Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies (part II)

ABSTRACT: Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC=0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC=0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Urine

SUBMITTER: Kevin Contrepois

PROVIDER: ST001890 | MetabolomicsWorkbench | Mon Jul 26 00:00:00 BST 2021

REPOSITORIES: MetabolomicsWorkbench

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	mwtab	Other

Items per page:

1 - 1 of 1

Similar Datasets

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies (part I)

Project description:Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC=0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC=0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

2021-07-26 | ST001889 | MetabolomicsWorkbench

Micro-RNA 150-5p predicts overt heart failure in patients with univentricular hearts

Project description:Background: In patients with left heart failure, microRNAs (miRNAs) have been shown to be of diagnostic and prognostic value. The present study aims to identify miRNAs in patients with univentricular heart (UVH) disease that may be predictive of overt heart failure. Methods: A large panel of human miRNA arrays were used to determine miRNA expression profiles in the blood of 48 UVH patients and 32 age and gender-matched healthy controls. For further selection, the most abundantly expressed miRNA arrays were related to clinical measures of heart failure and selected miRNAs validated by Reverse transcription quantitative polymerase chain reaction (RT-qPCR). These validated miRNAs were used for the prediction of overt heart failure and all-cause mortality. Results: According to microarray analysis, 50 miRNAs were found to be significantly abundant in UVH patients of which miR-150-5p was best related to heart failure parameters. According to ROC analysis, NT-proBNP levels (AUC 0.940, 95% CI 0.873-1.000; p=0.001), miR-150-5p expression levels (AUC 0.905, 95% CI 0.779-1.000; p=0.001) and a higher NYHA class ≥ III (AUC 0.893, 95% CI 0.713-1.000; p=0.002) were the 3 most significant predictors of overt heart failure. Using a combined biomarker model, AUC increased to 0.980 indicating an additive value of miR-150-5p. Moreover, in the multivariate analysis, a higher NYHA class ≥ III (p=0.005) and miR-150-5p (p=0.006) turned out to be independent predictors of overt heart failure. Conclusion: In patients with UVH, miR-150-5p has additive value to natriuretic peptides for the prediction of overt heart failure and thus may be used as additional biomarker for risk assessment in these patients.

2019-10-17 | GSE136547 | GEO

Validation of a histology-independent prognostic gene signature for early stage, non-small cell lung cancer including stage IA patients

Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes. Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). !Series_contributor = Sandy,D,Der

2013-12-22 | E-GEOD-50081 | biostudies-arrayexpress

MiR-27a-3p in peripheral blood mononuclear cells as a potential marker for pancreatic cancer screening

Project description:Background & Aims: MicroRNAs have been shown to offer great potential in the diagnosis of cancer. We aimed to identify microRNAs in peripheral blood mononuclear cells (PBMCs) for diagnosing pancreatic cancer (PC). Methods: PBMCs microRNA expression was investigated in three independent cohorts including 352 participants (healthy, benign pancreatic/peripancreatic diseases (BPD), and PC). First, we used sequencing technology to identify differentially expressed microRNAs in 60 PBMCs samples for diagnosing PC. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using an independent cohort. Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: We found that PBMCs miR-27a-3p could efficiently discriminate PC from BPD (AUC=0.840; 95% CI, 0.787 to 0.885; sensitivity=82.2%, specificity=76.7%). A panel composed of PBMCs miR-27a-3p and serum CA19-9 provided a high diagnostic accuracy in differentiating PC from BPD in the clinical setting (AUC=0.886; 95% CI, 0.837 to 0.923; sensitivity=85.3%, specificity=81.6%). The satisfactory diagnostic performance of the panel persisted regardless of disease status (AUCs for tumour-node-metastasis stages?,?, and ? were 0.881, 0.884, and 0.893, respectively). Conclusion: PBMCs miR-27a-3p could be a potential marker for PC screening. A panel composed of PBMCs miR-27a-3p and serum CA19-9 has considerable clinical value in diagnosing early-stage PC. Therefore, patients who would have otherwise missed the curative treatment window can benefit from optimal therapy. Examination of different MicroRNA profiles in 3 types of PBMCs samples

2012-05-03 | E-GEOD-37710 | biostudies-arrayexpress

Validation of a histology-independent prognostic gene signature for early stage, non-small cell lung cancer including stage IA patients

2013-12-22 | GSE50081 | GEO

Platelet Reactivity ExpreSsion Score Predicts Cardiovascular Risk

Project description:Platelets are key mediators of atherothrombosis; yet, limited tools exist to identify individuals with a hyperreactive platelet phenotype. Here we derive and validate a Platelet Reactivity ExpreSsion Score (PRESS) integrating platelet aggregation responses and RNA sequencing in multiple cohorts. PRESS performs well in identifying a hyperreactive phenotype in patients with cardiovascular disease (AUC [cross-validation] 0.81, 95% CI 0.68 to 0.94), with similar discrimination in an independent cohort of healthy participants (AUC [validation] 0.77, 95% CI 0.75 to 0.79); accuracy 80%, sensitivity 71%, and specificity 86%. Next, we validate PRESS in multiple cohorts of patients with platelet RNASeq available. Following multivariable adjustment, PRESS was significantly higher in MI (β=1.8, p=0.02), and individuals with a score above the median more frequently develop a future cardiovascular event (adjusted HR 1.90, CI 1.07 to 3.36), p=0.027). Altogether, we provide strong evidence that PRESS is a robust prognostic marker, which could have an essential role in facilitating a personalized approach to cardiovascular risk management.

2024-06-30 | GSE232027 | GEO

microRNA expression profile in endometrioid endometrial carcinoma (EEC)

Project description:The aim of the presented study was to define tissue and plasma miRNA signatures, which could potentially serve as diagnostic and prognostic markers in endometrioid endometrial cancer (EEC), and to investigate miRNA profiles in regard to clinicopathological characteristics of the tumors. Results: qPCR validation revealed regulation of 14 miRNAs in EEC tissues (miR-9, miR-141,miR-205,miR-203,miR-183,miR-200a*,miR-200b*,miR-200a,miR-200c,miR-429,miR-200b,miR-410,miR-92a,miR-1305) and 9 in plasma samples (miR-449a,miR-1290,miR-1228,miR-203,miR-200a,miR-141,miR-92a, miR-9, miR-301b). Expression of certain miRNAs showed association with FIGO stage, grade and relapse. Two miRNA signatures, miR-205/miR-410 and miR-410/miR-429/miR-92a, classified tumor tissues with higher accuracy in comparison to single miRNAs (AUC 0.972, 95% CI 0.919-0.995 and 0.991, 95% CI 0.948-1.000, resepctively). miRNA signature composed of miR-205 and miR-200a predicted relapse with AUC of 0.854 (95% CI 0.691-0.951). Tissue miRNA signatures were independent prognostic markers of overall (miR-1228/miR-200c/miR-429, HR 2.98) and progression-free survival (miR-1228/miR-429, HR 4.149). Plasma miRNA signatures classified EEC plasma samples with high accuracy. Conclusions: We conclude that miRNA signatures hold great promise for becoming non-invasive biomarkers for early EEC detection and prognosis.

2012-06-01 | GSE35794 | GEO

microRNA expression profile in endometrioid endometrial carcinoma (EEC)

2012-05-31 | E-GEOD-35794 | biostudies-arrayexpress

Seven-lncRNA signature in esophageal squamous cell carcinoma

Project description:A seven-lncRNA signature was identified in the training set, which is significantly associated with overall survival (OS) (Hazard Ratio [HR]: 3.535, 95% confidence interval [CI]: 2.113-5.915, P < 0.001) and disease-free survival (DFS) (Hazard Ratio [HR]: 2.413, 95% confidence interval [CI]: 1.573-3.703, P < 0.001). Patients in high-risk group have shorter overall survival (HR: 3.555, 95% CI: 2.195-5.757, p < 0.001) and disease-free survival (HR: 2.537, 95% CI: 1.646-3.909, p < 0.001) in the training set, and we found the same conclusion in the independent set for OS (HR 2.665, p < 0.001) and DFS (HR 2.360, p = 0.001). Combination of the signature and TNM staging system was more powerful than TNM staging system alone in predicting outcomes in both the training set (AUC: 0.772 vs 0.681, p = 0.002) and in the independent set (AUC: 0.772 vs 0.660, p = 0.003).

2020-02-10 | GSE92986 | GEO

MiR-27a-3p in peripheral blood mononuclear cells as a potential marker for pancreatic cancer screening

Project description:Background & Aims: MicroRNAs have been shown to offer great potential in the diagnosis of cancer. We aimed to identify microRNAs in peripheral blood mononuclear cells (PBMCs) for diagnosing pancreatic cancer (PC). Methods: PBMCs microRNA expression was investigated in three independent cohorts including 352 participants (healthy, benign pancreatic/peripancreatic diseases (BPD), and PC). First, we used sequencing technology to identify differentially expressed microRNAs in 60 PBMCs samples for diagnosing PC. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using an independent cohort. Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: We found that PBMCs miR-27a-3p could efficiently discriminate PC from BPD (AUC=0.840; 95% CI, 0.787 to 0.885; sensitivity=82.2%, specificity=76.7%). A panel composed of PBMCs miR-27a-3p and serum CA19-9 provided a high diagnostic accuracy in differentiating PC from BPD in the clinical setting (AUC=0.886; 95% CI, 0.837 to 0.923; sensitivity=85.3%, specificity=81.6%). The satisfactory diagnostic performance of the panel persisted regardless of disease status (AUCs for tumour-node-metastasis stagesⅠ,Ⅱ, and Ⅲ were 0.881, 0.884, and 0.893, respectively). Conclusion: PBMCs miR-27a-3p could be a potential marker for PC screening. A panel composed of PBMCs miR-27a-3p and serum CA19-9 has considerable clinical value in diagnosing early-stage PC. Therefore, patients who would have otherwise missed the curative treatment window can benefit from optimal therapy.

2012-05-04 | GSE37710 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data