目的 通過算法識別與實(shí)驗(yàn)驗(yàn)證相結(jié)合，高效識別抗胰腺導(dǎo)管腺癌（pancreatic ductal adenocarcinoma，PDAC）中藥活性成分，并評價其抗腫瘤活性。方法 利用公共數(shù)據(jù)庫的PDAC表達(dá)譜數(shù)據(jù)及檢索國內(nèi)外相關(guān)文獻(xiàn)，通過單樣本基因集富集分析（single sample gene set enrichment analysis，ssGSEA）方法驗(yàn)證其驅(qū)動基因，計(jì)算驅(qū)動基因之間的最大連通分量（largest connected component，LCC）并構(gòu)建疾病網(wǎng)絡(luò)，運(yùn)用網(wǎng)絡(luò)鄰近度算法量化中藥活性成分靶點(diǎn)與疾病網(wǎng)絡(luò)在全人類蛋白質(zhì)-蛋白質(zhì)相互作用網(wǎng)絡(luò)中的距離預(yù)測其治療潛力，并對候選中藥活性成分進(jìn)行實(shí)驗(yàn)驗(yàn)證。結(jié)果ssGSEA分析顯示，PDAC驅(qū)動基因在腫瘤組的富集得分顯著高于正常組（P≤0.001）。LCC分析表明，驅(qū)動基因形成了高度緊密的相互作用模塊（P＝0.021）。運(yùn)用網(wǎng)絡(luò)鄰近度算法對中藥活性成分進(jìn)行快速篩選，識別出地膚子皂苷Ic為潛在的抗PDAC候選藥物。進(jìn)一步實(shí)驗(yàn)驗(yàn)證表明，在人胰腺癌PANC-1和人胰腺導(dǎo)管癌MIA PaCa-2細(xì)胞中，地膚子皂苷Ic在8.5～9.5 μmol/L濃度下顯著抑制腫瘤細(xì)胞的增殖、遷移和集落形成（P＜0.05），并誘導(dǎo)細(xì)胞凋亡。GSEA富集分析顯示，缺氧反應(yīng)因子-1（hypoxia-inducible factor-1，HIF-1）信號通路活性顯著下調(diào)（normalized enrichment score，NES＝−0.83），生存分析揭示HIF1A的高表達(dá)與生存期呈負(fù)相關(guān)。KEGG富集分析顯示地膚子皂苷Ic主要富集于絲裂原活化蛋白激酶（mitogen-activated protein kinase，MAPK）、磷脂酰肌醇-3-羥激酶（phosphatidylinositol-3-hydroxykinase，PI3K）-蛋白激酶B（protein kinase B，Akt）及血管內(nèi)皮生長因子（vascular endothelial growth factor，VEGF）等信號通路。結(jié)論 基于網(wǎng)絡(luò)鄰近度算法，提出了一種中藥活性成分高效識別策略，成功識別出地膚子皂苷Ic為PDAC潛在治療藥物，為中藥新藥開發(fā)提供了新思路。

Objective Efficient identification of active ingredients from traditional Chinese medicine (TCM) with anti-pancreatic ductal adenocarcinoma (PDAC) properties is achieved through a combination of algorithm identification and experimental validation, followed by an evaluation of their antitumor activity. Methods PDAC expression profile data from public databases and relevant literature were analyzed. Single sample gene set enrichment analysis (ssGSEA) was used to validate PDAC driver genes. The largest connected component (LCC) of driver genes was calculated, and a disease network was constructed. A network proximity algorithm was applied to quantify the distance between TCM active ingredient targets and the disease network within the human protein-protein interaction network, predicting their therapeutic potential. Candidate TCM active ingredients were experimentally validated. Results The ssGSEA analysis revealed that the enrichment scores of PDAC driver genes were significantly higher in the tumor group than in the normal group (P ≤ 0.001). LCC analysis indicated that the driver genes formed a tightly connected interaction module (P = 0.021). Using the network proximity algorithm to rapidly screen the active components of TCM, momordin Ic was identified as a potential anti-PDAC candidate drug. Further experimental validation demonstrated that momordin Ic significantly inhibited tumor cell proliferation, migration, and colony formation in PANC-1 and MIA PaCa-2 cells at concentrations of 8.5—9.5 μmol/L (P < 0.05) and induced apoptosis. GSEA enrichment analysis showed that the hypoxia-inducible factor-1 (HIF-1) signaling pathway activity was significantly downregulated (normalized enrichment score, NES = −0.83). Survival analysis revealed a negative correlation between high HIF1A expression and survival period. KEGG enrichment analysis indicated that momordin Ic primarily affected the MAPK, PI3K-Akt, and VEGF signaling pathways. Conclusion This study proposes an efficient strategy for identifying active ingredients in traditional Chinese medicine based on a network proximity algorithm, successfully identifying momordin Ic as a potential therapeutic agent for PDAC, offering new insights for TCM drug development.

TP18;R285

國家重點(diǎn)研發(fā)計(jì)劃（2022YFC3502000）；國家自然科學(xué)基金重點(diǎn)項(xiàng)目（82430119）；上海市晨光計(jì)劃（23CGA45）