Identification of key genes and pathways associated with spinal cord injury

Identification of key genes and pathways associated with 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 Identification of key genes and pathways associated with spinal cord injury liu kai, weikang Ma, gang Wen, Xinnan Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6162182/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 Spinal cord injury (SCI) is a devastating traumatic injury, which can lead to severe motor, sensory and autonomic nervous dysfunction. This study is based on the GSE5296 expression profile dataset, including 42 control samples and 54 SCI samples. Differential analysis was used to identify differentially expressed genes between control and SCI mices, and weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression network. A protein-protein interaction network was constructed based on the intersection of differential genes and module genes. Nine hub genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, Cd86 and Itgax) were selected by Cytoscape's comprehensive algorithm based on MMC, MNC, EPC, degree and proximity. In addition, the results of the hub gene were verified, and the Receiver Operating Characteristic (ROC) curve was drawn to evaluate the diagnostic efficiency. Subsequently, the expression levels of these genes were confirmed and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). These hub genes are enriched in immune-related pathways, especially leukocyte-mediated immunity, phagocytosis and membrane transport pathways. Gene Set Enrichment Analysis (GSEA) was used to explore the molecular mechanism of biomarkers. In this study, CIBERSORT was used to identify cell types to evaluate the inflammatory state of SCI. To study the relationship between key markers and infiltrating immune cells. In addition, we also use Cytoscape software to construct the Competing Endogenous RNA (ceRNA) regulatory network of biomarkers and obtain potential targeted drugs in the DGIdb database (Drug-Gene Interaction database). Finally, the reliability of Hub gene was verified by using GSE132242 dataset. These findings provide a new target for future research and a new perspective for the pathogenesis of spinal cord injury. Spinal cord injury weighted gene co-expression network analysis co-expression network hub gene Therapeutic target Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Spinal cord injury, as a severely traumatic type of central nervous system damage, is closely associated with significant dysfunction of the autonomic nervous system. It exhibits relatively high morbidity and mortality rates, frequently leading to permanent neurological deficits, which severely disrupt patients' quality of life and impose heavy physical and mental burdens and suffering on them 1 .Spinal cord injuries are typically induced by high-intensity external forces, such as traffic accidents, fall injuries, and violent assaults 2 . Additionally, infections, tumors, spinal degenerative diseases, ischemia-reperfusion injuries, and vascular-related factors can also trigger this condition 3 . After injury, patients present abnormal manifestations of sensory, motor, and autonomic nerve functions below the injury level 4 . Given the irreversible nature of spinal cord injury and the long treatment cycle, the treatment of this disease has become a crucial problem that urgently needs to be overcome in the medical field 5 . By 2021, there were 759,302 patients with traumatic spinal cord injury in China, with an annual increase of 66,374 cases, showing a continuously rising trend year by year 6 . Spinal cord injury can be divided into two major stages in the process: primary injury and secondary injury 7 . During the primary injury stage, the spine directly endures external force impacts, thereby resulting in fractures, vertebral body displacement, bone fragments, and spinal cord ligament tears, which cause the destruction of nerve parenchyma, rupture of the axonal network, bleeding, and tissue edema, ultimately leading to the imbalance of the homeostasis of the spinal cord environment 6 . Secondary injury is a series of cascade reactions triggered on the basis of primary injury, which can last from several weeks to several months. The main characteristics include the infiltration of pro-inflammatory cytokines, an increase in reactive oxygen species, a loss of ion homeostasis, mitochondrial dysfunction, and cell death 8 . Despite extensive research efforts on spinal cord injury, no substantial major breakthroughs have been achieved in terms of translation into clinical treatment 9 . Therefore, in-depth exploration of its internal