Table_2_A prognostic estimation model based on mRNA-sequence data for patients with oligodendroglioma.XLSX

BackgroundThe diagnosis of oligodendroglioma based on the latest World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS 5) criteria requires the codeletion of chromosome arms 1p and 19q and isocitrate dehydrogenase gene (IDH) mutation (mut). Previously identified prognostic indicators may not be completely suitable for patients with oligodendroglioma based on the new diagnostic criteria. To find potential prognostic indicators for oligodendroglioma, we analyzed the expression of mRNAs of oligodendrogliomas in Chinese Glioma Genome Atlas (CGGA).
MethodsWe collected 165 CGGA oligodendroglioma mRNA-sequence datasets and divided them into two cohorts. Patients in the two cohorts were further classified into long-survival and short-survival subgroups. The most predictive mRNAs were filtered out of differentially expressed mRNAs (DE mRNAs) between long-survival and short-survival patients in the training cohort by least absolute shrinkage and selection operator (LASSO), and risk scores of patients were calculated. Univariate and multivariate analyses were performed to screen factors associated with survival and establish the prognostic model. qRT-PCR was used to validate the expression differences of mRNAs.
ResultsA total of 88 DE mRNAs were identified between the long-survival and the short-survival groups in the training cohort. Seven RNAs were selected to calculate risk scores. Univariate analysis showed that risk level, age, and primary-or-recurrent status (PRS) type were statistically correlated with survival and were used as factors to establish a prognostic model for patients with oligodendroglioma. The model showed an optimal predictive accuracy with a C-index of 0.912 (95% CI, 0.679–0.981) and harbored a good agreement between the predictions and observations in both training and validation cohorts.
ConclusionWe established a prognostic model based on mRNA-sequence data for patients with oligodendroglioma. The predictive ability of this model was validated in a validation cohort, which demonstrated optimal accuracy. The 7 mRNAs included in the model would help predict the prognosis of patients and guide personalized treatment.

背景：基于最新版《世界卫生组织中枢神经系统肿瘤分类（第5版）》（WHO CNS 5）诊断标准，少突胶质细胞瘤（oligodendroglioma）的确诊需满足1号与19号染色体臂共缺失，以及异柠檬酸脱氢酶基因（isocitrate dehydrogenase gene, IDH）突变（mut）。根据新的诊断标准，此前已确认的预后指标可能并不完全适用于少突胶质细胞瘤患者。为挖掘少突胶质细胞瘤潜在的预后标志物，本研究对中国胶质瘤基因组图谱（Chinese Glioma Genome Atlas, CGGA）数据库中的少突胶质细胞瘤mRNA表达数据进行了分析。
方法：本研究收集了165份CGGA数据库中的少突胶质细胞瘤mRNA测序数据集，并将其划分为两个队列。将两个队列中的患者进一步分为长生存期亚组与短生存期亚组。通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）算法，从训练队列中长、短生存期患者间的差异表达mRNA（differentially expressed mRNAs, DE mRNAs）中筛选出最具预测价值的mRNA，并计算患者的风险评分。采用单因素与多因素分析筛选与生存相关的因素，构建预后模型。通过实时定量逆转录聚合酶链反应（qRT-PCR）验证目标mRNA的表达差异。
结果：训练队列中长、短生存期患者组间共鉴定出88个差异表达mRNA。最终筛选出7个mRNA用于构建风险评分模型。单因素分析显示，风险等级、年龄以及原发/复发状态（primary-or-recurrent status, PRS）类型与患者生存具有统计学相关性，遂以此构建少突胶质细胞瘤患者的预后模型。该模型展现出优异的预测准确性，一致性指数（C-index）为0.912（95%置信区间：0.679~0.981），且在训练队列与验证队列中均实现了预测值与观测值的良好契合。
结论：本研究基于mRNA测序数据构建了少突胶质细胞瘤患者的预后模型，其预测能力在验证队列中得到了验证，展现出最优的预测精度。模型所包含的7个mRNA标志物将有助于预测患者预后，并指导个体化治疗。