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

Elsevier BV
Publication Type:
Journal Article
Journal of proteomics, 2019, 206
Issue Date:
Filename Description Size
1-s2.0-S1874391919302180-main.pdfPublished version1.42 MB
Adobe PDF
Full metadata record
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.
Please use this identifier to cite or link to this item: