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

• Main Authors: Goh, Wilson Wen Bin, Zhao, Yaxing, Sue, Andrew Chi-Hau, Guo, Tiannan, Wong, Limsoon
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2021
• Subjects: Science::Biological sciences; Proteomics; Networks
• Online Access: https://hdl.handle.net/10356/150776
• Institution: Nanyang Technological University

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.