Sun Jian-Rong, Kong Chen-Fan, Lou Yan-Ni, Yu Ran, Qu Xiang-Ke, Jia Li-Qun
Graduate School, Beijing University of Chinese Medicine, Beijing, China.
Oncology Department of Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital, Beijing, China.
Front Genet. 2020 Jul 22;11:796. doi: 10.3389/fgene.2020.00796. eCollection 2020.
Alternative splicing (AS) is a molecular event that drives protein diversity through the generation of multiple mRNA isoforms. Growing evidence demonstrates that dysregulation of AS is associated with tumorigenesis. However, an integrated analysis in identifying the AS biomarkers attributed to esophageal carcinoma (ESCA) is largely unexplored.
AS percent-splice-in (PSI) data were obtained from the TCGA SpliceSeq database. Univariate and multivariate Cox regression analysis was successively performed to identify the overall survival (OS)-associated AS events, followed by the construction of AS predictor through different splicing patterns. Then, a nomogram that combines the final AS predictor and clinicopathological characteristics was established. Finally, a splicing regulatory network was created according to the correlation between the AS events and the splicing factors (SF).
We identified a total of 2389 AS events with the potential to be used as prognostic markers that are associated with the OS of ESCA patients. Based on splicing patterns, we then built eight AS predictors that are highly capable in distinguishing high- and low-risk patients, and in predicting ESCA prognosis. Notably, the area under curve (AUC) value for the exon skip (ES) prognostic predictor was shown to reach a score of 0.885, indicating that ES has the highest prediction strength in predicting ESCA prognosis. In addition, a nomogram that comprises the pathological stage and risk group was shown to be highly efficient in predicting the survival possibility of ESCA patients. Lastly, the splicing correlation network analysis revealed the opposite roles of splicing factors (SFs) in ESCA.
In this study, the AS events may provide reliable biomarkers for the prognosis of ESCA. The splicing correlation networks could provide new insights in the identification of potential regulatory mechanisms during the ESCA development.
可变剪接（AS）是一种通过产生多种mRNA异构体来驱动蛋白质多样性的分子事件。越来越多的证据表明，AS失调与肿瘤发生有关。然而，在鉴定食管癌（ESCA）相关的AS生物标志物方面的综合分析在很大程度上尚未得到探索。
从TCGA SpliceSeq数据库中获取AS剪接百分率（PSI）数据。先后进行单变量和多变量Cox回归分析以识别与总生存期（OS）相关的AS事件，随后通过不同的剪接模式构建AS预测模型。然后，建立一个结合最终AS预测模型和临床病理特征的列线图。最后，根据AS事件与剪接因子（SF）之间的相关性创建一个剪接调控网络。
我们共鉴定出2389个可能用作预后标志物的AS事件，这些事件与ESCA患者的OS相关。基于剪接模式，我们构建了八个AS预测模型，它们在区分高风险和低风险患者以及预测ESCA预后方面具有很高的能力。值得注意的是，外显子跳跃（ES）预后预测模型的曲线下面积（AUC）值达到了0.88分，表明ES在预测ESCA预后方面具有最高的预测强度。此外，一个包含病理分期和风险组的列线图在预测ESCA患者的生存可能性方面显示出很高的效率。最后，剪接相关性网络分析揭示了剪接因子（SFs）在ESCA中的相反作用。
在本研究中，AS事件可能为ESCA的预后提供可靠的生物标志物。剪接相关性网络可为ESCA发生发展过程中潜在调控机制的识别提供新的见解。
