The Combination of Machine Learning and Metabolomics for the Clinical Diagnosis of Hepatocellular Carcinoma. (PubMed, J Proteome Res)
Nontargeted and targeted metabolomic analyses were used to explore dysregulated metabolites, and many bile acids (such as DCA, GUDCA, GCDCA, GCA, TCDCA, TDCA, TCA, LCA, and TUDCA) and steroid hormones (such as DHEAS, DHEA, Aldo, Cortisone, and 18-OHF) were found to be dysregulated in HCC...It exhibited good diagnostic performance in the detection of early-stage and small-size HCC (AUC = 0.896 and AUC = 0.830) and performed better than the classical biomarker alpha-fetoprotein (AFP). In conclusion, our study established a novel XGBoost model based on bile acids and steroid hormones and might be helpful for the early diagnosis of HCC.