目的 基于LC-MS代謝組學技術研究胡桃醌抑制胃癌細胞BGC-823和肝癌細胞HepG 3B增殖的機制。方法 采用MTS法檢測胡桃醌（6.25、12.50、25.00、50.00、100.00 μmol·L^-1）對BGC-823、HepG 3B細胞活力的影響，并計算半數(shù)抑制濃度（IC₅₀）作為后續(xù)給藥濃度；使用胡桃醌處理BGC-823、HepG 3B細胞，提取代謝物，基于LC-MS檢測樣品，進行代謝組學分析；使用Progenesis QI軟件進行數(shù)據(jù)處理，將得到的數(shù)據(jù)導入EZinfo進行多元統(tǒng)計分析，利用主成分分析（PCA）及正交偏最小二乘-判別分析（OPLS-DA）得到變量權重值（VIP），選取VIP＞1且具有統(tǒng)計學意義的差異代謝物（P＜ 0.05）作為潛在的生物標記物。通過人類代謝組數(shù)據(jù)庫（HMDB）、京都基因和基因組百科全書（KEGG）等代謝物數(shù)據(jù)庫對生物標記物進行鑒定，利用MetaboAnalyst平臺進行代謝通路分析。結果 胡桃醌對HepG 3B細胞的IC₅₀為14.17 μmol·L^-1，對BGC-823細胞的IC₅₀為11.19 μmol·L^-1。作用于HepG 3B細胞，篩選出尿苷二磷酸葡萄糖、谷胱甘肽、氧化谷胱甘肽、檸檬酸、L-酪氨酸、L-異亮氨酸、L-苯丙氨酸、泛酸、L-色氨酸、花生四烯酸、棕櫚酸共11個生物標記物，涉及谷胱甘肽代謝，泛酸鹽和輔酶A（CoA）生物合成，花生四烯酸代謝，淀粉和蔗糖代謝，氨基糖和核苷酸糖代謝，苯丙氨酸代謝，纈氨酸、亮氨酸和異亮氨酸的生物合成，酪氨酸代謝，色氨酸代謝，檸檬酸循環(huán)，脂肪酸代謝共11條代謝通路；作用于胃癌BGC-823細胞，篩選出泛硫乙胺、肌酸、鞘氨醇、丙酰肉堿、異戊酰肉堿、L-苯丙氨酸、L-異亮氨酸、L-色氨酸8個生物標記物，涉及鞘脂類代謝，纈氨酸、亮氨酸、異亮氨酸生物合成，苯丙氨酸、酪氨酸、色氨酸生物合成，苯丙氨酸代謝，色氨酸代謝，泛酸與輔酶A生物合成，精氨酸與脯氨酸代謝共7條代謝通路。結論 胡桃醌可抑制BGC-823、HepG3B細胞活力，其機制與影響多種能量及氨基酸代謝通路相關。

Objective To investigate the mechanism of inhibition of proliferation of gastric cancer cell line BGC-823 and hepatocellular carcinoma cell line HepG 3B by juglone based on LC-MS metabolomics. Methods The MTS method was used to detect the effects of juglone (6.25, 12.50, 25.00, 50.00, 100.00 μmol·L^-1) on the viability of BGC-823 and HepG 3B cells and calculate the half-maximal inhibitory concentration (IC₅₀) as the subsequent drug administration concentration. BGC-823 and HepG 3B cells were treated with juglone, and the metabolites were extracted. The samples were detected by LC-MS and subjected to metabolomics analysis. Data processing was performed using Progenesis QI software, and the obtained data were imported into EZinfo for multivariate statistical analysis. Principal component analysis (PCA) and orthogonal partial least squares analysis (OPLS-DA) were used to obtain variable importance in projection (VIP) values. Metabolites with VIP > 1 and statistical significance (P <0.05) were selected as potential biomarkers. The biomarkers were identified using metabolite databases such as HMDB and KEGG, and metabolic pathway analysis was conducted using the MetaboAnalyst platform. Results The IC₅₀ of juglone for HepG 3B cells was 14.17 μmol·L^-1, and for BGC-823 cells, it was 11.19 μmol·L^-1. When acting on HepG 3B cells, 11 biomarkers were screened out, including uridine diphosphate glucose, glutathione, oxidized glutathione, citric acid, L-tyrosine, L-isoleucine, L-phenylalanine, pantothenic acid, L-tryptophan, arachidonic acid, and palmitic acid, involving 11 metabolic pathways such as glutathione metabolism, pantothenate and CoA biosynthesis, arachidonic acid metabolism, starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, phenylalanine metabolism, valine, leucine and isoleucine biosynthesis, tyrosine metabolism, tryptophan metabolism, citric acid cycle, and fatty acid metabolism. When acting on gastric cancer BGC-823 cells, eight biomarkers were screened out, including pantethine, creatine, sphingosine, propionylcarnitine, isovalerylcarnitine, L-phenylalanine, L-isoleucine, and L-tryptophan, involving seven metabolic pathways such as sphingolipid metabolism, valine, leucine and isoleucine biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, tryptophan metabolism, pantothenate and CoA biosynthesis, and arginine and proline metabolism. Conclusion Juglone can inhibit the viability of BGC-823 and HepG 3B cells, and its mechanism is related to the influence on multiple energy and amino acid metabolic pathways.

R285.5

黑龍江省中醫(yī)藥學會青年人才托舉工程項目（2022-QNRC1-14）