CpG methylation signature defines human temporal lobe epilepsy and predicts drug-resistant

被引:15
作者
Xiao, Wenbiao [1 ]
Liu, Chaorong [1 ]
Zhong, Kuo [2 ]
Ning, Shangwei [3 ]
Hou, Rui [4 ]
Deng, Na [1 ]
Xu, Yuchen [1 ]
Luo, Zhaohui [1 ]
Fu, Yujiao [1 ]
Zeng, Yi [5 ]
Xiao, Bo [1 ]
Long, Hongyu [1 ]
Long, Lili [1 ]
机构
[1] Cent South Univ, Xiangya Hosp, Dept Neurol, Changsha 410008, Hunan, Peoples R China
[2] Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Shenzhen, Peoples R China
[3] Harbin Med Univ, Coll Bioinformat Sci & Technol, Harbin, Peoples R China
[4] Shanghai Biotechnol Corp, Shanghai, Peoples R China
[5] Cent South Univ, Xiangya Hosp 2, Dept Geriatr, Changsha, Peoples R China
基金
中国国家自然科学基金;
关键词
biomarker; DNA methylation; machine learning; nomogram; temporal lobe epilepsy; DNA METHYLATION; HIPPOCAMPAL SCLEROSIS; PROMOTER METHYLATION; GENES; EPIGENETICS; MECHANISMS; HYPOTHESIS; BIOMARKERS; MANAGEMENT; BLOOD;
D O I
10.1111/cns.13394
中图分类号
Q189 [神经科学];
学科分类号
071006 [神经生物学];
摘要
Aims Temporal lobe epilepsy (TLE) is the most common focal epilepsy syndrome in adults and frequently develops drug resistance. Studies have investigated the value of peripheral DNA methylation signature as molecular biomarker for diagnosis or prognosis. We aimed to explore methylation biomarkers for TLE diagnosis and pharmacoresistance prediction. Methods We initially conducted genome-wide DNA methylation profiling in TLE patients, and then selected candidate CpGs in training cohort and validated in another independent cohort by employing machine learning algorithms. Furthermore, nomogram comprising DNA methylation and clinicopathological data was generated to predict the drug response in the entire patient cohort. Lastly, bioinformatics analysis for CpG-associated genes was performed using Ingenuity Pathway Analysis. Results After screening and validation, eight CpGs were identified for diagnostic biomarker with an area under the curve (AUC) of 0.81 and six CpGs for drug-resistant prediction biomarker with an AUC of 0.79. The nomogram for drug-resistant prediction comprised methylation risk score, disease course, seizure frequency, and hippocampal sclerosis, with AUC as high as 0.96. Bioinformatics analysis indicated drug response-related CpGs corresponding genes closely related to DNA methylation. Conclusions This study demonstrates the ability to use peripheral DNA methylation signature as molecular biomarker for epilepsy diagnosis and drug-resistant prediction.
引用
收藏
页码:1021 / 1030
页数:10
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