The Breast Cancer Protein Co-Expression Landscape
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Stage 1: Data Acquisition, Quality Control, and Data Curation
2.2. Stage 2: Differential Protein Abundance and Pathway Analysis
- Type of process: biological process non-redundant;
- Number of genes per category: 5–2000;
- Fit test: Benjamini and Hochberg (BH);
- Multiple testing correction: false discovery rate (FDR);
- Reference gene list: genome protein-coding.
2.3. Stage 3: Inference and Analysis of Protein Co-Expression Networks
2.4. Stage 4: Network Modularity Analysis and Protein Interaction Networks
3. Results
3.1. The Protein Co-Expression Network Shows a Scale-Free Topology
3.2. There Is a Hierarchical Organization in the PCN
3.3. Subcommunities Are Significantly Associated with Specific Biological Processes
3.4. Subcommunities with Similar Enrichment Are Part of the Same Community, and They Share Several Interactions
3.5. The PCN Is Clustered by Differential Expression Trend
3.6. Protein Co-Expression Is Not Distance-Dependent
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ruhle, M.; Espinal-Enríquez, J.; Hernández-Lemus, E. The Breast Cancer Protein Co-Expression Landscape. Cancers 2022, 14, 2957. https://doi.org/10.3390/cancers14122957
Ruhle M, Espinal-Enríquez J, Hernández-Lemus E. The Breast Cancer Protein Co-Expression Landscape. Cancers. 2022; 14(12):2957. https://doi.org/10.3390/cancers14122957
Chicago/Turabian StyleRuhle, Martín, Jesús Espinal-Enríquez, and Enrique Hernández-Lemus. 2022. "The Breast Cancer Protein Co-Expression Landscape" Cancers 14, no. 12: 2957. https://doi.org/10.3390/cancers14122957