Renal Cell Carcinoma Gene Research Based On Differential Network

The complex diseases represented by cancer pose a serious threat to people's lives.With the development of high-flux technology,more and more genes have been explored.The difference of the interaction between the different biomolecules and the molecules in the network is different,and the difference network is formed.The construction of the topology and the exploration of these difference networks and the genetic relationship in the network are a key step to understand the biological mechanism behind them;meanwhile,the differential network analysis method can go deep into the biological metabolism level to further understand the mechanism of the disease.Therefore,to explore how the structure of the genetic differentiation network can change between two groups of different disease states It is an important task in the gene group research.In this paper,the differential network analysis method of genomics was used in disease samples and normal samples,and the related genes with higher probability of occurrence and development of disease were obtained.Firstly,the Gaussian graph model is used to estimate the network model of the global relationship,and the correlations among all genes are specified.Then the precise regression model is used to calculate the intra-group network model of a particular group.The residual data are used to eliminate the influence of the global relationship,and the covariance regression model is fitted.The difference gene pairs are obtained by using the maximum expectation algorithm to estimate the accuracy matrix.Finally,the Bootstrap algorithm is used to obtain the difference score and then construct the differential network to screen.The key gene is selected and the enrichment path of the key gene is analyzed.In particular,in the construction of differential network precision matrix estimation model,compared with MLE and GLasso model estimation method,through the sum of the squared error(SSE),receiver operating characteristic curve(ROC)and precision-recall curve(P-R)are compared to the experimental results and the results show that the proposed method can detect the differentially expressed genes between normal and tumor samples better than other methods.In this paper,we download the data from GEO database to get the data of renal cell carcinoma,and carry out experimental research on 40 related gene chips,get thedifferential expression results and select the top 20 differentially expressed genes.Twelve of them have been shown to be highly correlated with the progression of renal cell carcinoma in the study.The path synthesis analysis of differentially expressed genes also showed that the pathways involved in these differentially expressed genes had a direct impact on tumor production.The genome-based differential network analysis method is an effective and applicable model to estimate the specific groups of networks and to infer the related differential genes in the differential networks.