کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8361961 | 1542527 | 2017 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A global view of regulatory networks in lung cancer: An approach to understand homogeneity and heterogeneity
ترجمه فارسی عنوان
دیدگاه جهانی شبکه های نظارتی در زمینه سرطان ریه: رویکردی برای درک همگنی و ناهمگونی
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کلمات کلیدی
سرطان ریه، شبکه نظارتی عامل رونویسی، زیرمجموعه، بیوانفورماتیک،
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
زیست شیمی
چکیده انگلیسی
A number of new biotechnologies are used to identify potential biomarkers for the early detection of lung cancer, enabling a personalized therapy to be developed in response. The combinatorial cross-regulation of hundreds of biological function-specific transcription factors (TFs) is defined as the understanding of regulatory networks of molecules within the cell. Here we integrated global databases with 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without cancer with the understanding of TF functions. The present review aims at the homogeneity or heterogeneity of gene expression profiles among subtypes of lung cancer. About 5, 136, 52, or 16 up-regulated or 19, 24, 122, or 97down-regulated type-special TF genes were identified in ADC, SCC, LCC or SCLC, respectively. DNA-binding and transcription regulator activity associated genes play a dominant role in the differentiation of subtypes in lung cancer. Subtype-specific TF gene regulatory networks with elements should be an alternative for diagnostic and therapeutic targets for early identification of lung cancer and can provide insightful clues to etiology and pathogenesis.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Seminars in Cancer Biology - Volume 42, February 2017, Pages 31-38
Journal: Seminars in Cancer Biology - Volume 42, February 2017, Pages 31-38
نویسندگان
Jiapei Lu, William Wang, Menglin Xu, Yuping Li, Chengshui Chen, Xiangdong Wang,