ArticleBMC genomics2013
Enhancing the utility of Proteomics Signature Profiling (PSP) with Pathway Derived Subnets (PDSs), performance analysis and specialised ontologies.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 36 citations in OpenAlex.
- Biological functions and theranostic potential of HMGB family members in human cancers.Therapeutic advances in medical oncology · 2020Review
- The function and mechanism of HMGB1 in lung cancer and its potential therapeutic implications.Oncology letters · 2018Review
- High Mobility Group Box Protein 1 Serves as a Potential Prognostic Marker of Lung Cancer and Promotes Its Invasion and Metastasis by Matrix Metalloproteinase-2 in a Nuclear Factor-BioMed research international · 2018Article
- Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics.BMC genomics · 2017Article
- Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics.BMC medical genomics · 2016Article
- Quantitative proteomics signature profiling based on network contextualization.Biology direct · 2015Article
- A semi-automated methodology for finding lipid-related GO terms.Database : the journal of biological databases and curation · 2014Article
- Contemporary network proteomics and its requirements.Biology · 2013Article
- Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice.Journal of proteome research · 2013Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundProteomics Signature Profiling (PSP) is a novel hit-rate based method that proved useful in resolving consistency and coverage issues in proteomics. As a follow-up study, several points need to be addressed: 1/ PSP's generalisability to pathways, 2/ understanding the biological interplay between significant complexes and pathway subnets co-located on the same pathways on our liver cancer dataset, 3/ understanding PSP's false positive rate and 4/ demonstrating that PSP works on other suitable proteomics datasets as well as expanding PSP's analytical resolution via the use of specialised ontologies.
results1/ PSP performs well with Pathway-Derived Subnets (PDSs). Comparing the performance of PDSs derived from various pathway databases, we find that an integrative approach is best for optimising analytical resolution. Feature selection also confirms that significant PDSs are closely connected to the cancer phenotype.2/ In liver cancer, correlation studies of significant PSP complexes and PDSs co-localised on the same pathways revealed an interesting relationship between the purine metabolism pathway and two other complexes involved in DNA repair. Our work suggests progression to poor stage requires additional mutations that disrupt DNA repair enzymes.3/ False positive analysis reveals that PSP, applied on both complexes and PDSs, is powerful and precise.4/ Via an expert-curated lipid ontology, we uncovered several interesting lipid-associated complexes that could be associated with cancer progression. Of particular interest is the HMGB1-HMGB2-HSC70-ERP60-GAPDH complex which is also involved in DNA repair. We also demonstrated generalisability of PSP using a non-small-cell lung carcinoma data set.
conclusionsPSP is a powerful and precise technique, capable of identifying biologically coherent features. It works with biological complexes, network-predicted clusters as well as PDSs. Here, an instance of the interplay between significant PDSs and complexes, possibly significantly involved in liver cancer progression but not well understood as yet, is demonstrated. Also demonstrated is the enhancement of PSP's analytical resolution using specialised ontologies.
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