To Reveal the Potential Mechanism of Quercetin against NSCLC Based on Network Pharmacology and Experimental Validation.

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Baibai Ye, Ping Chen, Cheng Lin, Xinyu Liu, Jia Chen, Chenning Zhang, Linfu Li
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引用次数: 0

Abstract

Purpose: This study aimed to initially clarify the potential mechanism of quercetin in the treatment of non-small cell lung cancer (NSCLC) based on network pharmacology, molecular docking and in vitro experiments.

Method: TCMSP, SwissTargetPrediction, TCMIP, STITCH, and ETCM databases were applied to obtain the targets of quercetin. NSCLC-related targets were retrieved from GeneCards, OMIM, PharmGKB, TTD, and NCBI databases. Their intersection targets were imported into the STRING database to construct a protein-protein interaction (PPI) network and core targets were identified through the Cytoscape 3.10.0 soft and the CytoHubba tool. Furthermore, Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersection targets. A compound-targetspathways network was subsequently constructed to screen for key targets and pathways. Molecular docking was performed with Discovery Studio software to verify the interactions between quercetin and core targets. In vitro validations were conducted employing CCK-8 assays, flow cytometry, and Western blotting (WB).

Results: 193 potential targets of quercetin for treating NSCLC were obtained. The top ten core targets identified within the PPI network included TP53, HSP90AA1, AKT1, JUN, SRC, EGFR, ACTB, TNF, MAPK1, and VEGFA. GO analysis yielded 2319 items, and KEGG analysis resulted in 211 enriched pathways. Molecular docking results demonstrated a high affinity of quercetin towards the core targets. Based on the compound-targets-pathways network and molecular docking, the PI3K/AKT/P53 pathway and its key-related proteins (PIK3R1, AKT1, and TP53) were selected for further validation. Quercetin(20 and 40 μg/mL) significantly decreased the viability of A549 NSCLC cells but not BEAS-2B normal cells via CCK-8 assays. Flow cytometry and WB analyses further corroborated that quercetin could promote apoptosis of A549 cells by downregulating and upregulating the expression of Bcl-2 and Bax (P<0.05), respectively. Notably, quercetin did not significantly alter the total protein levels of PI3K, AKT, and P53 but downregulated the phosphorylation levels of PI3K and AKT (P<0.05) and upregulated the phosphorylation level of P53 (P<0.05).

Conclusion: Quercetin exhibits therapeutic potential in NSCLC by regulating the PI3K/AKT/P53 pathway to promote cell apoptosis.

基于网络药理学和实验验证,揭示槲皮素抗 NSCLC 的潜在机制。
目的:本研究旨在基于网络药理学、分子对接和体外实验,初步阐明槲皮素治疗非小细胞肺癌(NSCLC)的潜在机制:方法:应用 TCMSP、SwissTargetPrediction、TCMIP、STITCH 和 ETCM 数据库获取槲皮素的靶点。从 GeneCards、OMIM、PharmGKB、TTD 和 NCBI 数据库中检索 NSCLC 相关靶标。通过Cytoscape 3.10.0软件和CytoHubba工具确定了核心靶点。此外,还对交叉靶点进行了基因本体(GO)功能分析和京都基因组百科全书(KEGG)通路富集分析。随后构建了化合物-靶点-通路网络,以筛选关键靶点和通路。利用 Discovery Studio 软件进行了分子对接,以验证槲皮素与核心靶点之间的相互作用。利用 CCK-8 检测法、流式细胞术和 Western 印迹法(WB)进行了体外验证:结果:获得了193个槲皮素治疗NSCLC的潜在靶点。在PPI网络中发现的前十大核心靶点包括TP53、HSP90AA1、AKT1、JUN、SRC、表皮生长因子受体、ACTB、TNF、MAPK1和VEGFA。GO 分析产生了 2319 个项目,KEGG 分析产生了 211 个富集通路。分子对接结果表明,槲皮素对核心靶点具有很高的亲和力。根据化合物-靶标-通路网络和分子对接,选择了 PI3K/AKT/P53 通路及其关键相关蛋白(PIK3R1、AKT1 和 TP53)进行进一步验证。通过CCK-8测定,槲皮素(20和40 μg/mL)能显著降低A549 NSCLC细胞的活力,但不能降低BEAS-2B正常细胞的活力。流式细胞术和WB分析进一步证实,槲皮素可通过下调和上调Bcl-2和Bax的表达来促进A549细胞的凋亡:槲皮素通过调节PI3K/AKT/P53通路促进细胞凋亡,对NSCLC具有治疗潜力。
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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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