RESUMO
PURPOSE: Hepatocellular carcinoma (HCC) is a prevalent malignant tumor, often arising from hepatitis induced by the hepatitis B virus (HBV) in China. However, effective biomarkers for early diagnosis are lacking, leading to a 5-year overall survival rate of less than 20% among patients with advanced HCC. This study aims to identify serum biomarkers for early HCC diagnosis to enhance patient survival rates. METHODS: We established an independent cohort comprising 27 healthy individuals, 13 patients with HBV-induced cirrhosis, 13 patients with hepatitis B-type HCC, and 8 patients who progressed from cirrhosis to hepatocellular carcinoma during follow-up. Serum metabolic abnormalities during the progression from cirrhosis to HCC were studied using untargeted metabolomics. Liquid chromatography-mass spectrometry-based metabolomics methods characterized the subjects' serum metabolic profiles. Partial least squares discriminant analysis (PLS-DA) was employed to elucidate metabolic profile changes during the progression from cirrhosis to HCC. Differentially expressed metabolites (DEMs) between cirrhosis and HCC groups were identified using the LIMMA package in the R language. Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest Classifier (RF), were used to identify key metabolic biomarkers involved in the progression from cirrhosis to HCC. Key metabolic biomarkers were further validated using targeted metabolomics in a new independent validation cohort comprising 25 healthy individuals and 25 patients with early-stage hepatocellular carcinoma. RESULTS: A total of 155 serum metabolites were identified, of which 21/54 metabolites exhibited significant changes in HCC patients compared with cirrhosis patients and healthy individuals, respectively. PLS-DA clustering results demonstrated a significant change trend in the serum metabolic profile of patients with HBV-induced cirrhosis during the progression to HCC. Utilizing LASSO regression and RF algorithms, we confirmed 10 key metabolic biomarkers. Notably, 1-Methylnicotinamide (1-MNAM) exhibited a persistent and significant decrease in healthy individuals, cirrhosis, and HCC patients. Moreover, 1-MNAM levels in developing patients were significantly higher during the cirrhosis stage than in the HCC stage. Targeted metabolomic validation in an external cohort further confirmed the good diagnostic performance of 1-MNAM in early HCC detection. CONCLUSION: Our findings imply that 1-MNAM may be a specific biomarker for the progression of cirrhosis to HCC.
Assuntos
Biomarcadores Tumorais , Carcinoma Hepatocelular , Progressão da Doença , Cirrose Hepática , Neoplasias Hepáticas , Niacinamida , Humanos , Carcinoma Hepatocelular/sangue , Carcinoma Hepatocelular/virologia , Carcinoma Hepatocelular/diagnóstico , Neoplasias Hepáticas/sangue , Neoplasias Hepáticas/diagnóstico , Cirrose Hepática/sangue , Cirrose Hepática/diagnóstico , Masculino , Biomarcadores Tumorais/sangue , Feminino , Pessoa de Meia-Idade , Niacinamida/análogos & derivados , Niacinamida/sangue , Adulto , Metabolômica/métodos , Estudos de Coortes , IdosoRESUMO
Aim: Investigation of the pharmacokinetics of sorafenib (SRF) in rats with hepatocellular carcinoma (HCC). Methods: A reproducible ultra-HPLC-MS method for simultaneous determination of serum SRF, N-hydroxymethyl sorafenib and N-demethylation sorafenib. Results: Both the maximum serum concentrations (2.5-times) and the area under the serum concentration-time curve from 0 h to infinity (4.5-times) of SRF were observed to be significantly higher, with a greater than 3.0-fold decrease in the clearance rate in the HCC-bearing rats compared with these values in healthy animals. Further study revealed approximately 3.8- and 3.2-times increases in the apparent Michaelis constant for N-hydroxymethyl sorafenib and N-demethylation sorafenib conversions in the HCC-bearing rats. Conclusion: The low efficiency for the SRF conversions was a key contributor to the increased serum concentrations of SRF.
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