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- W2777280710 abstract "Breast cancer is characterized with enormous heterogeneity, which represents the major hurdle for accurate diagnosis and curative therapy. It is generally believed that genome unstability and molecular evolvability underlie the robustness of cancer cells in hostile microenvironment and their resilience to therapeutic intervention. Conventional histopathological classification of breast cancer falls short of providing sufficient prognostic and predictive power, and thus biomarkers indicative of tumor intrinsic features at molecular levels have been actively pursued in biomedical researches. Currently, a number of molecular biomarkers are being used in standard clinical practice, including the hormone receptors for breast cancer subtyping and several genes involved in genome maintenance for prediction of breast cancer susceptibility. In addition, a number of biomarkers of single genes or multigene signatures have been approved for clinical use for breast cancer prognosis. A growing body of molecular biomarkers are being studied and tested to facilitate disease diagnosis and management, especially for breast cancer early detection, accurate prediction of metastatic behaviors, and selection of therapy. However, most of them are still at the preclinical stages. Finally, biomarkers of noninvasive protocols, such as serological molecules, have advantages in detection convenience over other biomarker types and therefore are of particular interest in translational and clinical development to improve diagnosis, prognosis, and treatment." @default.
- W2777280710 created "2018-01-05" @default.
- W2777280710 creator A5027986193 @default.
- W2777280710 creator A5047346331 @default.
- W2777280710 creator A5081112182 @default.
- W2777280710 date "2017-01-01" @default.
- W2777280710 modified "2023-10-06" @default.
- W2777280710 title "Biomarker Studies in Early Detection and Prognosis of Breast Cancer" @default.
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- W2777280710 doi "https://doi.org/10.1007/978-981-10-6020-5_2" @default.
- W2777280710 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29282678" @default.
- W2777280710 hasPublicationYear "2017" @default.
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