Comparative proteomic study of two closely related ovarian endometrioid adenocarcinoma cell lines using cIEF fractionation and pathway analysis
dc.contributor.author | Dai, Lan | en_US |
dc.contributor.author | Li, Chen | en_US |
dc.contributor.author | Shedden, Kerby A. | en_US |
dc.contributor.author | Misek, David E. | en_US |
dc.contributor.author | Lubman, David M. | en_US |
dc.date.accessioned | 2009-05-04T18:24:22Z | |
dc.date.available | 2010-05-07T17:40:09Z | en_US |
dc.date.issued | 2009-04 | en_US |
dc.identifier.citation | Dai, Lan; Li, Chen; Shedden, Kerby A.; Misek, David E.; Lubman, David M. (2009). "Comparative proteomic study of two closely related ovarian endometrioid adenocarcinoma cell lines using cIEF fractionation and pathway analysis." Electrophoresis 30(7): 1119-1131. <http://hdl.handle.net/2027.42/62121> | en_US |
dc.identifier.issn | 0173-0835 | en_US |
dc.identifier.issn | 1522-2683 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/62121 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19288585&dopt=citation | en_US |
dc.description.abstract | The proteomic profiles from two distinct ovarian endometrioid tumor-derived cell lines, (MDAH-2774 and TOV-112D) each with different morphological characteristics and genetic mutations, have been studied. Characterization of the differential global protein expression between these two cell lines has important implications for the understanding of the pathogenesis of ovarian endometrioid carcinoma. In this comparative proteomic study, extensive fractionation of peptides generated from whole-cell trypsin digestion was achieved by coupling cIEF in the first-dimensional separation with capillary LC (RP-HPLC) in the second dimensional separation. Online analysis was performed using tandem mass spectra acquired by a linear ion trap mass spectrometer from triplicate runs. A total of 1749 and 1955 proteins with protein probability above 0.95 were identified from MDAH-2774 and TOV-112D after filtering through Peptide Prophet/Protein Prophet software. Differentially expressed proteins were further investigated by ingenuity pathway analysis (IPA) to reveal the association with important biological functions. Canonical pathway analysis using IPA demonstrates that important signaling pathways are highly associated with one of these two cell lines versus the other, such as the PI3K/AKT pathway, which is found to be significantly predominant in MDAH-2774 but not in TOV-112D. Also, protein network analysis using IPA highlights p53 as a central hub relating to other proteins from the connectivity map. These results illustrate the utility of high throughput proteomics methods using large-scale proteome profiling combined with bioinformatics tools to identify differential signaling pathways, thus contributing to the understanding of mechanisms of deregulation in neoplastic cells. | en_US |
dc.format.extent | 463461 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | WILEY-VCH Verlag | en_US |
dc.subject.other | Chemistry | en_US |
dc.subject.other | Biochemistry and Biotechnology | en_US |
dc.title | Comparative proteomic study of two closely related ovarian endometrioid adenocarcinoma cell lines using cIEF fractionation and pathway analysis | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Chemical Engineering | en_US |
dc.subject.hlbsecondlevel | Chemistry | en_US |
dc.subject.hlbsecondlevel | Materials Science and Engineering | en_US |
dc.subject.hlbsecondlevel | Molecular, Cellular and Developmental Biology | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Bioinformatics Program, University of Michigan Medical Center, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationum | Department of Chemistry, University of Michigan, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationum | Department of Statistics, University of Michigan, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationum | Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationum | Bioinformatics Program, University of Michigan Medical Center, Ann Arbor, MI, USA ; Department of Chemistry, University of Michigan, Ann Arbor, MI, USA ; Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, USA ; Department of Pathology, University of Michigan Medical Center, Ann Arbor, MI, USA ; Department of Surgery, The University of Michigan Medical Center, 1150 West Medical Center Dr., Building MSRB1 Rm A510B, Ann Arbor, MI 48109-0656, USA Fax: +1-734-615-2088 | en_US |
dc.identifier.pmid | 19288585 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/62121/1/1119_ftp.pdf | |
dc.identifier.doi | 10.1002/elps.200800505 | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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