【Abstract】Objective To explore the application of RNA interference (RNAi) in colorectal cancer gene therapy. Methods The related literatures in recent years were reviewed. Results RNAi causes a high effective and distinctive degradation of mRNA homologous in sequence to the dsRNA. This new technology has been successfully applied to research the genesis and the growth of colorectal cancer.Conclusion RNAi has been a new focus in gene therapy for colorectal cancer.
Objective To systematically review the correlation between polymorphism of DNA methyltransferase 1(DNMT1) rs16999593 and the susceptibility of breast cancer. Methods Databases such as PubMed, EMbase, Web of Science, Chinese Biomedical Literature Database, CNKI, WanFang, and VIP database were searched from inception to Mar. 2017 to collect case-control studies on the correlation between DNMT1 rs16999593 C/T polymorphism and the susceptibility of breast cancer. Two reviewers independently identified the literatures according to inclusion and exclusion criterias, extracted data, and assessed the quality of the included studies. The meta-analysis was performed by using RevMan 5.3 software. Results A total of 5 studies involving 1 741 cases and 1 917 control subjects were included. The results of meta-analysis showed that, dominate model ［TT+TC vs. CC: OR=0.63, 95% CI was (0.30, 1.30), P=0.21］, homozygous model ［TT vs. CC: OR=1.01, 95% CI was (0.70, 1.47), P=0.95］, heterozygous model ［TC vs. CC: OR=0.44, 95% CI was (0.18, 1.04), P=0.06］, and additive model ［T vs. C: OR=1.29, 95% CI was (0.90, 1.86), P=0.16］ were not significantly related to breast cancer, but recessive gene model was related to breast cancer ［TT vs. TC+CC: OR=1.74, 95% CI was (1.01, 3.00), P=0.04］. Conclusion The current studies showed that, DNMT1 rs16999593 TT genotype decreases the susceptibility of breast cancer.
Network plots can clearly present the relationships among the direct comparisons of various interventions in a network meta-analysis. Currently, there are some methods of drawing network plots. However, the information provided by a network plot and the interface-friendly degree to a user differ in the kinds of software. This article briefly introduces how to draw network plots using the network package and gemtc package that base on R Software, Stata software, and ADDIS software, and it also compares the similarities and differences among them.
Stata is statistical software that combines programming and un-programming, which is easy to operate, of high efficiency and good expansibility. In performing meta-analysis, Stata software also presents powerful function. The mvmeta package of Stata software is based on a multiple regression model to conduct network meta-analysis, and it also processes "multiple outcomes-multivariate" data. Currently, the disadvantages of mvmeta package include relatively cumbersome process, poor interest-risk sorting, and lack of drawing function in the process of conducting network meta-analysis. In this article, we introduce how to implement network meta-analysis using this package based on cases.
R Software is an open, free of use and charge statistical software which has a powerful graphic capability; however, it requires more complex codes and commands to perform network meta-analysis, which causes errors and difficulties in operation. WinBUGS software is based on Bayesian theory, which has a powerful data processing capability, and especially its codes are simple and easy to operate for dealing with network meta-analysis. However, its function of illustrating statistical results is very poor. In order to fully integrate the advantages of R software and WinBUGS software, an R2WinBUGS package based on R software has been developed which builds a “bridge” across two of them, making network meta-analysis process conveniently, quickly and result illustration more beautiful. In this article, we introduced how to use the R2WinBUGS package for performing network meta-analysis using examples.
The netmeta package is specialized for implementing network meta-analysis. This package was developed based on the theories of classical frequentist under R language framework. The netmeta package overcomes some difficulties of the software and/or packages based on the theories of Bayesian, for these software and/or packages need to set prior value when conducting network meta-analysis. The netmeta package also has the advantages of simple operation process and ease to operate. Moreover, this package can calculate and present the individual matched and pooled results based on the random and fixed effect model at the same time. It also can draw forest plots. This article gives a briefly introduction to show the process to conduct network meta-analysis using netmeta package.
The theoretical foundation of relevant packages of R software for network meta-analysis is mainly based on Bayesian statistical model and a few of them use generalized linear model. Network meta-analysis is performed using GeMTC R package through calling the corresponding rjags package, BRugs package, or R2WinBUGS package (namely, JAGS, OpenBUGS, and WinBUGS software, respectively). Meanwhile, GeMTC R package can generate data storage files for GeMTC software. Techonically, network meta-analysis is performed through calling the software based on Markov Chain Monte Carlo method. In this article, we briefly introduce how to use GeMTC R package to perform network meta-analysis through calling the OpenBUGS software.
Objective To investigate the effect of dexamethasone, recombinant human fibroblast growth factor (rhFGF) and recombinant human bone morphogenetic protein 2 (rhBMP-2) on the proliferation and differentiation of marrow stromal stem cells (MSCs) for their further application in tissue engineering. Methods MSCs were isolated and cultured in vitro, and then exposed to different dose of dexamethasone (10-8 mol/L,10-7 mol/L,10 -6 mol/L), rhFGF (50 ng/ml,200 ng/ml,500 ng/ml) and rhBMP-2 (50 ng/ml,500 ng/ml,1 000 ng/ml) respectively. The total protein and alkaline phosphatase (ALP) activity of each group was measured on 4th and 7th day. Results Exposure of MSCs with 10-6mol/L dexamethasone inhibited protein synthesis without obvious effects on ALP expression. The application of rhFGF significantly promoted cell proliferation but inhibited ALP activity. In comparison, ALP expression was significantly enhanced by treatment of rhBMP-2 at concentration of 500 ng/ml，1 000 ng/ml. Conclusion The exposure of dexamethasone as well as rhBMP-2 to MSCs with an appropriate concentration promotes osteogenic expression without reverse effects on cell proliferation, which indicates the great potential value in cell-based strategy of bone tissue engineering.
The aggregate data drug information system (ADDIS) software is a non-programming software which is based on the Bayesian framework and using the Markov chain Monte Carlo (MCMC) method for prior assessment and implementation. The operation is fairly easy for users. The consequent results and relevant plots could be output automatically by the software after users assess the consistency of model and convergence diagnostics. The major disadvantage of ADDIS is the more complicated data entry. This article introduces how to perform network meta-analysis using ADDIS software.