Name: YangziChen2024 - Metabolic Machine Learning Predictor Model for Diagnosis of Gastric Cancer
Keywords: Machine Learning Model

Dataset Information

YangziChen2024 - Metabolic Machine Learning Predictor Model for Diagnosis of Gastric Cancer

ABSTRACT: 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.

SUBMITTER: Akshat Pandey

PROVIDER: MODEL2407230001 | BioModels | 2024-07-23

REPOSITORIES: BioModels

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Publications

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.

Chen Yangzi Y Wang Bohong B Zhao Yizi Y Shao Xinxin X Wang Mingshuo M Ma Fuhai F Yang Laishou L Nie Meng M Jin Peng P Yao Ke K Song Haibin H Lou Shenghan S Wang Hang H Yang Tianshu T Tian Yantao Y Han Peng P Hu Zeping Z

Nature communications 20240223 1

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine ...[more]

PMID: 38395893

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Project description:Background & Aims: Cholangiocarcinomas (CCAs), heterogeneous biliary tumors with dismal prognosis, lack accurate early-diagnostic methods, especially important for individuals at high-risk (i.e., primary sclerosing cholangitis (PSC)). Here, we searched for protein biomarkers in serum extracellular vesicles (EVs). Methods: EVs from patients with isolated PSC (n=45), concomitant PSC-CCA (n=42), PSC who developed CCA during follow-up (PSC to CCA; n=25), CCAs from non-PSC etiology (n=56), hepatocellular carcinoma (n=34) and healthy individuals (n=55) were characterized by mass-spectrometry. Diagnostic biomarkers of PSC-CCA, non-PSC CCA or CCAs regardless etiology (pan-CCAs) were defined, and their expression was evaluated in human organs/tissues and within CCA tumors at single-cell level. Prognostic EV-biomarkers for CCA were investigated. Results: High-throughput proteomics identified candidate diagnostic biomarkers for PSC-CCA, non-PSC CCA or pan-CCA, as well as and for differential diagnosis of intrahepatic CCA and HCC, that were cross-validated by ELISA using total serum. Machine learning logit modelling disclosed CRP/FRIL/Fibrinogen algorithm with diagnostic value for early-stage PSC-CCA vs isolated PSC (AUC=0.944; OR=82.0), overpowering CA19-9 (AUC=0.735; OR=9.3). An algorithm combining CRP/VWF/PIGR/ /Fibrinogen allowed the diagnosis of early-stage non-PSC CCAs compared to healthy individuals (AUC=0.999; OR=1115). Noteworthy, levels of Fibrinogen/CRP/PIGR/FRIL showed predictive capacity for CCA development in patients with PSC before clinical evidences of malignancy. Multi-organ transcriptomic analysis revealed that serum EV-biomarkers were mostly expressed in hepatobiliary tissues, and scRNA-seq and immunofluorescence analysis of CCA tumors showed their presence mainly in malignant cholangiocytes. Multivariable analysis unveiled EVprognostic biomarkers independent to clinical features, with COMP/GNAI2/CFAI and ACTN1/MYCT1/PF4V associated negatively or positively to patients’ survival, respectively. Conclusions: Serum EVs contain protein biomarkers for the prediction, early diagnosis and prognosis estimation of CCA, representing a novel tumor cell-derived liquid biopsy for personalized medicine.

Dataset Information

YangziChen2024 - Metabolic Machine Learning Predictor Model for Diagnosis of Gastric Cancer

Publications

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.

Similar Datasets

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