LIU Xiyang, AN Yingying, YU Baoguo, QIN Guimin. Analysis of Commonly and Specifically Dysregulated Pathways in Three Women Cancers[J]. Chinese Journal of Electronics, 2018, 27(5): 1043-1049. doi: 10.1049/cje.2017.09.028
Citation: LIU Xiyang, AN Yingying, YU Baoguo, QIN Guimin. Analysis of Commonly and Specifically Dysregulated Pathways in Three Women Cancers[J]. Chinese Journal of Electronics, 2018, 27(5): 1043-1049. doi: 10.1049/cje.2017.09.028

Analysis of Commonly and Specifically Dysregulated Pathways in Three Women Cancers

doi: 10.1049/cje.2017.09.028
Funds:  This work is supported by the National Natural Science Foundation of China (No.61472311), National Defense Basic Research Project of China (No.jcky2016110c006), the Fundamental Research Funds for the Central Universities (No.BDZ011401), and the Natural Science Foundation of Shaanxi Province (No.2017JM6038).
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  • Corresponding author: QIN Guimin (corresponding author) was born in 1977. She currently is an associate professor at Xidian University. She has been conducting research for many years on complex networks, bioinformatics, and machine learning. (Email:gmqin@mail.xidian.edu.cn)
  • Received Date: 2016-12-01
  • Rev Recd Date: 2017-05-01
  • Publish Date: 2018-09-10
  • Breast, ovarian and endometrial cancer are three most prevalent gynaecological malignancies. Identifying their common and specific biomarkers is significant for cancer prediction and therapy in females. We propose a method to identify dysregulated pathways in cancer through scoring pathways based on the molecular interaction data and genomic data. Commonly and specifically dysregulated pathways are analyzed across the above three female cancers, which have not been studied as a whole to the best of our knowledge. Our results demonstrate that all the three cancers have close relationships with Type Ⅱ diabetes and cell cycle-related biology processes. Breast cancer is specifically related to immune system while ovarian cancer and endometrial cancer are associated with blood vascular-related systems such as renin-angiotensin system and coagulation system. In addition, dysregulated pathways are used to predict potential driver genes effectively according to their topological structure and biological information.
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