Uncovering New Biomarkers for Prostate Cancer Through Proteomic and Network Analysis
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Sample Collection and Isolation of Total Proteins from Urine
2.2. Protein Extraction and Enzymatic Digestion
2.3. Proteomic Analysis by nanoLC-MS/MS
2.4. MS/MS Data Processing
2.5. Enrichment Analysis
2.6. Label-Free Quantitative Analysis
2.7. Reconstruction of PPI Network Model and Functional Modules Identification
2.8. Topological Analysis of PPI and Co-Expression Network Models
2.9. TCGA Bioinformatic Analysis
3. Results
3.1. Protein Profiling of Urine from Healthy Controls and Patients Affected by Prostate Cancer at Low- and High-Risk Level
3.2. Differentially Abundant Proteins (DAPs) by Comparing Urine Protein Profiles from Healthy Controls and Patients Affected by Prostate Cancer at Low- and High-Risk Level
3.3. Functional Modules Marking the Urine Proteome of Healthy Controls and Patients Affected by Prostate Cancer at Low- and High-Risk Levels
3.4. Network Hubs and Bottlenecks in Urine of Healthy Controls and Patients Affected by Prostate Cancer at Low- and High-Risk Levels
3.5. TCGA Bioinformatic Analysis: HD vs. PCa
3.6. TCGA Bioinformatic Analysis: LRPCa vs. HRPCa
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
PSA | Prostate-Specific Antigen |
PCa | Prostate Cancer |
TCGA | The Cancer Genome Atlas |
DRE | Digital Rectal Examination |
TRUS | Transrectal Ultrasound |
MRI | Magnetic Resonance Imaging |
GS | Gleason Score |
mpMRI | multi-parametric Magnetic Resonance Imaging |
CT | Computed Tomography |
PET | Positron Emission Tomography |
ISUP | International Society of Urological Pathology |
NCCN | National Comprehensive Cancer Network |
CAPRA | Cancer of the Prostate Risk Assessment |
AI | Artificial Intelligence |
PPI | Protein-Protein Interaction |
HD | Healthy Donors |
LRPCa | Low-risk PCa patients |
HRPCa | High-risk PCa patients |
TFA | Trifluoroacetic Acid |
LC | Liquid Chromatography |
MS | Mass Spectrometry |
FDR | False Discovery Rates |
BP | Biological Process |
MF | Molecular Function |
CC | Cellular Component |
LDA | Linear Discriminant Analysis |
PSM | Peptide Spectrum Match |
DAPs | Differentially Abundant Proteins |
IF | Identification Frequency |
DAve | Differential Average |
PCA | Principal Component Analysis |
nLC-hrMS/MS | Nano-Liquid Chromatography-Mass Spectrometry/ |
High Resolution Mass Spectrometry | |
PC1 | Principal Component1 |
PC2 | Principal Component2 |
HSPs | Heat Shock Proteins (HSPs) |
BPH | Benign Prostatic Hyperplasia |
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UNIPROT ID | Protein Name | Gene Name | HD | PCa | p-Value |
---|---|---|---|---|---|
F8W111 | Carboxypeptidase M | CPM | 5 (100%) | 0 (0%) | 0.00063 |
O75882 | Attractin | ATRN | 5 (100%) | 1 (9%) | 0.0034 |
A0A3B3ISU3 | Low affinity immunoglobulin gamma Fc region receptor III-B | FCGR3B | 5 (100%) | 2 (18%) | 0.012 |
Q16651 | Prostasin | PRSS8 | 5 (100%) | 2 (18%) | 0.012 |
Q8WZ75 | Roundabout homolog 4 | ROBO4 | 5 (100%) | 2 (18%) | 0.012 |
Q9H8L6 | Multimerin-2 | MMRN2 | 5 (100%) | 2 (18%) | 0.012 |
P19022 | Cadherin-2 | CDH2 | 5 (100%) | 2 (18%) | 0.012 |
P05787 | Keratin, type II cytoskeletal 8 | KRT8 | 0 (0%) | 10 (91%) | 0.0034 |
P25815 | Protein S100-P | S100P | 1 (20%) | 10 (91%) | 0.024 |
A8MY60 | Leucine-rich repeat and IQ domain-containing protein 1 | LRRIQ1 | 0 (0%) | 9 (82%) | 0.012 |
UNIPROT ID | Protein Name | Gene Name | LRPCa | HRPCa | p-Value |
---|---|---|---|---|---|
P19823 | Inter-alpha-trypsin inhibitor heavy chain H2 | ITIH2 | 4 (100%) | 0 (0%) | 0.034 |
Q13018 | Secretory phospholipase A2 receptor | PLA2R1 | 4 (100%) | 1 (14%) | 0.034 |
Q15293 | Reticulocalbin-1 | RCN1 | 0 (0%) | 6 (86%) | 0.034 |
O94919 | Endonuclease domain-containing 1 protein | ENDOD1 | 0 (0%) | 6 (86%) | 0.034 |
P11684 | Uteroglobin | SCGB1A1 | 1 (25%) | 7 (100%) | 0.047 |
P08571 | Monocyte differentiation antigen CD14 | CD14 | 1 (25%) | 7 (100%) | 0.047 |
P02750 | Leucine-rich alpha-2-glycoprotein | LRG1 | 1 (25%) | 7 (100%) | 0.047 |
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Rossi, R.; Borroni, E.M.; Yusuf, I.; Lomagno, A.; Hegazi, M.A.A.A.; Mauri, P.L.; Grizzi, F.; Taverna, G.; Di Silvestre, D. Uncovering New Biomarkers for Prostate Cancer Through Proteomic and Network Analysis. Biology 2025, 14, 256. https://doi.org/10.3390/biology14030256
Rossi R, Borroni EM, Yusuf I, Lomagno A, Hegazi MAAA, Mauri PL, Grizzi F, Taverna G, Di Silvestre D. Uncovering New Biomarkers for Prostate Cancer Through Proteomic and Network Analysis. Biology. 2025; 14(3):256. https://doi.org/10.3390/biology14030256
Chicago/Turabian StyleRossi, Rossana, Elena Monica Borroni, Ishak Yusuf, Andrea Lomagno, Mohamed A. A. A. Hegazi, Pietro Luigi Mauri, Fabio Grizzi, Gianluigi Taverna, and Dario Di Silvestre. 2025. "Uncovering New Biomarkers for Prostate Cancer Through Proteomic and Network Analysis" Biology 14, no. 3: 256. https://doi.org/10.3390/biology14030256
APA StyleRossi, R., Borroni, E. M., Yusuf, I., Lomagno, A., Hegazi, M. A. A. A., Mauri, P. L., Grizzi, F., Taverna, G., & Di Silvestre, D. (2025). Uncovering New Biomarkers for Prostate Cancer Through Proteomic and Network Analysis. Biology, 14(3), 256. https://doi.org/10.3390/biology14030256