DataSheet_1_The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.zip

Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM were applied for validating clustering results. Somatic mutation and copy number variation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of risk scores. Monocytes were associated with glioma patients’ survival and exhibited high predictive value. The prognostic value of risk score in glioma was validated by the abundant expression of immune checkpoint and metabolic profile. Additionally, high risk score was positively associated with the expression of immunogenic and antigen presenting factors, which indicated high immune infiltration. A prognostic model based on risk score demonstrated high accuracy rate of receiver operating characteristic curves. Compared with previous studies, our research dissected functional roles of monocytes from large-scale analysis. Findings of our analyses strongly support an immune modulatory and prognostic role of monocytes in glioma progression. Notably, monocyte could be an effective predictor for therapy responses of glioma patients.

胶质瘤系原发性脑部恶性肿瘤。已证实单核细胞在肿瘤生长过程中发挥着积极作用。本研究采用加权基因共表达网络分析，旨在识别与单核细胞相关的基因并进行聚类分析。通过神经网络和SVM技术对聚类结果进行验证。利用体细胞突变和拷贝数变异来定义识别出的簇的特征。通过弹性回归和主成分分析对分层组进行差异表达基因（DEGs）分析，以此构建风险评分。单核细胞与胶质瘤患者的生存状况密切相关，并展现出高度的预测价值。通过验证免疫检查点和代谢谱的丰富表达，胶质瘤风险评分的预后价值得到证实。此外，高风险评分与免疫原性和抗原呈递因子的表达呈正相关，这表明了高免疫浸润。基于风险评分的预后模型展现了高接收者操作特征曲线的准确率。与先前研究相比，本研究从大规模分析中剖析了单核细胞的功能作用。我们分析的结果强烈支持单核细胞在胶质瘤进展中具有免疫调节和预后作用的观点。值得注意的是，单核细胞可以作为胶质瘤患者治疗反应的有效预测指标。