A prognostic estimation model based on mRNA-sequence data for patients with oligodendroglioma

• Published in: Frontiers in neurology 2022-12, Vol.13, p.1074593-1074593
• Main Authors: Zhu, Qinghui, Shen, Shaoping, Yang, Chuanwei, Li, Mingxiao, Zhang, Xiaokang, Li, Haoyi, Zhao, Xuzhe, Li, Ming, Cui, Yong, Ren, Xiaohui, Lin, Song
• Format: Article
• Language: English
• Subjects: 1p/19q codeletion; mRNA-sequence; Neurology; oligodendroglioma; prognostic model; WHO CNS 5
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2022.1074593

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.