Please use this identifier to cite or link to this item: https://doi.org/10.1021/pr200698c
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dc.titleProteomics signature profiling (PSP): A novel contextualization approach for cancer proteomics
dc.contributor.authorGoh, W.W.B.
dc.contributor.authorLee, Y.H.
dc.contributor.authorRamdzan, Z.M.
dc.contributor.authorSergot, M.J.
dc.contributor.authorChung, M.
dc.contributor.authorWong, L.
dc.date.accessioned2013-07-04T08:08:04Z
dc.date.available2013-07-04T08:08:04Z
dc.date.issued2012
dc.identifier.citationGoh, W.W.B., Lee, Y.H., Ramdzan, Z.M., Sergot, M.J., Chung, M., Wong, L. (2012). Proteomics signature profiling (PSP): A novel contextualization approach for cancer proteomics. Journal of Proteome Research 11 (3) : 1571-1581. ScholarBank@NUS Repository. https://doi.org/10.1021/pr200698c
dc.identifier.issn15353893
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40601
dc.description.abstractTraditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method in proteomics that utilizes all detected proteins. We demonstrate its efficacy in a proteomics screen of 5 and 7 liver cancer patients in the moderate and late stage, respectively. Utilizing biological complexes as a cluster vector, and augmenting it with submodules obtained from partitioning an integrated and cleaned protein-protein interaction network, we calculate a Proteomics Signature Profile (PSP) for each patient based on the hit rates of their reported proteins, in the absence of fold change thresholds, against the cluster vector. Using this, we demonstrated that moderate- and late-stage patients segregate with high confidence. We also discovered a moderate-stage patient who displayed a proteomics profile similar to other poor-stage patients. We identified significant clusters using a modified version of the SNet approach. Comparing our results against the Proteomics Expansion Pipeline (PEP) on which the same patient data was analyzed, we found good correlation. Building on this finding, we report significantly more clusters (176 clusters here compared to 70 in PEP), demonstrating the sensitivity of this approach. Gene Ontology (GO) terms analysis also reveals that the significant clusters are functionally congruent with the liver cancer phenotype. PSP is a powerful and sensitive method for analyzing proteomics profiles even when sample sizes are small. It does not rely on the ratio scores but, rather, whether a protein is detected or not. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets. © 2012 American Chemical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/pr200698c
dc.sourceScopus
dc.subjectbioinformatics
dc.subjectHCC (hepatocellular carcinoma)
dc.subjectliver cancer
dc.subjectprotein networks
dc.subjectproteomics
dc.subjectsystems biology
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1021/pr200698c
dc.description.sourcetitleJournal of Proteome Research
dc.description.volume11
dc.description.issue3
dc.description.page1571-1581
dc.description.codenJPROB
dc.identifier.isiut000300916200012
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