https://scholars.lib.ntu.edu.tw/handle/123456789/429260
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | T.H. Hsiao | en_US |
dc.contributor.author | Y.H. Chen | en_US |
dc.contributor.author | H.I. Chen | en_US |
dc.contributor.author | Y.C. Chiu | en_US |
dc.contributor.author | E.Y. Chuang | en_US |
dc.contributor.author | Y. Chen | en_US |
dc.contributor.author | ERIC YAO-YU CHUANG | en_US |
dc.creator | ERIC YAO-YU CHUANG;Y. Chen;E.Y. Chuang;Y.C. Chiu;H.I. Chen;Y.H. Chen;T.H. Hsiao | - |
dc.date.accessioned | 2019-10-31T06:49:31Z | - |
dc.date.available | 2019-10-31T06:49:31Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 13862073 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/429260 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047800630&doi=10.2174%2f1574888X13666180105125347&partnerID=40&md5=8b157c3b126cc6f1a4a767d6008e5a73 | - |
dc.description.abstract | Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time. © 2018 Bentham Science Publishers. | - |
dc.relation.ispartof | Combinatorial Chemistry & High Throughput Screening | - |
dc.subject | Anticancer drugs; Breast cancer; Connectivity map; Drug reposition; Expression profiling; Gene set analysis | - |
dc.subject.classification | [SDGs]SDG3 | - |
dc.subject.other | 1 (5 isoquinolinesulfonyl) 2 methylpiperazine; anisomycin; antineoplastic agent; ceftazidime; dexverapamil; etoposide; gw 8510; irinotecan; josamycin; kawain; mg 262; unclassified drug; antineoplastic agent; adult stem cell; Article; breast cancer; cancer chemotherapy; cancer patient; cancer prognosis; cancer stem cell; cancer survival; clinical outcome; cohort analysis; drug repositioning; gene expression profiling; gene function; gene ontology; genetic analysis; human; human cell; MCF-7 cell line; priority journal; stroma cell; survival time; systems biology; telomere homeostasis; breast tumor; female; gene regulatory network; genetics; procedures; survival analysis; systems biology; Antineoplastic Agents; Breast Neoplasms; Cohort Studies; Drug Repositioning; Female; Gene Expression Profiling; Gene Regulatory Networks; Humans; Survival Analysis; Systems Biology | - |
dc.title | Utilizing Cancer–Functional Gene set–Compound Networks to Identify Putative Drugs for Breast Cancer | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.2174/1574888x13666180105125347 | - |
dc.identifier.scopus | 2-s2.0-85047800630 | - |
dc.relation.pages | 47-55 | - |
dc.relation.journalvolume | 21 | - |
dc.relation.journalissue | 2 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Biomedical Electronics and Bioinformatics | - |
crisitem.author.dept | Center for Biotechnology | - |
crisitem.author.dept | Genome and Systems Biology Degree Program | - |
crisitem.author.orcid | 0000-0003-2530-0096 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Life Science | - |
顯示於: | 生醫電子與資訊學研究所 |
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