目的 建立雞骨草Abrus cantoniensis和毛雞骨草A.mollis的HPLC指紋圖譜，比較不同批次雞骨草和毛雞骨草樣品的化學(xué)成分差異，并對維采寧-2和夏佛塔苷進(jìn)行定量測定，結(jié)合網(wǎng)絡(luò)藥理學(xué)和體外實(shí)驗(yàn)驗(yàn)證，初步探討雞骨草和毛雞骨草的同用合理性及治療肝癌的潛在作用機(jī)制。方法 采用HPLC方法，Ultimate^Ⓡ AQ-C₁₈色譜柱（250 mm×4.6 mm，5 μm），流動(dòng)相為甲醇-水溶液，梯度洗脫，體積流量1.0 mL·min^-1，檢測波長270 nm，柱溫30℃。建立12批雞骨草和6批毛雞骨草的指紋圖譜并進(jìn)行相似度評價(jià)和特征峰匹配。結(jié)合聚類分析（CA）、主成分分析（PCA）及正交偏最小二乘法-判別分析（OPLS-DA）確定雞骨草和毛雞骨草的差異性特征成分，并對其中2種成分進(jìn)行定量測定。通過中藥系統(tǒng)藥理學(xué)數(shù)據(jù)庫與分析平臺（TCMSP）篩選雞骨草活性成分及其潛在靶點(diǎn)，并利用GeneCards數(shù)據(jù)庫篩選肝癌相關(guān)靶點(diǎn)，結(jié)合韋恩圖分析獲取共有靶點(diǎn)?；诠灿邪悬c(diǎn)，進(jìn)行基因本體（GO）和京都基因與基因組百科全書（KEGG）分析，揭示雞骨草治療肝癌的關(guān)鍵通路。使用STRING 11.5數(shù)據(jù)庫構(gòu)建靶點(diǎn)蛋白質(zhì)與蛋白質(zhì)相互作用（PPI）網(wǎng)絡(luò)，并構(gòu)建藥物-成分-疾病-靶點(diǎn)-通路網(wǎng)絡(luò)，確定主要活性成分。采用MTT法確定雞骨草醇提物（ACEE）對HepG2細(xì)胞的最佳用藥劑量，并應(yīng)用實(shí)時(shí)熒光定量PCR（qRTPCR）實(shí)驗(yàn)檢測排名前5的mRNA表達(dá)量。結(jié)果 建立了12批雞骨草和6批毛雞骨草樣品的指紋圖譜，標(biāo)定10個(gè)共有峰，共指認(rèn)其中5個(gè)主要特征峰：峰1為相思子堿、峰2為刺桐堿、峰5為維采寧-2、峰8為夏佛塔苷、峰9為異夏佛塔苷。通過化學(xué)模式識別篩選得到峰3、峰5（維采寧-2）、峰6、峰7、峰8（夏佛塔苷）、峰9（異夏佛塔苷）所代表的成分是區(qū)分不同批次樣品的差異性標(biāo)志物，其中維采寧-2和夏佛塔苷的質(zhì)量分?jǐn)?shù)分別為0.05～4.36 mg·g^-1、0.10～4.34 mg·g^-1，不同批次間差異較大。網(wǎng)絡(luò)藥理學(xué)結(jié)合指紋圖譜指認(rèn)共確定9個(gè)活性成分，265個(gè)潛在靶點(diǎn)，與肝癌共有靶點(diǎn)130個(gè)。KEGG富集分析共得到112條信號通路，主要涉及癌癥通路、脂質(zhì)與動(dòng)脈粥樣硬化通路等。根據(jù)活性成分-靶點(diǎn)-通路網(wǎng)絡(luò)篩選出5個(gè)肝癌關(guān)鍵靶點(diǎn)。根據(jù)MTT結(jié)果，最終選擇質(zhì)量濃度0.25 mg·mL-1的ACEE進(jìn)行qRT-PCR實(shí)驗(yàn)，與對照組比較，關(guān)鍵靶點(diǎn)AKT1、PIK3CA、STAT3、BCL2、GSK3B的mRNA表達(dá)水平顯著下降（P＜0.001）。結(jié)論 建立的指紋圖譜及含量測定方法簡便可行，網(wǎng)絡(luò)藥理學(xué)篩選出的關(guān)鍵靶點(diǎn)經(jīng)體外實(shí)驗(yàn)證明與雞骨草抗肝癌的作用機(jī)制密切相關(guān)，為雞骨草質(zhì)量的控制和藥效機(jī)制的研究提供參考。

