目的 通過網(wǎng)絡(luò)藥理學(xué)、分子對(duì)接及實(shí)驗(yàn)驗(yàn)證探討雷公藤紅素通過調(diào)控鐵死亡抑制胃癌的機(jī)制。方法 通過TCMSP、SwissTargetPrediction數(shù)據(jù)庫(kù)收集藥物作用靶點(diǎn)，Genecards、Drukbank、OMIM、TTD及Pharmgkb數(shù)據(jù)庫(kù)收集胃癌疾病作用靶點(diǎn)，通過jvenn在線網(wǎng)站獲取二者交集靶點(diǎn)，通過STRING數(shù)據(jù)庫(kù)及Cytoscape 3.7.2軟件進(jìn)行網(wǎng)絡(luò)可視化，利用CytoNCA插件及CytoHubba擴(kuò)展程序計(jì)算節(jié)點(diǎn)得分，獲得Hub基因，然后使用R軟件進(jìn)行基因本體（gene ontology，GO）功能、基因組百科全書（Kyoto encyclopedia of genes and genomes，KEGG）通路富集分析；通過FerrDb V2數(shù)據(jù)庫(kù)檢索鐵死亡相關(guān)靶點(diǎn)，與藥物-疾病靶點(diǎn)取交集獲得核心靶點(diǎn)，并根據(jù)度值大小初步確定雷公藤紅素通過調(diào)控鐵死亡治療胃癌的作用靶點(diǎn)；采用SYBYL-X 2.0、RCSB、AutoDock Vina、Discovery Studio等軟件進(jìn)行分子對(duì)接驗(yàn)證。體外培養(yǎng)人胃腺癌AGS細(xì)胞，通過細(xì)胞計(jì)數(shù)試劑盒-8（cell counting kit-8,CCK-8）檢測(cè)細(xì)胞活力；通過克隆形成實(shí)驗(yàn)和EdU實(shí)驗(yàn)探討雷公藤紅素對(duì)胃癌細(xì)胞增殖能力的影響；劃痕實(shí)驗(yàn)和Transwell實(shí)驗(yàn)探討雷公藤紅素對(duì)細(xì)胞遷移能力的影響；流式細(xì)胞術(shù)檢測(cè)細(xì)胞內(nèi)活性氧（reactive oxygen species，ROS）積累水平；熒光顯微鏡觀察線粒體膜電位水平的變化；試劑盒檢測(cè)細(xì)胞內(nèi)亞鐵離子（Fe²⁺）、丙二醛（malondialdehyde，MDA）及還原型谷胱甘肽/氧化型谷胱甘肽（glutathione/oxidized glutathione，GSH/GSSG）水平；Western blotting檢測(cè)核心靶點(diǎn)及鐵死亡相關(guān)蛋白表達(dá)情況。結(jié)果 共獲得124個(gè)雷公藤紅素靶點(diǎn)、2 343個(gè)胃癌相關(guān)靶點(diǎn)，取交集得到89個(gè)共有靶點(diǎn)。蛋白質(zhì)-蛋白質(zhì)相互作用（protein-protein interaction，PPI）網(wǎng)絡(luò)篩選得到10個(gè)Hub基因。GO功能富集分析顯示，雷公藤紅素主要通過影響細(xì)胞增殖、轉(zhuǎn)錄、凋亡和氧化應(yīng)激等來發(fā)揮功能，KEGG通路富集分析顯示，雷公藤紅素參與調(diào)控多條腫瘤相關(guān)信號(hào)通路。獲得雷公藤紅素-胃癌-鐵死亡交集靶點(diǎn)25個(gè)，結(jié)合度值大小及PPI得分確定信號(hào)轉(zhuǎn)導(dǎo)和轉(zhuǎn)錄激活因子3（signal transducer and activator of transcription 3，STAT3）為雷公藤紅素通過調(diào)控鐵死亡治療胃癌的核心作用靶點(diǎn)。分子對(duì)接結(jié)果提示，雷公藤紅素與STAT3有較好的結(jié)合活性。與對(duì)照組比較，雷公藤紅素組AGS細(xì)胞活力、克隆數(shù)量、EdU陽性率、細(xì)胞遷移率顯著降低（P＜0.05、0.01），ROS水平明顯升高（P＜0.01），線粒體膜電位明顯下降（P＜0.01），MDA及Fe²⁺水平顯著升高（P＜0.01），GSH/GSSG值顯著降低（P＜0.01），且呈劑量相關(guān)性。給予鐵死亡抑制劑（ferrostatin-1，F(xiàn)er-1）干預(yù)后顯著逆轉(zhuǎn)雷公藤紅素對(duì)以上指標(biāo)的作用（P＜0.05、0.01）。結(jié)論 雷公藤紅素通過調(diào)控STAT3誘導(dǎo)胃癌細(xì)胞鐵死亡，抑制胃癌細(xì)胞的惡性增殖及遷移。

Objective To investigate the mechanism of celastrol on inhibiting gastric cancer through regulating ferroptosis using network pharmacology, molecular docking and experimental validation. Methods Drug targets were collected from TCMSP and SwissTargetPrediction databases, while gastric cancer disease targets were obtained from Genecards, DrugBank, OMIM, TTD and PharmGKB databases. The intersection of these targets was identified using jvenn, followed by network visualization using STRING and Cytoscape 3.7.2 software. CytoNCA and CytoHubba plugins were used to calculate node scores and identify Hub genes. Gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed using R software. Ferroptosis-related targets were retrieved from FerrDb V2, and the