基于机器学习和分子对接,验证芳樟醇是治疗乳腺癌的潜在药物。

IF 3.5 4区 医学 Q2 CHEMISTRY, MEDICINAL
Qian Zhang, Dengfeng Chen
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引用次数: 0

摘要

乳腺癌(BC)是女性常见癌症。本研究旨在通过机器学习方法构建乳腺癌的预后风险模型并确定预后生物标志物,同时阐明芳樟醇发挥抑瘤作用的机制。研究人员从基因表达总库(Gene Expression Omnibus)和癌症基因组图谱(The Cancer Genome Atlas)数据库中获得了三个mRNA芯片/RNA测序数据集(GSE25055、GSE103091和TCGA-BRCA),并通过单变量COX分析获得了预后基因。采用多种机器学习方法筛选核心基因并构建预后风险模型。利用 DAVID 数据库对关键基因进行了富集分析。利用 UALCAN、人类蛋白质图谱、geneMANIA 和 LinkedOmics 数据库分析基因表达和共表达基因。应用分子对接和分子动力学模拟验证了芳樟醇与磷酸甘油酸激酶 1(PGK1)的结合亲和力。使用细胞计数试剂盒 8(CCK-8)、Edu、transwell、流式细胞术和 Western 印迹分析法分析细胞活性、凋亡、细胞周期和蛋白质表达。通过生物信息学分析和机器学习获得了八个预后基因,并构建了预后风险模型。该模型能很好地预测患者的预后,其风险评分可作为 BC 的独立风险因素。高危组和低危组的总生存率(OS)和免疫细胞浸润特征各不相同。PGK1在BC中高表达,PGK1高表达患者的OS较短。PGK1与细胞周期和PPAR信号通路有关。芳樟醇与PGK1具有良好的结合活性,芳樟醇能抑制BC细胞的活力、增殖、迁移和侵袭,促进细胞凋亡,诱导G0/G1停滞。此外,芳樟醇还能促进PPARγ蛋白的表达,抑制PGK1的表达。芳樟醇通过抑制PGK1的表达和激活PPAR信号通路,对BC发挥抑制肿瘤的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validating linalool as a potential drug for breast cancer treatment based on machine learning and molecular docking

Breast cancer (BC) is a common cancer for women. This study aims to construct a prognostic risk model of BC and identify prognostic biomarkers through machine learning approaches, and clarify the mechanism by which linalool exerts tumor-suppressive function. Three mRNA microarray/RNA sequencing data sets (GSE25055, GSE103091, and TCGA-BRCA) were obtained from Gene Expression Omnibus database and The Cancer Genome Atlas database, and prognostic genes were obtained by univariate COX analysis. Multiple machine learning methods were used to screen core genes and construct prognostic risk models. The enrichment analysis of crucial genes was analyzed using the DAVID database. UALCAN, human protein atlas, geneMANIA, and LinkedOmics databases were used to analyze gene expression and co-expressed genes. Molecular docking and molecular dynamics simulation was applied to verify the binding affinity between linalool and phosphoglycerate kinase 1 (PGK1). Cell counting kit 8 (CCK-8, Edu, transwell, flow cytometry, and Western blot assay were used to analyze cell activity, apoptosis, cell cycle and protein expression. Eight prognostic genes were obtained by bioinformatics analysis and machine learning, and prognostic risk models were constructed. This model could well predict the prognosis of patients, and the risk score could be used as an independent risk factor for BC. Overall survival (OS) and immune cell infiltration characteristics were distinct between high and low risk groups. PGK1 was highly expressed in BC and the OS of patients with high PGK1 expression was shorter. PGK1 was related to cell cycle and PPAR signaling pathway. Linalool and PGK1 had good binding activity, and linalool could inhibit the viability, proliferation, migration, and invasion of BC cells, promote cell apoptosis, and induce G0/G1 arrest. In addition, linalool can promote PPARγ protein expression and inhibit PGK1 expression. Machine learning and molecular docking were promising for exploration of new drug targets for BC, and linalool exerts tumor-suppressive effects in BC by inhibiting PGK1 expression and activating PPAR signaling pathway.

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来源期刊
CiteScore
6.40
自引率
2.60%
发文量
104
审稿时长
6-12 weeks
期刊介绍: Drug Development Research focuses on research topics related to the discovery and development of new therapeutic entities. The journal publishes original research articles on medicinal chemistry, pharmacology, biotechnology and biopharmaceuticals, toxicology, and drug delivery, formulation, and pharmacokinetics. The journal welcomes manuscripts on new compounds and technologies in all areas focused on human therapeutics, as well as global management, health care policy, and regulatory issues involving the drug discovery and development process. In addition to full-length articles, Drug Development Research publishes Brief Reports on important and timely new research findings, as well as in-depth review articles. The journal also features periodic special thematic issues devoted to specific compound classes, new technologies, and broad aspects of drug discovery and development.
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