Feature selection for the identification of antitumor compounds in the alcohol total extracts of Curcuma longa.

Antitumor activity has been reported for turmeric, the dried rhizome of Curcuma longa. This study proposes a new feature selection method for the identification of the antitumor compounds in turmeric total extracts. The chemical composition of turmeric total extracts was analyzed by gas chromatography-mass spectrometry (21 ingredients) and high-performance liquid chromatography-mass spectrometry (22 ingredients), and their cytotoxicity was detected through an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay against HeLa cells. A support vector machine for regression and a generalized regression neural network were used to research the composition-activity relationship and were later combined with the mean impact value to identify the antitumor compounds. The results showed that six volatile constituents (three terpenes and three ketones) and seven nonvolatile constituents (five curcuminoids and two unknown ingredients) with high absolute mean impact values exhibited a significant correlation with the cytotoxicity against HeLa cells. With the exception of the two unknown ingredients, the identified 11 constituents have been reported to exhibit cytotoxicity. This finding indicates that the feature selection method may be a supplementary tool for the identification of active compounds from herbs.

Targeted metabolomics study of fatty-acid metabolism in lean metabolic-associated fatty liver disease patients.

BACKGROUND: The annual incidence of metabolic-associated fatty liver disease (MAFLD) in China has been increasing and is often overlooked owing to its insidious characteristics. Approximately 50% of the patients have a normal weight or are not obese. They are said to have lean-type MAFLD, and few studies of such patients are available. Because MAFLD is associated with abnormal lipid metabolism, lipid-targeted metabolomics was used in this study to provide experimental evidence for early diagnosis and pathogenesis. AIM: To investigate the serum fatty-acid metabolic characteristics in lean-type MAFLD patients using targeted serum metabolomic technology. METHODS: Between January and June 2022, serum samples were collected from MAFLD patients and healthy individuals who were treated at Shanghai Putuo District Central Hospital for serum metabolomics analysis. Principal component analysis and orthogonal partial least squares-discriminant analysis models were developed, and univariate analysis was used to screen for biomarkers of lean-type MAFLD and analyze metabolic pathways. UPLC-Q-Orbitrap/MS content determination was used to determine serum palmitic acid (PA), oleic acid (OA), linoleic acid (LA), and arachidonic acid (AA) levels in lean-type MAFLD patients. RESULTS: Urea nitrogen and uric acid levels were higher in lean-type MAFLD patients than in healthy individuals (P < 0.05). Alanine transaminase and cholinesterase levels were higher in lean-type MAFLD patients than in healthy individuals (P < 0.01). The expression of high-density lipoprotein and apolipoprotein A-1 were lower in lean-type MAFLD patients than in healthy individuals (P < 0.05) and the expression of triglycerides and fasting blood glucose were increased (P < 0.01). A total of 65 biomarkers that affected the synthesis and metabolism of fatty acids were found with P < 0.05 and variable importance in projection > 1". The levels of PA, OA, LA, and AA were significantly increased compared with healthy individuals. CONCLUSION: The metabolic profiles of lean-type MAFLD patients and healthy participants differed significantly, yielding 65 identified biomarkers. PA, OA, LA, and AA exhibited the most significant changes, offering valuable clinical guidance for prevention and treatment of lean-type MAFLD.