Abstract
INTRODUCTION: The biological roles of histone lactylation (HLA) modification-related genes (HLMRGs) in spinal cord injury (SCI) remain unclear. This study aimed to investigate the expression patterns and molecular mechanisms of HLMRGs in SCI through bioinformatics approaches. METHODS: Data from GSE151371, GSE47681, and 10 HLMRGs were analyzed. Subsequently, biomarkers were identified based on receiver operating characteristic (ROC) curves, followed by logistic regression modeling and nomogram construction. Gene set enrichment analysis (GSEA) was performed to assess the functional roles of these biomarkers. Clustering analysis of samples based on biomarkers revealed distinct groups, and differentially expressed genes between these groups were analyzed. Inter-cluster comparisons were conducted to examine Hallmark pathways, HLA genes, and immune functions. Weighted gene co-expression network analysis (WGCNA) was applied to identify cluster-related module genes, which were further used for protein-protein interaction (PPI) network construction to pinpoint key proteins. Networks linking miRNAs, transcription factors (TFs), and biomarkers, as well as drug-biomarker interactions, were established. The expression of biomarkers was validated through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: In GSE151371, eight biomarkers (HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 [KAT2A]) exhibited area under the curve (AUC) > 0.7 and were significantly differentially expressed between SCI and control samples. These biomarkers also showed differential expression across the two identified clusters. Differential expression analysis between clusters 1 and 2 revealed enrichment in pathways such as the 'phosphatidylinositol signaling system.' Finally, a miRNA-TF-biomarker network involving the eight biomarkers was constructed, and their expression was validated by RT-qPCR. It is noteworthy that the expression of HDAC2, GCN5 (KAT2A), LDHA, HDAC3, and SIRT3 showed significant differences between SCI and control samples. This suggests that these genes may have potential clinical significance in SCI and warrant further validation. Additionally, by exploring their mechanisms of action in depth, they may provide important biomarkers for the early diagnosis, treatment strategy optimization, and personalized medicine of SCI, thereby advancing clinical research and drug development related to SCI. CONCLUSION: In summary, 8 biomarkers playing an important role in SCI were identified, providing a reference for the application of HLMRGs in SCI.