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10.15171/ijb.1308 http://scihub22266oqcxt.onion/10.15171/ijb.1308 C5582248!5582248!28959347     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Iran+J+Biotechnol 2017 ; 15 (1): 1-9 Nephropedia Template TP {{tp|p=28959347|t=2017. Gene Regulation Network Based Analysis Associated with TGF-?eta Stimulation in Lung Adenocarcinoma Cells |pdf=|usr=}}{{28959347}} gab.com Text Gene Regulation Network Based Analysis Associated with TGF- eta Stimulation in Lung Adenocarcinoma Cells JO: Iran J Biotechnol http://www.kidney.de/pget.php?&tw=1&pmid=28959347 #FOAvip Background: Transforming growth factor (TGF)-? is over-expressed in a wide variety of cancers such as lung adenocarcinoma. TGF-? plays a major role in cancer progression through regulating cancer cell proliferation and remodeling of the tumor micro-environment. However, it is still a great challenge to explain the phenotypic effects caused by TGF-? stimulation and the effect of TGF-? stimulation on tumor micro-environment. Objectives: To address this issue, in the present study we used two time-course microarray data in human lung adenocarcinoma cells and applied bioinformatics methods to explore the gene regulation network responding to TGF-? stimulation in lung adenocarcinoma cells. Materials and Methods: The time-dependent reverse-engineering method, protein-protein interaction network analyses, and calculation of the similarity measures between the links were used to construct gene regulatory network and to extract gene clusters. Results: Utilizing the constructed gene regulation network, we predicted NEFL and LUC7A show the opposite and the same change with C21orf90 if HAND2 is knocked-out after treatment with TGF-?1 for 4 hours and for 12 hours respectively. FGG and HSPC009 are predicted to display the opposite change with NEFL if CSMD1 is knocked out after treatment with TGF-?1 for 12 hours. Additionally, by integrating two datasets, we specially identified several nested clusters which included those genes regulated by TGF-? stimulation in lung adenocarcinoma cells. Conclusions: Our analysis can help a better understanding regarding how TGF-? stimulation causes the expression change of a number of the genes and provide a novel insight into TGF-? stimulation effect on lung adenocarcinoma cells. Twit Text FOAVip Gene Regulation Network Based Analysis Associated with TGF- eta Stimulation in Lung Adenocarcinoma Cells ????Iran J Biotechnol http://www.kidney.de/pget.php?&tw=1&pmid=28959347 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28959347 #FOAvip English Wikipedia <ref>{{Cite pmid|28959347}}</ref> Gene Regulation Network Based Analysis Associated with TGF-?eta Stimulation in Lung Adenocarcinoma Cells #MMPMID28959347 Hua L; Xia H; Zheng Wy; An L Iran J Biotechnol 2017[Mar]; 15 (1): 1-9 PMID28959347show ga Background: Transforming growth factor (TGF)-? is over-expressed in a wide variety of cancers such as lung adenocarcinoma. TGF-? plays a major role in cancer progression through regulating cancer cell proliferation and remodeling of the tumor micro-environment. However, it is still a great challenge to explain the phenotypic effects caused by TGF-? stimulation and the effect of TGF-? stimulation on tumor micro-environment. Objectives: To address this issue, in the present study we used two time-course microarray data in human lung adenocarcinoma cells and applied bioinformatics methods to explore the gene regulation network responding to TGF-? stimulation in lung adenocarcinoma cells. Materials and Methods: The time-dependent reverse-engineering method, protein-protein interaction network analyses, and calculation of the similarity measures between the links were used to construct gene regulatory network and to extract gene clusters. Results: Utilizing the constructed gene regulation network, we predicted NEFL and LUC7A show the opposite and the same change with C21orf90 if HAND2 is knocked-out after treatment with TGF-?1 for 4 hours and for 12 hours respectively. FGG and HSPC009 are predicted to display the opposite change with NEFL if CSMD1 is knocked out after treatment with TGF-?1 for 12 hours. Additionally, by integrating two datasets, we specially identified several nested clusters which included those genes regulated by TGF-? stimulation in lung adenocarcinoma cells. Conclusions: Our analysis can help a better understanding regarding how TGF-? stimulation causes the expression change of a number of the genes and provide a novel insight into TGF-? stimulation effect on lung adenocarcinoma cells. äGene Regulation Network Based Analysis Associated with TGF-?eta Stimul(epmc).pdf DeepDyve Pubget Overpricing