A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients
| Autor: | Giuseppe Toffoli, Davide Busato, Monica Mossenta, Luca Bedon, Michele Dal Bo, Maurizio Polano |
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| Přispěvatelé: | Bedon, L., Dal Bo, M., Mossenta, M., Busato, D., Toffoli, G., Polano, M. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Hepatocellular carcinoma Kaplan-Meier Estimate computer.software_genre hepatocellular carcinoma DNA methylation Epigenesis Genetic lcsh:Chemistry Machine Learning Tumor Microenvironment Medicine lcsh:QH301-705.5 Spectroscopy Tumor Liver Neoplasms Epigenetic General Medicine hepatocellular carcinoma Middle Aged Prognosis Primary tumor Progression-Free Survival Computer Science Applications Algorithm prediction model Gene Expression Regulation Neoplastic Hepatocellular carcinoma DNA methylation Prediction model Tumor microenvironment Adult Aged Algorithms Biomarkers Tumor Carcinoma Hepatocellular CpG Islands DNA DNA Methylation Decision Making Female Gene Expression Profiling Genome-Wide Association Study Humans Proportional Hazards Models Regression Analysis Risk Disease Progression Liver Neoplasm DNA methylation epigenetic Human Prognosi Machine learning Regression Analysi Catalysis Article Inorganic Chemistry Genetic Epigenetics Progression-free survival Physical and Theoretical Chemistry Molecular Biology Survival analysis Neoplastic Proportional hazards model business.industry Organic Chemistry Carcinoma Hepatocellular medicine.disease Gene expression profiling lcsh:Biology (General) lcsh:QD1-999 Gene Expression Regulation Tumor progression Proportional Hazards Model Artificial intelligence CpG Island business computer Biomarkers Epigenesis |
| Zdroj: | International Journal of Molecular Sciences International Journal of Molecular Sciences, Vol 22, Iss 1075, p 1075 (2021) Volume 22 Issue 3 |
| ISSN: | 1422-0067 |
| Popis: | Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment. |
| Databáze: | OpenAIRE |
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