ArticleScientific reports2020
Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing.BMC medical genomics · 2024Article
- A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer.International journal of molecular sciences · 2024Article
- Enhancing selection of alcohol consumption-associated genes by random forest.The British journal of nutrition · 2024Article
- Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.Scientific reports · 2023Article
- Review
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma.BMC genomics · 2023Article
- Machine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkers.Medeniyet medical journal · 2022Article
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy.JCO clinical cancer informatics · 2022Article
- Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery.Frontiers in oncology · 2022Article
- Cell-Free DNA Analysis by Whole-Exome Sequencing for Hepatocellular Carcinoma: A Pilot Study in Thailand.Cancers · 2021Article
- Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine.Biomedicines · 2021Review
- Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery.Frontiers in medicine · 2021Review
- Analysis of the TCGA Dataset Reveals that Subsites of Laryngeal Squamous Cell Carcinoma are Molecularly Distinct.Cancers · 2020Article
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10 authors.
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Abstract
Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recurrence in HCC patients using databases (TCGA, AMC and Inserm) and machine learning methods and obtain the gene signature that could predict early relapse of HCC. Statistical methods, such as feature selection, survival analysis and Chi-Square test in R software, were used to analyze and select mutant genes related to disease free survival (DFS), race and vascular invasion. In addition, whole-exome sequencing was performed on 10 HCC patients recruited from our center, and the sequencing results were compared with the databases. Using the databases and machine learning methods, the prediction model of recurrence was constructed and optimized, and the selected mutant genes were verified in the test group. The accuracy of prediction was 74.19%. Moreover, these 10 patients from our center were used to verify these mutant genes and the prediction model, and a success rate of 80% was achieved. Collectively, we discovered recurrence-related genes and established recurrence prediction model of recurrence for HCC patients, which could provide significant guidance for clinical prediction of recurrence.
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