mechanism to identify potential therapeutic targets is of particular importance. Circulating immune cells can effectively reflect tissue damage and the body's immune response and can be conveniently obtained from routine blood samples 10 . By regulating the immune-inflammatory response in the microenvironment, it is expected to limit the extent of spinal cord injury and promote the functional recovery process 11 . In recent years, numerous research reports have indicated that immune cell infiltration plays a central role in the healing process of spinal cord injury. Chronic spinal cord injury can impair the normal function of CD8 T cells by up-regulating the expression level of programmed cell death protein 1 12 . γδ T cells are recruited to the spinal cord injury site, thereby exacerbating the inflammatory response and further deteriorating nerve injury. The CCL2/CCR2 signal transduction pathway plays a crucial role in the aggregation of T cells at the spinal cord injury site and holds the potential to be developed into a novel therapeutic target in the future 13 .In summary, accurate assessment of the degree of immune cell infiltration and in-depth analysis of the composition of infiltrating immune cells are of indispensable significance for deeply dissecting the molecular mechanism of spinal cord injury and exploring new immunotherapy targets. Therefore, this study is dedicated to searching for relevant biomarkers of spinal cord injury and deeply exploring the specific role and mechanism of immune cell infiltration in the occurrence and development of spinal cord injury. Methods Data sources and processing Genes and clinical information were downloaded from the GEO dataset (https://www.ncbi.nlm.nih.gov/geo/) 14 , which was employed to identify differentially expressed genes (DEGs) and hub genes related to SCI. The GSE5296 contains gene expression profile data from 96 mice in different groups at different time points. During the procedure, moderate injury was induced at the T8 spinal segment under isoflurane anesthesia. The mice were euthanized at 0.5 h, 4 h, 24 h, 7 days, and 28 days after injury, and tissues were taken from the spinal cord section of 0.4 cm in length from the site of impact and the immediately adjacent rostral and caudal regions, in both SCI group per three mice and control group per two mice. A total of 12 individuals were used for each injury time point. Screening of DEGs To highlight DEGs, gene expression analysis was conducted between the SCI and control groups using the “limma” package of R. Genes that met the criteria (|logFC| > 1, p < 0.05) were defined as DEGs. WGCNA construction WGCNA was used to analyze the gene expression patterns of multiple samples and identify highly analogous expression modules and significant module genes, which are extensively utilized in the exploration of associations between expression profiles and clinical information 15 .The optimal soft threshold was first determined, and based on the topological overlap matrix, hierarchical clustering trees were constructed, allowing genes with similar expression patterns to be selected and categorized into modules. The modules were then tested for correlations with the target clinical traits, and the gene network and core genes were identified. We focused on the modules that were highly correlated with SCI, and the genes of the featured module were ascertained for further research. Selection of candidate genes and functional pathway analysis As DEGs involved in the development of SCI warrant extensive study, the intersection of DEGs and focal module genes was retained for further analysis. To explore the biological functions of these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on the obtained genes using the “clusterProfiler” package in R 16 . The GO analysis was pooled and divided into three sections: biological process, molecular function, and cell component 17 . Selection of hub genes in the protein–protein interaction (PPI) network Using the STRING database 18 ,a functional PPI association network of module genes was generated. Cytoscape was used to visualize the network of genes clustered in the module 19 . Subsequently, the top 10 genes in the gene regulatory network were calculated according to the MMC, MNC, EPC, degree, and closeness method related to algorithms using the CytoHubba plug-in 20,21 . The genes that recurred in each result of the top 10 hub genes were selected as the core genes to further explore the potential mechanism and biological functions. Performance of the hub genes related to SCI The performance of the top nine hub genes was measured based on the following three aspects: GO analysis was performed to identify the biological pathways significantly related to the hub genes. The expression levels of the hub genes between the SCI and control groups were compared using the t-test or Wilcoxon rank-sum test. We then evaluated the differences in gene expression at different time points after injury. Moreover, receiver operating characteristic (ROC) curve analysis was performed to describe the discriminative power for each hub gene, and the area under the ROC curve (AUC) was calculated. Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) was employed to functionally elucidate the biological significance of characteristic genes 22 . The gene set of “c2.cp.kegg.v11.0.symbols” from the Molecular Signatures Database (MSigDB, http://software.broadinstitute.org/gsea/msigdb) 23 was selected as the reference set. To obtain the normalized enrichment score for each analysis, 1,000 gene set permutations were carried out. An FDR < 0.05 was considered as significant enrichment. Immune Cell Infiltration Analysis CIBERSORT method was employed to determine the relative proportion of immune cells within the tissue of mice with spinal cord injury (SCI). Simultaneously, the merged expression data were annotated, and the immune cell infiltration was calculated based on the expression profiles of mouse tissues 24 . Subsequently, a comparison was made regarding the relative levels of 25 immune cells between the SCI group and the control group. With the aid of the correlation heatmap generated by the "corrplot" package, the interrelationships among these 25 types of infiltrating immune cells were uncovered. Finally, the "ggstatplot" and "ggplot2" packages were utilized to analyze and visually display the Spearman correlation between the key biomarkers and the immune infiltrating cells. ceRNA Network Construction The target miRNAs of biomarkers were predicted by employing the ENCORI database (https://starbase.sysu.edu.cn/, with the screening criterion of CLIP - DATA ≥ 1) and the miRWalk database (http://mirwalk.uni - heidelberg.de/, where the screening criterion is a score ≥ 0.95). Subsequently, the lncRNAs that target the miRNAs were predicted using the ENCORI database (with the screening criteria of clipExpands > 1 and degradeExpands > 1). The lncRNA - miRNA - mRNA network was then constructed with the utilization of Cytoscape (version 3.9.0). Prediction of Potential Drugs By utilizing the Drug-Gene Interaction Database (https://www.dgidb.org/search interactions), the potential drugs applicable to the treatment of spinal cord injury were predicted. Subsequently, the Cytoscape software (version 3.9.0) was employed to conduct the visual display of the biomarker-compound pairing network, so as to more intuitively present the correlative characteristics and potential action patterns between the two, thereby providing data support and a visual basis for the in-depth exploration of spinal cord injury treatment strategies. Statistical Analysis All statistical tests were carried out using R software version 4.0.3. All statistical p-values were two-sided, and a p-value less than 0.05 was considered statistically significant. This criterion serves as a reliable basis and a rigorous guideline for the data analysis throughout the entire research. Results Identification of DEGs The expression profiles of 54 mice with SCI and 42 control mice were downloaded from the GEO database. Of these genes, 149 upregulated genes were screened at the filter condition (p 1) and were selected for subsequent analysis. The volcano plot and heatmap are shown in Figure 1A and B, and DEGs are listed in Supplemental Table 1. WGCNA and key modules To further construct the co-expression network and clarify the correlations between the expression profiles and disease, WGCNA was performed. By calculating the inter-gene correlation coefficients, a suitable soft threshold was selected to make the network close to the scale-free network. After determining a soft threshold value of 12 (Figure 1C-D), a scale-free network was constructed following scale independence and mean connectivity. After hierarchical clustering, nine modules were detected using a dynamic tree cut algorithm. After calculating the dissimilarity of module eigengenes and setting a cut-off height of 0.2, corresponding to a correlation of 0.8, the modules were merged, and the four key modules (blue, green, turquoise, and gray) were finally retained. As shown in Figure 1E, each branch of the clustering tree represents a DEG, and each color represents a module. The number of genes in each module was 5,401 in the turquoise module, 1,788 in the blue module, 1,643 in the brown module, 