PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein–Protein Interactions, Co-Expression Networks, and Expression Data
Abstract
:1. Introduction
2. Results
2.1. Performance Evaluation of PPIA-coExp in ENCODE and TCGA-BRCA Database
2.2. Transcriptomic Profiling Reveals the Differentially Expressed Genes Between Human Aging and Alzheimer’s Disease
2.3. Genome-Wide Optimization Model for TF-Biomarkers and DEG-Biomarkers Identification
2.4. Difference in Histone Modification Distribution in the Younger, Old, and AD Groups
2.5. AD-Specific and Old-Specific Active Enhancers
3. Discussion
4. Materials and Methods
4.1. Estimating the Protein–Protein Interaction Affinity by the Law of Mass Action
4.2. Construct Context-Specific Gene Co-Expression Networks
4.3. Overview of the PPIA-coExp
4.4. Evaluation Metrics
4.5. Data Collection and Pre-Processing
4.6. Differentially Expressed Genes and Pathway Enrichment Analysis
4.7. The Histone Modification Levels Flanking the Transcription Start Site (TSS)
4.8. Enhancer Annotation and Motif Enrichment
4.9. Hierarchical Clustering
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
PPIA-coExp | protein–protein interaction affinity–co-expression network |
PPI | protein–protein interaction |
PPIA | protein–protein interaction affinity |
AD | Alzheimer’s disease |
PCC | Pearson correlation coefficient |
BioGRID | Biological General Repository for Interaction Datasets |
Sensitivity | |
Specificity | |
Accuracy | |
RNA-Seq | RNA sequencing |
TPM | transcripts per million |
GSEA | gene set enrichment analysis |
DEGs | differentially expressed genes |
HMs | histone modifications |
TSS | transcription start site |
TFs | transcription factors |
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Model | Sn | Sp | ACC | AUC |
---|---|---|---|---|
Gene-node | 0.7895 | 0.9535 | 0.9032 | 0.9547 |
t-test | 0.8947 | 0.9302 | 0.9194 | 0.9621 |
PPIA-coExp | 0.9474 | 0.9535 | 0.9516 | 0.9851 |
Model | Sn | Sp | ACC | AUC |
---|---|---|---|---|
Gene-node | 0.9027 | 0.9425 | 0.9326 | 0.9437 |
t-test | 0.4124 | 0.9737 | 0.8765 | 0.8835 |
PPIA-coExp | 0.8850 | 0.9762 | 0.9650 | 0.9811 |
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Yan, D.; Fan, Z.; Li, Q.; Chen, Y. PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein–Protein Interactions, Co-Expression Networks, and Expression Data. Int. J. Mol. Sci. 2024, 25, 12608. https://doi.org/10.3390/ijms252312608
Yan D, Fan Z, Li Q, Chen Y. PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein–Protein Interactions, Co-Expression Networks, and Expression Data. International Journal of Molecular Sciences. 2024; 25(23):12608. https://doi.org/10.3390/ijms252312608
Chicago/Turabian StyleYan, Dongsheng, Zhiyu Fan, Qianzhong Li, and Yingli Chen. 2024. "PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein–Protein Interactions, Co-Expression Networks, and Expression Data" International Journal of Molecular Sciences 25, no. 23: 12608. https://doi.org/10.3390/ijms252312608
APA StyleYan, D., Fan, Z., Li, Q., & Chen, Y. (2024). PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein–Protein Interactions, Co-Expression Networks, and Expression Data. International Journal of Molecular Sciences, 25(23), 12608. https://doi.org/10.3390/ijms252312608