Prediction of breast cancer aggression-related genetic markers based on weighted gene co-expression network analysis

Currently, aggressive breast cancer has become a difficult problem in the clinical treatment of breast cancer patients. Weighted Gene Co-expression Network Analysis (WGCNA) is widely used for genetic markers finding. Therefore, this paper studies the clustering method in WGCNA. In this paper, k-means clustering, maximum expectation clustering, hierarchical clustering and WGCNA commonly used in bioinformatics are selected to compare and verify the quality of clustering methods. Firstly, three clustering methods were applied in the WGCNA process. Then, the modules obtained from the three clusters were used to screen genetic markers using the centrality method. Finally, survival analysis was performed on the genetic markers obtained by each clustering method and WGCNA to verify whether the gene markers found by each clustering method combined with WGCNA had a good prognostic effect. Therefore, it can be concluded that hierarchical clustering can be replaced by K-means clustering.