Xu Wenhao, Anwaier Aihetaimujiang, Liu Wangrui, Tian Xi, Zhu Wen-Kai, Wang Jian, Qu Yuanyuan, Zhang Hailiang, Ye Dingwei
Department of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032 People's Republic of China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People's Republic of China.
Phenomics. 2021 Oct 29;1(6):243-256. doi: 10.1007/s43657-021-00026-x. eCollection 2021 Dec.
Alternative splicing (AS) in the tumor biological process has provided a novel perspective on carcinogenesis. However, the clinical significance of individual AS patterns of adrenocortical carcinoma (ACC) has been underestimated, and in-depth investigations are lacking. We selected 76 ACC samples from the Cancer Genome Atlas (TCGA) SpliceSeq and SpliceAid2 databases, and 39 ACC samples from Fudan University Shanghai Cancer Center (FUSCC). Prognosis-related AS events (PASEs) and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm. In total, 23,984 AS events and 3,614 PASEs were detected in the patients with ACC. The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients ( < 0.001). Prognostic models produced AUC values of 0.907 in all PASEs' groups. Eight splicing factors (SFs), including and , were identified in regulatory networks of ACC. was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts. In conclusion, the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts. Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS, providing valuable insights into the potential usages of for predicting prognosis for patients with ACC.
The online version contains supplementary material available at 10.1007/s43657-021-00026-x.
肿瘤生物学过程中的可变剪接(AS)为癌症发生提供了新视角。然而,肾上腺皮质癌(ACC)个体AS模式的临床意义被低估,且缺乏深入研究。我们从癌症基因组图谱(TCGA)的SpliceSeq和SpliceAid2数据库中选取了76例ACC样本,以及复旦大学附属上海肿瘤医院(FUSCC)的39例ACC样本。基于机器学习算法构建的预测模型评估了预后相关AS事件(PASEs)并进行生存分析。在ACC患者中总共检测到23,984个AS事件和3,614个PASEs。每位患者的预测风险评分表明,八个PASEs组与这些患者的临床结局显著相关(<0.001)。预后模型在所有PASEs组中的AUC值为0.907。在ACC的调控网络中鉴定出八个剪接因子(SFs),包括……和……。在来自TCGA和FUSCC队列的115例ACC患者中,……被鉴定并验证为一种新的临床促进因子和治疗靶点。总之,本研究中使用的严格标准确保了利用全基因组队列系统地发现AS事件谱。我们的发现有助于全面了解AS的格局和潜在机制,为……在预测ACC患者预后方面的潜在用途提供有价值的见解。
在线版本包含可在10.1007/s43657-021-00026-x获取的补充材料。