Weighted gene co-expression network analysis reveals modules and hub genes associated with the development of breast cancer

This study aimed to identify modules associated with breast cancer (BC) development by constructing a gene co-expression network, and mining hub genes that may serve as markers of invasive breast cancer (IBC). We downloaded 2 gene expression datasets from the Gene Expression Omnibus (GEO) database, and used weighted gene co-expression network analysis (WGCNA) to dynamically study the changes of co-expression genes in normal breast tissues, ductal carcinoma in situ (DCIS) tissues, and IBC tissues. Modules that highly correlated with BC development were carried out functional enrichment analysis for annotation, visualization, and integration discovery. The hub genes detected by WGCNA were also confirmed using the Oncomine dataset. We detected 17 transcriptional modules in total and 4 — namely tan, greenyellow, turquoise, and brown — were highly correlated with BC development. The functions of these 4 modules mainly concerned cell migration (tan module, P = 3.03 × 10(−4)), the cell cycle (greenyellow module, P = 3.08 × 10(−13)), cell–cell adhesion (turquoise module, P = .002), and the extracellular exosome (brown module, P = 1.38 × 10(−22)). WGCNA also mined the hub genes, which were highly correlated with the genes in the same module and with BC development. The Oncomine database confirmed that the expressions levels of 6 hub genes were significantly higher in BC tissues than in normal tissues, with fold changes larger than 2 (all P < .05). Apart from the 2 well-known genes EPCAM and MELK, during the development of BC, KRT8, KRT19, KPNA2, and ECT2 also play key roles, and may be used as new targets for the detection or treatment of BC. In summary, our study demonstrated that hub genes such as EPCAM and MELK are highly correlated with breast cancer development. However, KRT8, KRT19, KPNA2, and ECT2 may also have potential as diagnostic and prognostic biomarkers of IBC.