Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
Department of General Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, Sichuan, China.
Aging (Albany NY). 2021 Oct 8;13(19):23149-23168. doi: 10.18632/aging.203608.
As a key mechanism, alternative splicing (AS) plays a role in the cancer initiation and development. However, in papillary thyroid cancer (PTC), data for the comprehensive AS event profile and its clinical implications are lacking. Herein, a genome-wide AS event profiling using RNA-Seq data and its correlation with matched clinical information was performed using a 389 PTC patient cohort from the project of The Cancer Genome Atlas (TCGA). We identified 1,925 cancer-associated AS events (CASEs) by comparing paired tumors and neighboring healthy tissues. Parent genes with CASEs remarkably enriched in the pathways were linked with carcinogenesis, such as P53, KRAS, IL6-JAK-STAT3, apoptosis, and MYC signaling. The regulatory networks of AS implied an obvious correlation between the expression of splicing factor and CASE. We identified eight CASEs as predictors for overall survival (OS) and disease-free survival (DFS). The established risk score model based on DFS-associated CASEs successfully predicted the prognosis of PTC patients. From the unsupervised clustering analysis results, it is found that different clusters based on AS correlated with prognosis, molecular features, and immune characteristics. Taken together, the comprehensive genome-wide AS landscape analysis in PTC showed new AS events linked with tumorigenesis and prognosis, which provide new insights for clinical monitoring and therapy for PTC.
作为一个关键机制，可变剪接（AS）在癌症的发生和发展中发挥作用。然而，在甲状腺乳头状癌（PTC）中，缺乏全面的 AS 事件谱及其临床意义的数据。在此，我们使用 RNA-Seq 数据对来自癌症基因组图谱（TCGA）项目的 389 名 PTC 患者队列进行了全基因组 AS 事件分析，并将其与匹配的临床信息进行了关联。通过比较配对的肿瘤和邻近的健康组织，我们确定了 1925 个与癌症相关的 AS 事件（CASEs）。具有 CASEs 的亲本基因在途径中显著富集，与致癌作用有关，如 P53、KRAS、IL6-JAK-STAT3、细胞凋亡和 MYC 信号通路。AS 的调控网络暗示了剪接因子表达与 CASE 之间的明显相关性。我们确定了 8 个 CASEs 作为总生存（OS）和无病生存（DFS）的预测因子。基于与 DFS 相关的 CASEs 建立的风险评分模型成功预测了 PTC 患者的预后。从无监督聚类分析结果中发现，基于 AS 的不同聚类与预后、分子特征和免疫特征相关。综上所述，PTC 的全基因组 AS 景观分析显示了与肿瘤发生和预后相关的新 AS 事件，为 PTC 的临床监测和治疗提供了新的见解。
