Department of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, The Second Affiliated Hospital, Cancer Institute, Zhejiang University School of Medicine, Hangzhou, China.
Front Endocrinol (Lausanne). 2021 Mar 12;12:538364. doi: 10.3389/fendo.2021.538364. eCollection 2021.
This paper aims to identify alternative RNA splicing landscape and its prognostic value in adrenocortical carcinoma.
The alternative splicing events data with corresponding clinical information data of 79 ACC patients were obtained from the Cancer Genome Atlas and SpliceSeq package. Prognosis-associated AS events by using univariate Cox regression analysis were selected. Gene functional enrichment analysis demonstrated the potential pathways enriched by survival-associated AS. Prognosis-related splicing events were submitted to develop moderate predictors using Lasso regression model.
One thousand five survival-associated alternative splicing events were identified. The prognostic genes included , , . Functional enrichment analysis suggested that prognostic splicing events are associated with Wnt signaling pathway. A prediction model including 12 alternative splicing events was constructed by Lasso regression using train set. ROC analysis showed good performance of the prediction model in test set. Then, a nomogram integrating the clinical-pathological factors and riskscore was constructed for predicting 1- and 3-year survival rate.
Our data provide a comprehensive bioinformatics analysis of AS events in ACC, providing biomarkers for disease progression and a potentially rich source of novel therapeutic targets.
本研究旨在确定肾上腺皮质癌中替代性 RNA 剪接图谱及其预后价值。
从癌症基因组图谱和 SpliceSeq 包中获取了 79 例 ACC 患者的替代性剪接事件数据及其相应的临床信息数据。使用单因素 Cox 回归分析筛选与预后相关的 AS 事件。通过生存相关 AS 富集的基因功能分析，揭示了潜在的通路。通过 Lasso 回归模型，对预后相关的剪接事件进行了中度预测因子的构建。
鉴定出 1500 个与生存相关的替代性剪接事件。预后相关基因包括、、。功能富集分析表明，预后剪接事件与 Wnt 信号通路相关。通过 Lasso 回归在训练集中构建了一个包含 12 个替代性剪接事件的预测模型。ROC 分析显示，该预测模型在测试集中具有良好的性能。然后，构建了一个包含临床病理因素和风险评分的列线图，用于预测 1 年和 3 年的生存率。
本研究提供了 ACC 中 AS 事件的全面生物信息学分析，为疾病进展提供了生物标志物，并为潜在的新型治疗靶点提供了丰富的来源。
