摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-乳腺癌是一篇报道的主题。根据NewsRx记者在中国荆州的新闻报道，研究表明：“乳腺癌(BC)是女性常见的癌症，本研究旨在构建乳腺癌预后风险模型，通过机器学习方法识别预后标志物，并阐明芳樟醇抑制肿瘤的作用机制。”新闻记者引用长江大学的研究，“从基因表达综合数据库和LAS数据库获得三个mRNA微阵列/RNA测序数据集(GSE25055、GSE103091和TcGA-BR CA)，采用多种机器学习方法筛选核心基因,构建预测风险模型,利用DAVID数据库对关键基因进行富集分析,UALCAN、人类蛋白图谱、基因狂热、基因多态性等。采用Molecular Doc King和分子动力学模拟方法验证芳樟醇与磷酸甘油酸激酶1(PGK1)的结合关系,采用细胞计数试剂盒8(CC K-8、Edu、Transwell、流式细胞术和Western blot分析细胞活性、凋亡和凋亡。通过生物信息学分析和机器学习获得了8个预后基因,并构建了预测风险模型,该模型能很好地预测患者的预后。总生存率(OS)和免疫细胞浸润特征在高、低危组之间差异不大,PGK1在BC中高表达,PGK1高表达的患者OS较短,PGK1与细胞周期和PPAR信号通路有关,芳樟醇和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阻滞，并能促进PPARg蛋白的表达，抑制PGK1的表达。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Breast Canc er is the subject of a report. According to news reporting originating in Jingzh ou, People’s Republic of China, by NewsRx journalists, research stated, “Breast cancer (BC) is a common cancer for women. This study aims to construct a prognos tic risk model of BC and identify prognostic biomarkers through machine learning approaches, and clarify the mechanism by which linalool exerts tumor-suppressiv e function.” The news reporters obtained a quote from the research from Yangtze University, “ Three mRNA microarray/RNA sequencing data sets (GSE25055, GSE103091, and TCGA-BR CA) were obtained from Gene Expression Omnibus database and The Cancer Genome At las database, and prognostic genes were obtained by univariate COX analysis. Mul tiple machine learning methods were used to screen core genes and construct prog nostic risk models. The enrichment analysis of crucial genes was analyzed using the DAVID database. UALCAN, human protein atlas, geneMANIA, and LinkedOmics data bases were used to analyze gene expression and co-expressed genes. Molecular doc king and molecular dynamics simulation was applied to verify the binding affinit y between linalool and phosphoglycerate kinase 1 (PGK1). Cell counting kit 8 (CC K-8, Edu, transwell, flow cytometry, and Western blot assay were used to analyze cell activity, apoptosis, cell cycle and protein expression. Eight prognostic g enes were obtained by bioinformatics analysis and machine learning, and prognost ic 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 distinc t between high and low risk groups. PGK1 was highly expressed in BC and the OS o f 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 li nalool could inhibit the viability, proliferation, migration, and invasion of BC cells, promote cell apoptosis, and induce G0/G1 arrest. In addition, linalool c an promote PPARg protein expression and inhibit PGK1 expression.”
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