Wang Boshen, Tian Peijie, Sun Qianyu, Zhang Hengdong, Han Lei, Zhu Baoli
Jiangsu Provincial Center for Disease Prevention and Control, Nanjing 210000, Jiangsu, China.
Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China.
Heliyon. 2023 Jul 7;9(7):e18075. doi: 10.1016/j.heliyon.2023.e18075. eCollection 2023 Jul.
Patients with low-grade glioma (LGG) may survive for long time periods, but their tumors often progress to higher-grade lesions. Currently, no cure for LGG is available. A-to-I RNA editing accounts for nearly 90% of all RNA editing events in humans and plays a role in tumorigenesis in various cancers. However, little is known regarding its prognostic role in LGG. On the basis of The Cancer Genome Atlas (TCGA) data, we used LASSO and univariate Cox regression to construct an RNA editing site signature. The results derived from the TCGA dataset were further validated with Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) datasets. Five machine learning algorithms (Decision Trees C5.0, XGboost, GBDT, Lightgbm, and Catboost) were used to confirm the prognosis associated with the RNA editing site signature. Finally, we explored immune function, immunotherapy, and potential therapeutic agents in the high- and low-risk groups by using multiple biological prediction websites. A total of 22,739 RNA editing sites were identified, and a signature model consisting of four RNA editing sites (PRKCSH|chr19:11561032, DSEL|chr18:65174489, UGGT1|chr2:128952084, and SOD2|chr6:160101723) was established. Cox regression analysis indicated that the RNA editing signature was an independent prognostic factor, according to the ROC curve (AUC = 0.823), and the nomogram model had good predictive power (C-index = 0.824). In addition, the predictive ability of the RNA editing signature was confirmed with the machine learning model. The sensitivity of PCI-34051 and Elephantin was significantly higher in the high-risk group than the low-risk group, thus potentially providing a marker to predict the effects of lung cancer drug treatment. RNA editing may serve as a novel survival prediction tool, thus offering hope for developing editing-based therapeutic strategies to combat LGG progression. In addition, this tool may help optimize survival risk assessment and individualized care for patients with low-grade gliomas.
低级别胶质瘤（LGG）患者可能存活很长时间，但其肿瘤往往会进展为更高级别的病变。目前，尚无治愈LGG的方法。A-to-I RNA编辑占人类所有RNA编辑事件的近90%，并在各种癌症的肿瘤发生中起作用。然而，关于其在LGG中的预后作用知之甚少。基于癌症基因组图谱（TCGA）数据，我们使用LASSO和单变量Cox回归构建了一个RNA编辑位点特征。从TCGA数据集中得出的结果在基因表达综合数据库（GEO）和中国胶质瘤基因组图谱（CGGA）数据集中进一步得到验证。使用五种机器学习算法（决策树C5.0、XGBoost、梯度提升决策树、Lightgbm和Catboost）来确认与RNA编辑位点特征相关的预后。最后，我们通过使用多个生物预测网站探索了高风险和低风险组中的免疫功能、免疫治疗和潜在治疗药物。共鉴定出22739个RNA编辑位点，并建立了一个由四个RNA编辑位点（PRKCSH|chr19:11561032、DSEL|chr18:65174489、UGGT1|chr2:128952084和SOD2|chr6:160101723）组成的特征模型。Cox回归分析表明，根据ROC曲线（AUC = 0.823），RNA编辑特征是一个独立的预后因素，列线图模型具有良好的预测能力（C指数 = 0.824）。此外，机器学习模型证实了RNA编辑特征的预测能力。PCI-34051和Elephantin在高风险组中的敏感性显著高于低风险组，从而有可能提供一个预测肺癌药物治疗效果的标志物。RNA编辑可能作为一种新的生存预测工具，从而为开发基于编辑的治疗策略以对抗LGG进展带来希望。此外，该工具可能有助于优化低级别胶质瘤患者的生存风险评估和个性化护理。
