ArticleScientific reports2022
Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrated Multi-Omics Analysis Reveals Cytokine Network Dynamics and Prognostic Signatures in Hepatitis B Virus-Associated Hepatocellular Carcinoma.Applied biochemistry and biotechnology · 2026Article
- Leveraging complementary multi-omics data integration methods for mechanistic insights in kidney diseases.JCI insight · 2025Article
- Strategies for discovering novel hepatocellular carcinoma biomarkers.World journal of hepatology · 2025Review
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches.Cancers · 2024Review
- Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.ACS pharmacology & translational science · 2024Review
- DecipheringEpigenomics · 2024Article
- Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer.Health data science · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell's concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.
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