1,583 in the yellow module, 816 in the green module, 486 in the red module, 478 in the gray module, 385 in the pink module, and 478 in the gray module, and two genes were not assigned to any module (Supplemental Table 2). According to the correlation coefficient between the eigengene network and clinical traits, the turquoise module showed high adjacency to SCI (r = 0.47, p < 0.001, Figure 1F), and was selected as the biologically meaningful module for further study. Candidate genes and functional analysis Based on the results obtained from the differential analysis and WGCNA, the DEG highly associated with SCI were emphasized, so DEGs and genes that belonged to the turquoise module were filtered as candidate genes. GO analysis showed that in the biological process category, the candidate genes were mainly enriched in pathways related to phagocytosis, such as positive regulation of phagocytosis, myeloid leukocyte activation, and positive regulation of cytokine production (Figure 2A). The membrane raft, membrane microdomain, and NADPH oxidase complex genes were enriched in the cellular component category (Figure 2B). The genes in the molecular function process were involved in immunoglobulin binding, immune receptor activity, and pattern recognition receptor activity (Figure 2C). Moreover, KEGG analysis of DEGs demonstrated that 24 pathways were enriched, and the candidates were involved in osteoclast differentiation, phagosome, neutrophil extracellular trap formation, B cell receptor signaling pathway, and FcgR-mediated phagocytosis (Figure 2D). The threshold for the functional analysis was a p-value of <0.05. Detection of the hub genes in the PPI network The PPI network was constructed based on the genes included in the critical turquoise module, which contained 97 nodes and 906 edges in the network (Figure 3A). Using the CytoHubba plug-in in Cytoscape, the top 10 genes were confirmed, and the nine overlapping genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, CD86, and Itgax) were maintained as core genes by the integrated sorting of algorithms, and the network was visualized (Supplemental Table 3, Figure 3B). Underlying mechanism and differences in hub genes between the groups As shown in figure 4A-B, based on GO pathway enrichment analysis, immune-related pathways, especially leukocyte-mediated immunity, are significantly enriched, and membrane transport is a common feature of the nine genes. Through KEGG enrichment analysis, it was found that key genes were potentially associated with immune response and inflammatory signals (Figure 4C). Distinguishing ability and validation of the hub genes ROC analysis was performed to further investigate the roles of the hub genes. As illustrated in Figure 4D、E, the AUC of the time-dependent ROC curve was >0.7, which indicates the good discriminative performance of the nine hub genes. The most accurate variable, as determined by the AUC, was CD86 (AUC = 0.898), followed by Ptprc (AUC = 0.869) and Fcgr3 (AUC = 0.847). The combined AUC of these nine hub genes is 0.936.(95%CI=0.887-0.976) As shown in Figure 5, the expression level varied markedly during the short-term period after injury, but the difference was not significant (p > 0.05). At ≥24 h after the induction of SCI, the expression levels significantly differed between the two groups, and we also observed that the expression of hub genes in the SCI group increased over time and then stabilized after 7 days. Signaling Pathways Involved in hub Genes Through GSEA analysis, we evaluated the signal pathways involved in hub genes. Our results show that CD86 (Figure 6A), FCGR3 (Figure 6B), TYROBP (Figure 6C), ITGB2 (Figure 6D), FCGR1 (Figure 6E), ITGAX (Figure 6F), PTPRC (Figure 6G) FCER1G (Figure 6H) and MPEG1 (Figure 6I) are all positively correlated with complement and coagulation cascade, NF-κB signal pathway and extracellular matrix-receptor interaction. Immune cell infiltration in SCI As shown in figure 7A, we use the CIBERSORT algorithm to calculate the proportion of immune cell infiltration in each sample. Figure 7B shows the proportion of immune cell infiltration in SCI and the control group, respectively. Compared with the control group, the activated proportion of T cells CD8 Naive，M0 macrophages and DC active cell in SCI group was significantly higher. Compared with the SCI group, the activated proportion of Neutrophils cell, Monocyte cell, T cells CD4 memory, Plasma cell and Th17 cells in the control group increased significantly. The results showed that there were significant differences in immune microenvironment between the SCI group and the control group. Then the correlation between immune cells was further estimated, and the results showed that plasma cells were positively correlated with mast cells and neutrophils. There was a significant negative correlation between M0 macrophages and neutrophils, initial B cells and plasma cells (Figure. 