Alam Md Shahin, Sultana Adiba, Sun Hongyang, Wu Jin, Guo Fanfan, Li Qing, Ren Haigang, Hao Zongbing, Zhang Yi, Wang Guanghui
Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China.
Department of Pharmacology, College of Pharmaceutical Science, Soochow University, Suzhou, Jiangsu, China.
Front Pharmacol. 2022 Sep 20;13:942126. doi: 10.3389/fphar.2022.942126. eCollection 2022.
Accurate identification of molecular targets of disease plays an important role in diagnosis, prognosis, and therapies. Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study was to accurately identify a set of molecular targets and small molecular drugs that might be effective for BC diagnosis, prognosis, and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles (GSE54002, GSE29431, GSE124646, GSE42568, GSE45827, GSE10810, GSE65216, GSE36295, and GSE109169) collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages, LIMMA and clusterProfiler, in were used to identify overlapping differential expressed genes (oDEGs) and significant GO and KEGG enrichment terms. We constructed a PPI (protein-protein interaction) network through the STRING database and identified eight key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using six topological measures, betweenness, closeness, eccentricity, degree, MCC, and MNC, in the Analyze Network tool in Cytoscape. Three online databases GSCALite, Network Analyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks, and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm support vector machine (SVM). We suggested four TFs (TP63, MYC, SOX2, and KDM5B) and four miRNAs (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, and hsa-mir-23b-3p) as key transcriptional and posttranscriptional regulators of KGs. Finally, we proposed 16 candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat for BC through molecular docking analysis. Using BC cell lines, we validated that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed results might play an effective role in the treatment of BC patients.
准确识别疾病的分子靶点在诊断、预后和治疗中起着重要作用。乳腺癌（BC）是全球女性中最常见的恶性肿瘤之一。因此，本研究的目的是通过使用现有的生物信息学和基于网络的方法，准确识别一组可能对BC诊断、预后和治疗有效的分子靶点和小分子药物。本研究使用从基因表达综合数据库（GEO）收集的九个基因表达谱（GSE54002、GSE29431、GSE124646、GSE42568、GSE45827、GSE10810、GSE65216、GSE36295和GSE109169）进行生物信息学分析。使用R语言中的LIMMA和clusterProfiler两个软件包来识别重叠差异表达基因（oDEGs）以及显著的基因本体（GO）和京都基因与基因组百科全书（KEGG）富集项。我们通过STRING数据库构建了一个蛋白质-蛋白质相互作用（PPI）网络，并在Cytoscape的网络分析工具中使用介数、紧密性、离心率、度、MCC和MNC这六种拓扑度量方法，识别出八个关键基因（KGs），即表皮生长因子受体（EGFR）、纤连蛋白1（FN1）、EZH2、间质上皮转化因子（MET）、细胞周期蛋白依赖性激酶1（CDK1）、极光激酶A（AURKA）、拓扑异构酶IIα（TOP2A）和凋亡抑制蛋白5（BIRC5）。使用三个在线数据库GSCALite、Network Analyst和GEPIA分析KGs的药物富集、调控相互作用网络和基因表达水平。我们通过使用流行的机器学习算法支持向量机（SVM）的预测模型来检验KGs的预后能力。我们提出四个转录因子（TP63、MYC、SOX2和赖氨酸特异性去甲基化酶5B（KDM5B））和四个微小RNA（hsa-mir-16-5p、hsa-mir-34a-5p、hsa-mir-1-3p和hsa-mir-
