Zhao Liang, Zhang Jiayue, Liu Zhiyuan, Wang Yu, Xuan Shurui, Zhao Peng
Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Front Oncol. 2021 Jan 26;10:555632. doi: 10.3389/fonc.2020.555632. eCollection 2020.
Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.
据广泛报道，前体mRNA的可变剪接（AS）与恶性肿瘤的进展有关。然而，迫切需要对胶质母细胞瘤（GBM）中AS事件的预后价值进行系统研究。GBM患者的基因表达谱和匹配的AS事件数据分别从癌症基因组图谱计划（TCGA）和TCGA SpliceSeq数据库中获得。使用单变量Cox回归分析将775个AS事件确定为预后因素。采用最小绝对收缩和选择算子（LASSO）Cox模型缩小候选AS事件范围，随后建立基于多个AS事件的风险评分模型。基于风险评分的特征被证明是总生存期的有效预测指标，并且与GBM中的肿瘤纯度和免疫抑制密切相关。联合相似性网络融合和一致性聚类（SNF-CC）分析基于预后AS事件揭示了两种不同的GBM亚型，并进一步探索了这种新的分子分类与临床病理因素、免疫细胞浸润以及免疫原性特征之间的关联。我们还构建了一个调控网络来描述预后剪接因子（SFs）如何调节GBM中的剪接模式的潜在机制。最后，构建了一个包含AS事件特征和其他临床相关协变量的列线图用于临床应用。这项综合分析突出了其在预测GBM预后和临床管理方面的潜在意义。
