Huang Shiqiong, Sun Ji
Department of Pharmacy, The First Hospital of Changsha, Changsha, China.
Front Pharmacol. 2025 May 14;16:1547320. doi: 10.3389/fphar.2025.1547320. eCollection 2025.
Although the role of adenosine-to-inosine RNA editing (ATIRE) has gained widespread attention in multiple cancers, its predictive role in hepatocellular carcinoma (HCC) remains little known. We aimed to establish a predicting signature based on ATIRE for the prognosis of HCC.
A total of 200 HCC patients with survival data and ATIRE profiles from The Cancer Genome Atlas (TCGA) database were divided into training (n = 140) and validation (n = 60) cohorts. Survival-related ATIRE sites were identified by the least absolute shrinkage and selection operator algorithm. ATIRE-based risk score was then generated with these ATIRE sites. Cox proportional hazards regression was employed to construct the ATIRE-based nomogram signature. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of the signature. Harrell's C-index and calibration plot was utilized to evaluate the significant prognostic factors.
Nine ATIRE sites were screened to establish the ATIRE risk score, and it was found to be associated with prognosis of HCC. Survival analysis revealed that higher ATIRE-based risk scores were significantly associated with worse overall survival (OS) in both the training dataset ( < 0.001) and the validation dataset ( = 0.011), as well as in the combined dataset ( < 0.001). The ROC curve displayed a good predictive efficiency of the risk score regarding 1-year, 2-year, and 3-year OS. Furthermore, ATIRE sites were significantly correlated with the expression of host genes and were likely to be involved in certain cancer-related pathways.
Our findings provided a novel ATIRE-based nomogram, which could serve as a potential tool for predicting HCC prognosis.
尽管腺苷到肌苷RNA编辑（ATIRE）在多种癌症中的作用已受到广泛关注，但其在肝细胞癌（HCC）中的预测作用仍鲜为人知。我们旨在建立基于ATIRE的预测模型以预测HCC的预后。
从癌症基因组图谱（TCGA）数据库中获取了200例具有生存数据和ATIRE图谱的HCC患者，将其分为训练组（n = 140）和验证组（n = 60）。通过最小绝对收缩和选择算子算法识别与生存相关的ATIRE位点。然后利用这些ATIRE位点生成基于ATIRE的风险评分。采用Cox比例风险回归构建基于ATIRE的列线图模型。使用受试者工作特征（ROC）曲线评估该模型的预测效能。利用Harrell's C指数和校准图评估重要的预后因素。
筛选出9个ATIRE位点以建立ATIRE风险评分，发现其与HCC的预后相关。生存分析显示，在训练数据集（<0.001）、验证数据集（=0.011）以及合并数据集（<0.001）中，基于ATIRE的风险评分越高，总生存期（OS）越差。ROC曲线显示该风险评分对1年、2年和3年OS具有良好的预测效能。此外，ATIRE位点与宿主基因的表达显著相关，并且可能参与某些癌症相关通路。
我们的研究结果提供了一种基于ATIRE的新型列线图，可作为预测HCC预后的潜在工具。
