Department of Neurosurgery, Pu'er People's Hospital, Pu'er, China.
Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Medicine (Baltimore). 2024 Oct 4;103(40):e39205. doi: 10.1097/MD.0000000000039205.
Glioblastoma (GBM) is a common primary malignant brain tumor and the prognosis of these patients remains poor. Therefore, further understanding of cell cycle-related molecular mechanisms of GBM and identification of appropriate prognostic markers and therapeutic targets are key research imperatives. Based on RNA-seq expression datasets from The Cancer Genome Atlas database, prognosis-related biological processes in GBM were screened out. Gene Set Variation Analysis (GSVA), LASSO-COX, univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis, and Pearson correlation analysis were performed for constructing a predictive prognostic model. A total of 58 cell cycle-related genes were identified by GSVA and analysis of differential expression between GBM and control samples. By univariate Cox and LASSO regression analyses, 8 genes were identified as prognostic biomarkers in GBM. A nomogram with superior performance to predict the survival of GBM patients was established regarding risk score, cancer status, recurrence type, and mRNAsi. This study revealed the prognostic value of cell cycle-related genes in GBM. In addition, we constructed a reliable model for predicting the prognosis of GBM patients. Our findings reinforce the relationship between cell cycle and GBM and may help improve the prognostic assessment of patients with GBM. Our predictive prognostic model, based on independent prognostic factors, enables tailored treatment strategies for GBM patients. It is particularly useful for subgroups with uncertain prognosis or treatment challenges.
胶质母细胞瘤（GBM）是一种常见的原发性恶性脑肿瘤，这些患者的预后仍然较差。因此，进一步了解 GBM 与细胞周期相关的分子机制，并确定合适的预后标志物和治疗靶点是关键的研究重点。本研究基于癌症基因组图谱数据库中的 RNA-seq 表达数据集，筛选出与 GBM 预后相关的生物学过程。通过基因集变异分析（GSVA）、LASSO-COX、单因素和多因素 Cox 回归分析、Kaplan-Meier 生存分析和 Pearson 相关性分析，构建了预测预后模型。通过 GSVA 和 GBM 与对照样本之间的差异表达分析，共鉴定出 58 个与细胞周期相关的基因。通过单因素 Cox 和 LASSO 回归分析，鉴定出 8 个与 GBM 预后相关的基因。根据风险评分、癌症状态、复发类型和 mRNAsi 构建了一个预测 GBM 患者生存的列线图，具有优越的性能。本研究揭示了细胞周期相关基因在 GBM 中的预后价值。此外，我们构建了一个可靠的模型来预测 GBM 患者的预后。我们的研究结果证实了细胞周期与 GBM 之间的关系，并可能有助于改善 GBM 患者的预后评估。我们的预测预后模型基于独立的预后因素，可以为 GBM 患者制定个体化的治疗策略。对于预后不确定或治疗有挑战的亚组患者尤其有用。
