Breast cancer (BC) is a significant contributor to cancer-related deaths in women globally, impacting approximately one out of every eight women in developed nations. Over the past few years, RNA Sequencing (RNA-seq) has emerged as a remarkable and functional technology for analyzing gene expression patterns. Methods: In this particular study, the RNA-seq dataset labeled as BioProject: PRJNA۸۵۵۳۲۴ was obtained from the SRA database and subsequently subjected to analysis within the Linux Ubuntu v۲۲.۰۴.۴ and RStudio v۴.۳ environments. The “EdgeR” package was utilized to process the file and detect differentially expressed genes (DEGs) between BC samples. Next, ۸۰۲ genes were chosen based on criteria of absolute logFC greater than one and an adjusted p-value less than ۰.۰۵ to build the co-expression network. Weighted gene co‐expression network analysis (WGCNA) was utilized to build a gene co‐expression network. The soft-thresholding parameter was set to β = ۱۴. After that, the adjacency matrix was converted to a topological overlap matrix (TOM). Afterward, the TOM-based dissimilarity measure was used to classify highly correlated genes into gene modules using average linkage hierarchical clustering, with a minimum threshold of ۱۵. Subsequently, the correlations between the module eigengenes and clinical features were calculated. Finally, Gene Ontology and pathway analysis of the modules that hold clinical relevance were conducted through R packages such as “clusterProfiler”, “AnnotationDbi” and “enrichplot”. Results: two modules were screened out using the average linkage hierarchical clustering, which showed a strong correlation with the Luminal B subtype (cor=۰.۴۱, p=۵.۶e−۱۳), and was selected for further analysis. Among them, two genes (i.e., CDK۱ and EZH۲) were considered hub genes in the brown module, which had a high “Degree Distribution” and “Bottleneck”, respectively. Based on the information provided by the Gene Ontology and pathway analysis, these genes exert regulatory control over various crucial pathways, including cell cycle and DNA repair. Conclusion: The Luminal B subtype of breast cancer could potentially be treated more effectively by targeting the DEGs that have been identified.