Multi-omics analyses develop and validate the optimal prognostic model on overall survival prediction for resectable hepatocellular carcinoma
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a multi-omics model to predict prognosis in resectable hepatocellular carcinoma (HCC). The model integrates genetic mutations, copy number variations, gene expression, and methylation data with clinical factors for improved survival prediction.
Area Of Science
- Oncology
- Genomics
- Bioinformatics
Background
- Single-omics profiling for hepatocellular carcinoma (HCC) prognosis is established.
- Prognosis prediction using multi-omics biomarkers in HCC remains underexplored.
Purpose Of The Study
- To develop and validate a predictive model for resectable HCC prognosis.
- To integrate multi-omics data with clinicopathological factors for enhanced prediction.
Main Methods
- Utilized multi-omics data (mutational, CNV, transcriptional, methylation) from TCGA and Beijing Youan Hospital.
- Performed univariate and multivariate analyses to identify independent risk factors.
- Developed and validated a prognostic model using ROC analysis.
Main Results
- Identified key genes and genomic alterations associated with HCC prognosis (e.g., TP53, FBN1, MAP1B, CSMD1, CCNJL, CXorf15).
- Established single-omics models and a comprehensive multi-omics prognostic model.
- Achieved high predictive accuracy with AUCs of 0.98 (1 year) and 0.88 (2 years) in validation.
Conclusions
- A multi-omics model integrating molecular and clinical data provides optimal prognosis prediction for resectable HCC.
- This model can aid in therapeutic strategy selection and survival assessment for HCC patients.
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