School Of Medicine, Guizhou University, Guiyang, Guizhou, China.
Department of Oral and Maxillofacial Surgery, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
J Cell Physiol. 2019 Dec;234(12):22753-22764. doi: 10.1002/jcp.28840. Epub 2019 May 29.
There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival-related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein-protein interaction analysis was conducted for the prognostic AS-related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival-specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival-specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <-0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.
越来越多的证据表明，可变剪接（AS）在癌症发展中起着重要作用。然而，目前缺乏对肾透明细胞癌（KIRC）中 AS 特征的全面分析，这是非常有必要的。目前尚不清楚 AS 是否可以作为诊断生物标志物来预测 KIRC 患者的预后。在这项工作中，从癌症基因组图谱（TCGA）中获取了 KIRC 的基因表达和临床数据，并从 SpliceSeq 数据库中下载了 AS 事件的图谱。整合了 RNA 序列/AS 数据和临床信息，我们通过 Cox 回归分析筛选与生存相关的 AS 事件和信使 RNA（mRNA）。使用 Pearson 相关系数分析了预后 AS 事件与基因表达之间的相关性。对预后 AS 相关基因进行蛋白质-蛋白质相互作用分析，并使用 Cytoscape（版本 3.6.1）构建潜在的调控网络。同时，进行了功能富集分析。然后，基于在 KIRC 患者风险分类中表现出高性能的七个关键基因（KRT222、LENG8、APOB、SLC3A1、SCD5、AQP1 和 ADRA1A）建立了一个预后风险评分模型。在 537 名 KIRC 患者中，共鉴定出 46415 个包括 10601 个基因的 AS 事件。在单变量 Cox 回归分析中，检测到 13362 个与生存相关的 AS 事件和 8694 个与生存特异性 mRNAs。通过重叠 13362 个与生存相关的 AS 事件和 8694 个与生存特异性 mRNAs，筛选出 1305 个常见基因。通过 Pearson 相关分析，发现 13 个基因与 AS 事件显著相关（Pearson 相关系数>0.8 或<-0.8）。然后，我们进行了多变量 Cox 回归分析，以选择潜在的预后 AS 基因。确定了七个与 OS 显著相关的基因。基于七个基因构建了一个预后模型。ROC 曲线下面积为 0.767。在本研究中，为 KIRC 患者构建了一个稳健的预后预测模型，研究结果表明，AS 事件可以作为 KIRC 的潜在预后生物标志物。
