Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network
Abstract
:1. Introduction
2. Results
2.1. ceRNA Network
2.2. Feature Selection
2.3. Integrative Analysis of the Transcriptional Signature Components
2.3.1. Genomic Alteration Analysis
2.3.2. Risk Analysis
2.3.3. Functional Annotation Analysis
2.4. Gene Signature and ceRNA Network
3. Discussion
3.1. Gene Signature
3.2. Validation and Biological Interpretation
3.2.1. Genomic and Functional Alterations
3.2.2. Gene Cluster Analysis
4. Materials and Methods
4.1. Data
4.2. ceRNA Network Construction
4.3. Dataset Construction, Feature Selection, and Gene Signature Construction
4.4. Somatic and Copy Number Alteration Analysis
4.5. Risk Analysis
4.6. Functional Annotation Analysis
4.7. Development
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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Method | Accuracy | AUC | Brier Score |
---|---|---|---|
Random forest 1 | 72.2% | 81.48% | 0.1955442 |
SVM | 50% | 66.67% | 0.2500714 |
xgBoost | 61.1% | 62.34% | 0.2343498 |
kNN | 50% | 61.72% | 0.4817816 |
Naïve Bayes | 50% | 54.32% | 0.5000000 |
Cluster | Gene | First Ligands |
---|---|---|
1 | AF117829.1 | hsa-miR-361-5p, POLE2, HMMR |
2 | BTBD11 | hsa-miR-374a-5p, hsa-miR-374b-5p, MAGI2-AS3 |
3 | HECW2 | hsa-miR-130a-3p, hsa-miR-130b-3p, hsa-miR-454-3p, hsa-miR-4295, hsa-miR-3666, H19 |
1 | HMMR | hsa-miR-361-5p, POLE2, AF117829.1 |
3 | hsa-miR-130a-3p | HECW2, WNK3, RASD1, PFKFB3, SCARA3, LDLR, PMEPA1, TCF4, PXDB, BCL11A, NHSL1, H19 |
4 | hsa-miR-381-3p | RSRP1, CORO1C, ATAD5, RNF149, AC016876.2 |
3 | INSR | hsa-miR-16-5p, hsa-miR-424-5p, C1RL-AS1. |
5 | PTTG1 | hsa-miR-186-5p, AC021078.1 |
3 | RFLNB | hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-16-5p, hsa-miR-424-5p, H19, AC005154.1 |
3 | RASD1 | hsa-miR-130a-3p, hsa-miR-130b-3p, hsa-miR-3666, hsa-miR-4295, hsa-miR-454-3p |
6 | SNHG15 | hsa-miR-24-3p, IL2RB, NFKBIE, CITED4 |
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Farias, E.; Terrematte, P.; Stransky, B. Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network. Int. J. Mol. Sci. 2024, 25, 4214. https://doi.org/10.3390/ijms25084214
Farias E, Terrematte P, Stransky B. Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network. International Journal of Molecular Sciences. 2024; 25(8):4214. https://doi.org/10.3390/ijms25084214
Chicago/Turabian StyleFarias, Epitácio, Patrick Terrematte, and Beatriz Stransky. 2024. "Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network" International Journal of Molecular Sciences 25, no. 8: 4214. https://doi.org/10.3390/ijms25084214