Dataset Information

UPLC-MS-Based Serum Metabolic Profiling Reveals Potential Biomarkers for Predicting Propofol Responsiveness in Females

ABSTRACT:

Although previous studies have shown that certain factors interfere with the sensitivity of propofol, the mechanisms for interindividual variability in response to propofol remain unclear. This study aimed to screen the metabolites to predict patients' sensitivity to propofol and to identify metabolic pathways to explore possible mechanisms associated with propofol resistance. Sera from 40 female patients undergoing elective hysteroscopic surgery in a prospective cohort propofol study were obtained before the administration of propofol. The patients' responsiveness to propofol was differentiated based on propofol effect-site concentration. Serum samples from two sets, a discovery set (n = 24) and an independent validation set (n = 16), were analyzed using ultraperformance liquid chromatography coupled with mass spectrometry based untargeted metabolomics. In the discovery set, 494 differential metabolites were screened out, and then 391 potential candidate biomarkers with the area under receiver operating characteristic curve >0.80 were selected. Pathway analysis showed that the pathway of glycerophospholipid metabolism was the most influential pathway. In the independent validation set, six potential biomarkers enabled the discrimination of poor responders from good and intermediate responders, which might be applied to predict propofol sensitivity.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Ruiyun Li

PROVIDER: MTBLS2311 | MetaboLights | 2021-09-29

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MTBLS2311	Other
	FILES	Other
	a_MTBLS2311_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS2311_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other

Items per page:

1 - 5 of 9

Publications

UPLC-MS-Based Serum Metabolic Profiling Reveals Potential Biomarkers for Predicting Propofol Responsiveness in Females.

Li Ruiyun R Yang Lu L Guan Su S Lin Ming M Lai Hanjin H Liu Kun K Liu Zimeng Z Zhang Xuyu X

Journal of proteome research 20210812 9

PMID: 34384217

Similar Datasets

Project description:A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. In this study, we have conducted a prospective sample collection and retrospective blinded validation (PRoBE design) to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. The Affymetrix HG U133 Plus 2.0 Array and 2D-DIGE were used to profile transcriptomes and proteomes in saliva supernatants respectively. Significant variations of salivary transcriptomic and proteomic profiles were observed between breast cancer patients and healthy controls. Eleven transcriptomic biomarker candidates and two proteomic biomarker candidates were selected for a preclinical validation using an independent sample set. Transcriptomic biomarkers were validated by RT-qPCR and proteomic biomarkers were validated by quantitative protein immunoblot. Eight mRNA biomarkers and one protein biomarker have been validated for breast cancer detection, yielding ROC-plot AUC values between 0.665 and 0.959. This report provides proof of concept of salivary biomarkers for the non-invasive detection of breast cancer. The salivary biomarkersâ discriminatory power paves the way for a PRoBE-design definitive validation study. Keywords: Salivary biomarker, Breast cancer, Early detection, Salivary transcriptome, Salivary proteome This study, which was approved by the UCLA and Cedars-Sinai Medical Center Institutional Review Boards (#06-07-043 and #3870, respectively), started sample collection in February 2007. The sample collection followed the PRoBE principle (prospective specimen collection). The saliva bank for breast cancer project at the UCLA Dental Research Institute in collaboration with Cedars Sinai Medical Center has collected 200 saliva samples since 2007 with all subjects recruited from the Saul and Joyce Brandman Breast Cancer Center. Of these, 113 samples, including 41 breast cancer patients and 72 healthy control individuals, were selected for the discovery and validation phase of this study. Inclusion criteria of cancer patients consisted of a confirmed diagnosis of breast cancer. Exclusion criteria of cancer patients included therapy/surgery and/or a diagnosis of other malignancies within 5 years prior to saliva collection. Exclusion criteria of control patients included a diagnosis of any malignancies within 5 years prior to saliva collection. Written informed consents and questionnaire data sheets were obtained from all patients who agreed to serve as saliva donors. Unstimulated saliva samples were consistently collected, stabilized, and preserved as previously described. The sample supernatants were reserved at -80 C prior to assay. This study consisted of a discovery phase, followed by an independent preclinical validation phase. Of the 113 samples, 10 breast cancer samples and 10 healthy control samples were chosen for the discovery phase. All breast cancer cases are invasive ductal carcinoma (IDC), the most common type of breast cancer. Biomarkers identified from the discovery studies were first verified using the discovery sample set. An independent sample set, including 31 breast cancer patients and 62 healthy control subjects, was used for the biomarker validation phase.

