Optimisation Models for Pathway Activity Inference in Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. A Novel Optimisation-Based Pathway Activity Inference Model
2.3. Comparison of Pathway Activity Inference Approaches
2.4. Method Evaluation
2.4.1. Classification
2.4.2. Robustness against Noise in Data
2.4.3. Survival Analysis
2.5. Sample Classification through DIOPTRA
2.5.1. Pathway and Gene Ranking
2.5.2. Assessing Sample Classification through DIOPTRA
3. Results
3.1. Classification Comparison
3.2. Robustness Comparison
3.3. Survival Comparison
3.4. DIOPTRA Prediction Performance and Identification of Biologically Relevant Pathways
4. Discussion
4.1. Computational Efficiency Improvements in DIOPTRA
4.2. Exploration of Top-Ranked Pathways
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Tumour or Normal Label | Molecular Subtype Label |
---|---|---|
COAD | Tumour: 480 Normal: 41 | CMS1: 85 |
CMS2: 165 | ||
CMS3: 58 | ||
CMS4: 120 | ||
BRCA | Tumour: 1091 Normal: 120 | LumA: 579 |
LumB: 217 | ||
Basal: 191 | ||
Her2: 82 | ||
Normal-Like: 22 |
Classifier | BRCA | COAD |
---|---|---|
DIOPTRA | 0.67 (0.064) | 0.75 (0.043) |
DIOPTRA+KNN | 0.74 (0.046) | 0.76 (0.068) |
DIOPTRA+RF | 0.84 (0.031) | 0.85 (0.060) |
KEGG Pathway Name | No. Gene | Top Genes and Weights 1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pancreatic secretion | 104 | CA2 | 4.36 | CPA1 | 0.72 | CHRM3 | 0.69 | PRSS2 | 0.53 | CPA2 | 0.33 |
Circadian rhythm | 32 | RORB | 3.24 | ROR1 | 0.78 | PRKAA2 | 0.49 | RORA | 0.49 | CUL1 | 0.47 |
Peroxisome | 87 | AGXT | 5.39 | HAO2 | 3.21 | ACSL6 | 0.23 | PEX11A | 0.10 | IDH2 | 0.08 |
Chemical carcinogenesis | 81 | GSTM5 | 6.07 | PTGS2 | 0.71 | GSTA1 | 0.66 | GSTA2 | 0.44 | CYP1A1 | 0.30 |
Platinum drug resistance | 73 | GSTM5 | 6.71 | GSTA2 | 1.30 | GSTA1 | 0.82 | CDKN2A | 0.21 | GSTT2B | 0.15 |
Drug metabolism cytochrome P450 | 70 | GSTM5 | 6.33 | GSTA1 | 0.91 | GSTA2 | 0.69 | UGT2B11 | 0.22 | FMO2 | 0.21 |
Folate biosynthesis | 30 | TPH1 | 4.11 | PAH | 3.88 | ALPL | 0.79 | MOCOS | 0.34 | FPGS | 0.20 |
Drug metabolism other enzymes | 79 | GSTM5 | 5.98 | GSTA1 | 0.93 | GSTA2 | 0.65 | XDH | 0.55 | GSTT2B | 0.21 |
Cocaine addiction | 50 | SLC18A2 | 3.56 | DRD1 | 1.16 | GRIN2A | 0.93 | CREB3L3 | 0.51 | SLC18A1 | 0.41 |
Carbon metabolism | 118 | AGXT | 3.468 | HAO2 | 2.178 | ALDOB | 1.962 | PHGDH | 0.605 | PSAT1 | 0.147 |
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Chen, Y.; Liu, S.; Papageorgiou, L.G.; Theofilatos, K.; Tsoka, S. Optimisation Models for Pathway Activity Inference in Cancer. Cancers 2023, 15, 1787. https://doi.org/10.3390/cancers15061787
Chen Y, Liu S, Papageorgiou LG, Theofilatos K, Tsoka S. Optimisation Models for Pathway Activity Inference in Cancer. Cancers. 2023; 15(6):1787. https://doi.org/10.3390/cancers15061787
Chicago/Turabian StyleChen, Yongnan, Songsong Liu, Lazaros G. Papageorgiou, Konstantinos Theofilatos, and Sophia Tsoka. 2023. "Optimisation Models for Pathway Activity Inference in Cancer" Cancers 15, no. 6: 1787. https://doi.org/10.3390/cancers15061787
APA StyleChen, Y., Liu, S., Papageorgiou, L. G., Theofilatos, K., & Tsoka, S. (2023). Optimisation Models for Pathway Activity Inference in Cancer. Cancers, 15(6), 1787. https://doi.org/10.3390/cancers15061787