Lin Changwei, Yu Bowen, Zhang Mao, Chen Yifei, Li Liang, Zhao Deze
School of Life Sciences, Central South University, Changsha, China.
Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, China.
Front Genet. 2020 Nov 17;11:522831. doi: 10.3389/fgene.2020.522831. eCollection 2020.
Accumulation of evidence has indicated a close relationship between alternative splicing (AS) and gastric cancer (GC), whereas systematic analyses of the differentially expressed AS events (DEAS) between GC and normal tissues are lacking. RNA-Seq data and the corresponding clinical information were downloaded from TCGA GC cohort. The percent spliced-in (PSI) value calculated in the GC tissues and normal tissues was employed to quantify the DEAS. Further, survival-associated DEAS and DEAS signatures were identified by univariate and multivariate cox regression analyses. To evaluate the association between DEAS and patients' clinical features, Kaplan-Meier analysis, receiver operator characteristic (ROC) curve, Cox proportional regression and nomograms incorporating the DEAS signatures were performed. DEAS and their splicing networks were finally analyzed by bioinformatics methods. In addition, we use the method of random grouping to divide the samples into the training group and the test group. The final results of the two groups are consistent. After strict filtering, a total of 44,935 AS events were identified, among which 11,141 DEAS were preliminarily screened from 5032 genes. A total of 454 DEAS was associated with OS, and 872 DEAS were associated with DFS. The final prognostic signatures were constructed from the survival-associated DEAS with an area under the receiver operating characteristic (ROC) curve (AUC) greater than 0.6. Only ES in ABI1 was simultaneously associated with OS and DFS. Finally, we identified the splicing correlation network between the prognostic splicing factors (SF) and DEAS in GC. Our study provided a systematic portrait of survival-associated DEAS in GC and uncovered splicing networks that are valuable in deciphering the underlying mechanisms of AS in GC.
越来越多的证据表明可变剪接（AS）与胃癌（GC）之间存在密切关系，然而，目前缺乏对GC组织与正常组织之间差异表达的可变剪接事件（DEAS）的系统分析。从TCGA GC队列下载了RNA-Seq数据和相应的临床信息。利用在GC组织和正常组织中计算的剪接百分率（PSI）值来量化DEAS。此外，通过单变量和多变量Cox回归分析确定了与生存相关的DEAS和DEAS特征。为了评估DEAS与患者临床特征之间的关联，进行了Kaplan-Meier分析、受试者工作特征（ROC）曲线分析、Cox比例回归分析以及纳入DEAS特征的列线图分析。最后，通过生物信息学方法分析了DEAS及其剪接网络。此外，我们采用随机分组的方法将样本分为训练组和测试组。两组的最终结果一致。经过严格筛选，共鉴定出44935个AS事件，其中从5032个基因中初步筛选出11141个DEAS。共有454个DEAS与总生存期（OS）相关，872个DEAS与无病生存期（DFS）相关。从生存相关的DEAS构建最终的预后特征，其受试者工作特征（ROC）曲线下面积（AUC）大于0.6。只有ABI1中的外显子跳过（ES）同时与OS和DFS相关。最后，我们确定了GC中预后剪接因子（SF）与DEAS之间的剪接相关网络。我们的研究提供了GC中生存相关DEAS的系统概况，并揭示了在解读GC中AS潜在机制方面有价值的剪接网络。
