ArticleCancer reports (Hoboken, N.J.)2023
Cellular senescence-related long noncoding ribonucleic acids: Predicting prognosis in hepatocellular carcinoma.
Article in Cancer reports (Hoboken, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-coding RNAs participate in interactions between senescence and gastrointestinal cancers.Frontiers in genetics · 2024Pooled it
- Association of lncRNA AGAP11 with prognosis and malignant progression in triple-negative breast cancer.Discover oncology · 2026Article
- Long non-coding RNA-based single and combination independent prognostic biomarkers for hepatocellular carcinoma.Discover oncology · 2025Review
- Prognostic cellular senescence-related lncRNAs patterns to predict clinical outcome and immune response in colon cancer.Frontiers in immunology · 2024Article
- Cellular senescence-related long noncoding ribonucleic acids: Predicting prognosis in hepatocellular carcinoma.Cancer reports (Hoboken, N.J.) · 2023Article
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5 authors.
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Abstract
backgroundDue to their inherent role in cell function, long non-coding ribonucleic acids (lncRNAs) mediate changes in the microenvironment, and thereby participate in the development of cellular senescence.
aimsThis study aimed to identify cellular senescence-related lncRNAs that could predict the prognosis of liver cancer. METHODS AND
resultsGene expression and clinical data were downloaded from the UCSC Xena platform, ICGC, and TCGA databases. Cox regression and LASSO regression were used to establish a cellular senescence-related lncRNA model. ROC curves and Kaplan-Meier survival curves were then constructed to predict patient prognosis. Cox regression analysis and clinical characteristics were used to evaluate the capability of the model. Tumor mutational burden and tumor-infiltrating immune cell analyses were subsequently performed in the risk subgroups and the samples in the entire cohort were reclustered. Finally, potential small molecule immune-targeted drugs were identified based on the model. The cellular senescence-related prognostic model that was constructed based on AGAP11 and FAM182B. Along with the results of Cox regression and Lasso regression, the risk score was found to be an independent factor for predicting overall survival in cohorts. In the subgroup analysis, the prognosis of the low-risk group in each cohort was significantly higher than that of the high-risk group; the area under temporal ROC curves and clinical ROC curves were all greater than 0.65, respectively. C-index shows that the risk scores are greater than 0.6, showing the stability of the model. The high-risk group demonstrated lower tumor microenvironment and higher tumor mutational burden scores, further verifying the reliability of the model grouping results. Analysis of tumor-infiltrating immune cells indicated that CD8+ and γδ T cells were more abundant among patients in the low-risk group; cluster reorganization indicated that the two groups had different prognoses and proportions of immune cells. The p value of potential drugs predicted based on the expression of model lncRNAs were all less than .05, demonstrating the potential of model lncRNAs as therapeutic targets to some extent.
conclusionA prognostic model based on cellular senescence-associated lncRNAs was established and this may be used as a potential biomarker for the prognosis assessment of liver cancer patients.
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