7D). In order to understand the value of Hub gene in immunity, we analyzed the correlation between 9 Hub genes and immune cell infiltration (Figure 7C). The results showed that the hub genes were positively correlated with resting NK cells, M1 and M2 macrophages, monocytes and resting dendritic cells, and negatively correlated with initial B cells, T cell helper follicular cells, plasma cells, activated NK cells and M0 macrophages, suggesting that Hub gene may be involved in the immune process of SCI. Diagnostic genes based ceRNA network To deeply analyze the regulatory mechanisms of biomarkers, this research initiated the construction of a ceRNA regulatory network, with the relevant illustration shown in Figure 8. The construction process began by using the miRWalk and ENCORI databases to predict microRNAs (miRNAs) associated with biomarkers. Through this step, 126 pairs of messenger RNA (mRNA)-miRNA associations were successfully obtained. Subsequently, based on the 118 miRNAs acquired above, a search for related long non-coding RNAs (lncRNAs) was carried out in the ENCORI database, and finally 155 pairs of miRNA-lncRNA combinations were determined. After completing the above preliminary data preparation work, the ceRNA network architecture was established with the assistance of Cytoscape software. This network encompasses 258 nodes, specifically including 7 mRNAs, 118 miRNAs, and 133 lncRNAs, and the interconnections among the nodes form 281 edges. (Supplemental Table 4) The successful construction of this network lays a solid foundation for further exploring the role of biomarkers under the ceRNA regulatory mechanism. Potential drugs targeting the diagnostic genes In the process of exploring therapeutic drugs for spinal cord injury, this study focused on the discovery of potential drugs. By conducting a comprehensive search in the DGIdb database for specific biomarkers, it aimed to obtain drug information with targeted therapeutic effects. From the search results, among the numerous potential drugs discovered, the numbers of drugs targeting different biomarkers varied. Specifically, there were a total of 9 drugs targeting CD86, while the numbers of drugs targeting FCER1G, ITGAX, ITGB2, and PTPRC were 3, 3, 36, and 18 respectively, and the relevant data were clearly presented in Figure 9.Based on the association information between the above drugs and the corresponding biomarkers, a network system composed of messenger RNA (mRNA) and drugs was further constructed. This network encompassed 69 nodes, including 5 mRNAs and 64 drugs, and the interconnections among the nodes formed 69 edges.(Supplemental Table 5）It is worth noting that in the field of clinical trials for spinal cord injury, it has been found through practical verification that 31 of the drugs discovered above have been proven to have definite therapeutic effectiveness. It provided an important basis for drug selection and research in the clinical treatment of spinal cord injury. Cross‑validation of external datasets To ensure the precision of the research results, we carried out cross-validation using the GSE 132242 dataset, with the focus on detecting and analyzing the expression levels of nine key genes. This dataset was sourced from the GEO database, and after downloading, we immediately initiated an in-depth analysis.Upon comparison, it was found that among these nine key genes, except for Mpeg1, the expression level characteristics of the remaining eight genes in the GSE 132242 dataset were generally consistent with those in the GSE 5296 dataset. Specifically, in terms of expression levels, the control group was significantly lower than the spinal cord injury (SCI) group, and these eight key genes exhibited significant differences between the two groups. (Figure 10.) Discussion Spinal cord injury, as a highly destructive neurological disorder, can lead to abnormalities in body movement function, sensory ability, and autonomic nerve function below the injury level, which has an extremely severe negative impact on the quality of life of patients 4 . The pathological process can generally be divided into two stages: primary injury and secondary injury. The primary injury refers to the initial mechanical damage, while the secondary injury includes a series of changes mainly characterized by inflammatory responses and apoptosis 25 . At the same time, current research findings show that the