intersection with drug-disease targets was used to identify core targets. Molecular docking was carried out using SYBYL-X 2.0, RCSB, AutoDock Vina and Discovery Studio. AGS cells were cultured in vitro, and cell viability was measured using cell counting kit-8 (CCK-8). Cloning formation experiments and EdU experiments were used to investigate the effect of celastrol on cell proliferation. Scratch assays and Transwell assays were used to assess the effects of celastrol on cell migration. Flow cytometry was used to detect intracellular reactive oxygen species (ROS) levels. Fluorescence microscopy was used to observe mitochondrial membrane potential (MMP) changes. Kits were used to measure the levels of Fe²⁺, malondialdehyde (MDA) and glutathione/oxidized glutathione (GSH/GSSG). Western blotting was performed to determine the expression levels of key target and ferroptosis-related proteins. Results A total of 124 potential targets for celastrol and 2 343 gastric cancer-related targets were identified. The intersection of these targets revealed 89 common targets. Protein-protein interaction (PPI) network analysis identified 10 Hub genes. GO function enrichment analysis showed that celastrol mainly exerted its effects by influencing cell proliferation, transcription, apoptosis and oxidative stress. KEGG pathway enrichment analysis showed that celastrol was involved in regulating multiple tumor-related signaling pathways. A total of 25 common targets between celastrol, gastric cancer and ferroptosis were identified, signal transducer and activator of transcription 3 (STAT3) was determined as the core target through which celastrol regulateing ferroptosis in gastric cancer treatment based on docking score and PPI score. Molecular docking results indicated a strong binding affinity between celastrol and STAT3. Compared with control group, the viability, colony number, EdU positivity rate and cell migration rate of AGS cells in celastrol group were significantly decreased (P < 0.05, 0.01), ROS level was significantly increased (P < 0.01), MMP was significantly decreased (P < 0.01), levels of MDA and Fe²⁺ were significantly increased (P < 0.01), GSH/GSSG value was significantly decreased (P < 0.01), and showed a dose-dependent relationship. After intervention with ferrostatin-1 (Fer-1), the effects of celastrol on the above indicators were significantly reversed (P < 0.05, 0.01). Conclusion Celastrol induces ferroptosis in gastric cancer cells by regulating STAT3, inhibiting malignant proliferation and migration of gastric cancer cells.

國(guó)家自然科學(xué)基金資助項(xiàng)目（82460561）；甘肅省自然科學(xué)基金資助項(xiàng)目（24JRRA586）；國(guó)家衛(wèi)健委重點(diǎn)實(shí)驗(yàn)室碩博基金項(xiàng)目（NHCDP2022005）；甘肅省人民醫(yī)院院內(nèi)科研基金項(xiàng)目（22GSSYD-38）