How to Screen and Prevent Metabolic Syndrome in Patients of PCOS Early: Implications From Metabolomics.

Background: Polycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. And metabolic syndrome (MS) is an important bridge for PCOS patients to develop other diseases, such as diabetes and coronary heart disease. Our aim was to study the potential metabolic characteristics of PCOS-MS and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis, and treatment. Methods: In this study, 44 PCOS patients with MS, 34 PCOS patients without MS, and 32 healthy controls were studied. Plasma samples of subjects were tested by ultraperformance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators. Results: There were significant differences in general characteristics, reproductive hormone, and metabolic parameters in the PCOS-MS group when compared with the PCOS group and healthy controls. We found 40 differential metabolites which were involved in 23 pathways when compared with the PCOS group. The metabolic network further reflected the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions, and metabolites. In the correlation analysis, there were 11 differential metabolites whose correlation coefficient with clinical parameters was greater than 0.4, which were expected to be taken as biomarkers for clinical diagnosis. Besides, these 11 differential metabolites were assessed by ROC, and the areas under curve (AUCs) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid + leucine + phenylalanine and carnitine C 4: 0 + carnitine C18:1 + carnitine C5:0 were expected to be sensitive combinational biomarkers in clinical practice. Conclusion: Our study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.

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.

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.

Metabolic characterization and pathway analysis of berberine protects against prostate cancer.

Recent explosion of biological data brings a great challenge for the traditional methods. With increasing scale of large data sets, much advanced tools are required for the depth interpretation problems. As a rapid-developing technology, metabolomics can provide a useful method to discover the pathogenesis of diseases. This study was explored the dynamic changes of metabolic profiling in cells model and Balb/C nude-mouse model of prostate cancer, to clarify the therapeutic mechanism of berberine, as a case study. Here, we report the findings of comprehensive metabolomic investigation of berberine on prostate cancer by high-throughput ultra performance liquid chromatography-mass spectrometry coupled with pattern recognition methods and network pathway analysis. A total of 30 metabolite biomarkers in blood and 14 metabolites in prostate cancer cell were found from large-scale biological data sets (serum and cell metabolome), respectively. We have constructed a comprehensive metabolic characterization network of berberine to protect against prostate cancer. Furthermore, the results showed that berberine could provide satisfactory effects on prostate cancer via regulating the perturbed pathway. Overall, these findings illustrated the power of the ultra performance liquid chromatography-mass spectrometry with the pattern recognition analysis for large-scale biological data sets may be promising to yield a valuable tool that insight into the drug action mechanisms and drug discovery as well as help guide testable predictions.

Screening the active compounds of Phellodendri Amurensis cortex for treating prostate cancer by high-throughput chinmedomics.

Screening the active compounds of herbal medicines is of importance to modern drug discovery. In this work, an integrative strategy was established to discover the effective compounds and their therapeutic targets using Phellodendri Amurensis cortex (PAC) aimed at inhibiting prostate cancer as a case study. We found that PAC could be inhibited the growth of xenograft tumours of prostate cancer. Global constituents and serum metabolites were analysed by UPLC-MS based on the established chinmedomics analysis method, a total of 54 peaks in the spectrum of PAC were characterised in vitro and 38 peaks were characterised in vivo. Among the 38 compounds characterised in vivo, 29 prototype components were absorbed in serum and nine metabolites were identified in vivo. Thirty-four metabolic biomarkers were related to prostate cancer, and PAC could observably reverse these metabolic biomarkers to their normal level and regulate the disturbed metabolic profile to a healthy state. A chinmedomics approach showed that ten absorbed constituents, as effective compounds, were associated with the therapeutic effect of PAC. In combination with bioactivity assays, the action targets were also predicted and discovered. As an illustrative case study, the strategy was successfully applied to high-throughput screening of active compounds from herbal medicine.