infiltration of immune cells in the spinal cord injury area plays a non-negligible role in the occurrence and development process of this disorder 26 . Based on this, this study is committed to exploring the biomarkers associated with spinal cord injury and deeply analyzing the role and mechanism of immune cell infiltration therein. During this research process, in order to deeply explore the potential genes and signaling pathways involved in the pathogenesis of spinal cord injury, we adopted a variety of expression analysis methods and combined with the weighted gene co-expression network analysis (WGCNA) technique. Through the intersection analysis of differentially expressed genes (DEGs) and module genes that show a high correlation with spinal cord injury, a series of candidate genes were successfully screened out 27 . Through comparative analysis, 149 differentially expressed genes were identified between the spinal cord injury group and the control group. Further research found that nine key hub genes are mainly concentrated in immune-related, phagocytosis-related, and inflammatory signal transduction pathways. These genes play a crucial role in maintaining and regulating the physiological functions of the body. After more in-depth verification experiments, we finally determined that these hub genes are the key molecular targets involved in the pathogenesis of spinal cord injury, providing an important theoretical basis for further exploring the mechanism of spinal cord injury and developing targeted treatment strategies. Among the DEGs that were found to be upregulated after SCI, CCL3 was found to be the most highly upregulated. CCL3 is an important member of the CC chemokine family and contributes to the mediation of acute inflammation 28 . CCL3 is reportedly involved in the inflammatory response and secondary damage after SCI, and could be considered a possible target to maintain the immune response for functional recovery 29 . Among the nine hub genes identified (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, CD86, and Itgax), Itgb2 was at the top of the ranking according to the MCC algorithm. Itgb2 encodes a protein called CD18 (integrin beta chain-2), which is a member of the integrin family 30 . Itgb2 plays a critical role as a membrane receptor in cell adhesion and cell recognition and is involved in many biological processes, including embryogenesis, hemostasis, and immune response 31 . In our study, Itgb2 was determined to be associated with the progression of SCI, which is consistent with the findings of previous studies. Further, Mpeg1, which encodes Perforin-2, was found to be highly expressed in macrophages and other phagocytes. Perforin-2 acts in the phagosome and breaks down the phagocytosed cells 32 ,but the exact mechanism underling intracellular transport and delivery to phagosomes remains unclear. Tyrobp has been developed as a prognostic biomarker of gastric cancer and clear cell renal cell carcinoma 33 . A previous study reported a clear reduction in Tyrobp gene expression after injury to sensory neurons 34 , which has been confirmed as an essential component of adult microglial functionality and ongoing neuropathic pain. Fcgr1, which encodes the proteins CD64, belongs to the leukocyte IgG receptor family, and Fcgr1 is the only high-affinity receptor for the Fc region of immunoglobulin gamma 35 . Further, Negishi-Koga et al. reported that the intensity of Frcg signaling regulates the level of osteoclastogenesis36. CD86 is a costimulatory molecule and also a protein marker of M1-type macrophages. Related studies have shown that macrophages can undergo M1-type or M2-type polarization after spinal cord injury, thus exerting different functions, which is crucial for the tissue immune response and homeostasis repair 37 . The protein encoded by protein tyrosine phosphatase, receptor type C (Ptprc) belongs to the protein tyrosine phosphatase (PTP) family, and it exerts its effects by directly interacting with the components of the antigen receptor complex or activating various Src family kinases necessary for antigen receptor signaling 38 . This study indicates an association between Ptprc and oxidative stress after SCI. The integrin αX encoded by Itgax is a cell surface protein that is involved in the adhesion between cells and the extracellular matrix, as well as cell migration and other processes 39 . After SCI, Itgax may play a role in the migration of immune cells to the injury site, as well as the migration and differentiation of neural stem cells. The receptors encoded by Fcgr1 and Fcgr3 are expressed on immune cells and play a key role in spinal cord injury (SCI) 40 . Fcgr1 