Changmeng Cui,1 Biao Xu,1 Hui Liu,2 Changshui Wang,1 Tao Zhang,2 Pei Jiang,2 Lei Feng3 

1Department of Neurosurgery, Affiliated Hospital of Jining Medical University, Jining, Shandong, 272000, People’s Republic of China; 2Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, Shandong, 272000, People’s Republic of China; 3Department of Neurosurgery, Jining First People’s Hospital, Shandong First Medical University, Jining, Shandong, 272000, People’s Republic of China

Correspondence: Lei Feng, Department of Neurosurgery, Jining First People’s Hospital, Shandong First Medical University, No. 6 Jiankang Road, Jining City, Shandong Province, 272000, People’s Republic of China, Tel +86-0537-2337201, Email flneuro@163.com Pei Jiang, Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, No. 6 Jiankang Road, Jining City, Shandong Province, 272000, People’s Republic of China, Tel +86-0537-2337200, Email jiangpeicsu@sina.com

Background: Traumatic brain injury (TBI) is associated with disturbances in energy metabolism. This study aimed to construct a lncRNA-miRNA-mRNA network through bioinformatics methods to explore energy metabolism-related genes in the pathogenesis of TBI.
Methods: Data from datasets GSE171718, GSE131695, and GSE223245 obtained from the Gene Expression Omnibus, were analyzed to identify differentially expressed (DE) genes. Regulatory relationships were investigated through miRDB, miRTarBase, and TargetScan, thereby forming a lncRNA-miRNA-mRNA network. The Molecular Signatures Database (MSigDB) was utilized to identify energy metabolism-related genes, and a protein-protein interaction (PPI) network was established through the STRING database. Functional annotation and enrichment analysis were conducted using GO and KEGG. The TBI mouse model was established to detect the expression levels of GOLGA8B, ZNF367, and SMPD3 in brain tissues.
Results: SMPD3 emerged as the key DE gene linked to energy metabolism in TBI, demonstrating a negative correlation with miR-218-5p and being associated with moderate unconsciousness and female patients. The PPI network revealed SMPD3 interactions with proteins associated with cell death, sphingolipid metabolism, and neurodegenerative diseases such as Alzheimer’s disease. In vivo, GOLGA8B, ZNF367, and SMPD3 mRNA levels were significantly lower in TBI mice.
Conclusion: In summary, SMPD3 represents a crucial metabolic gene in the progression of TBI. It potentially provides a new therapeutic target for metabolic disorders caused by traumatic brain injury (TBI) and holds significant theoretical value for further research.

Keywords: traumatic brain injury, bioinformatics analysis, lncRNA-miRNA-mRNA network, energy metabolism, SMPD3