LC-HRMS based plasma metabolomics analysis for biomarker discovery of neuroblastoma: 3-O-methyldopa is a new biomarker of poor prognosis of metastatic disease
Ontology highlight
ABSTRACT:
ORGANISM(S): Human Homo Sapiens
TISSUE(S): Blood
DISEASE(S): Cancer
SUBMITTER: 
Chiara Lavarello
PROVIDER: ST002238 | MetabolomicsWorkbench | Fri Oct 15 00:00:00 BST 2021
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| mwtab | Other |
Items per page: 5 1 - 1 of 1 |
Similar Datasets
Project description:Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of 'high-risk' at diagnosis could help improving patient stratification and predicting outcome. In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.
2023-07-14 | MTBLS4294 | MetaboLights
Project description:Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of "high-risk" at diagnosis could help improving patient stratification and predicting outcome. In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.
| S-EPMC9231354 | biostudies-literature
Project description:Pooled human plasma was spiked separately with two different sets of 11 metabolite standards (22 “true biomarkers”) to mimic plasma samples collected from diseased subjects and non-diseased subjects. Metabolite extracts of individual samples were subjected to biomarker discovery using LC-HRMS.
2019-02-05 | ST001132 | MetabolomicsWorkbench
Project description:1. Profiling of sialylated glycopeptides from rEPO was performed via LC–HRMS 2. LC–HRMS methods for analyzing sialylated glycopeptide in urine samples were developed 3. The method was validated and applied to detection of rEPO biosimilars in urine
2023-05-10 | PXD038932 | Pride
Project description:As part of the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) a dataset was obtained from 35 participants, including 31 Myotonic Dystrophy type 1 (DM1) cases and four unaffected controls. All DM1 cases in this research were heterozygous for the abnormally expanded CTG repeat. The mode of the length of the DM1 CTG expansion (Modal Allele Length, MAL) was determined by small-pool PCR of blood DNA for 35/36 patients. For this work we did not attempt to measure the repeat length from muscle, due to a very high degree of repeat instability in muscle cells, and associated difficulties in its experimental measurement. One patient refused blood donation. For each of the 35 blood-donating patients mRNA expression profiling of blood was performed using Affymetrix GeneChip™ Human Exon 1.0 ST microarray. For 28 of 36 patients a successful quadriceps muscle biopsy was obtained. The muscle tissue was mRNA profiled using the same type of microarray. In total, a complete set of samples (blood and muscle) was obtained for 27 of 36 patients; samples were given a disease staging score based on muscle impairment rating. mRNA profiling was carried out by the GeneLogic service lab (on a fee-for-service basis) using standard Affymetrix hybridisation protocol.
2019-05-31 | E-MTAB-7983 | biostudies-arrayexpress
Project description:The expanding and dynamic market of new psychoactive substances (NPSs) poses challenges for laboratories worldwide. The retrospective data analysis (RDA) of previously analyzed samples for new targets can be used to investigate analytes missed in the first data analysis. However, RDA has historically been unsuitable for routine evaluation because reprocessing and reevaluating large numbers of forensic samples are highly work- and time-consuming. In this project, we developed an efficient and scalable retrospective data analysis workflow that can easily be tailored and optimized for groups of NPSs. The objectives of the study were to establish a retrospective data analysis workflow for benzodiazepines in whole blood samples and apply it on previously analyzed driving-under-the-influence-of-drugs (DUID) cases. The RDA workflow was based on a training set of hits in ultrahigh-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to common benzodiazepines that also had been analyzed with a complementary UHPLC-tandem mass spectrometry (MS/MS) method. Quantitative results in the training set were used as the true condition to evaluate whether a hit in the UHPLC-QTOF-MS data file was true or false positive. The training set was used to evaluate and set filters. The RDA was used to extract information from 47 DBZDs in 13,514 UHPLC-QTOF-MS data files from DUID cases analyzed from 2014 to 2020, with filters on the retention time window, count level, and mass error. Sixteen designer and uncommon benzodiazepines (DBZDs) were detected, where 47 identifications had been confirmed by using complementary methods when the case was open (confirmed positive finding), and 43 targets were not reported when the case was open (tentative positive finding). The most common tentative and confirmed findings were etizolam (n = 26), phenazepam (n = 13), lorazepam (n = 9), and flualprazolam (n = 8). This method efficiently found DBZDs in previously acquired UHPLC-QTOF-MS data files, with only nine false-positive hits. When the standard of an emerging DBZD becomes available, all previously acquired DUID data files can be screened in less than 1 min. Being able to perform a fast and accurate retrospective data analysis across previously acquired data files is a major technological advancement in monitoring NPS abuse.
| S-EPMC9175026 | biostudies-literature
Project description:Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens' pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.
| S-EPMC5006994 | biostudies-literature
Project description:Diluted urine for biomarker discovery.
Processed in MZmine 2.37.corr17.7_kei_merge2
GNPS link for IIN paper:
https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=a5480529261b4a13bb867f2edad1dcbe
2019-07-29 | MSV000084148 | GNPS
Project description:Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed good performance using p-values alone; the best of these PLS-DA models was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between high-risk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLS-DA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.
| S-EPMC4304798 | biostudies-literature
Project description:Cannabis is an ancient plant that has been used for therapeutic and recreational purposes. Nowadays, industrial hemp, a variety with low concentration of the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC) and high concentration of non-psychoactive cannabinoids, is getting more and more interest in the food, pharmaceutical, and cosmetic industry. However, cannabis not only contains cannabinoids as bioactive components but also other metabolites like terpenes and phenolic compounds, and the content of these interesting secondary metabolites greatly differs with the genetic variety of the plant. Due to the huge complexity of composition of the cannabis matrix, in this work, a comprehensive two-dimensional liquid chromatography (LC × LC) method has been developed as a very power separation technique coupling a pentafluorophenyl (PFP) and a C18 in the first and second dimensions. Two industrial hemp strains (cookie and gelato) were analyzed to determine the difference in their content of cannabinoids and phenolic compounds. To do this, a new demodulation process was applied for the first time to transform 2D raw data into 1D data which allowed carrying out the chemometric analysis needed to determine the statistical differences between the hemp strains. The cookie strain presented a total of 41 cannabinoid markers, while the gelato strain presented more representative phenolic compounds, in total 24 phenolic compounds were detected as potential markers of this sample. These differences in the chemical composition could determine the industrial destiny of the different hemp strains.
| S-EPMC9242925 | biostudies-literature