AUTHOR=Zhu Qinghui , Shen Shaoping , Yang Chuanwei , Li Mingxiao , Zhang Xiaokang , Li Haoyi , Zhao Xuzhe , Li Ming , Cui Yong , Ren Xiaohui , Lin Song 

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

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1074593

DOI=10.3389/fneur.2022.1074593

ISSN=1664-2295

ABSTRACT=Background:The diagnosis of oligodendroglioma based on the latest 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 oligendendroglioma based on the new diagnostic criteria. To find potential prognositic indicators for oligodendroglioma, we analyzed the expression of mRNAs of oligodendrogliomas in Chinese Glioma Genome Atlas (CGGA).
Methods: We collected 165 CGGA oligodendroglioma mRNA sequence datasets and devided them into 2 cohorts. Patients in the 2 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 training cohort by 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 were used to validate the expression differences of mRNAs.
Results: Eighty-eight DE mRNAs were identified between the long survival and the short survival groups in training cohort. Seven RNAs were selected to calculate risk scores. Univariate analysis showed 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 to 0.981) and harbored a good agreement between the predictions and observations in both training and validation cohorts.
Conclusion: 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 an optimal accuracy. The 7 mRNAs included in the model would be helpful in predicting the prognosis of patients and guiding personalized treatment.