Study On Gene Markers Of Aggressive Breast Cancer Based On Cluster Analysis

Breast cancer has now become one of the most common cancers in the world,and breast cancer is a problem that cannot be ignored in China or other countries in the world.As a type of breast cancer with high mortality,aggressive breast cancer plays an important role in the pathogenesis of aggressive breast cancer and the development of targeted drugs by screening aggressive breast cancer gene markers.The traditional methods for discovering gene markers are mainly based on biological experiments,sequencing,quantitative PCR,and fluorescence immunofluorescence in situ hybridization.Traditional methods require a long experimental cycle and consume a lot of manpower and material resources.With the rapid development of bioinformatics,it is possible to use data analysis to screen gene markers.Compared to traditional methods,using data analysis to screen gene markers has the advantages of fast experimental speed and saving manpower and material resources.Therefore,this paper is mainly based on cluster analysis to screen gene markers of aggressive breast cancer.By constructing a weighted gene co expression network,different clustering methods are applied to cluster the weighted gene co expression network into different gene modules,and a central approach is applied to screen gene markers in the gene modules.The research work of this article is mainly divided into two parts.In the first part,cluster analysis was used to screen gene modules highly related to aggressive breast cancer.First of all,the weighted gene co expression network was constructed from the preprocessed aggressive breast cancer data and non aggressive breast cancer data.Then,K-means clustering,hierarchical clustering and maximum expectation clustering are applied to cluster the weighted gene co expression networks of aggressive breast cancer and non aggressive breast cancer,respectively.Finally,gene modules highly related to aggressive breast cancer were obtained by enrichment analysis.The second part is to screen aggressive breast cancer gene markers in the cluster module based on the central method.First,the integrated centrality method was used to screen aggressive breast cancer genes.Then,prognostic analysis is performed on the selected genes to determine the clustering effect through prognostic analysis.Finally,the screened genes were verified to obtain genes that can be used as genetic markers of aggressive breast cancer.Through prognostic analysis and comparison,it was found that the clustering effect of K-means was better than hierarchical clustering and maximum expected clustering.Through K-means clustering,10 genes were selected using the integrated centrality method,namely FN1,TGFB1,CXCL12,IL1 B,TP53,CDH1,ACTB,CXCR4,SOX2,and JUN.Among these10 genes,6 genes FN1,CXCL12,TP53,CXCR4,SOX2 and JUN have been verified to be related to aggressive breast cancer and can be used as genetic markers of aggressive breast cancer.The validity of the four undiscovered genes ACTB,TGFB1,IL1 B and CDH1 was verified.It was found that ACTB and TGFB1 genes were effective and could be used as new gene markers for aggressive breast cancer.