ArticleFrontiers in oncology2022
A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A cuproptosis-related lncRNAs-based risk signature for predicting prognosis and immune status in glioma.Translational cancer research · 2026Article
- Exploring Cuproptosis-Related Prognostic Signature for Hepatocellular Carcinoma: Bioinformatics and In Vitro Analyses.Digestive diseases and sciences · 2026Article
- Cuproptosis and Cardiovascular Diseases: Mechanisms, Pathophysiology, and Therapeutic Strategies-A Narrative Review.Reviews in cardiovascular medicine · 2025Review
- Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.Scientific reports · 2024Article
- An alternative extension of telomeres related prognostic model to predict survival in lower grade glioma.Journal of cancer research and clinical oncology · 2023Article
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3 authors.
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Abstract
Background: As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG. Methods: RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA. Results: We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well. Conclusion: Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients' survival of LGG and provide ideas for the exploration of new targets for LGG in the future.
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