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.

Red raspberry contains a variety of bioactive ingredients and has high edible and medicinal value. Red raspberry extractions (RREs) have strong antioxidant capacity and anticancer ability in vivo and in vitro. This study was to explore the specific mechanism of RREs inhibiting the proliferation of liver cancer HepG2 cells and provide a theoretical basis for the prevention and treatment of liver cancer by RREs. HepG2 cells were cultured in vitro, and MTT assay was adopted to detect the effect of RREs on HepG2 cell activity. Colony formation assay was applied to detect the growth and proliferation of cells, cell apoptosis was detected by flow cytometry, and dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay was adopted to detect the effect of RREs on the production of reactive oxygen species (ROS) in cells. The effect of RREs on cell mitochondrial membrane potential was evaluated by mitochondrial membrane potential assay kit with JC-1 (JC-1 assay), and western blot was used to detect the expression of apoptosis-related proteins (B-cell lymphoma-2 (Bcl-2), Bcl-2-associated x (Bax), and Caspase-3), thus investigating the effect of RREs on the molecular mechanism of HepG2 cell apoptosis. The results showed that RREs could inhibit the proliferation activity of HepG2 cells and promote their apoptosis in a concentration-dependent manner. The level of ROS in HepG2 cells interfered by RREs increased markedly, while the cell mitochondrial membrane potential decreased sharply. As the concentration of HepG2 increased, the mitochondrial membrane potential reduced steeply. Western blot results showed that the expression of apoptosis-related protein Bcl-2 in the RREs treatment group dropped, but the expression of Bax and Caspase-3 rose. In summary, RREs could inhibit the proliferation of liver cancer HepG2 cells and promote their apoptosis. This inhibition might be executed by inducing HepG2 cells to produce ROS, a decrease in Bcl-2/Bax protein ratio, and an obvious reduction in mitochondrial membrane potential.