Puerarin modulation of CENPA affects downstream PLK1 and CCNB1 expression to inhibit bladder cancer cell proliferation

Background:The treatment alternatives for bladder cancer(BLCA),the 10th most prevalent cancer in the world,need to be further investigated,and many active substances like Puerarin in herbal medicine were found to be effective in treating BLCA.The purpose of this study was to investigate the potential treating mechanisms of Puerarin on BLCA.Methods:The cell counting kit 8 assay and flow cytometry were performed to confirm Puerarin’s ability to suppress BLCA.The differentially expressed proteins(DEPs)were obtained by Tandem Mass Tags technology and functional enrichment analysis performed by R studio.The most enriched pathways were selected for study and the DEPs were screened out.Protein-protein interaction network maps were created using String and Cytoscape and key proteins,which will be analyzed for survival,expression,and upstream transcription factor prediction,were screened out using the cytoHubba plugin.CHEA3 was used to obtain upstream transcription factor validated by molecular docking and western blotting experiments.Results:Cell counting kit 8 showed that Puerarin inhibited BLCA cells,with 50%inhibitory concentration of 218μmol/L in T24 and 198μmol/L in 5637.Flow cytometry reveals that Puerarin blocks T24 and 5637 cells in G1 phase.1,385 DEPs were obtained and the enrichment analysis revealed that cell cycle and DNA replication were the two main areas in which DEPs were enriched.Cyclin-B-cyclin dependent kinase 1(CDK1),cyclin B1(CCNB1),and polo-like kinase 1(PLK1)were identified as key proteins,and their upstream transcription factor was predicted to be centromere protein A(CENPA).Puerarin’s binding energy to CENPA was determined by molecular docking to be−6.3 kcal/mol,indicating a strong binding interaction.Western blot showed that Puerarin significantly reduced the expression of CENPA.Conclusion:We hypothesize that Puerarin may inhibit the proliferation of bladder cancer cells by inhibiting CENPA expression to regulate PLK1 and CCNB1 expression,thereby affecting cell cycle.

A model for automatic identification of human pulse signals

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.