Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.
Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China.
Brain Behav. 2022 May;12(5):e2575. doi: 10.1002/brb3.2575. Epub 2022 Apr 16.
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.
We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.
We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.
We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.
胶质母细胞瘤（GBM）是成人中最常见的原发性恶性脑肿瘤。对于 GBM 患者，中位总生存期（OS）为 14.6 个月，5 年生存率为 7.2%。开发一种可靠的模型来预测新 GBM 患者的生存概率至关重要。迄今为止，大多数预测 GBM 患者生存的预后模型都是基于批量 RNA-seq 数据集构建的，这些模型未能准确反映肿瘤核心和周围区域之间的差异，因此预测能力较低。在临床实践中迫切需要一种有效的预后模型。
我们研究了单细胞 RNA-seq 数据集和癌症基因组图谱-胶质母细胞瘤（TCGA-GBM）数据集，以确定影响 GBM 患者 OS 的差异表达基因（DEGs）。然后，我们应用最小绝对收缩和选择算子（LASSO）Cox 惩罚回归分析来确定包含在我们风险评分预后模型中的最佳基因。然后，我们使用另一个数据集来测试我们的风险评分预后模型的准确性。
我们从单细胞 RNA-seq 数据集和批量 RNA-seq 数据集分别鉴定了 2128 个 DEGs 和 6461 个 DEGs。此外，还获得了 896 个与 GBM 患者 OS 相关的 DEGs。从这些基因中选择了 5 个基因（LITAF、MTHFD2、NRXN3、OSMR 和 RUFY2）来生成风险评分预后模型。使用训练和验证数据集，我们发现低风险组的患者 OS 明显好于高风险组。我们使用训练和验证数据集验证了我们的风险评分模型，表明它可以有效预测 GBM 患者的 OS。
我们通过整合单细胞 RNA-seq 数据集和批量 RNA-seq 数据集构建了一种新的预测 GBM 患者生存的预后模型。我们的研究结果可能为开发新的治疗靶点和改善 GBM 患者的临床结局提供依据。
