Development of an integrated predictive model for postoperative glioma-related epilepsy using gene-signature and clinical data

BMC Cancer

Research

Chong Qi¹  Yuhao Guo¹  Lianwang Li²  Zheng Wang³  Chuanbao Zhang³  Zhong Zhang³  Yinyan Wang³  Gan You³  Xing Fan⁴  Tao Jiang⁵ 

[1] Beijing Neurosurgical Institute, Capital Medical University, 100070, Beijing, China;Department of Neuro-Oncology and Neurosurgery, Tianjin Medical University Cancer Institute and Hospital, 300060, Tianjin, China;Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China;Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China;Beijing Neurosurgical Institute, Capital Medical University, 100070, Beijing, China;Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China;Beijing Neurosurgical Institute, Capital Medical University, 100070, Beijing, China;Research Units of Accurate Diagnosis and Treatment of Brain Tumors and Translational Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China;

关键词: Glioma-related epilepsy;    Diffuse high-grade gliomas;    Gene-signature;    Clinical data;    Integrated prediction model;   

DOI  :  10.1186/s12885-022-10385-x

 received in 2022-07-17, accepted in 2022-11-30,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThis study aimed to develop an integrated model for predicting the occurrence of postoperative seizures in patients with diffuse high-grade gliomas (DHGGs) using clinical and RNA-seq data.MethodsPatients with DHGGs, who received prophylactic anti-epileptic drugs (AEDs) for three months following surgery, were enrolled into the study. The patients were assigned randomly into training (n = 166) and validation (n = 42) cohorts. Differentially expressed genes (DEGs) were identified based on preoperative glioma-related epilepsy (GRE) history. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to construct a predictive gene-signature for the occurrence of postoperative seizures. The final integrated prediction model was generated using the gene-signature and clinical data. Receiver operating characteristic analysis and calibration curve method were used to evaluate the accuracy of the gene-signature and prediction model using the training and validation cohorts.ResultsA seven-gene signature for predicting the occurrence of postoperative seizures was developed using LASSO logistic regression analysis of 623 DEGs. The gene-signature showed satisfactory predictive capacity in the training cohort [area under the curve (AUC) = 0.842] and validation cohort (AUC = 0.751). The final integrated prediction model included age, temporal lobe involvement, preoperative GRE history, and gene-signature-derived risk score. The AUCs of the integrated prediction model were 0.878 and 0.845 for the training and validation cohorts, respectively.ConclusionWe developed an integrated prediction model for the occurrence of postoperative seizures in patients with DHGG using clinical and RNA-Seq data. The findings of this study may contribute to the development of personalized management strategies for patients with DHGGs and improve our understanding of the mechanisms underlying GRE in these patients.

【 授权许可】

CC BY   
© The Author(s) 2022

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