Objective To establish high-performance liquid chromatography (HPLC) Fingerprints for Abrus cantoniensis and A. mollis, comparison of chemical composition differences among different batches of A. cantoniensis and A. mollis samples, quantitative determination of vicenin-2 and schaftoside, combined with network pharmacology and in vitro experiments to preliminary explore the rationality of co-use of A. cantoniensis and A. mollis and the potential mechanism of anti-hepatocellular carcinoma.Methods The HPLC method was used with an Ultimate^Ⓡ AQ-C₁₈ column (250 mm×4.6 mm, 5 μm), a mobile phase of methanol-water solution, gradient elution; flow rate of 1.0 mL·min^-1; detection at 270 nm, 30 ℃. Fingerprints for 12 batches of A. cantoniensis and six batches of A. mollis were established and evaluated for similarity and characteristic peak matching. Combined with Cluster Analysis (CA), principal component analysis (PCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA), the differential characteristic components of A. cantoniensis and A. mollis were determined, and two of these components were quantitatively measured. Active components of A. cantoniensis and their potential targets were screened through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and liver cancer-related targets were screened using the GeneCards database, with common targets obtained through venn diagram analysis. Based on common targets, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to reveal key pathways of A. cantoniensis in treating liver cancer. The STRING 11.5 database was used to construct a protein-protein interaction (PPI) network of target proteins, and an active component-target-pathway network was built to identify main active components. The MTT method was used to determine the optimal dosage of A. cantoniensis ethanol extract (ACEE) for HepG2 cells, and real-time fluorescence quantitative PCR (qRT-PCR) was used to detect the expression levels of the top five mRNA.Results Fingerprints for 12 batches of A. cantoniensis and 6 batches of A. mollis samples were established, with ten common peaks calibrated and five main characteristic peaks identified: peak 1 as abrine, peak 2 as hypaphorine, peak 5 as vicenin-2, peak 8 as schaftoside, and peak 9 as isoschaftoside. Chemical pattern recognition identified components represented by peaks 3, 5 (vicenin-2), 6, 7, 8 (schaftoside), and 9 (isoschaftoside) as differential markers distinguishing different batch samples, with mass fractions of vicenin-2 and schaftoside ranging from 0.05—4.36 mg·g^-1 and 0.10—4.34 mg·g^-1, respectively, showing significant differences among batches. Network pharmacology combined with fingerprint identification confirmed nine active components and 265 potential targets, with 130 common targets related to liver cancer. KEGG yielded 112 signaling pathways, mainly involving cancer pathways, lipid and atherosclerosis pathways, etc. Five key liver cancer targets were screened based on the active component-target-pathway network. According to MTT results, 0.25 mg·mL^-1 ACEE was selected for qRT-PCR, compared with the control group, the mRNA expression levels of key targets AKT1, PIK3CA, STAT3, BCL2 and GSK3B were significantly decreased (P＜0.001).Conclusion The established HPLC fingerprint and content determination method are simple and feasible. The key targets screened by network pharmacology have been verified by in vitro experiments to be closely related to the effect of A. cantoniensis in the treatment of liver cancer, providing a reference for the quality control and the study of its pharmacological mechanisms.

R286.2

國家自然科學(xué)基金委區(qū)域創(chuàng)新發(fā)展聯(lián)合基金重點(diǎn)支持項(xiàng)目（U23A20521）；廣西科技重大專項(xiàng)研究項(xiàng)目（桂科AA22096029）