Proteomic investigation of intra-tumor heterogeneity using network-based contextualization — A case study on prostate cancer

Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Qua...

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Bibliographic Details
Published in:Journal of proteomics 2019-08, Vol.206, p.103446, Article 103446
Main Authors: Goh, Wilson Wen Bin, Zhao, Yaxing, Sue, Andrew Chi-Hau, Guo, Tiannan, Wong, Limsoon
Format: Article
Language:English
Subjects:
Bioinformatics
Biomarker
Networks
Proteomics
Systems biology
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Summary:Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery. [Display omitted] •Traditional analytical approaches typically exaggerate tumor heterogeneity.•Network-based analysis suggests heterogeneity is over-estimated.•Network-based analysis leads towards good cross-validation accuracy.•Network-based analysis is a useful means of circumventing heterogeneity.
ISSN:1874-3919
DOI:10.1016/j.jprot.2019.103446