Identification of Hub Genes Associated with Breast Cancer Using Integrated Gene Expression Data with Protein-Protein Interaction Network

Breast cancer (BC) is the most incident cancer type among women. BC is also ranked as the second leading cause of death among all cancer types. Therefore, early detection and prediction of BC are significant for prognosis and in determining the suitable targeted therapy. Early detection using morphological features poses a significant challenge for physicians. It is therefore important to develop computational techniques to help determine informative genes, and hence help diagnose cancer in its early stages. Eight common hub genes were identified using three methods: the maximal clique centrality (MCC), the maximum neighborhood component (MCN), and the node degree. The hub genes obtained were CDK1, KIF11, CCNA2, TOP2A, ASPM, AURKB, CCNB2, and CENPE. Enrichment analysis revealed that the differentially expressed genes (DEGs) influenced multiple pathways. The most significant identified pathways were focal adhesion, ECM-receptor interaction, melanoma, and prostate cancer pathways. Additionally, survival analysis using Kaplan-Meier was conducted, and the results showed that the obtained eight hub genes are promising candidate genes to serve as prognostic and diagnostic biomarkers for BC. Furthermore, a correlation study between the clinicopathological factors in BC and the eight hub genes was performed. The results showed that all eight hub genes are associated with the clinicopathological variables of BC. Using an integrated analysis of RNASeq and microarray data, a protein-protein interaction (PPI) network was developed. Eight hub genes were identified in this study, and they were validated using previous studies. Additionally, Kaplan-Meier was used to verify the prognostic value of the obtained hub genes.

Automated summary

Breast cancer is the most common cancer type among women and the second leading cause of death. Early detection and prediction of breast cancer are crucial for prognosis and targeted therapy. Developing computational techniques to determine informative genes and aid in early diagnosis is important.