ArticleCancer cell international2023
Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.
Article in Cancer cell international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 11 citations in OpenAlex.
- Prognostic stratification in hepatocellular carcinoma using a telomerase-related lncRNA signature derived from TCGA database.PloS one · 2026Article
- The molecular characteristics of DNA damage and repair related to P53 mutation for predicting the recurrence and immunotherapy response in hepatocellular carcinoma.Scientific reports · 2025Article
- Long non-coding rnas as key modulators of the immune microenvironment in hepatocellular carcinoma: implications for Immunotherapy.Frontiers in immunology · 2025Review
- Hypoxia- and Anoikis-Related lncRNA Signature Defines Molecular Subtypes and Predicts Prognosis and Immunotherapy Response in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Article
- Hepatoma-Targeting and ROS-Responsive Polymeric Micelle-Based Chemotherapy Combined with Photodynamic Therapy for Hepatoma Treatment.International journal of nanomedicine · 2024Article
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Authors and funding
11 authors at 3 institutions in 1 country.
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Abstract
Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.
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