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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/150776 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| 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. |
|---|