Prediction Of Key Genes In Breast Cancer Based On Complex Network

Studies have shown that the occurrence and development of cancer cannot be separated from the regulation and mutation of genes.The mutation information of multiple genes and protein information are combined to be used in molecular regulatory networks to generate various disease phenotypes,which in turn affect the functioning of organisms.With the development of second generation sequencing technology,researchers have begun to combine tumor gene expression information with biological network information to explore the pathogenesis of tumors,which has practical significance for the treatment and development of tumors.Based on breast cancer gene expression data and protein interaction network(PPIN)data,this paper explores the key genes that affect the occurrence and development of breast cancer.Firstly,the co-expression and differential expression information of breast cancer genes were integrated with PPIN data,and a specific protein network containing the gene expression information of breast cancer was innovatively constructed.Secondly,the control classification algorithm based on the controllability of complex network structure is used to classify the nodes in the network,and the key gene set containing breast cancer information is obtained.Next,use enrichment analysis to further reduce the size of the key gene set.Finally,hub genes in the set are obtained according to TOPSIS central algorithm.After excluding key genes existing in the significant gene set,select the top genes as candidate key genes for breast cancer.For the predicted key genes obtained,check whether there are confirmed significant genes.The results show that 4 of the 10 candidate genes obtained by prediction are confirmed key genes for breast cancer.The neighbor nodes of other candidate genes also have a certain proportion of significant key genes for breast cancer.The gene with the highest TOPSIS score was selected,and relevant literature confirmed that the gene was associated with the occurrence of breast cancer.Therefore,the network construction method and node classification method used in this article can effectively identify candidate gene markers that play a key role in the occurrence and development of diseases.The identification results of key genes have reference significance for precise oncology,promoting the development of targeted drugs and the treatment of breast cancer.