Zhu Qinghui, Shen Shaoping, Yang Chuanwei, Li Mingxiao, Zhang Xiaokang, Li Haoyi, Zhao Xuzhe, Li Ming, Cui Yong, Ren Xiaohui, Lin Song
Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Front Neurol. 2022 Dec 14;13:1074593. doi: 10.3389/fneur.2022.1074593. eCollection 2022.
The 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).
We 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.
A 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.
We 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.
根据世界卫生组织中枢神经系统肿瘤分类（WHO CNS 5）最新标准诊断少突胶质细胞瘤，需要1号染色体短臂和19号染色体长臂共缺失以及异柠檬酸脱氢酶基因（IDH）突变。先前确定的预后指标可能不完全适用于基于新诊断标准的少突胶质细胞瘤患者。为了寻找少突胶质细胞瘤的潜在预后指标，我们分析了中国胶质瘤基因组图谱（CGGA）中少突胶质细胞瘤的mRNA表达。
我们收集了165个CGGA少突胶质细胞瘤mRNA序列数据集，并将它们分为两个队列。两个队列中的患者进一步分为长生存和短生存亚组。通过最小绝对收缩和选择算子（LASSO）从训练队列中长生存和短生存患者之间的差异表达mRNA（DE mRNA）中筛选出最具预测性的mRNA，并计算患者的风险评分。进行单因素和多因素分析以筛选与生存相关的因素并建立预后模型。采用qRT-PCR验证mRNA的表达差异。
在训练队列的长生存组和短生存组之间共鉴定出88个DE mRNA。选择了7个RNA来计算风险评分。单因素分析表明，风险水平、年龄和原发或复发状态（PRS）类型与生存具有统计学相关性，并被用作建立少突胶质细胞瘤患者预后模型的因素。该模型显示出最佳预测准确性，C指数为0.912（95%CI，0.679-0.981），并且在训练和验证队列中的预测与观察结果之间具有良好的一致性。
我们基于mRNA序列数据为少突胶质细胞瘤患者建立了一个预后模型。该模型的预测能力在验证队列中得到验证，显示出最佳准确性。该模型中包含的7个mRNA将有助于预测患者的预后并指导个性化治疗。