Project description:The early and accurate identification of predictive biomarkers for antiviral treatment efficacy remains a significant clinical challenge, particularly in the management of chronic hepatitis B (CHB). This study aimed to assess whether the plasma metabolome could reliably predict the success of antiviral therapy in CHB patients. We conducted a retrospective analysis on 56 treatment-naive CHB patients at the First Affiliated Hospital of Zhejiang University from December 2013 to March 2016. Patients were randomly assigned to either a discovery cohort (n=29) or a validation cohort (n=27) and underwent a 48-week treatment regimen of entecavir (ETV) and interferon-alpha (IFN-α). Based on the outcome of the treatment, patients were classified as HBeAg seroconversion either (High responders, Hrp) or the non-remission group (Low responder, Lrp). Our methodology involved an untargeted analysis of the amine/phenol and carboxylic acid submetabolomes in the CHB patients under treatment, utilizing chemical isotope labeling (CIL) techniques with liquid chromatography-mass spectrometry (LC-MS).Several metabolites were identified as having significant diagnostic potential for distinguishing Hrp from Lrp, with areas under the receiver operating characteristic curve (AUC) exceeding those typically clinical indicators. Notably, four metabolites, namely 2-methyl-3-ketovaleric acid, 2-ketohexanoic acid, 6-oxo-1,4,5,6-tetrahydronicotinic acid, and α-ketoisovaleric acid, demonstrated exceptionally high sensitivity and specificity in both cohorts, nearing 100%. In contrast, the clinical indicators, including HBcAb, log(HBsAg), and HBeAb, demonstrated lower and inconsistent sensitivity and specificity between the discovery and validation cohorts. Using HBcAb as a marker, the sensitivity was 87.5% with 76.9% specificity in the discovery cohort; however, the sensitivity dropped to 46.7% with 91.7% specificity in the validation cohort. Using log(HBsAg), the sensitivity was 84.6% with 69.2% specificity in the discovery cohort, compared to 85.7% sensitivity and 83.3% specificity in the validation cohort. For HBeAb, the separation of Hrp and Lrp had a sensitivity of 87.5% with 69.2% specificity in the discovery cohort, while the validation cohort showed 86.7% sensitivity and 91.7% specificity.

Project description:Most (70%) epithelial ovarian cancers (EOCs) are diagnosed late. Non-invasive biomarkers that facilitate disease detection and predict outcome are needed. The microRNAs (miRNAs) represent a new class of biomarkers. This study was to identify and validate plasma miRNAs as biomarkers in EOC. We evaluated plasma samples of 360 EOC patients and 200 healthy controls from two institutions. All samples were grouped into screening, training and validation sets. We scanned the circulating plasma miRNAs by TaqMan low-density array in the screening set and identified/validated miRNA markers by real-time polymerase chain reaction assay in the training set. Receiver operating characteristic and logistic regression analyses established the diagnostic miRNA panel, which were confirmed in the validationsets. We found higher plasma miR-205 and lower let-7f expression in cases than in controls. MiR-205 and let-7f together provided high diagnostic accuracy for EOC, especially in patients with stage I disease. The combination of these two miRNAs and carbohydrate antigen-125 (CA-125) further improved the accuracy of detection. MiR-483-5p expression was elevated in stages III and IV compared with in stages I and II, which was consistent with its expression pattern in tumor tissues. Furthermore, lower levels of let-7f were predictive of poor prognosis in EOC patients. Our findings indicate that plasma miR-205 and let-7f are biomarkers for ovarian cancer detection that complement CA-125; let-7f may be predictive of ovarian cancer prognosis. We designed a multi-stage, retrospective, nested case-control study to determine whether serum miRNA profiles can be used to predict EOC development. All samples were grouped into screening, training, and validation sets. A total of 560 plasma samples (360 cases and 200 controls) were obtained from Tianjin Medical University Cancer Institute and Hospital (TCIH) and Cancer Center of Nanjing Medical University. The cases and controls were well matched for age. The International Federation of Gynaecology and Obstetrics (FIGO) staging system was used to stage cases. All patients and controls were genetically unrelated, ethnic Han Chinese women who were permanent residents of the urban area of Tianjin and Nanjing. Controls with cardiovascular, respiratory, digestive, urinary, reproductive, and endocrine diseases were excluded. The study protocols were approved by the Tianjin and Nanjing Center review committees. In screening set, to detect generalizable miRNA signatures, we pooled serum samples of 10 early-stage cases (stage I), 10 late-stage cases (stage IIIc-IV), and 10 healthy controls, respectively, and analyzed these three pool samples using a TaqMan low-density array (TLDA set 2.0, Applied Biosystems) chip screening in the discovery stage. In training set, the miRNAs identified in the screening set were validated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on individual plasma samples of 76 EOC patients and 30 normal controls. In validation set, the validation set comprised a two-phase process. Promising associations from the screening and training set were evaluated in the validation phase I set, comprising 134 cases and 70 controls from Tianjin Center. The validation phase II set included samples of 77 EOC cases and 50 normal controls were from Tianjin Center and 73 EOC cases and 50 normal controls were from Nanjing Center.