Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA357547 | ENA |
REPOSITORIES: ENA
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Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancer.
Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma.
In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the disease state. For pancreatic cancer, a separation by stage was achieved.
Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.
2021-10-01 | MSV000088180 | MassIVE
Project description:The objective is to obtain miRNA representative signatures both in plasma and bronchoalveolar cell fraction that could serve as biomarker in respiratory diseases. The identification of new less invasive biomarkers is necessary to improve the detection and prognostic outcome of respiratory pathological processes. The measurement of miRNA expression through less invasive techniques such as plasma and serum have been suggested to analysis of several lung malignancies including lung cancer. These studies are assuming a common deregulated miRNA expression both in blood and lung tissue. The present study aimed to obtain miRNA representative signatures both in plasma and bronchoalveolar cell fraction that could serve as biomarker in respiratory diseases. we have compared circulating plasma miRNA with the bronchoalveolar cell fraction-derived miRNA patterns from 10 patients with several lung disease using a RT-qPCR assay.
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