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Development and validation of an individual alternative splicing prognostic signature in gastric cancer

机译:胃癌中个体替代剪接预后签名的开发与验证

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摘要

Gastric cancer (GC) is a heterogeneous disease with different clinical manifestations and prognoses. Alternative splicing (AS) is a determinant of gene expression and contributes to protein diversity from a rather limited gene transcript in metazoans. AS events are associated with different aspects of cancer biology, including cell proliferation, apoptosis, invasion, etc. Here, we present a comprehensive analysis of the prognostic AS profile in GC. GC-specific AS (GCAS) events were analyzed, and overall survival-associated GCAS (OS-GCAS) events were verified among the genome-wide AS events identified in The Cancer Genome Atlas (TCGA) database. In total, 1,287 GCAS events of 837 genes and 173 OS-GCAS events of 130 genes were identified. The parental genes of OS-GCAS events were significantly enriched in the development of GC. Protein-protein interaction (PPI) and OS-GCAS-associated splicing factor (SF) interaction networks were constructed. Multivariate Cox regression analysis with least absolute shrinkage and selection operator (LASSO) penalty was performed to establish a prognostic risk formula, representing 23 OS-GCAS events. The low-risk group had better OS than the high-risk group and lower immune and stromal scores. Cox proportional hazard regression was applied to generate an AS-clinical integrated prognostic model with a considerable area under the curve (AUC) value in both the training and validation datasets. Our study provides a profile of OS-GCAS events and an AS-clinical nomogram to predict the prognosis of GC.
机译:胃癌(GC)是一种异质疾病,具有不同的临床表现和预测。替代剪接(AS)是基因表达的决定因素,并导致蛋白质的多样性来自美唑烷的相当有限的基因转录物。由于事件与癌症生物学的不同方面有关,包括细胞增殖,细胞凋亡,侵袭等,我们在GC中对预后作为外形的综合分析。分析了GC特异性的(GCAS)事件,并且在基因组Atlas(TCGA)数据库中鉴定的基因组中核实了整体存活相关的GCAS(OS-GCAS)事件。总共有1,287个GCAS事件837基因和173个OS-GCAS事件的130个基因。在GC的发展中,OS-GCAS事件的父母基因显着富集。构建蛋白质 - 蛋白质相互作用(PPI)和OS-GCAS相关剪接因子(SF)相互作用网络。具有最小绝对收缩和选择操作员(套索)惩罚的多变量COX回归分析,以建立预后风险公式,代表23个OS-GCAS事件。低风险组具有比高风险群体更好的卫生组,较低的免疫和基质分数。 Cox比例危害回归应用于在训练和验证数据集中的曲线(AUC)值下具有相当大区域的临床综合预后模型。我们的研究提供了OS-GCAS事件和临床载体的简介,以预测GC的预后。

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