Integrating machine learning and retrospective cohort to explore the effects of AEDs on the prognosis of LGG and identify related molecular targets
Ming Zhou, Qinhong Huang, Hui Liang, Hanjie Yang, Min Li · 2026
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Description
Ming Zhou leads a five-author team with a 2026 study of whether the choice of anti-seizure drug changes survival in low-grade glioma, where epilepsy strikes most patients. The team combined lenses: machine learning over 105 targets of ten anti-seizure drugs, molecular docking, a past-records patient cohort, and cell experiments. A thirteen-gene signature predicted prognosis. One sodium channel gene, SCN9A, emerged as a hazard factor, while two glutamate receptor genes, GRIN2C and GRIN3A, acted as protectors.
Three drugs, carbamazepine, oxcarbazepine, and lacosamide, were predicted to extend survival by blocking the hazard gene's product. Phenobarbital pointed the other way, shortening survival by suppressing the two protective genes. The cohort check supported those directions, and bench tests agreed: blocking SCN9A curbed malignant cell behaviour, while suppressing the protective pair enhanced it. The authors offer the work as guidance for drug choice.
Catalog record
| CREATOR | Ming Zhou, Qinhong Huang, Hui Liang, Hanjie Yang, Min Li |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44520 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |