Qian Chen, Xiufu Wu, Jianxun Tang, Zihao Chen, Wenjie Shi, Jingfeng Tang, Kahlert Ulf D, Renfei Du
Department of Cerebrovascular Diseases, The Second Affiliated Hospital of Guilin Medical University, Guilin, China.
University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany.
Front Genet. 2022 Jun 17;13:851427. doi: 10.3389/fgene.2022.851427. eCollection 2022.
Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes. The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis. Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high- and low-ECM index groups with significantly different overall survival rates in four datasets. High-ECM index patients exhibited a worse prognosis than low-ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/). In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients.
胶质母细胞瘤（GBM）是最常见的脑肿瘤类型之一，其预后极差。细胞外基质（ECM）基因可激活许多促进肿瘤发展的关键通路。本研究旨在提供基于ECM基因预测GBM患者生存情况的在线模型。分析了ECM基因与GBM预后的关联，并在CGGA数据集中使用显著的预后相关基因构建ECM指数。此外，该ECM指数随后在三个数据集（即GSE16011、TCGA - GBM和GSE83300）上进行了验证。得出了预后差异、差异表达基因及潜在药物。选择多种机器学习方法构建模型，以预测GBM患者在诊断后6、12、18、24、30和36个月的生存状态。识别出五个ECM基因特征（AEBP1、F3、FLNC、IGFBP2和LDHA）与预后相关。在四个数据集中，GBM患者被分为高ECM指数组和低ECM指数组，其总生存率有显著差异。高ECM指数患者的预后比低ECM指数患者更差。通过Cmap数据集预测了四种可能抑制GBM发展的小分子（鬼臼毒素、拉沙洛西、MG - 262和制霉菌素）。在独立数据集（GSE83300）中，6、12、18、24、30和36个月时预测准确率的最大值分别为0.878、0.769、0.748、0.720、0.705和0.868。这些机器学习模型可在一个公开访问的开源网站（https://ospg.shinyapps.io/OSPG/）上获取。总之，我们的研究结果表明，ECM基因是患者生存的预后指标。本研究提供了一个用于预测GBM患者生存曲线的在线服务器。
