揭示毗黎勒油在肺癌中的抗癌作用:代谢组学和网络药理学方法

Uncovering the anticancer effects of Bhallataka Taila in lung cancer: A metabolomic and network pharmacology approach.

作者信息

Suchitha G P, Upadhyay Shubham S, Pervaje Ravishankar, Prasad T S Keshava, Dagamajalu Shobha

机构信息

Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.

Sushrutha Ayurveda Hospital, Puttur 574201, Karnataka, India.

出版信息

Bioimpacts. 2025 Apr 16;15:30568. doi: 10.34172/bi.30568. eCollection 2025.

Abstract

INTRODUCTION

Bhallataka ( Linn.) is used in traditional medicine to treat various ailments. The nut extract of Bhallataka, known as Bhallataka taila, has anticancer properties. Although several studies have explored to verify and evaluate its anticancer properties and efficacy against various cancers, the specific target proteins, mode of action, and associated metabolites have not yet been identified. This study aimed to elucidate the biological mechanisms of Bhallataka taila using an integrated metabolomics and systems pharmacology approach with validation.

METHODS

Untargeted metabolomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to evaluate the metabolites in Bhallataka taila, identify key protein targets and link them to cellular pathways through bioinformatics-based network pharmacology. Protein targets were mapped using BindingDB, and pathway enrichment was analyzed using STRINGdb. An study of A549 cells assessed the impact of Bhallataka taila on cellular viability (MTT assay), apoptosis (AO-EB staining), reactive oxygen species (ROS) production (fluorescent spectroscopy and DCFDA staining), and marker validation (immunoblotting and qRT-PCR). The integration of metabolomics, network pharmacology, and experiments offers a significant understanding of the anticancer mechanisms and pathways influenced by Bhallataka taila in non-small cell lung cancer (NSCLC) cells. Statistical analysis was performed using GraphPad Prism using one-way ANOVA.

RESULTS

Metabolomics combined with network pharmacology detected 2023 unique metabolites at the MS1 level and 216 metabolites at the MS2 level. Bhallataka taila metabolites were found to interact with 180 human target proteins identified through BindingDB analysis. These target proteins were mapped to key cancer regulatory signaling pathways, along with TNF-related apoptosis-inducing ligand (TRAIL), protease-activated receptor-1 (PAR1)-mediated thrombin signaling, Syndecan-1 and Glypican pathways, and vascular endothelial growth factor receptor (VEGFR)1/2 pathways. validation demonstrated that Bhallataka taila significantly regulated apoptosis (57%) and ROS production (56%) in A549 cells compared to control while modulating other cancer-related regulatory pathways.

CONCLUSION

This data-driven study can help researchers identify promising cancer treatment candidates and validate their efficacy. This approach integrates traditional knowledge with modern scientific techniques to reinforce the anticancer potential of Bhallataka taila and its mechanisms.

摘要

引言

印加毒籽(Linn.)在传统医学中用于治疗各种疾病。印加毒籽的坚果提取物,即印加毒籽油,具有抗癌特性。尽管多项研究已探索验证和评估其抗癌特性及对各种癌症的疗效,但具体的靶蛋白、作用方式和相关代谢物尚未确定。本研究旨在通过综合代谢组学和系统药理学方法并进行验证,阐明印加毒籽油的生物学机制。

方法

采用液相色谱 - 串联质谱(LC-MS/MS)进行非靶向代谢组学分析,以评估印加毒籽油中的代谢物,通过基于生物信息学的网络药理学鉴定关键蛋白靶点并将其与细胞通路联系起来。使用BindingDB绘制蛋白靶点,并使用STRINGdb分析通路富集情况。对A549细胞进行的研究评估了印加毒籽油对细胞活力(MTT法)、凋亡(AO-EB染色)、活性氧(ROS)产生(荧光光谱法和DCFDA染色)以及标志物验证(免疫印迹和qRT-PCR)的影响。代谢组学、网络药理学和实验的整合为理解印加毒籽油在非小细胞肺癌(NSCLC)细胞中影响的抗癌机制和通路提供了重要依据。使用GraphPad Prism进行单因素方差分析以进行统计分析。

结果

代谢组学与网络药理学相结合在MS1水平检测到2023种独特代谢物,在MS2水平检测到216种代谢物。发现印加毒籽油代谢物与通过BindingDB分析鉴定的180种人类靶蛋白相互作用。这些靶蛋白被映射到关键的癌症调节信号通路,以及肿瘤坏死因子相关凋亡诱导配体(TRAIL)、蛋白酶激活受体-1(PAR1)介导的凝血酶信号通路、Syndecan-1和Glypican通路以及血管内皮生长因子受体(VEGFR)1/2通路。实验验证表明,与对照组相比,印加毒籽油显著调节A549细胞中的凋亡(57%)和ROS产生(56%),同时调节其他癌症相关调节通路。

结论

这项数据驱动的研究可帮助研究人员识别有前景的癌症治疗候选物并验证其疗效。这种方法将传统知识与现代科学技术相结合,以增强印加毒籽油的抗癌潜力及其机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3421/12204778/a56cba793256/bi-15-30568-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索