Data from: An eighteen serum cytokine signature for discriminating glioma from normal healthy individuals

Glioblastomas (GBM) are largely incurable as they diffusely infiltrate adjacent brain tissues and are difficult to diagnose at early stages. Biomarkers derived from serum, which can be obtained by minimally invasive procedures, may help in early diagnosis, prognosis and treatment monitoring. To develop a serum cytokine signature, we profiled 48 cytokines in sera derived from normal healthy individuals (n = 26) and different grades of glioma patients (n = 194). We divided the normal and grade IV glioma/GBM serum samples randomly into equal sized training and test sets. In the training set, the Prediction Analysis for Microarrays (PAM) identified a panel of 18 cytokines that could discriminate GBM sera from normal sera with maximum accuracy (95.40%) and minimum error (4.60%). The 18-cytokine signature obtained in the training set discriminated GBM sera from normal sera in the test set as well (accuracy 96.55%; error 3.45%). Interestingly, the 18-cytokine signature also differentiated grade II/Diffuse Astrocytoma (DA) and grade III/Anaplastic Astrocytoma (AA) sera from normal sera very efficiently (DA vs. normal–accuracy 96.00%, error 4.00%; AA vs. normal–accuracy 95.83%, error 4.17%). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using 18 cytokines resulted in the enrichment of two pathways, cytokine-cytokine receptor interaction and JAK-STAT pathways with high significance. Thus our study identified an 18-cytokine signature for distinguishing glioma sera from normal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology.

胶质母细胞瘤（Glioblastomas, GBM）因弥漫性浸润邻近脑组织且早期难以确诊，目前大多无法治愈。通过微创操作即可获取的血清来源生物标志物，有望辅助胶质瘤的早期诊断、预后评估与治疗监测。为构建血清细胞因子特征模型，本研究对26名健康个体及194名不同级别胶质瘤患者的血清样本中的48种细胞因子进行了定量分析。随后将健康对照样本与IV级胶质瘤/GBM血清样本随机均分，构建训练集与测试集。在训练集中，微阵列预测分析（Prediction Analysis for Microarrays, PAM）筛选出由18种细胞因子组成的标志物组，该组可最大程度区分GBM血清与健康对照血清，准确率达95.40%，错误率为4.60%。训练集中得到的18种细胞因子特征模型在测试集中同样表现优异，可有效区分GBM血清与健康对照血清（准确率96.55%，错误率3.45%）。值得注意的是，该18细胞因子特征模型还可高效区分II级弥漫性星形细胞瘤（Diffuse Astrocytoma, DA）、III级间变性星形细胞瘤（Anaplastic Astrocytoma, AA）血清与健康对照血清（DA vs 健康对照：准确率96.00%，错误率4.00%；AA vs 健康对照：准确率95.83%，错误率4.17%）。基于这18种细胞因子的京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析显示，细胞因子-细胞因子受体相互作用通路与JAK-STAT信号通路得到显著富集。综上，本研究筛选出可区分胶质瘤血清与健康个体血清的18细胞因子特征模型，并证实了细胞因子表达差异在胶质瘤生物学中的重要作用。