ORIGINAL RESEARCH article

Front. Aging Neurosci.

Sec. Neuroinflammation and Neuropathy

Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1531317

Bioinformatics-driven exploration of key genes and mechanisms underlying oxidative stress in traumatic brain injury

Provisionally accepted
Bin  RenBin Ren1Jifang  LiangJifang Liang2Yang  LeifangYang Leifang1Xiaocong  WeiXiaocong Wei1Min  GuoMin Guo1Li  HongLi Hong3*
  • 1Department of Neurosurgery, Shanxi Bethune Hospital Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Tai Yuan, China
  • 2Department of Intensive Care Unit, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Tai Yuan, China
  • 3Department of Gynaecology and Obstetrics, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Third Hospital of Shanxi Medical University,Tongji Shanxi Hospital, Taiyuan, China

The final, formatted version of the article will be published soon.

Background: Oxidative stress is a pivotal mechanism implicated in the onset of traumatic brain injury (TBI), yet its precise role remains elusive. This study aims to elucidate the potential molecular interactions between key genes associated with oxidative stress and their influence on TBI pathogenesis.Methods: TBI dataset and oxidative stress-related genes sourced from Public databases. Differential expression analysis and machine learning models were executed to select key genes, which were further validated using receiver operating characteristic (ROC) curves. A nomogram was constructed for diagnostic prediction, and enrichment analysis explored pathways associated with key genes.Immune infiltration analysis and regulatory network construction were conducted. Molecular validation included RT-qPCR and Western blotting using rat brain tissue to assess gene and protein expression levels.Results: In our study, we identified 400 differentially expressed genes (DEGs) between TBI and normal samples, including 20 oxidative stress-related genes. Machine learning analysis highlighted AKR1C2, QDPR, CYP3A5, CNTF, and PNPT1 as key genes with diagnostic potential (AUC > 0.6). Functional analysis revealed significant involvement of these genes in immune processes and metabolic regulation. Further, immune cell infiltration analysis showed notable differences in effector memory CD8 T cells. Molecular validation through RT-qPCR and Western blot confirmed the overexpression of key genes PNPT1 and QDPR in TBI models, substantiating their potential role in TBI pathology. Our study revealed the potential mechanisms of action for PNPT1 and QDPR in TBI, offering valuable insights into their roles in TBI pathology. These findings opened new avenues for future therapeutic strategies in TBI treatment.

Keywords: Traumatic Brain Injury, Oxidative Stress, machine learning, modified neuropathy symptom score, key genes

Received: 20 Nov 2024; Accepted: 09 Apr 2025.

Copyright: © 2025 Ren, Liang, Leifang, Wei, Guo and Hong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Li Hong, Department of Gynaecology and Obstetrics, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Third Hospital of Shanxi Medical University,Tongji Shanxi Hospital, Taiyuan, China

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