 patients had a low expression of MCT1 in tumor and MCT4 in stroma. There were 232 patients who had a high expression of MCT1 and a low expression of MCT4 in stroma. Only 11 patients had a low tumoral MCT1 expression and a high stromal MCT4 expression, and 26 patients (5%) had a high expression of both. Patients with a high-high combination had a significantly reduced BFFS (P = 0.011), and when adjusting for other factors, its effect was significant and independent (HR = 1.99, CI 95%: 1.09-3.62; P = 0.024).

CONCLUSIONS

This study adds to the current understanding of the reversed Warburg effect to be a significant phenotype in PC. High coexpression of MCT1 in tumor and MCT4 in stroma is independently associated to a worse BFFS, and the strength of this association is as strong as having a Gleason score of ≥9.