Shao Xiang-Yang, Dong Jin, Zhang Han, Wu Ying-Song, Zheng Lei
Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Nanfang Hospital, Southern Medical University, Guangzhou, China.
Front Genet. 2020 Jul 10;11:726. doi: 10.3389/fgene.2020.00726. eCollection 2020.
Increasing evidence suggests that aberrant alternative splicing (AS) events are associated with progression of cancer. This study evaluated the prognostic value and clarify the role of AS events in cervical cancer (CC).
Based on RNA-seq AS event data and clinical information of CC patients in The Cancer Genome Atlas (TCGA) database, we sought to identify prognosis-related AS events in this setting. We selected several survival-associated AS events to construct a prognostic predictor for CC through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Moreover, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed on genes with prognosis-related AS events and constructed an AS-splicing factors (SFs) regulatory network.
2770 AS events were significantly correlated with overall survival (OS). The area under the curve (AUC) values of receiver-operator characteristic curve (ROC) for the final prognostic predictor were 0.926, 0.946 and 0.902 at 3, 5, and 10 years, respectively. These values indicated efficiency in prognostic risk stratification for patients with CC. The final prognostic predictor was an independent predictor of OS (HR: 1.24; 95% CI: 1.020-1.504; < 0.05). The AS-SFs correlation network may reveal an underlying regulatory mechanism of AS events.
AS events are essential participants in the prognosis of CC and hold great potentials for the prognostic stratification and development of treatment strategy.
越来越多的证据表明,异常可变剪接(AS)事件与癌症进展相关。本研究评估了AS事件在宫颈癌(CC)中的预后价值并阐明其作用。
基于癌症基因组图谱(TCGA)数据库中CC患者的RNA测序AS事件数据和临床信息,我们试图在这种情况下识别与预后相关的AS事件。我们选择了几个与生存相关的AS事件,通过最小绝对收缩和选择算子(LASSO)和多变量Cox回归构建CC的预后预测模型。此外,对具有预后相关AS事件的基因进行京都基因与基因组百科全书(KEGG)和基因本体(GO)分析,并构建AS剪接因子(SFs)调控网络。
2770个AS事件与总生存期(OS)显著相关。最终预后预测模型的受试者工作特征曲线(ROC)的曲线下面积(AUC)值在3年、5年和10年时分别为0.926、0.946和0.902。这些值表明该模型在CC患者预后风险分层方面具有有效性。最终预后预测模型是OS的独立预测因子(HR:1.24;95%CI:1.020-1.504;P<0.05)。AS-SFs相关网络可能揭示了AS事件的潜在调控机制。
AS事件是CC预后的重要参与者,在预后分层和治疗策略制定方面具有巨大潜力。