Table5_Comprehensive Analysis of Splicing Factor and Alternative Splicing Event to Construct Subtype-Specific Prognosis-Predicting Models for Breast Cancer.XLSX

Recent evidence suggests that splicing factors (SFs) and alternative splicing (AS) play important roles in cancer progression. We constructed four SF-risk-models using 12 survival-related SFs. In Luminal-A, Luminal-B, Her-2, and Basal-Like BRCA, SF-risk-models for three genes (PAXBP1, NKAP, and NCBP2), four genes (RBM15B, PNN, ACIN1, and SRSF8), three genes (LSM3, SNRNP200, and SNU13), and three genes (SRPK3, PUF60, and PNN) were constructed. These models have a promising prognosis-predicting power. The co-expression and protein-protein interaction analysis suggest that the 12 SFs are highly functional-connected. Pathway analysis and gene set enrichment analysis suggests that the functional role of the selected 12 SFs is highly context-dependent among different BRCA subtypes. We further constructed four AS-risk-models with good prognosis predicting ability in four BRCA subtypes by integrating the four SF-risk-models and 21 survival-related AS-events. This study proposed that SFs and ASs were potential multidimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

最新研究证据表明，剪接因子（splicing factors, SFs）与可变剪接（alternative splicing, AS）在癌症进展过程中发挥关键调控作用。本研究基于12个与患者生存相关的剪接因子，构建了4种剪接因子风险模型（SF-risk-models）。在管腔A型（Luminal-A）、管腔B型（Luminal-B）、HER-2过表达型（Her-2）以及基底样型乳腺癌（BRCA）这四种乳腺癌分子亚型中，我们分别构建了基于3个基因（PAXBP1、NKAP与NCBP2）、4个基因（RBM15B、PNN、ACIN1与SRSF8）、3个基因（LSM3、SNRNP200与SNU13）以及3个基因（SRPK3、PUF60与PNN）的剪接因子风险模型。上述模型均展现出优异的预后预测效能。共表达分析与蛋白质相互作用分析结果显示，这12个剪接因子之间存在高度紧密的功能关联网络。通路分析与基因集富集分析（gene set enrichment analysis）结果表明，所筛选得到的12个剪接因子的功能作用在不同乳腺癌亚型中呈现出显著的情境依赖性。本研究进一步整合上述4种剪接因子风险模型与21个与生存相关的可变剪接事件，在四种乳腺癌亚型中构建了4种可变剪接风险模型（AS-risk-models），该类模型同样具备良好的预后预测能力。本研究提出，剪接因子与可变剪接可作为乳腺癌（BRCA）诊断、预后评估与临床治疗的潜在多维生物标志物。