Screening of Serum Biomarkers of Coal Workers' Pneumoconiosis by Metabolomics Combined with Machine Learning Strategy.

ABSTRACT: Pneumoconiosis remains one of the most serious global occupational diseases. However, effective treatments are lacking, and early detection is crucial for disease prevention. This study aimed to explore serum biomarkers of occupational coal workers' pneumoconiosis (CWP) by high-throughput metabolomics, combining with machine learning strategy for precision screening. A case-control study was conducted in Beijing, China, involving 150 pneumoconiosis patients with different stages and 120 healthy controls. Metabolomics found a total of 68 differential metabolites between the CWP group and the control group. Then, potential biomarkers of CWP were screened from these differential metabolites by three machine learning methods. The four most important differential metabolites were identified as

SUBMITTER: Chen Z 

PROVIDER: S-EPMC9222502 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature