Exploration of biomarkers associated with histone lactylation modification in spinal cord injury

Exploration of biomarkers associated with histone lactylation modification in spinal cord injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploration of biomarkers associated with histone lactylation modification in spinal cord injury Yisong Sun, Jie Gao, Juehua Jing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4884820/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The biological functions of histone lactylation (HLA) modification-related genes (HLMRGs) in spinal cord injury (SCI) are unknown. Therefore, we explored the expression and molecular mechanism of HLMRGs in SCI by bioinformatics means. Methods GSE151371, GSE47681, and 10 HLMRGs were incorporated in this study. Biomarkers were screened based on the receiver operating characteristic curves for the modeling of logistic regression and nomogram. Additionally, gene set enrichment analysis (GSEA) was executed to detect biomarkers’ functions. Samples were clustered based on biomarkers, identifying distinct groups. Differential expressed genes between these clusters were determined, and inter-cluster analyses of Hallmark pathways, HLA genes, and immune functions were conducted. Weighted gene co-expression network analysis (WGCNA) was used to select cluster-related module genes for protein-protein interaction (PPI) network construction, pinpointing key proteins. miRNA-TF-biomarker and drug-biomarker networks were established. Biomarker expression was validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results In GSE151371, 8 biomarkers (HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A)) with AUC > 0.7 were significantly different expressed between SCI and control samples. 8 biomarkers were different expressed in 2 clusters. By differential expression analysis of cluster 1 versus cluster 2, enriched in ‘phosphatidylinositol signaling system’ etc. Finally, a miRNA-TF-biomarker network comprising eight biomarkers were constructed. The expression validation of eight biomarkers by RT-qPCR, LDHA were high expression, while HDAC3 and SIRT3 were low expression in SCI. Conclusion In summary, 8 biomarkers playing an important role in SCI were identified, which provided in-depth references for HLMRGs in SCI. Spinal cord injury Histone lactylation modification Cluster Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Spinal cord injury (SCI) can lead to motor and sensory disorders below the injury plane, and even cause limb paralysis, and urinary and fecal incontinence, seriously affecting the quality of life of patients. About 90% of SCI are caused by trauma, such as traffic accidents, falls from heights, and so on (Huang et al. 2021). There is a lack of reliable treatment interventions for patients with severe neurological loss. Most medical measures focus on stabilizing the patient, preventing further damage, treating complications caused by paralysis (Eli et al. 2021 ; Eckert et al. 2017). At present, the treatment methods used in clinical practice include surgical intervention (Bühren et al. 1999 ), drug therapy (Liu et al. 2019 ; Torelli et al. 2020), hyperbaric oxygen therapy (Siglioccolo et al. 2023 ), etc. At the same time, stem cell transplantation (Zipser et al. 2022 ), gene therapy (Smith et al. 2022 ) also have been proven effective through research and are expected to gradually be applied in clinical practice, opening up new paths for the rehabilitation of SCI patients. After spinal cord injury, spinal cord nerve cells undergo apoptosis and necrosis. Due to the non renewability of nerve cells and the inhibitory effect of the local microenvironment after injury, it is difficult to recover from spinal cord nerve injury. Emerging treatment methods such as stem cell transplantation and gene therapy are gradually being explored and applied, especially in the research and application of bone marrow mesenchymal stem cells (BMSCs), which have made positive progress (Li et al. 2019 ; Cofano et al. 2019 ).Although surgery or medication can partially restore nerve function in SCI patients, various neurological dysfunction still exists or worsen in the secondary injury stage. Nowadays, the pathophysiology, multi-molecular interactions, neuroprotection, immune regulation, and neural regeneration pathways of SCI have received attention from scholars around the world (Anjum et al. 2020 ), however, there are also lacking effective treatment methods for neurological damage. Therefore, exploring potential biomarkers and their specific molecular mechanisms in SCI is crucial for its diagnosis and treatment. The basic unit of chromatin is the nucleosome, which is an octamer formed by DNA winding histones. As one of the main components of chromatin, the chemical modification of histones after translation is one of the important ways of epigenetics (Galle et al. 2022 ). Histone modifications include acetylation, butyrylation, methylation, and phosphorylation. Histone lactylation modification is a newly discovered method of histone modification. Lactic acid can generate lactyl CoA, which provides a lactyl group to the lysine tail of histones through acyltransferase, resulting in a histone modification called lysine lactylation. The lactoyl group in histone lactylation is provided by lactic acid (Zhang et al. 2019 ; Lv et al. 2023 ; Dai et al. 2022 ). In central nervous system diseases, histone lactylation is involved in various cellular events and plays a crucial role in immune regulation and homeostasis maintenance[Yang et al. 2025 ]. In addition, Alzheimer's disease (AD) is a degenerative disease of the central nervous system. Research has shown that elevated levels of histone lactylation in brain samples of AD mice exacerbate dysfunction of microglia in AD; Inhibiting histone lactate levels can improve the function of microglia and enhance spatial learning and memory in AD mice. This indicates that histone lactylation plays an important role in neurological diseases, but its specific function in spinal cord injury, a neurological disease, is still unclear. Therefore, it is necessary to explore biomarkers related to histone lactylation modification in spinal cord injury (Pan et al. 2022 ). This study integrates SCI-related transcriptome data, screens and identifies biomarkers related to histone lactate in SCI, constructs diagnostic models using bioinformatics, and analyzes the biological functions of these biomarkers, in order to provide new references for the clinical diagnosis of SCI. 2. Materials and methods 2.1 Data source The study utilized datasets obtained from the Gene Expression Omnibus (GEO) website ( https://www.ncbi.nlm.nih.gov/geo/ ). The training set, GSE151371 (GPL20301), comprised sequencing data from 38 SCIs and 10 healthy controls. Validation set, GSE47681 (GPL1261), contained sequencing data from mouse spinal cord tissue. GSE47681 contained 9 control mice, 9 mice on day 3 after SCI, and 9 mice on day 7 after SCI. There were 10 Histone lactylation modification-related genes (HLMRGs): HDAC1, HDAC2, HDAC3, SIRT1, SIRT2, SIRT3 (Moreno-Yruela et al. 2022 ), LDHA, LDHB (Yu et al. 2021 ); P300 (EP300) (Zhang et al. 2019 ), GCN5 (KAT2A) (Wang et al. 2022 ). 2.2 Expression of HLMRGs in SCI To understand the expression of HLMRGs in SCI, 10 HLMRGs were first analyzed by Wilcoxon’s test between SCI and control samples in GSE151371. At the same time, the expression of 10 HLMRGs in all samples was shown in the heatmap by Pheatmap package (v 1.0.12, https://CRAN.R-project.org/package=pheatmap ). Correlations between the 10 HLMRGs were then analyzed by Spearman. In addition, we localized 10 HLMRGs on chromosomes by RCircos package (v 1.2.2) (Zhang et al. 2013). 2.3 Construction and evaluation of logistic regression model The receiver operating characteristic (ROC) curves of individual genes were plotted for the differentially expressed HLMRGs (DE-HLMRGs) in SCI and control samples of GSE151371 using pROC package (v 1.1.1) (Robin et al. 2011) The genes with the area under the ROC curve (AUC) values of greater than 0.7 were recorded as biomarkers for the construction of logistic regression model. The performance of the model was evaluated by ROC curve. 2.4 Construction and evaluation of nomogram model Based on the biomarkers in the GSE151371 dataset, the functions nomogram and calibrate in the R package rms (v 1.6) ( https://CRAN.R-project.org/package=rmda ) were used to construct column line plots to assess the clinical applicability of the model as well as calibration curves. The calibration curves were expressed using the magnitude of the C-statistic (correlation). A value of the C-statistic > 0.7 generally indicated a higher correlation and a better model. Decision curve analysis (DCA) curves were plotted by the R package rms (v 1.6) to assess whether the nomogram prediction could be beneficial to patients with SCI. 2.5 Gene Set Enrichment Analysis (GSEA) for biomarkers To comprehend the biological functions and the involved signaling pathways of the biomarkers, the correlation coefficients were calculated between the expression of all genes and biomarkers. Then, we ranked all genes according to their correlation coefficients for GSEA on each biomarker, using screening criteria of |NES| > 1, p < 0.05, and FDR < 0.25. Single gene GSEA for each biomarker was conducted in GSE151371 using the GSEA function of the ClusterProfiler package (v 3.18.1) (Yu et al. 2012 ). The top 5 most significant pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO)-Biological Process (BP), GO-Cellular Component (CC), and GO-Molecular Function (MF) for each biomarker were demonstrated. 2.6 Immune infiltration analysis To observe the inter-sample immune cell composition, the samples in GSE151371 were analyzed for immune infiltration using CIBERSORT to observe the distributional proportion of immune cell types. These immune cells between the control and SCI groups were compared using the Wilcoxon’s test ( P < 0.05). Correlation of biomarkers with differential immune cells was demonstrated by Spearman correlation analysis at P 0.3. 2.7 Clusters associated with histone lactonization modification Based on the biomarkers, 38 SCI samples of GSE151371 were subjected to unsupervised consistent cluster analysis to identify their clusters. The expression profiles of SCI were then analyzed by principal component analysis (PCA) to assess the differentiation ability of the clusters. Then, Wilcoxon’s method was used to examine the differences in biomarker expression among clusters. 2.8 Acquisition of DEGs The limma package (version 3.56.2) (Ritchie et al. 2015 ) was applied to analyze the differential expressed genes (DEGs) of samples from disparate clusters with adj. P 0.5. Then, GO and KEGG for DEG were analyzed to detect function of DEGs by ClusterProfiler package with P < 0.05. 2.9 Functional exploration between clusters The scores of Hallmark pathway gene sets were calculated by gene set variation analysis (GSVA) with the help of GSVA package (v 1.38.2) (Hänzelmann et al. 2013) and Wilcoxon’s test was utilized to compare the differences of these pathways inter-cluster at P < 0.05. Next, differences in infiltrating immune cells in different clusters were analyzed using Wilcoxon’s test ( P < 0.05). Also, the correlation of differential immune cells with biomarkers between 2 clusters was analyzed. Moreover, the study analyzed differences in immune function pathways and HLA genes (He et al. 2018 ) using the Wilcoxon’s test. Meanwhile, the correlation between the 10 HLMRGs with immune function pathways and HLA genes was analyzed by Spearman analysis. 2.10 Identification of module genes and key proteins To identify the gene modules most strongly related to different clusters, weighted gene co-expression network analysis (WGCNA) was performed through WGCNA package (v 1.72-1(Langfelder et al. 2008). Firstly, all samples from different clusters were included in sample clustering tree to eliminate outliers. The soft threshold (β) was then chosen based on the nearly scale-free topology criterion. Next, the obtained topology matrix was clustered using differences between genes (minModuleSize = 100, TOMType = "unsigned", mergeCutHeight = 0.45, power = 16). The tree was then partitioned into modules using a dynamic tree-cutting algorithm. Different colors represented different gene modules and gray represented genes that did not fit into any of the known modules. Heat map of the relationships for module traits were then plotted to assess the association of each module with disparate clusters as traits, and finally the most relevant of the module genes with P 0.8 and |gene significance (GS)| > 0.2, the module genes were obtained for building a protein-protein interaction (PPI) network. Specifically, the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/ ) database was used to obtain protein interaction information to construct a PPI network, and then the Cytohubba plug-in (MCC, top = 10) was utilized to screen 10 genes to be recorded as key proteins for clusters. Correlations between 10 key proteins and biomarkers were then analyzed by Spearman analysis ( P 0.3). 2.11 Molecular networking Transcription factors (TFs) of biomarkers were predicted using the ChEA3 database. miRWalk ( http://mirwalk.umm.uni-heidelberg.de/ ), Encyclopedia of RNA Interactome (ENOCRI, http://starbase.sysu.edu.cn/index.php ), and miRTarBase ( https://mirtarbase.cuhk.edu.cn/ ) databases were utilized to predict miRNAs of biomarkers, and the intersection was taken as common miRNAs to construct a miRNA-TF-biomarker interaction network. Furthermore, the Drug-Gene Interaction (DGI) database was employed to predict potential drugs for biomarkers. 2.12 Expression validation of biomarkers For better validating the biomarker expression, we merged the SCI samples from GSE47681 into SCI groups separately to validate biomarker expression between SCI and control groups. To verify the expression of biomarkers, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed on tissues from 5 SCI samples and 5 control samples. RT-qPCR amplification was conducted under 40 cycles of 95°C for 1 min, 95°C for 20 s, 55°C for 20 s, and 72°C for 30 s. The primers for RT-qPCR were shown in Table 1 , and the reference gene was GAPDH. The relative expression levels of the biomarkers were calculated using the 2 −ΔΔCT method. Table 1 List of primers for PCR. genes primers HDAC1 F TGGCCTCTTACCCATGTATCAC HDAC1 R ATTCTGAGGAGGCAACACCG HDAC2 F GAGCTTTCGGCACCTCTGC HDAC2 R GAAACGTGGGGGCGATAGTC HDAC3 F GAGCAGGGACTTCAGCCTAC HDAC3 R GGGATTGTGTGAACGCCAAC SIRT1 F GGTCGGTGACAGCCTCAAG SIRT1 R ATGTCTGCTTCTCCACCAGC SIRT3 F GTAGTTGAACGGGTCGAGGC SIRT3 R TAATAATCGTCCCTGCCGCC LDHA F TGCCTTGGGCTTGAGCTTTG LDHA R CACAGCCAGGCTTCTCAAGT LDHB F GCCTTCTCTCTCCTGTGCAA LDHB R GACGGACTCCTGCAGTTACC GCN5 F GATTGGTCCCTCCTCTCCCT GCN5 R CCTCTTCTCGCCTGGCATAG GAPDH F CGAAGGTGGAGTCAACGGATTT GAPDH R ATGGGTGGAATCATATTGGAAC 2.13 Statistical analysis R software (v 4.2.2) was applied to execute all statistical analysis. Differences between groups were analyzed by Wilcoxon's test, and P < 0.05 was statistical significance. 3. Results 3.1 Eight DE-HLMRGs were significantly expressed between SCI and control samples In GSE151371, 8 DE-HLMRGs showed significant expression differences between SCI and control samples ( Fig. 1 a ) . These genes were HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A). LDHA was upregulated in SCI samples compared to controls, while the remaining genes were downregulated. Additionally, Fig. 1 b displayed the heatmap of 10 HLMRGs’ expression in the 2 groups. Correlation analysis revealed significantly positive correlations among most of the HLMRGs. The strongest positive correlation was found between HDAC2 and SIRT1 (r = 0.811, P < 0.01), while the strongest negative correlation was observed between LDHA and KAT2A (r = -0.489, P < 0.01) ( Fig. 1 c ) . According to the chromosome localization map, HDAC1 was located on chromosome 1, HDAC3 on chromosome 5, HDAC2 on chromosome 6, etc. ( Fig. 1 d ) . 3.2 Logistic regression model had a great ability to discriminate control and SCI The AUCs of ROC curves for 8 DE-HLMRGs (biomarkers) all exceeded 0.7, indicating that these biomarkers had the ability to discriminate control and SCI ( Fig. 2 a ) . Meanwhile, ROC curve for logistic regression model had an AUC = 0.979, which suggested a great performance with logistic regression model ( Fig. 2 b ) . 3.3 Nomogram model had a great ability to predict SCI On the basis of biomarkers in GSE151371, a nomogram model was constructed to predict SCI ( Fig. 3 a ) . The C-statistic of the calibration curve was 0.987, suggesting that there was some covariance with apparent and the model worked better ( Fig. 3 b ) . DCA curve demonstrated that nomogram model could be advantageous for SCI patients, with higher overall benefit rates than individual biomarkers ( Fig. 3 c ) . 3.4 Biomarkers were enriched in some metabolic pathways As an example, HDAC1 was enriched in 37 KEGG pathways ( Fig. 4 a ) , 917 GO-BP ( Fig. 4 b ) , 209 GO-CC ( Fig. 4 c ) , and 218 GO-MF ( Fig. 4 d ) by GSEA, including ‘ubiquitin-mediated proteolysis’, ‘oxidative phosphorylation’ and ‘valine, leucine, and isoleucine degradation’ pathways, etc. The enrichment results for the remaining 7 biomarkers: HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A) were shown in Online Resource 1–7 , respectively. 3.5 Totally 15 immune cells differed in SCI and control groups The proportional distribution of immune cell types in each sample was shown in Fig. 5 a. Between SCI and control groups, 15 immune cells differed, including memory B cells, eosinophils, M0 Macrophages and so on ( Fig. 5 b ) . By Spearman correlation analysis, the differential immune cells most significantly correlated with the biomarkers were obtained, as HDAC1-memory B cells (r = 0.44, P < 0.01), HDAC2-memory B cells (r = 0.51, P < 0.01), HDAC3-neutrophils (r = -0.31, P < 0.05), KAT2A-naive CD4 T cells/monophages (both r = 0.49, P < 0.01), LDHA-plasma cells (r = 0.47, P < 0.01), LDHB-resting memory CD4 T cells (r = 0.75, P < 0.01), SIRT1-resting memory CD4 T cells (r = 0.64, P < 0.01), and SIRT3-neutrophils (r = -0.39, P < 0.01) ( Fig. 5 c ) . 3.6 The 2 clusters were able to distinguish The 38 SCI samples from GSE151371 were classified into 2 clusters based on biomarkers ( Fig. 6 a ) . The PCA revealed that 2 clusters possessed some degree of differentiation ( Fig. 6 b ) . The expression levels of all biomarkers were higher in cluster 2, and only HDAC1, HDAC3, GCN5 (KAT2A), and SIRT3 had significant differences between both groups ( P < 0.05) ( Fig. 6 c ) . Inter-cluster expression of 8 biomarkers displayed in the heatmap ( Fig. 6 d ) . 3.7 Totally 700 DEGs were enriched to 140 GO entries and 4 KEGG pathways A sum of 700 DEGs was screened (287 upregulated and 413 downregulated) between cluster 1 and cluster 2 ( Fig. 7 a ) . These DEGs were enriched to 140 GO entries and 4 KEGG pathways including ‘Base excision repair’, ‘phosphatidylinositol signaling system’, ‘inositol phosphate metabolism’ and so on ( Fig. 7 b ) . 3.8 Hallmark pathways between clusters There were 7 pathways that differed significantly between two clusters, including ‘apical junction’, ‘apical surface’, ‘estrogen response early’ etc. in Hallmark ( Fig. 8 a ) . Only 4 types of immune cells were differed between clusters, activated dendritic cells, monocytes, activated CD4 memory T cells, and gamma delta T cells ( Fig. 8 b ) . Meanwhile, monocytes showed significantly positive correlation with HDAC3 and KAT2A, and activated dendritic cells significantly negatively correlated with HDAC3 and SIRT3 ( Fig. 8 c ) . Cytolytic activity and parainflammation showed differences in clusters ( Fig. 8 d ) . Additionally, there were differences in the expression of HLA-A, HLA-B, HLA-DMA, HLA-DMB, HLA-DPB1, HLA-DQB2, HLA-DRA, and HLA-E among the clusters ( Fig. 8 e ) . Regarding immune function, the study found strongly positive correlations between HDAC2 and APC co stimulation (r = 0.69, P < 0.01), as well as LDHB and HLA (r = 0.69, P < 0.01) ( Fig. 8 f ) . On the other hand, strongly negative correlations were found between LDHA and Type I IFN Response (r = -0.73, P < 0.01). The strongest positive correlations for HLA were found between LDHB and HLA-DQA1 (r = 0.73, P < 0.01) ( Fig. 8 g ) . The strongest negative correlations for HLA were found between LDHA and HLA-DMB (r = -0.43, P < 0.01). 3.9 A sum of 10 key proteins were selected For WGCNA, there were no outlier samples in clusters ( Fig. 9 a ) . The β was set to 16 to nearly conform to the scale-free network ( Fig. 9 b ) . Then, 6 gene modules were obtained by dynamic tree cutting ( Fig. 9 c ) . By correlation analysis, blue module (r = -0.60, P < 0.05) was considered as a key module ( Fig. 9 d ) . After the screening, 1,615 module genes were gained for further analysis ( Fig. 9 e ) . According to the PPI network, 10 key proteins were selected, which were RPS9, RPS28, RPS2, RPS15, RPL13, FAU, RPL18, RPL36, RPL28 and RPL8 ( Fig. 9 f ) . Correlation analysis revealed that KAT2A (GCN5) was most strongly positively correlated with RPL28 (r = 0.797, P < 0.01) and LDHA was most strongly negatively correlated with RPL28 (r = -0.478, P < 0.01) ( Fig. 9 g ) . 3.10 The miRNA-TF-biomarker and drug-biomarker networks A total of 41 TFs of biomarkers were predicted through the ChEA3 database, and the top 10 TFs were selected for network construction as ZNF883, ZNF614, ZNF644, ZNF280C, TP53, ZNF140, RBPJ, ZNF692, E2F4, and USF2. And 57 common miRNAs were predicted using miRWalk, ENOCRI, and miRTarBase databases. A miRNA-TFs-biomarker network was created based on the 8 biomarkers, top 10 TFs, and 57 common miRNAs, which demonstrated complex regulatory relationships ( Fig. 10 a ). With the DGIdb database, 47 potential drugs were predicted by HDAC1, 39 by HDAC2, 38 by HDAC3, 24 by SIRT1, 3 by SIRT3, 1 by LDHA, no prediction by LDHB, and 126 potential drugs were predicted by GCN5 (KAT2A). Thus, a drug-biomarker interaction network was constructed containing 279 edges and 208 nodes ( Fig. 10 b ) , like niacinamide-SIRT3, suramin-SIRT3, and lapachone-SIRT1. 3.11 LDHA was high expression in SCI group, while HDAC2, HDAC3, GCN5 (KAT2A), LDHB, and SIRT3 were reversed For the 8 biomarkers in GSE151371 (Fig. 1 a ) , only LDHA was highly expressed in SCI group. In GSE47681, HDAC1, GCN5 (KAT2A), LDHA, and SIRT1 were high expression in SCI samples, while HDAC2, HDAC3, LDHB, and SIRT3 were low expression in SCI group ( Fig. 11 a ) . By RT-qPCR, HDAC1 (not significant), HDAC2, GCN5 (KAT2A), LDHA, SIRT1 (not significant) were high expression, whereas HDAC3, LDHB (not significant), and SIRT3 were low expression in SCI samples ( Fig. 11 b ) . Among 8 biomarkers, LDHA high expression in SCI, HDAC3, LDHB, and SIRT3 were all low expression although LDHB was not significant in RT-qPCR. 4. Discussion SCI is a deadly and disabling disease that has been proven to be a polygenic disease, and its pathogenesis is related to changes in many genes. In SCI and brain injury, histone lactate is involved in various cellular events and plays a crucial role in immune regulation and homeostasis maintenance. This study was based on the GSE151371 and GSE47681 datasets, investigating 10 HLMRGs and selecting 8 biomarkers (HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A)) based on the ROC curve LDHA. And conduct a series of bioinformatics analyses on it to provide reference for the exploration of new biomarkers and molecular mechanisms in SCI. In our study, there were expression differences among 8 HLMRGs in different datasets and clinical blood samples, possibly due to the inconsistency of the samples. We will explore and verify this in future experiments. In the data, validation set, and tissue samples obtained by the author, LDHA and GCN5 (KAT2A) was highly expressed in the SCI group, while HDAC3 and SIRT3 were low expressed. Guan et al. (2021) established SCI models through in vivo and in vitro experiments, and detected an increase in LDHA and lactate levels in SCI rats and LPS induced PC12 cells. In previous studies, GCN5 (KAT2A) can promote axonal growth by regulating acetylated microtubule proteins (Lin et al. 2022 ). The loss of GCN5 (KAT2A) activity promotes neuronal apoptosis through upregulation of Egr-1-dependent BH3-only protein Bim (Wu et al. 2017 ). This indicates that GCN5/KAT2A has a positive effect on the recovery of nerve injury. In our study, GCN5/KAT2A was upregulated in SCI samples, indicating that after spinal cord injury, GCN5/KAT2A is activated and promotes the process of injury repair. However, the specific mechanism still needs further research. Meanwhile, this study also found low expression of HDAC3 and SIRT3 in SCI. Wahane (Wahane et al. 2021 ) also found that HDAC3 activity mediates multiple transcriptional responses in SCI in the myeloid and glial cells. Studies have shown that the silencing and inhibition of HDAC3 can improve neurological function and spinal cord edema after SCI, playing a neuroprotective role (Dai et al. 2021 ; Zhou et al. 2020 ). In a rat SCI model, Downregulating HDAC3 can inhibit the activation of the JAK2/STAT3 pathway by upregulating miR-19b-1-5p, thereby promoting the recovery of SCI. The low expression of HDAC3 and SIRT3 in SCI is a protective mechanism for SCI (Niu et al. 2022 ). Zinc defends Parthanatos and promotes functional recovery after SCI through sirt3-mediated antioxidant stress and mitochondrial phagocytosis (Jiang et al. 2023 ). In addition, activation of the NMDAR/AMPK/PGC-1α/SIRT3 signaling pathway through distal limb ischemic preconditioning can protect against spinal cord ischemia-reperfusion injury in mice (Gu et al. 2023 ). Therefore, SIRT3 can affect SCI and prognosis through various mechanisms. In clinical applications, SIRT3 inhibitors such as Suramin have been used for the treatment of tumors. In summary, LDHA and SIRT3 may be effective biomarkers for the diagnosis or treatment of SCI, providing new mechanisms and pathways for the treatment of spinal cord injury. During SCI, multiple immune cells are involved in the progression and recovery of the disease (Xu et al. 2021 ; Girón et al. 2023 ; Milich et al. 2019 ; Han et al. 2024 ). This study found that there are differences in four types of immune cells, including activated dendritic cells, monocytes, activated CD4 memory T cells, and gamma delta T cells. In previous studies, dendritic cells have been influenced by HDAC3 in various diseases such as Alzheimer's disease (AD) and tumors (Han et al.) 2022; Deng et al. 2019 ). Dendritic cells are divided into plasmacytoid dendritic cells (PDCs) and conventional dendritic cells (CDCs). In both vivo and vitro experiments,it has been confirmed that histone deacetylase 3 (HDAC3), as an important epigenetic regulatory factor, is highly expressed in PDCs. The lack of Hdac3 can seriously affect the development of dendritic cells (Zhang et al. 2023 ). In this study, there was a negative correlation between Dendritic cells and HDAC3. Therefore, in the immune microenvironment of SCI, Dendritic cells may be influenced by multiple factors, and further mechanism research is needed. Monocytes are important immune cells. HDAC3 mediates the inflammatory response of human monocytes and macrophages, determining the effects of HDAC inhibitors (HDACI) on human monocytes and macrophages, including their polarization, activation, and ability to induce endotoxin tolerance (Ghiboub et al. 2020 ). This study also found a positive correlation between Monocytes and HDAC3 in SCI diseases, adding new directions for Monocytes research and providing new ideas for the study of inflammatory mechanisms in SCI. In the study of mice with HDAC3 expression deficiency, Eshleman (Eshleman et al. 2023 ) found an increase in the accumulation of symbiotic-specific activated CD4 memory T cells in the gut of mice, revealing a negative correlation between HDAC3 and activated CD4 memory T cells in inflammatory diseases. The same phenomenon was also observed in SCI diseases in this study. At the same time, we found a negative correlation between activated CD4 memory T cells and SIRT3, and a significant positive correlation with LDHA, which is consistent with previous research results in tumors and inflammatory diseases (Hou et al. 2024 ; Zou et al. 2024 ). MicroRNAs (miRNAs) are approximately 22 nt RNA fragments that primarily regulate protein expression by binding to one or more target sites of mRNA transcription and inhibiting translation. They may be involved in multiple developmental pathways and gene regulatory mechanisms, and serve as the basis for a series of genetic disease processes and phenotype determinants. There are a large number of miRNAs in the central nervous system, playing an important role in the progression of SCI (Silvestro et al. 2022). Studies have shown that modulation of the NEAT1/miR-128-3p/AQP4 axis can alleviate neuropathic pain induced by SCI (Xian et al. 2021 ). Long non-coding RNA CASC9 (lncRNA CASC9) regulates levels of MDA, lactate, TNF - α, and IL-1 β through miR-383-5p, playing a protective role in antioxidant stress, inflammation, and cell apoptosis in SCI (Guan et al. 2021). Furthermore silencing miR-324-5p can alleviate Sirt1-induced SCI in rats (Wang et al. 2021 ). Based on our research, we speculate that miRNAs may be a key factor in the progression of SCI. The study identified and validated biomarkers associated with histone lactylation modification in spinal cord injury. Revealed the relationship between immune infiltrating cells and predicted drug targets. This provides a reference for exploring and discovering new biomarkers in SCI, and thus for the prevention and treatment of SCI. This informs the mining and exploration of novel biomarkers in SCI, which in turn reference the prevention and treatment of SCI. However, this study has certain limitations, such as a relatively limited sample size compared to other papers. Secondly, although our bioinformatics analysis suggests that HLMRGs may be a new direction for studying SCI mechanisms and potential therapeutic approaches.this conclusion still needs to be validated through in vitro and in vivo experiments. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of TheSecond Hospital of Anhui Medical University (Data 2023/7/6. /No YW2023-118). Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish The authors affirm that human research participants provided informed consent for publication of the datas. Funding This work was supported by Anhui Provincial Clinical Research Transformation Project (Grant number 202304295107020009). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Y S, J G and J J. The first draft of the manuscript was written by Y S and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. References Anjum A, Yazid MD, Fauzi Daud M et al (2020) Spinal Cord Injury: Pathophysiology, Multimolecular Interactions, and Underlying Recovery Mechanisms. Int J Mol Sci 21:7533. https://doi.org/10.3390/ijms21207533 Bühren V, Potulski M, Jaksche H (1999) Chirurgische Versorgung bei Tetraplegie [Surgical management of tetraplegia]. 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Nat Immunol 25:66–76. https://doi.org/10.1038/s41590-023-01682-z Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformations.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4884820","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346885762,"identity":"a435be5d-632e-4f32-beea-d9b643a31c99","order_by":0,"name":"Yisong Sun","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yisong","middleName":"","lastName":"Sun","suffix":""},{"id":346885764,"identity":"1caa284c-2f4f-4ee5-9164-0931be45da8a","order_by":1,"name":"Jie Gao","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Gao","suffix":""},{"id":346885765,"identity":"edc3bdb9-4d25-4ffc-a5c1-37088a4a5f65","order_by":2,"name":"Juehua Jing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACxmYgIdlwgIGfgbGBRC2SDcRqgegDajE4QKxq5nbewy8sd9xJ3Hz+cNuDHwx2crqELGNs5kuzkDzzLHHbjcR2wx6GZGMzQtYxNvOYGUi2HQZqYWyT4GE4kLiNaC2b+w+2Sf4hUovxA5CWDQyJbdJE28IA1GI84wZQi4wBEX4x7D9j/BmoRba///gzyTcVdnKEtTQwsElLwLkGBJSDgDwwaj5+IELhKBgFo2AUjGAAAOxrRYwffoIWAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Juehua","middleName":"","lastName":"Jing","suffix":""}],"badges":[],"createdAt":"2024-08-09 06:33:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4884820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4884820/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64056088,"identity":"46702a6f-2e4e-4a39-a23a-e6d3501883e7","added_by":"auto","created_at":"2024-09-05 18:59:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":492968,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of DE-HLMRGs in the SCI \u003cstrong\u003e(a)\u003c/strong\u003e Expression of the 8 DE-HLMRGs In GSE151371, Horizontal coordinates indicate HLMRGs and vertical coordinates indicate expression values, Asterisks represent P less than 0.05, the more asterisks, the more significant the P value, and ns means not significant \u003cstrong\u003e(b)\u003c/strong\u003eThe heatmap of 10 HLMRGs’ expression in the 2 groups, Red is high expression, and blue is low expression \u003cstrong\u003e(c)\u003c/strong\u003e Correlation analysis of the 8 DE-HLMRGs. \u003cstrong\u003e(d)\u003c/strong\u003eThe chromosome localization map of the 8 DE-HLMRGs\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/0a77f46d99105679c4ed4b44.png"},{"id":64055957,"identity":"36867992-e512-4509-8ff0-e6068b532385","added_by":"auto","created_at":"2024-09-05 18:51:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171380,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for 8 DE-HLMRGs \u003cstrong\u003e(a) \u003c/strong\u003eThe AUCs of ROC curves for 8 DE-HLMRGs (biomarkers) \u003cstrong\u003e(b)\u003c/strong\u003eROC curve for logistic regression model\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/618d681b6765966743dd589e.png"},{"id":64055967,"identity":"7f6b4c78-05e9-4676-bf8e-6d69b39558de","added_by":"auto","created_at":"2024-09-05 18:51:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":295436,"visible":true,"origin":"","legend":"\u003cp\u003eThe biomarker-based nomogram model, calibration curve and DCA curve \u003cstrong\u003e(a) \u003c/strong\u003eNomogram model constructed to predict SCI \u003cstrong\u003e(b)\u003c/strong\u003e The C-statistic of calibration curve was 0.987\u003cstrong\u003e (c)\u003c/strong\u003e DCA curve\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/480cd11b7bdc2d2b3700da43.png"},{"id":64056089,"identity":"f511f98a-e21a-403d-ae0d-2e4516a841d4","added_by":"auto","created_at":"2024-09-05 18:59:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":513972,"visible":true,"origin":"","legend":"\u003cp\u003eBiomarkers were enriched in some metabolic pathways, HDAC1 was enriched in 37 \u003cstrong\u003e(a) \u003c/strong\u003eKEGG pathways \u003cstrong\u003e(b)\u003c/strong\u003e 917 GO-BP \u003cstrong\u003e(c) \u003c/strong\u003e209 GO-CC and \u003cstrong\u003e(d) \u003c/strong\u003e218 GO-MF by GSEA\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/637793f900a53087d4f987ab.png"},{"id":64055964,"identity":"dabaf0b8-d94c-459d-8b84-12e0d6bda960","added_by":"auto","created_at":"2024-09-05 18:51:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":387551,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune cells with DE-HLMRGs \u003cstrong\u003e(a)\u003c/strong\u003e The proportional distribution of immune cell types in each sample \u003cstrong\u003e(b)\u003c/strong\u003e 15 immune cells differed between SCI and control groups \u003cstrong\u003e(c)\u003c/strong\u003e Spearman correlation analysis, the differential immune cells most significantly correlated with the biomarkers\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/37f594f6b493a124ee5f0e6a.png"},{"id":64055959,"identity":"743e38ee-e939-4e86-a547-831f7141ae9a","added_by":"auto","created_at":"2024-09-05 18:51:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":186354,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus ClusterPlus-Analysis and PCA analysis in different Molecular patterns \u003cstrong\u003e(a)\u003c/strong\u003eThe 38 SCI samples from GSE151371 were classified into 2 clusters based on biomarkers \u003cstrong\u003e(b)\u003c/strong\u003e The PCA revealed some degree of differentiation of the 2 clusters\u003cstrong\u003e (c)\u003c/strong\u003e The expression levels of all biomarkers\u003cstrong\u003e (d)\u003c/strong\u003eInter-cluster expression of 8 biomarkers displayed in the heatmap\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/a914c952dd9694df297ccea0.png"},{"id":64056091,"identity":"4c997316-aa6f-46e5-8b36-8aa0dedffcc4","added_by":"auto","created_at":"2024-09-05 18:59:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":785100,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed genes \u003cstrong\u003e(a) \u003c/strong\u003eVolcano plot and heatmap of the differentially expressed genes between the different molecular patterns\u003cstrong\u003e (b) \u003c/strong\u003eEnrichment analysis of the differentially expressed genes between the different molecular patterns\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/e9c96708c34f8b7315b5e3af.png"},{"id":64055963,"identity":"8af1a24b-c520-45cd-bb69-013a1719d87f","added_by":"auto","created_at":"2024-09-05 18:51:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":801504,"visible":true,"origin":"","legend":"\u003cp\u003eHallmark Pathway analysis, analysis of immune cell distribution, HLA genes, and immune function pathway score distribution \u003cstrong\u003e(a)\u003c/strong\u003e Heatmap of differential Hallmark pathways between different molecular patterns \u003cstrong\u003e(b)\u003c/strong\u003e Distribution of immune cells between different molecular patterns, Only 4 types of immune cells differed between clusters \u003cstrong\u003e(c)\u003c/strong\u003e Analysis of the correlation of differential immune cells and biomarkers in different molecular patterns \u003cstrong\u003e(d)\u003c/strong\u003e Distribution of immune function pathway scores among different molecular patterns \u003cstrong\u003e(e)\u003c/strong\u003eDistribution of HLA genes between different molecular patterns \u003cstrong\u003e(f)\u003c/strong\u003eCorrelation analysis of function pathway scores of 10 HLMRGs \u003cstrong\u003e(g)\u003c/strong\u003eCorrelation analysis of HLA genes of 10 HLMRGs\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/35778b216fd788f2daff6fb6.png"},{"id":64056090,"identity":"575feaef-8b03-4d77-aa28-1b64634cba44","added_by":"auto","created_at":"2024-09-05 18:59:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":719141,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of 10 key proteins \u003cstrong\u003e(a)\u003c/strong\u003e Sample clustering dendrogram. \u003cstrong\u003e(b)\u003c/strong\u003e Squared correlation coefficients and network connectivity analysis \u003cstrong\u003e(c) \u003c/strong\u003eHierarchical clustering dendrogram for each module, the top is the clustering dendrogram and the bottom corresponds to the corresponding gene modules, with different colours representing different gene modules and grey representing genes that do not fit into any of the known modules \u003cstrong\u003e(d)\u003c/strong\u003e Correlation graph between modules and phenotypes. Modules with redder colours indicate a highly positive correlation with the phenotypic trait with the genes of the module, while the bluer colour indicates a highly negative correlation\u003cstrong\u003e (e)\u003c/strong\u003e GS-MM plot \u003cstrong\u003e(f)\u003c/strong\u003eInteraction network analysis of PPIs encoded by 10 key proteins\u003cstrong\u003e (g)\u003c/strong\u003eCorrelation analysis of differential markers with 10 genes\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/f5fbd14ef1c5be861697a7e5.png"},{"id":64055961,"identity":"8ebab05b-c41b-4454-b460-6afbf75168a2","added_by":"auto","created_at":"2024-09-05 18:51:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3544839,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of miRNA / TF-biomarker interaction network and Drug-biomarker interaction network \u003cstrong\u003e(a) \u003c/strong\u003eThe miRNA / TF-biomarker interaction network\u003cstrong\u003e (b) \u003c/strong\u003eDrug-biomarker interaction network\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/1158ab9fd492e8c8ecef8ea4.png"},{"id":64055960,"identity":"548d9e9b-9654-41fe-9894-01687c534652","added_by":"auto","created_at":"2024-09-05 18:51:37","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":187566,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the HLMRGs \u003cstrong\u003e(a)\u003c/strong\u003e the 8 biomarkers in GSE47681 \u003cstrong\u003e(b)\u003c/strong\u003e qRT-PCR to verify the expression of eight HLMRGs\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/5755dbb9f4a90e45a83032fb.png"},{"id":75648372,"identity":"63125fc7-bd8f-4a69-8e3e-15d109576009","added_by":"auto","created_at":"2025-02-06 17:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8928983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/e0b0da89-40e9-4e45-8705-1f873a7686de.pdf"},{"id":64056092,"identity":"27027658-2e5f-4e7f-b7e4-f231f92c0b3b","added_by":"auto","created_at":"2024-09-05 18:59:37","extension":"zip","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14513616,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformations.zip","url":"https://assets-eu.researchsquare.com/files/rs-4884820/v1/2551bd85ab6537ef9072c07b.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration of biomarkers associated with histone lactylation modification in spinal cord injury","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSpinal cord injury (SCI) can lead to motor and sensory disorders below the injury plane, and even cause limb paralysis, and urinary and fecal incontinence, seriously affecting the quality of life of patients. About 90% of SCI are caused by trauma, such as traffic accidents, falls from heights, and so on (Huang et al. 2021). There is a lack of reliable treatment interventions for patients with severe neurological loss. Most medical measures focus on stabilizing the patient, preventing further damage, treating complications caused by paralysis (Eli et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eckert et al. 2017). At present, the treatment methods used in clinical practice include surgical intervention (B\u0026uuml;hren et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), drug therapy (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Torelli et al. 2020), hyperbaric oxygen therapy (Siglioccolo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), etc. At the same time, stem cell transplantation (Zipser et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), gene therapy (Smith et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also have been proven effective through research and are expected to gradually be applied in clinical practice, opening up new paths for the rehabilitation of SCI patients. After spinal cord injury, spinal cord nerve cells undergo apoptosis and necrosis. Due to the non renewability of nerve cells and the inhibitory effect of the local microenvironment after injury, it is difficult to recover from spinal cord nerve injury. Emerging treatment methods such as stem cell transplantation and gene therapy are gradually being explored and applied, especially in the research and application of bone marrow mesenchymal stem cells (BMSCs), which have made positive progress (Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cofano et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Although surgery or medication can partially restore nerve function in SCI patients, various neurological dysfunction still exists or worsen in the secondary injury stage. Nowadays, the pathophysiology, multi-molecular interactions, neuroprotection, immune regulation, and neural regeneration pathways of SCI have received attention from scholars around the world (Anjum et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), however, there are also lacking effective treatment methods for neurological damage. Therefore, exploring potential biomarkers and their specific molecular mechanisms in SCI is crucial for its diagnosis and treatment.\u003c/p\u003e \u003cp\u003eThe basic unit of chromatin is the nucleosome, which is an octamer formed by DNA winding histones. As one of the main components of chromatin, the chemical modification of histones after translation is one of the important ways of epigenetics (Galle et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Histone modifications include acetylation, butyrylation, methylation, and phosphorylation. Histone lactylation modification is a newly discovered method of histone modification. Lactic acid can generate lactyl CoA, which provides a lactyl group to the lysine tail of histones through acyltransferase, resulting in a histone modification called lysine lactylation. The lactoyl group in histone lactylation is provided by lactic acid (Zhang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lv et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In central nervous system diseases, histone lactylation is involved in various cellular events and plays a crucial role in immune regulation and homeostasis maintenance[Yang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. In addition, Alzheimer's disease (AD) is a degenerative disease of the central nervous system. Research has shown that elevated levels of histone lactylation in brain samples of AD mice exacerbate dysfunction of microglia in AD; Inhibiting histone lactate levels can improve the function of microglia and enhance spatial learning and memory in AD mice. This indicates that histone lactylation plays an important role in neurological diseases, but its specific function in spinal cord injury, a neurological disease, is still unclear. Therefore, it is necessary to explore biomarkers related to histone lactylation modification in spinal cord injury (Pan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study integrates SCI-related transcriptome data, screens and identifies biomarkers related to histone lactate in SCI, constructs diagnostic models using bioinformatics, and analyzes the biological functions of these biomarkers, in order to provide new references for the clinical diagnosis of SCI.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThe study utilized datasets obtained from the Gene Expression Omnibus (GEO) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The training set, GSE151371 (GPL20301), comprised sequencing data from 38 SCIs and 10 healthy controls. Validation set, GSE47681 (GPL1261), contained sequencing data from mouse spinal cord tissue. GSE47681 contained 9 control mice, 9 mice on day 3 after SCI, and 9 mice on day 7 after SCI. There were 10 Histone lactylation modification-related genes (HLMRGs): HDAC1, HDAC2, HDAC3, SIRT1, SIRT2, SIRT3 (Moreno-Yruela et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), LDHA, LDHB (Yu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); P300 (EP300) (Zhang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), GCN5 (KAT2A) (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Expression of HLMRGs in SCI\u003c/h2\u003e \u003cp\u003eTo understand the expression of HLMRGs in SCI, 10 HLMRGs were first analyzed by Wilcoxon\u0026rsquo;s test between SCI and control samples in GSE151371. At the same time, the expression of 10 HLMRGs in all samples was shown in the heatmap by Pheatmap package (v 1.0.12, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=pheatmap\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=pheatmap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Correlations between the 10 HLMRGs were then analyzed by Spearman. In addition, we localized 10 HLMRGs on chromosomes by RCircos package (v 1.2.2) (Zhang et al. 2013).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction and evaluation of logistic regression model\u003c/h2\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curves of individual genes were plotted for the differentially expressed HLMRGs (DE-HLMRGs) in SCI and control samples of GSE151371 using pROC package (v 1.1.1) (Robin et al. 2011) The genes with the area under the ROC curve (AUC) values of greater than 0.7 were recorded as biomarkers for the construction of logistic regression model. The performance of the model was evaluated by ROC curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction and evaluation of nomogram model\u003c/h2\u003e \u003cp\u003eBased on the biomarkers in the GSE151371 dataset, the functions nomogram and calibrate in the R package rms (v 1.6) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=rmda\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=rmda\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to construct column line plots to assess the clinical applicability of the model as well as calibration curves. The calibration curves were expressed using the magnitude of the C-statistic (correlation). A value of the C-statistic\u0026thinsp;\u0026gt;\u0026thinsp;0.7 generally indicated a higher correlation and a better model. Decision curve analysis (DCA) curves were plotted by the R package rms (v 1.6) to assess whether the nomogram prediction could be beneficial to patients with SCI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene Set Enrichment Analysis (GSEA) for biomarkers\u003c/h2\u003e \u003cp\u003eTo comprehend the biological functions and the involved signaling pathways of the biomarkers, the correlation coefficients were calculated between the expression of all genes and biomarkers. Then, we ranked all genes according to their correlation coefficients for GSEA on each biomarker, using screening criteria of |NES| \u0026gt; 1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25. Single gene GSEA for each biomarker was conducted in GSE151371 using the GSEA function of the ClusterProfiler package (v 3.18.1) (Yu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The top 5 most significant pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO)-Biological Process (BP), GO-Cellular Component (CC), and GO-Molecular Function (MF) for each biomarker were demonstrated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eTo observe the inter-sample immune cell composition, the samples in GSE151371 were analyzed for immune infiltration using CIBERSORT to observe the distributional proportion of immune cell types. These immune cells between the control and SCI groups were compared using the Wilcoxon\u0026rsquo;s test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlation of biomarkers with differential immune cells was demonstrated by Spearman correlation analysis at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |correlation (r)| \u0026gt; 0.3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Clusters associated with histone lactonization modification\u003c/h2\u003e \u003cp\u003eBased on the biomarkers, 38 SCI samples of GSE151371 were subjected to unsupervised consistent cluster analysis to identify their clusters. The expression profiles of SCI were then analyzed by principal component analysis (PCA) to assess the differentiation ability of the clusters. Then, Wilcoxon\u0026rsquo;s method was used to examine the differences in biomarker expression among clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Acquisition of DEGs\u003c/h2\u003e \u003cp\u003eThe limma package (version 3.56.2) (Ritchie et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was applied to analyze the differential expressed genes (DEGs) of samples from disparate clusters with adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003efoldchange(FC)|\u0026gt;0.5. Then, GO and KEGG for DEG were analyzed to detect function of DEGs by ClusterProfiler package with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Functional exploration between clusters\u003c/h2\u003e \u003cp\u003eThe scores of Hallmark pathway gene sets were calculated by gene set variation analysis (GSVA) with the help of GSVA package (v 1.38.2) (H\u0026auml;nzelmann et al. 2013) and Wilcoxon\u0026rsquo;s test was utilized to compare the differences of these pathways inter-cluster at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Next, differences in infiltrating immune cells in different clusters were analyzed using Wilcoxon\u0026rsquo;s test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Also, the correlation of differential immune cells with biomarkers between 2 clusters was analyzed. Moreover, the study analyzed differences in immune function pathways and HLA genes (He et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) using the Wilcoxon\u0026rsquo;s test. Meanwhile, the correlation between the 10 HLMRGs with immune function pathways and HLA genes was analyzed by Spearman analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Identification of module genes and key proteins\u003c/h2\u003e \u003cp\u003eTo identify the gene modules most strongly related to different clusters, weighted gene co-expression network analysis (WGCNA) was performed through WGCNA package (v 1.72-1(Langfelder et al. 2008). Firstly, all samples from different clusters were included in sample clustering tree to eliminate outliers. The soft threshold (β) was then chosen based on the nearly scale-free topology criterion. Next, the obtained topology matrix was clustered using differences between genes (minModuleSize\u0026thinsp;=\u0026thinsp;100, TOMType = \"unsigned\", mergeCutHeight\u0026thinsp;=\u0026thinsp;0.45, power\u0026thinsp;=\u0026thinsp;16). The tree was then partitioned into modules using a dynamic tree-cutting algorithm. Different colors represented different gene modules and gray represented genes that did not fit into any of the known modules. Heat map of the relationships for module traits were then plotted to assess the association of each module with disparate clusters as traits, and finally the most relevant of the module genes with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was selected as the key module. With |module membership (MM)| \u0026gt; 0.8 and |gene significance (GS)| \u0026gt; 0.2, the module genes were obtained for building a protein-protein interaction (PPI) network. Specifically, the Search Tool for the Retrieval of Interacting Genes (STRING, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database was used to obtain protein interaction information to construct a PPI network, and then the Cytohubba plug-in (MCC, top\u0026thinsp;=\u0026thinsp;10) was utilized to screen 10 genes to be recorded as key proteins for clusters. Correlations between 10 key proteins and biomarkers were then analyzed by Spearman analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |r| \u0026gt; 0.3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Molecular networking\u003c/h2\u003e \u003cp\u003eTranscription factors (TFs) of biomarkers were predicted using the ChEA3 database. miRWalk (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Encyclopedia of RNA Interactome (ENOCRI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/index.php\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and miRTarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirtarbase.cuhk.edu.cn/\u003c/span\u003e\u003cspan address=\"https://mirtarbase.cuhk.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were utilized to predict miRNAs of biomarkers, and the intersection was taken as common miRNAs to construct a miRNA-TF-biomarker interaction network. Furthermore, the Drug-Gene Interaction (DGI) database was employed to predict potential drugs for biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Expression validation of biomarkers\u003c/h2\u003e \u003cp\u003eFor better validating the biomarker expression, we merged the SCI samples from GSE47681 into SCI groups separately to validate biomarker expression between SCI and control groups. To verify the expression of biomarkers, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed on tissues from 5 SCI samples and 5 control samples. RT-qPCR amplification was conducted under 40 cycles of 95\u0026deg;C for 1 min, 95\u0026deg;C for 20 s, 55\u0026deg;C for 20 s, and 72\u0026deg;C for 30 s. The primers for RT-qPCR were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the reference gene was GAPDH. The relative expression levels of the biomarkers were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of primers for PCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprimers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC1 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGCCTCTTACCCATGTATCAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC1 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATTCTGAGGAGGCAACACCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC2 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGCTTTCGGCACCTCTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC2 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAAACGTGGGGGCGATAGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC3 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGCAGGGACTTCAGCCTAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC3 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGGATTGTGTGAACGCCAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRT1 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGTCGGTGACAGCCTCAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRT1 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGTCTGCTTCTCCACCAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRT3 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTAGTTGAACGGGTCGAGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRT3 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAATAATCGTCCCTGCCGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDHA F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCCTTGGGCTTGAGCTTTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDHA R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACAGCCAGGCTTCTCAAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDHB F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCTTCTCTCTCCTGTGCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDHB R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGACGGACTCCTGCAGTTACC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCN5 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATTGGTCCCTCCTCTCCCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCN5 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTCTTCTCGCCTGGCATAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGAAGGTGGAGTCAACGGATTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGGGTGGAATCATATTGGAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Statistical analysis\u003c/h2\u003e \u003cp\u003eR software (v 4.2.2) was applied to execute all statistical analysis. Differences between groups were analyzed by Wilcoxon's test, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Eight DE-HLMRGs were significantly expressed between SCI and control samples\u003c/h2\u003e \u003cp\u003eIn GSE151371, 8 DE-HLMRGs showed significant expression differences between SCI and control samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. These genes were HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A). LDHA was upregulated in SCI samples compared to controls, while the remaining genes were downregulated. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb displayed the heatmap of 10 HLMRGs\u0026rsquo; expression in the 2 groups. Correlation analysis revealed significantly positive correlations among most of the HLMRGs. The strongest positive correlation was found between HDAC2 and SIRT1 (r\u0026thinsp;=\u0026thinsp;0.811, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the strongest negative correlation was observed between LDHA and KAT2A (r = -0.489, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. According to the chromosome localization map, HDAC1 was located on chromosome 1, HDAC3 on chromosome 5, HDAC2 on chromosome 6, etc. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Logistic regression model had a great ability to discriminate control and SCI\u003c/h2\u003e \u003cp\u003eThe AUCs of ROC curves for 8 DE-HLMRGs (biomarkers) all exceeded 0.7, indicating that these biomarkers had the ability to discriminate control and SCI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Meanwhile, ROC curve for logistic regression model had an AUC\u0026thinsp;=\u0026thinsp;0.979, which suggested a great performance with logistic regression model \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Nomogram model had a great ability to predict SCI\u003c/h2\u003e \u003cp\u003eOn the basis of biomarkers in GSE151371, a nomogram model was constructed to predict SCI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The C-statistic of the calibration curve was 0.987, suggesting that there was some covariance with apparent and the model worked better \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. DCA curve demonstrated that nomogram model could be advantageous for SCI patients, with higher overall benefit rates than individual biomarkers \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Biomarkers were enriched in some metabolic pathways\u003c/h2\u003e \u003cp\u003eAs an example, HDAC1 was enriched in 37 KEGG pathways \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e, 917 GO-BP \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e, 209 GO-CC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e, and 218 GO-MF \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e by GSEA, including \u0026lsquo;ubiquitin-mediated proteolysis\u0026rsquo;, \u0026lsquo;oxidative phosphorylation\u0026rsquo; and \u0026lsquo;valine, leucine, and isoleucine degradation\u0026rsquo; pathways, etc. The enrichment results for the remaining 7 biomarkers: HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A) were shown in \u003cb\u003eOnline Resource 1\u0026ndash;7\u003c/b\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Totally 15 immune cells differed in SCI and control groups\u003c/h2\u003e \u003cp\u003eThe proportional distribution of immune cell types in each sample was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea. Between SCI and control groups, 15 immune cells differed, including memory B cells, eosinophils, M0 Macrophages and so on \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. By Spearman correlation analysis, the differential immune cells most significantly correlated with the biomarkers were obtained, as HDAC1-memory B cells (r\u0026thinsp;=\u0026thinsp;0.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), HDAC2-memory B cells (r\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), HDAC3-neutrophils (r = -0.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), KAT2A-naive CD4 T cells/monophages (both r\u0026thinsp;=\u0026thinsp;0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), LDHA-plasma cells (r\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), LDHB-resting memory CD4 T cells (r\u0026thinsp;=\u0026thinsp;0.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), SIRT1-resting memory CD4 T cells (r\u0026thinsp;=\u0026thinsp;0.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and SIRT3-neutrophils (r = -0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The 2 clusters were able to distinguish\u003c/h2\u003e \u003cp\u003eThe 38 SCI samples from GSE151371 were classified into 2 clusters based on biomarkers \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The PCA revealed that 2 clusters possessed some degree of differentiation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. The expression levels of all biomarkers were higher in cluster 2, and only HDAC1, HDAC3, GCN5 (KAT2A), and SIRT3 had significant differences between both groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. Inter-cluster expression of 8 biomarkers displayed in the heatmap \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Totally 700 DEGs were enriched to 140 GO entries and 4 KEGG pathways\u003c/h2\u003e \u003cp\u003eA sum of 700 DEGs was screened (287 upregulated and 413 downregulated) between cluster 1 and cluster 2 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. These DEGs were enriched to 140 GO entries and 4 KEGG pathways including \u0026lsquo;Base excision repair\u0026rsquo;, \u0026lsquo;phosphatidylinositol signaling system\u0026rsquo;, \u0026lsquo;inositol phosphate metabolism\u0026rsquo; and so on \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Hallmark pathways between clusters\u003c/h2\u003e \u003cp\u003eThere were 7 pathways that differed significantly between two clusters, including \u0026lsquo;apical junction\u0026rsquo;, \u0026lsquo;apical surface\u0026rsquo;, \u0026lsquo;estrogen response early\u0026rsquo; etc. in Hallmark \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Only 4 types of immune cells were differed between clusters, activated dendritic cells, monocytes, activated CD4 memory T cells, and gamma delta T cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. Meanwhile, monocytes showed significantly positive correlation with HDAC3 and KAT2A, and activated dendritic cells significantly negatively correlated with HDAC3 and SIRT3 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. Cytolytic activity and parainflammation showed differences in clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Additionally, there were differences in the expression of HLA-A, HLA-B, HLA-DMA, HLA-DMB, HLA-DPB1, HLA-DQB2, HLA-DRA, and HLA-E among the clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. Regarding immune function, the study found strongly positive correlations between HDAC2 and APC co stimulation (r\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as well as LDHB and HLA (r\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. On the other hand, strongly negative correlations were found between LDHA and Type I IFN Response (r = -0.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The strongest positive correlations for HLA were found between LDHB and HLA-DQA1 (r\u0026thinsp;=\u0026thinsp;0.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg\u003cb\u003e)\u003c/b\u003e. The strongest negative correlations for HLA were found between LDHA and HLA-DMB (r = -0.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.9 A sum of 10 key proteins were selected\u003c/h2\u003e \u003cp\u003eFor WGCNA, there were no outlier samples in clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The β was set to 16 to nearly conform to the scale-free network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. Then, 6 gene modules were obtained by dynamic tree cutting \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. By correlation analysis, blue module (r = -0.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was considered as a key module \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. After the screening, 1,615 module genes were gained for further analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. According to the PPI network, 10 key proteins were selected, which were RPS9, RPS28, RPS2, RPS15, RPL13, FAU, RPL18, RPL36, RPL28 and RPL8 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. Correlation analysis revealed that KAT2A (GCN5) was most strongly positively correlated with RPL28 (r\u0026thinsp;=\u0026thinsp;0.797, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and LDHA was most strongly negatively correlated with RPL28 (r = -0.478, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eg\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.10 The miRNA-TF-biomarker and drug-biomarker networks\u003c/h2\u003e \u003cp\u003eA total of 41 TFs of biomarkers were predicted through the ChEA3 database, and the top 10 TFs were selected for network construction as ZNF883, ZNF614, ZNF644, ZNF280C, TP53, ZNF140, RBPJ, ZNF692, E2F4, and USF2. And 57 common miRNAs were predicted using miRWalk, ENOCRI, and miRTarBase databases. A miRNA-TFs-biomarker network was created based on the 8 biomarkers, top 10 TFs, and 57 common miRNAs, which demonstrated complex regulatory relationships \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e With the DGIdb database, 47 potential drugs were predicted by HDAC1, 39 by HDAC2, 38 by HDAC3, 24 by SIRT1, 3 by SIRT3, 1 by LDHA, no prediction by LDHB, and 126 potential drugs were predicted by GCN5 (KAT2A). Thus, a drug-biomarker interaction network was constructed containing 279 edges and 208 nodes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e, like niacinamide-SIRT3, suramin-SIRT3, and lapachone-SIRT1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.11 LDHA was high expression in SCI group, while HDAC2, HDAC3, GCN5 (KAT2A), LDHB, and SIRT3 were reversed\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the 8 biomarkers in GSE151371 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e, only LDHA was highly expressed in SCI group. In GSE47681, HDAC1, GCN5 (KAT2A), LDHA, and SIRT1 were high expression in SCI samples, while HDAC2, HDAC3, LDHB, and SIRT3 were low expression in SCI group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. By RT-qPCR, HDAC1 (not significant), HDAC2, GCN5 (KAT2A), LDHA, SIRT1 (not significant) were high expression, whereas HDAC3, LDHB (not significant), and SIRT3 were low expression in SCI samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. Among 8 biomarkers, LDHA high expression in SCI, HDAC3, LDHB, and SIRT3 were all low expression although LDHB was not significant in RT-qPCR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSCI is a deadly and disabling disease that has been proven to be a polygenic disease, and its pathogenesis is related to changes in many genes. In SCI and brain injury, histone lactate is involved in various cellular events and plays a crucial role in immune regulation and homeostasis maintenance. This study was based on the GSE151371 and GSE47681 datasets, investigating 10 HLMRGs and selecting 8 biomarkers (HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A)) based on the ROC curve LDHA. And conduct a series of bioinformatics analyses on it to provide reference for the exploration of new biomarkers and molecular mechanisms in SCI.\u003c/p\u003e \u003cp\u003eIn our study, there were expression differences among 8 HLMRGs in different datasets and clinical blood samples, possibly due to the inconsistency of the samples. We will explore and verify this in future experiments. In the data, validation set, and tissue samples obtained by the author, LDHA and GCN5 (KAT2A) was highly expressed in the SCI group, while HDAC3 and SIRT3 were low expressed. Guan et al. (2021) established SCI models through in \u003cem\u003evivo\u003c/em\u003e and in \u003cem\u003evitro\u003c/em\u003e experiments, and detected an increase in LDHA and lactate levels in SCI rats and LPS induced PC12 cells. In previous studies, GCN5 (KAT2A) can promote axonal growth by regulating acetylated microtubule proteins (Lin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The loss of GCN5 (KAT2A) activity promotes neuronal apoptosis through upregulation of Egr-1-dependent BH3-only protein Bim (Wu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This indicates that GCN5/KAT2A has a positive effect on the recovery of nerve injury. In our study, GCN5/KAT2A was upregulated in SCI samples, indicating that after spinal cord injury, GCN5/KAT2A is activated and promotes the process of injury repair. However, the specific mechanism still needs further research. Meanwhile, this study also found low expression of HDAC3 and SIRT3 in SCI. Wahane (Wahane et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also found that HDAC3 activity mediates multiple transcriptional responses in SCI in the myeloid and glial cells. Studies have shown that the silencing and inhibition of HDAC3 can improve neurological function and spinal cord edema after SCI, playing a neuroprotective role (Dai et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a rat SCI model, Downregulating HDAC3 can inhibit the activation of the JAK2/STAT3 pathway by upregulating miR-19b-1-5p, thereby promoting the recovery of SCI. The low expression of HDAC3 and SIRT3 in SCI is a protective mechanism for SCI (Niu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Zinc defends Parthanatos and promotes functional recovery after SCI through sirt3-mediated antioxidant stress and mitochondrial phagocytosis (Jiang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, activation of the NMDAR/AMPK/PGC-1α/SIRT3 signaling pathway through distal limb ischemic preconditioning can protect against spinal cord ischemia-reperfusion injury in mice (Gu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, SIRT3 can affect SCI and prognosis through various mechanisms. In clinical applications, SIRT3 inhibitors such as Suramin have been used for the treatment of tumors. In summary, LDHA and SIRT3 may be effective biomarkers for the diagnosis or treatment of SCI, providing new mechanisms and pathways for the treatment of spinal cord injury.\u003c/p\u003e \u003cp\u003eDuring SCI, multiple immune cells are involved in the progression and recovery of the disease (Xu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gir\u0026oacute;n et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Milich et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study found that there are differences in four types of immune cells, including activated dendritic cells, monocytes, activated CD4 memory T cells, and gamma delta T cells. In previous studies, dendritic cells have been influenced by HDAC3 in various diseases such as Alzheimer's disease (AD) and tumors (Han et al.) 2022; Deng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Dendritic cells are divided into plasmacytoid dendritic cells (PDCs) and conventional dendritic cells (CDCs). In both vivo and vitro experiments,it has been confirmed that histone deacetylase 3 (HDAC3), as an important epigenetic regulatory factor, is highly expressed in PDCs. The lack of Hdac3 can seriously affect the development of dendritic cells (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, there was a negative correlation between Dendritic cells and HDAC3. Therefore, in the immune microenvironment of SCI, Dendritic cells may be influenced by multiple factors, and further mechanism research is needed. Monocytes are important immune cells. HDAC3 mediates the inflammatory response of human monocytes and macrophages, determining the effects of HDAC inhibitors (HDACI) on human monocytes and macrophages, including their polarization, activation, and ability to induce endotoxin tolerance (Ghiboub et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ). This study also found a positive correlation between Monocytes and HDAC3 in SCI diseases, adding new directions for Monocytes research and providing new ideas for the study of inflammatory mechanisms in SCI. In the study of mice with HDAC3 expression deficiency, Eshleman (Eshleman et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found an increase in the accumulation of symbiotic-specific activated CD4 memory T cells in the gut of mice, revealing a negative correlation between HDAC3 and activated CD4 memory T cells in inflammatory diseases. The same phenomenon was also observed in SCI diseases in this study. At the same time, we found a negative correlation between activated CD4 memory T cells and SIRT3, and a significant positive correlation with LDHA, which is consistent with previous research results in tumors and inflammatory diseases (Hou et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zou et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) are approximately 22 nt RNA fragments that primarily regulate protein expression by binding to one or more target sites of mRNA transcription and inhibiting translation. They may be involved in multiple developmental pathways and gene regulatory mechanisms, and serve as the basis for a series of genetic disease processes and phenotype determinants. There are a large number of miRNAs in the central nervous system, playing an important role in the progression of SCI (Silvestro et al. 2022). Studies have shown that modulation of the NEAT1/miR-128-3p/AQP4 axis can alleviate neuropathic pain induced by SCI (Xian et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Long non-coding RNA CASC9 (lncRNA CASC9) regulates levels of MDA, lactate, TNF - α, and IL-1 β through miR-383-5p, playing a protective role in antioxidant stress, inflammation, and cell apoptosis in SCI (Guan et al. 2021). Furthermore silencing miR-324-5p can alleviate Sirt1-induced SCI in rats (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on our research, we speculate that miRNAs may be a key factor in the progression of SCI.\u003c/p\u003e \u003cp\u003eThe study identified and validated biomarkers associated with histone lactylation modification in spinal cord injury. Revealed the relationship between immune infiltrating cells and predicted drug targets. This provides a reference for exploring and discovering new biomarkers in SCI, and thus for the prevention and treatment of SCI. This informs the mining and exploration of novel biomarkers in SCI, which in turn reference the prevention and treatment of SCI. However, this study has certain limitations, such as a relatively limited sample size compared to other papers. Secondly, although our bioinformatics analysis suggests that HLMRGs may be a new direction for studying SCI mechanisms and potential therapeutic approaches.this conclusion still needs to be validated through in vitro and in vivo experiments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of TheSecond Hospital of Anhui Medical University (Data 2023/7/6. /No YW2023-118).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eThe authors affirm that human research participants provided informed consent for publication of the datas.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Anhui Provincial Clinical Research Transformation Project (Grant number 202304295107020009).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Y S, J G and J J. The first draft of the manuscript was written by Y S and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnjum A, Yazid MD, Fauzi Daud M et al (2020) Spinal Cord Injury: Pathophysiology, Multimolecular Interactions, and Underlying Recovery Mechanisms. 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Nat Immunol 25:66\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41590-023-01682-z\u003c/span\u003e\u003cspan address=\"10.1038/s41590-023-01682-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spinal cord injury, Histone lactylation modification, Cluster, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-4884820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4884820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe biological functions of histone lactylation (HLA) modification-related genes (HLMRGs) in spinal cord injury (SCI) are unknown. Therefore, we explored the expression and molecular mechanism of HLMRGs in SCI by bioinformatics means.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGSE151371, GSE47681, and 10 HLMRGs were incorporated in this study. Biomarkers were screened based on the receiver operating characteristic curves for the modeling of logistic regression and nomogram. Additionally, gene set enrichment analysis (GSEA) was executed to detect biomarkers\u0026rsquo; functions. Samples were clustered based on biomarkers, identifying distinct groups. Differential expressed genes between these clusters were determined, and inter-cluster analyses of Hallmark pathways, HLA genes, and immune functions were conducted. Weighted gene co-expression network analysis (WGCNA) was used to select cluster-related module genes for protein-protein interaction (PPI) network construction, pinpointing key proteins. miRNA-TF-biomarker and drug-biomarker networks were established. Biomarker expression was validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn GSE151371, 8 biomarkers (HDAC1, HDAC2, HDAC3, SIRT1, SIRT3, LDHA, LDHB, and GCN5 (KAT2A)) with AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were significantly different expressed between SCI and control samples. 8 biomarkers were different expressed in 2 clusters. By differential expression analysis of cluster 1 versus cluster 2, enriched in \u0026lsquo;phosphatidylinositol signaling system\u0026rsquo; etc. Finally, a miRNA-TF-biomarker network comprising eight biomarkers were constructed. The expression validation of eight biomarkers by RT-qPCR, LDHA were high expression, while HDAC3 and SIRT3 were low expression in SCI.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn summary, 8 biomarkers playing an important role in SCI were identified, which provided in-depth references for HLMRGs in SCI.\u003c/p\u003e","manuscriptTitle":"Exploration of biomarkers associated with histone lactylation modification in spinal cord injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-05 18:51:32","doi":"10.21203/rs.3.rs-4884820/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"415a57f8-260f-468e-b350-487f0d79c536","owner":[],"postedDate":"September 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T08:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-05 18:51:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4884820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4884820","identity":"rs-4884820","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}