Li Wan, Bai Xue, Hu Erqiang, Huang Hao, Li Yiran, He Yuehan, Lv Junjie, Chen Lina, He Weiming
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Institute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, P.R. China.
Oncol Lett. 2017 May;13(5):3935-3941. doi: 10.3892/ol.2017.5917. Epub 2017 Mar 27.
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.
乳腺癌是女性死亡的主要原因之一。已经确定了许多预后标志物，包括单基因、多基因特征和网络模块；然而，这些预后标志物的稳健性不足。因此，本研究提出了一种更稳健的方法，通过整合四组疾病相关表达谱，基于加权蛋白质-蛋白质相互作用网络来识别乳腺癌预后模块。通过Cox回归和Kaplan-Meier生存分析确定，三个识别出的预后模块与预后相关功能和生存时间密切相关。这些模块的稳健性通过来自另一个平台的独立图谱得到验证。来自这些模块的基因可能作为乳腺癌预后标志物有用。这些预后模块可用于确定乳腺癌患者的预后并表征患者的恢复情况。
