网络药理学和生物信息学揭示了潘立卡金对抗肝细胞癌耐药性的药理机制

IF 0.5 Q4 GENETICS & HEREDITY
Gajalakshmi Ramarajyam , Ramadurai Murugan , Selvam Rajendiran
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引用次数: 0

摘要

背景:肝细胞癌(HCC)是一项严峻的全球健康挑战,在不同地区的发病率差异很大。本研究探讨了石榴中富含的一种多酚--punicalagin 对 HCC 的潜在治疗意义。研究的主要目标包括利用综合生物信息学分析鉴定受punicalagin影响的有效分子靶点和丰富通路。材料和方法:通过基因组富集分析(Gene Set Enrichment Analysis,GSEA),该研究发现了肝癌中潜在的差异表达基因(DEGs)。通过整理来自 GEO2R、CTD 数据库和基因卡片等不同数据库的信息,发现了一组 20 个潜在靶点。随后使用 STITCH 进行了药理学网络分析,Cytoscape 软件通过 cytohubba 插件在 punicalagin 网络中精确定位了五个高度上调的基因,如 SRC、CASP3、AKT1、IL6 和 NOS3。此外,还利用基因本体(GO)分析预测了功能类别,揭示了punicalagin对潜在生物学影响的关键见解:结果:KEGG通路分析显示了AMPK信号、HIF1a和mTOR信号等关键通路的富集,揭示了受punicalagin影响的分子机制。利用 UALCAN 和 GEPIA 数据库中的数据集,通过分析已确定靶点的 mRNA 表达水平和总生存率进行了诊断评估。通过分子对接研究完成了Punicalagin与其靶点相互作用的结构确认,揭示了与SRC、CASP3、AKT1、IL6和NOS3等生物大分子的稳健结合关系。实验验证包括 RT-PCR,结果表明在使用 punicalagin 的 HepG2 细胞中,SRC、CASP3、AKT1、IL6 和 NOS3 等靶标生物大分子的表达水平均有所下降。结论这些发现强调了孔雀石绿作为肝癌治疗的一种有前途的治疗途径的潜力,提出了一种将计算见解与实验证据相结合的综合方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network pharmacology and bioinformatics illuminates punicalagin's pharmacological mechanisms countering drug resistance in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) poses a formidable global health challenge, exhibiting significant prevalence variations across diverse regions. This study delves into the potential therapeutic implications of punicalagin, a polyphenol abundant in pomegranates, for HCC. The primary objectives encompass the identification of potent molecular targets and enriched pathways influenced by punicalagin using integrated bioinformatic analysis. Materials and methods: Employing Gene Set Enrichment Analysis (GSEA), the study discerned potential differentially expressed genes (DEGs) in liver cancer. Collating information from diverse databases, including GEO2R, CTD database, and Gene Cards, revealed a set of 20 potential targets. A pharmacological network analysis was subsequently conducted using STITCH, with Cytoscape software pinpointing five highly upregulated genes within the punicalagin network such as SRC, CASP3, AKT1, IL6, and NOS3 via the cytohubba plugin. Furthermore, Gene Ontology (GO) analysis was employed to predict functional categories, unveiling key insights into the potential biological impact of punicalagin.

Results: KEGG pathway analysis demonstrated enrichment in crucial pathways such as AMPK signaling, HIF1a, and mTOR signaling, shedding light on the molecular mechanisms influenced by punicalagin. Diagnostic assessments were performed by analyzing mRNA expression levels and overall survival for the identified targets, utilizing datasets from UALCAN and GEPIA databases. Structural confirmation of punicalagin interactions with its targets was accomplished through molecular docking studies, revealing robust binding associations with biomolecules such as SRC, CASP3, AKT1, IL6, and NOS3. Experimental validation involved RT-PCR, showcasing reduced expression levels of target biomolecules such as SRC, CASP3, AKT1, IL6, and NOS3 in HepG2 cells treated with punicalagin. Conclusion: These findings underscore the potential of punicalagin as a promising therapeutic avenue for liver cancer treatment, presenting a comprehensive approach that integrates computational insights with experimental evidence.

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来源期刊
Human Gene
Human Gene Biochemistry, Genetics and Molecular Biology (General), Genetics
CiteScore
1.60
自引率
0.00%
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审稿时长
54 days
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