Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset and Their Pre-processing
2.2. Expression Biomarkers
2.3. Neighboring Patients: Patients with Similar Expression Profiles
2.4. Mutation Network
3. Results
3.1. Association of Mutation and Expression Profiles
3.2. Pipeline to Find Functional Mutations
3.3. Application to Breast Cancer
3.4. Mutation Types
3.5. Functional Mutated Genes in Other Cancers
3.6. Functional Association of Functional Mutations
3.7. Mutation Network
4. Discussion
Acknowledgments
Conflicts of Interest
References
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Mutated gene | No. somatic mutation | No. functional mutation | Percentage | p(MutSig) | p(MUSIC) | p(drGAP) |
---|---|---|---|---|---|---|
PIK3CA | 175 | 123 | 70.3% | 0 | ||
TP53 | 188 | 107 | 56.9% | 0 | 0 | |
MAP3K1 | 40 | 30 | 75.0% | 0 | ||
CDH1 | 35 | 29 | 82.6% | 0 | ||
GATA3 | 56 | 19 | 33.9% | 0 | ||
RUNX1 | 19 | 13 | 68.4% | 1 | ||
CTCF | 15 | 10 | 66.7% | 1 | 1 | |
CACNA1B | 14 | 9 | 64.2% | 1 | ||
DNAH17 | 14 | 8 | 57.1% | 1 | ||
MAP2K4 | 21 | 8 | 38.1% |
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Meng, G. Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations. High-Throughput 2018, 7, 6. https://doi.org/10.3390/ht7010006
Meng G. Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations. High-Throughput. 2018; 7(1):6. https://doi.org/10.3390/ht7010006
Chicago/Turabian StyleMeng, Guofeng. 2018. "Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations" High-Throughput 7, no. 1: 6. https://doi.org/10.3390/ht7010006