Song Linlin, Li Qi, Shi Hui, Zhang Pengxia
School of Basic Medicine, Jiamusi University, Jiamusi, 154007 Heilongjiang, China.
Department of Biochemistry, Mudanjiang Medical School, Mudanjiang, 157011 Heilongjiang, China.
Appl Bionics Biomech. 2022 Jun 10;2022:9294634. doi: 10.1155/2022/9294634. eCollection 2022.
Based on data mining, an innovative big data analysis platform was utilized to discuss the treatment of cancer in chronic myeloid leukemia (CML) by dasatinib, aiming to offer help to the diagnosis and treatment of cancer. An integrated gene expression analysis system (IEAS) was firstly constructed to automatically classify data in the online human Mendelian genetic database using clustering algorithms. At the same time, the gene expression profile was analyzed by principal component analysis (PCA) in the analysis system. In addition, the efficacy of dasatinib in the treatment of patients with advanced CML was then retrospectively analyzed. The results showed that the IEAS system could incorporate the gene expression analysis vectors it contained by JAVA-related technologies, and the generated clustering genes showed similar functions. The clustering algorithm could homogenize data and generate visual clustering heat maps. The analysis results of major elements were diverse under different experimental conditions. The characteristic value of the first major element was the largest. Messenger ribonucleic acid (mRNA) datasets of CML patients were selected from cancer genomic map, including 120 samples and 20,614 mRNA in total. In micro-RNA (miRNA) datasets, there were 202 samples including 1,406 miRNAs. Data were screened by miRNA-mRNA regulation template, and 20 differentially expressed mRNAs were obtained. In conclusion, the proposed IEAS system could mine and analyze the gene expression data. Dasatinib showed good efficacy in the treatment of patients with advanced CML. Besides, it could improve visual queries, and data mining had a broad application prospect in clinical application. Dasatinib was considered to be a good option for patients with advanced CML.
基于数据挖掘，利用一个创新的大数据分析平台来探讨达沙替尼对慢性粒细胞白血病（CML）癌症的治疗，旨在为癌症的诊断和治疗提供帮助。首先构建了一个整合基因表达分析系统（IEAS），使用聚类算法在在线人类孟德尔遗传数据库中自动对数据进行分类。同时，在分析系统中通过主成分分析（PCA）对基因表达谱进行分析。此外，随后对达沙替尼治疗晚期CML患者的疗效进行了回顾性分析。结果表明，IEAS系统可以通过与JAVA相关的技术整合其包含的基因表达分析向量，生成的聚类基因显示出相似的功能。聚类算法可以使数据同质化并生成可视化的聚类热图。在不同实验条件下主要元素的分析结果各不相同。第一个主要元素的特征值最大。从癌症基因组图谱中选取CML患者的信使核糖核酸（mRNA）数据集，共包括120个样本和20614个mRNA。在微小RNA（miRNA）数据集中，有202个样本，包括1406个miRNA。通过miRNA-mRNA调控模板对数据进行筛选，得到20个差异表达的mRNA。总之，所提出的IEAS系统可以挖掘和分析基因表达数据。达沙替尼在治疗晚期CML患者方面显示出良好的疗效。此外，它可以改善可视化查询，数据挖掘在临床应用中具有广阔的应用前景。达沙替尼被认为是晚期CML患者的一个良好选择。
