Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.

IF 1.5 4区 医学 Q4 CHEMISTRY, MEDICINAL Current computer-aided drug design Pub Date : 2024-01-01 DOI:10.2174/1573409920666230821102806
Yidong Zhu, Zhongping Ning, Ximing Li, Zhikang Lin
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Abstract

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease.

Objective: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL.

Methods: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms.

Results: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway.

Conclusion: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

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机器学习算法确定马特林抗弥漫大 B 细胞淋巴瘤的靶基因和分子机制
背景:弥漫大B细胞淋巴瘤(DLBCL)是全球最常见的非霍奇金淋巴瘤类型。这种疾病仍然需要新的治疗策略:本研究旨在系统地探讨matrine治疗DLBCL的潜在靶点和分子机制:方法:从多个平台收集潜在的matrine靶点。从公开数据库下载DLBCL的芯片数据和临床特征。应用差异表达分析和加权基因共表达网络分析(WGCNA),使用 R 软件识别 DLBCL 的枢纽基因。然后,将matrine和DLBCL之间的共享靶基因确定为matrine抗DLBCL的潜在靶点。利用最小绝对收缩和选择算子(LASSO)算法确定了最终的核心靶基因,并通过分子对接模拟和接收者操作特征曲线(ROC)分析进一步验证了这些基因。此外还进行了功能分析,以阐明潜在的机制:结果:通过多个数据库和机器学习算法,共获得了222个matrine靶基因和1269个DLBCL中心基因。结果:通过多个数据库和机器学习算法,共获得了 222 个 matrine 靶基因和 1269 个 DLBCL 中心基因,并从 Matrine 和 DLBCL 的 9 个共享靶基因中最终确定了 5 个核心靶基因,包括 CTSL、NR1H2、PDPK1、MDM2 和 JAK3。分子对接显示,马屈菜碱与核心基因的结合是稳定的。ROC曲线也表明核心基因与DLBCL密切相关。此外,功能分析显示,马屈菜碱对DLBCL的治疗效果可能与PI3K-Akt信号通路有关:结论:在治疗DLBCL时,马特林可针对五个基因和PI3K-Akt信号通路。
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来源期刊
Current computer-aided drug design
Current computer-aided drug design 医学-计算机:跨学科应用
CiteScore
3.70
自引率
5.90%
发文量
46
审稿时长
>12 weeks
期刊介绍: Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
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