Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks.

Breast cancer is one of the leading causes of mortality in females. A number of prognostic markers have been identified, including single genes, multi-gene signatures and network modules; however, the robustness of these prognostic markers is insufficient. Thus, the present study proposed a more robust method to identify breast cancer prognostic modules based on weighted protein-protein interaction networks, by integrating four sets of disease-associated expression profiles. Three identified prognostic modules were closely associated with prognosis-associated functions and survival time, as determined by Cox regression and Kaplan-Meier survival analyses. The robustness of these modules was verified with an independent profile from another platform. Genes from these modules may be useful as breast cancer prognostic markers. The prognostic modules could be used to determine the prognoses of patients with breast cancer and characterize patient recovery.