Liu Peidong, Li Yu, Zhang Yiming, Choi John, Zhang Jinhao, Shang Guanjie, Li Bailiang, Lin Ya-Jui, Saleh Laura, Zhang Liang, Yi Li, Yu Shengping, Lim Michael, Yang Xuejun
Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, United States.
Front Oncol. 2022 May 13;12:708272. doi: 10.3389/fonc.2022.708272. eCollection 2022.
Gliomas are the most common primary brain cancer. While it has been known that calcium-related genes correlate with gliomagenesis, the relationship between calcium-related genes and glioma prognosis remains unclear. We assessed TCGA datasets of mRNA expressions with differentially expressed genes (DEGs) and enrichment analysis to specifically screen for genes that regulate or are affected by calcium levels. We then correlated the identified calcium-related genes with unsupervised/supervised learning to classify glioma patients into 2 risk groups. We also correlated our identified genes with immune signatures. As a result, we discovered 460 calcium genes and 35 calcium key genes that were associated with OS. There were 13 DEGs between Clusters 1 and 2 with different OS. At the same time, 10 calcium hub genes (CHGs) signature model were constructed using supervised learning, and the prognostic risk scores of the 3 cohorts of samples were calculated. The risk score was confirmed as an independent predictor of prognosis. Immune enrichment analysis revealed an immunosuppressive tumor microenvironment with upregulation of checkpoint markers in the high-risk group. Finally, a nomogram was generated with risk scores and other clinical prognostic independent indicators to quantify prognosis. Our findings suggest that calcium-related gene expression patterns could be applicable to predict prognosis and predict levels of immunosuppression.
胶质瘤是最常见的原发性脑癌。虽然已知钙相关基因与胶质瘤发生相关,但钙相关基因与胶质瘤预后之间的关系仍不清楚。我们评估了TCGA数据集的mRNA表达,进行差异表达基因(DEG)分析和富集分析,以专门筛选调节或受钙水平影响的基因。然后,我们将鉴定出的钙相关基因与无监督/监督学习相关联,将胶质瘤患者分为2个风险组。我们还将鉴定出的基因与免疫特征相关联。结果,我们发现了460个钙基因和35个与总生存期(OS)相关的钙关键基因。在具有不同总生存期的1组和2组之间有13个差异表达基因。同时,使用监督学习构建了10个钙枢纽基因(CHG)特征模型,并计算了3个样本队列的预后风险评分。风险评分被确认为预后的独立预测因子。免疫富集分析显示高风险组中存在免疫抑制性肿瘤微环境,检查点标志物上调。最后,利用风险评分和其他临床预后独立指标生成列线图以量化预后。我们的研究结果表明,钙相关基因表达模式可用于预测预后和预测免疫抑制水平。