Machine learning-assisted prediction of PANoptosis-related molecular targets and precise screening of neuroprotective drugs for spinal cord injury.

Spinal cord injury (SCI) is a highly disabling central nervous system disease with complex pathology, and targeted neuroprotective drugs remain clinically lacking. However, traditional molecular target screening and drug prediction methods are inefficient, costly, and poorly targeted, failing to meet clinical precision treatment needs. To address this, we introduced machine learning to construct a multi-dimensional data integration framework. First, we established normal, acute- and subacute-phase SCI mouse complete transection models, and RNA-seq combined with single-cell sequencing revealed acute-phase may occur extensive neuronal PANoptosis. Using WGCNA and MCC algorithms, 25 candidate genes for extensive neuronal PANoptosis in the acute phase were screened out. Then, we comprehensively applied machine learning algorithms including Elastic Net-GLM, Random Forest, Support Vector Machine, and LASSO to predict and prioritize potential molecular targets, identifying 13 possible core genes for extensive neuronal PANoptosis, including Tacc3, Aurka, Mcm6, Mcm5, Ripk1, etc. With the help of the Connectivity Map, drug prediction was performed on these 13 genes, and the 8 candidate drugs with neuroprotective effects were screened out. Through protein domain screening, it was verified via proof-by-contradiction assays that the drug Xaliproden can establish robust interactions with the 7XMK, 7FCZ and 7FD0 domains of Ripk1, a core molecule of the PANoptosome, via a network of multiple hydrogen bonds. This finding provides a novel screening strategy for neuroprotective drugs for spinal cord injury and is of great significance for promoting the establishment of a precision treatment system for the acute phase of injury.