Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Metabolic detection of early-stage breast cancer through absolute quantitative metabolomics and machine learning

ABSTRACT: To achieve the best outcomes, breast cancer necessitates robust strategies for early detection. However, reliable blood-based tests for identifying early-stage disease remains elusive. Here we have employed plasma metabolomics and machine learning techniques to establish a non-invasive metabolic approach for early detection of breast cancer.

INSTRUMENT(S): Q-Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Songhuajie

PROVIDER: MSV000093325 | MassIVE | Mon Nov 06 23:46:00 GMT 2023

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs

Project description:In this study, we aim to reveal the value of plasma exo-miRNA in early diagnosis of breast cancer.In this study, after determining the success of plasma exocrine separation, we analyzed the expression of miRNA in plasma exocrine and selected 16 strong correlation features miRNA by Lasso logistic regression. Different machine learning algorithm models were constructed to evaluate the performance of 16 miRNA for early detection and diagnosis of breast cancer. The biological significance of 16 characteristic miRNAs was evaluated by bioinformatics analysis. Overall, these data highlight the value of exo-miRNA as a biomarker for breast cancer. They may be used for early detection and diagnosis of breast cancer in future clinical practice.

2022-02-21 | GSE197020 | GEO

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Multi-omic analysis identifies metabolic biomarkers for early detection of breast cancer and prediction of therapeutic responses

Project description:Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in breast tumor cells and activation of regulatory T cells (Tregs) in the tumor microenvironment, which show distinction in subtypes of triple-negative breast cancer (TNBC) and non-TNBC patients and likely to impact on prognosis of BC through A2AR-Treg pathway. Combining machine learning with absolute quantitative plasma metabolomics, we establish an effective diagnostic model for early-stage breast cancer, utilizing a four-metabolite panel including two nucleoside metabolites, inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across training, test, and independent validation cohorts. Surprisingly, inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in TNBC patients. This study deepens the understanding of nucleotide metabolism in development of breast cancer and introduces a promising non-invasive approach with low radiation exposure for early breast cancer detection and predicting NAC response in TNBC patients.

2024-08-03 | GSE268662 | GEO

miRNA expression profiling in early stage breast cancer

Project description:MiRNA profiling from whole blood (EDTA) was used to identify a miRNA profile applicable for early stage breast cancer detection

2012-02-02 | GSE31309 | GEO

miRNA expression profiling in early stage breast cancer

Project description:MiRNA profiling from whole blood (EDTA) was used to identify a miRNA profile applicable for early stage breast cancer detection In the study presented here, a consecutively collected, well-defined cohort of 48 patients with early stage breast cancer (invasive ductal carcinoma) at diagnosis was compared with a cohort of 57 healthy individuals for known homo sapiens miRNAs (mirbase 15).

2012-02-02 | E-GEOD-31309 | biostudies-arrayexpress

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:Aim is to identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identification of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Materials and Methods: Using MALDI MSI data, we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specificity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer

2022-02-15 | PXD019662 | Pride

Cancer tissue classication using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:To identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identication of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Using MALDI MSI data,we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specicity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer.

2022-02-15 | PXD019653 | Pride

Machine learning-based detection of immune-mediated diseases from genome-wide cell-free DNA sequencing datasets

Project description:The early detection of tissue and organ damage associated with autoimmune diseases (AID) has been identified as key to improve long-term survival, but non-invasive biomarkers are lacking. Elevated cell-free DNA (cfDNA) levels have been observed in AID and inflammatory bowel disease (IBD), prompting interest to use cfDNA as a potential non-invasive diagnostic and prognostic biomarker. Despite these known disease-related changes in concentration, it remains impossible to identify AID and IBD patients through cfDNA analysis alone. By using unsupervised clustering on large sets of shallow whole-genome sequencing (sWGS) cfDNA data, we uncover AID- and IBD-specific genome-wide patterns in plasma cfDNA in both the obstetric and general AID and IBD populations. Supervised learning of the genome-wide patterns allows AID prediction with 50% sensitivity at 95% specificity. Importantly, the method can identify pregnant women with AID during routine non-invasive prenatal screening. Since AID pregnancies have an increased risk of severe complications, early recognition or detection of new onset AID can redirect pregnancy management and limit potential adverse events. This method opens up new avenues for screening, diagnosis and monitoring of AID and IBD.

2022-09-14 | E-MTAB-11607 | biostudies-arrayexpress

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:Aim is to identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identification of a molecular pattern that can distin- guish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Materials and Methods: Using MALDI MSI data, we developed and validated a machine learning model that can be used for early screening of CRC. Results: Our model yields high sensitivity and speci- ficity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer

2022-02-15 | PXD019666 | Pride

GNPS Systematic analyses of early-stage lung cancer by scRNA-seq and lipidomic reveals aberrant lipid metabolism as detection biomarkers

Project description:Lung cancer is the leading cause of cancer mortality and early detection is the key to improve survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of early-stage lung cancer and found lipid metabolism was broadly dysregulated in different cell types and glycerophospholipid metabolism is the most significantly altered lipid metabolism-related pathway. Untargeted lipidomics were detected in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we have identified nine lipids as the most important detection features and developed a LC-MS-based targeted assay utilizing multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low dose CT and a prospective clinical cohort containing 109 participants, this assay reached over 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization-mass spectrometry imaging assay confirmed the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. Thus, this method, designated as Lung Cancer Artificial Intelligence Detector (LCAID), may be used for early detection of lung cancer or large-scale screening of high-risk populations in cancer prevention.

2021-11-05 | MSV000088324 | GNPS

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