10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs. Complementary and alternative medicine (CAM) may avoid these problems, in which traditional Chinese medicine (TCM) has been highlighted. In this section, to analyze the mechanism through which TCM act on breast cancer, we have built a virtual model consisting of the construction of database, oral bioavailability prediction, drug-likeness evaluation, target prediction, network construction. The 20 commonly employed herbs for the treatment of breast cancer were used as a database to carry out research. As a result, 150 ingredient compounds were screened out as active molecules for the herbs, with 33 target proteins predicted. Our analysis indicates that these herbs 1) takes a ‘Jun-Chen-Zuo-Shi” as rule of prescription, 2) which function mainly through perturbing three pathways involving the epidermal growth factor receptor, estrogen receptor, and inflammatory pathways, to 3) display the breast cancer-related anti-estrogen, anti-inflammatory, regulation of cell metabolism and proliferation activities. To sum it up, by providing a novel in silico strategy for investigation of the botanical drugs, this work may be of some help for understanding the action mechanisms of herbal medicines and for discovery of new drugs from plants.

          Related collections

          Most cited references39

          • Record: found
          • Abstract: found
          • Article: not found

          Elevated biomarkers of inflammation are associated with reduced survival among breast cancer patients.

          PURPOSE Chronic inflammation is believed to contribute to the development and progression of breast cancer. Systemic C-reactive protein (CRP) and serum amyloid A (SAA) are measures of low-grade chronic inflammation and potential predictors of cancer survival. PATIENTS AND METHODS We evaluated the relationship between circulating markers of inflammation and breast cancer survival using data from the Health, Eating, Activity, and Lifestyle (HEAL) Study (a multiethnic prospective cohort study of women diagnosed with stage 0 to IIIA breast cancer). Circulating concentrations of CRP and SAA were measured approximately 31 months after diagnosis and tested for associations with disease-free survival (approximately 4.1 years of follow-up) and overall survival (approximately 6.9 years of follow-up) in 734 disease-free breast cancer survivors. Cox proportional hazards models were used with adjustment for potential confounding factors to generate hazard ratios (HRs) and 95% CIs. Results Elevated SAA and CRP were associated with reduced overall survival, regardless of adjustment for age, tumor stage, race, and body mass index (SAA P trend < .0001; CRP P trend = .002). The HRs for SAA and CRP tertiles suggested a threshold effect on survival, rather than a dose-response relationship (highest v lowest tertile: SAA HR = 3.15; 95% CI, 1.73 to 5.65; CRP HR = 2.27; 95% CI, 1.27 to 4.08). Associations were similar and still significant after adjusting for self-reported history of cardiovascular events and censoring cardiovascular disease deaths. Elevated CRP and SAA were also associated with reduced disease-free survival, although these associations were of borderline significance (SAA P trend = .04; CRP P trend = .07). CONCLUSION Circulating SAA and CRP may be important prognostic markers for long-term survival in breast cancer patients, independent of race, tumor stage, and body mass index.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Molecular biology in breast cancer: intrinsic subtypes and signaling pathways.

            The last decade has brought a breakthrough in the knowledge of the biology of breast cancer. The technological development, and in particular the high throughput technologies, have allowed researchers to inquire more deeply into the nature of the disease through the comparative study of large numbers of samples. The classification of breast cancer by traditional parameters has been joined by rankings based on gene expression. Among the most popular platforms are MammaPrint®, Oncotype DX® the wound-response model, the rate of two genes model, the genomic grade index and the intrinsic subtype model. The latter one provides the amplest biological information and allows for the classification of breast cancer into six intrinsic subtypes: luminal A, luminal B, HER2-enriched, basal-like, normal breast and claudin-low. These new classifications are not yet fully applicable to clinical practice not only because they have not been standardized, but also because they entail a substantial economic outlay. Nevertheless, they have provided valuable information on tumor biology that has led to a better understanding of the signaling pathways governing the processes of formation, maintenance and expansion of the tumors. Researchers now know more about the HER2, estrogen receptor, IGF1R, PI3K/AKT, mTOR, AMPK and angiogenesis pathways which has allowed for the development of new targeted therapeutics now being tested in ongoing clinical trials. In general, one can say that the last decade has changed the way researchers understand, classify and study breast cancer, and it has reshaped the way doctors diagnose and treat this disease. In addition, it has undoubtedly changed the search for alternative therapies by integrating molecular studies and the selection of study populations based on their molecular markers into clinical trials. The present review summarizes the advances that have allowed researchers to both better classify the disease, as well as explore some of the most important signaling pathways. Copyright © 2011 Elsevier Ltd. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              TCM: Made in China.

                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 January 2017
                2017
                : 12
                : 1
                : e0169363
                Affiliations
                [1 ]Systems Biology Laboratory, Department of Computer Information Science and Engineering, University of Florida, Gainesville, Florida, United States of America
                [2 ]Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Faculty of Chemical, Environmental and Biological Science and Technology, Dalian University of Technology, Dalian, Liaoning, P R China
                Southern Illinois University School of Medicine, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: SC.

                • Data curation: FL YY.

                • Formal analysis: FL YY.

                • Funding acquisition: YL.

                • Investigation: SC YL JW FL YY.

                • Methodology: SC YL.

                • Project administration: SC.

                • Resources: SC YL.

                • Software: JW.

                • Supervision: SC.

                • Validation: JW.

                • Visualization: JW.

                • Writing – original draft: YL JW.

                • Writing – review & editing: YL.

                ‡ These authors are co-first authors on this work

                Article
                PONE-D-16-23018
                10.1371/journal.pone.0169363
                5222515
                28068355
                6f01e1b1-6e69-4828-bc07-791647d8700f
                © 2017 Li et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 July 2016
                : 15 December 2016
                Page count
                Figures: 5, Tables: 4, Pages: 25
                Funding
                This work was supported by Key Program of National Natural Science Foundation of China (Grant No. 81530100) and the Oasis Scholar Fund of Shihezi University.
                Categories
                Research Article
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Breast Tumors
                Breast Cancer
                Biology and Life Sciences
                Organisms
                Plants
                Herbs
                Medicine and Health Sciences
                Pharmacology
                Drug Interactions
                Medicine and Health Sciences
                Oncology
                Cancer Treatment
                Biology and Life Sciences
                Biochemistry
                Hormones
                Estrogens
                Medicine and Health Sciences
                Complementary and Alternative Medicine
                Herbal Medicine
                Biology and Life Sciences
                Immunology
                Immune Response
                Inflammation
                Medicine and Health Sciences
                Immunology
                Immune Response
                Inflammation
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Inflammation
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Inflammation
                Computer and Information Sciences
                Network Analysis
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article