ArticleFrontiers in immunology2023
Construction and systematic evaluation of a machine learning-based cuproptosis-related lncRNA score signature to predict the response to immunotherapy in hepatocellular carcinoma.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Targeting copper death-related long non-coding RNAs: a novel strategy to overcome immunotherapy resistance in liver cancer.Frontiers in immunology · 2026Review
- Targeting cuproptosis in liver cancer: Molecular mechanisms and therapeutic implications.Apoptosis : an international journal on programmed cell death · 2025Review
- Epigenetic modification of cuproptosis by non-coding RNAs in cancer drug resistance.Molecular cancer · 2025Review
- Analysis of Multiple Programmed Cell Death Patterns and Functional Validations of Apoptosis-Associated Genes in Lung Adenocarcinoma.Annals of surgical oncology · 2025Article
- Long non-coding rnas as key modulators of the immune microenvironment in hepatocellular carcinoma: implications for Immunotherapy.Frontiers in immunology · 2025Review
- Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer.Frontiers in immunology · 2025Article
- Prognostic analysis of hepatocellular carcinoma based on cuproptosis -associated lncRNAs.BMC gastroenterology · 2024Article
- Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology.European journal of medical research · 2024Article
- Commentary: Copper and cuproptosis-related genes in hepatocellular carcinoma: therapeutic biomarkers targeting tumor immune microenvironment and immune checkpoints.Frontiers in immunology · 2023Article
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Authors and funding
7 authors.
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Abstract
Introduction: Hepatocellular carcinoma (HCC) is a common malignant cancer with a poor prognosis. Cuproptosis and associated lncRNAs are connected with cancer progression. However, the information on the prognostic value of cuproptosis-related lncRNAs is still limited in HCC. Methods: We isolated the transcriptome and clinical information of HCC from TCGA and ICGC databases. Ten cuproptosis-related genes were obtained and related lncRNAs were correlated by Pearson's correlation. By performing lasso regression, we created a cuproptosis-related lncRNA prognostic model based on the cuproptosis-related lncRNA score (CLS). Comprehensive analyses were performed, including the fields of function, immunity, mutation and clinical application, by various R packages. Results: Ten cuproptosis-related genes were selected, and 13 correlated prognostic lncRNAs were collected for model construction. CLS was positively or negatively correlated with cancer-related pathways. In addition, cell cycle and immune related pathways were enriched. By performing tumor microenvironment (TME) analysis, we determined that T-cells were activated. High CLS had more tumor characteristics and may lead to higher invasiveness and treatment resistance. Three genes ( Conclusion: We created a prognostic CLS signature by machine learning, and we comprehensively analyzed the signature in the fields of function, immunity, mutation and clinical application.
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