can activate immune cells to trigger inflammation, is expressed in nerve cells, promotes nerve cell survival and axonal regeneration through signaling pathways, and mediates the activation of microglia to affect the repair microenvironment 41 ; Fcgr3 can recruit immune cells and regulate polarization, interact with the complement system to reduce damage, and affect neuronal signaling and connection reconstruction in synaptic plasticity 42 . Expression of all nine hub genes was significantly higher in the SCI group than in the control group at ≥24 h after injury. For the nine hub genes, CD86 achieved the highest AUC, and the AUC of the other eight genes ranged from 0.7 to 0.9, indicating the strong predictive accuracy of all genes, particularly CD86. CD86, which is extensively expressed on dendritic cells, monocytes, and activated B lymphocytes, was the main co-factor in the induction of T lymphocyte proliferation and interleukin-2 production 43 . SCI not only causes a huge psychological burden and severe limb dysfunction but also activates vascular, inflammatory, and immune processes. This study found a potential association between key genes and immune response and inflammatory signals. Inflammation plays an important role in the cascade of secondary injury of SCI. During SCI, injured cells release injury-related molecular patterns, activate pattern recognition receptors expressed by immune cells, and promote recruitment of resident and peripheral immune cells in the lesion site 44 . These cells can secrete a large number of pro-inflammatory cytokines, such as interleukin-1 (IL-1), IL-6 and tumor necrosis factor-α (TNF-α), which further aggravate the inflammatory response and seriously damage the microenvironment of nerve regeneration, resulting in the aggravation of SCI 45 . Therefore, effectively inhibiting the excessive inflammatory response of SCI can promote the recovery of neurological function and tissue regeneration, and improve the prognosis of SCI. Cytokines are an important part of innate immune response. There is a bidirectional interaction between the neurological and immune systems 46 . After SCI, due to the loss of microglia, an astrocytic fibrous scar gradually develops, leading to enhanced immune infiltration, neuronal necrosis, recovery of damage function, and severely impacted tissue regeneration 47 . In order to more accurately evaluate the effect of immune cell infiltration in SCI, we used CIBERSORT to analyze immune cell infiltration through mouse tissue expression profile. The immunocyte infiltration of M0 macrophages, immature CD-8 T cells and DC-activated cells increased significantly, indicating that it may be related to the occurrence and progression of SCI. In addition, the immune cell infiltration at different time points after spinal cord injury may have the characteristics of dynamic changes. Studies have shown that after SCI, macrophage M1 phenotypic polarization increases, inflammatory response intensifies, damaging neuronal cells 48 .our study also found that there is a correlation between immune cells and key genes. According to one study, Itgb2 can form various adhesion interactions with mononuclear cells, macrophages and granulocytes and mediate the uptake of complement coated particles 49 . Given that the nine hub genes identified in this study were mainly enriched in immune-related pathways and were of significance to modulation after injury, approaches targeting immune factors or proinflammatory cytokines may provide important insights in terms of therapeutic development. Within the scope of this research, in addition to the previously mentioned key hub genes, the non-coding RNAs closely related to these genes have also become the focus of investigation. A considerable number of previous studies have demonstrated that non-coding RNAs play an extremely crucial regulatory role in the entire process of the occurrence and development of spinal cord injury. Among them, differentially expressed miRNAs have been verified to have a close association with the pathological responses induced by spinal cord injury. Tigchelaar 50 et al carried out sequencing work on the blood and cerebrospinal fluid of 39 patients with spinal cord injury and 5 healthy controls. The research results indicated that in the cerebrospinal fluid of the spinal cord injury patient group, as many as 190 miRNAs exhibited differential expression states, while there were 19 in the serum. Moreover, 24 hours after the occurrence of injury, the miRNA expression profile in the cerebrospinal fluid could accurately determine whether the ASIA grade had improved. From this, it can be inferred that the level of miRNAs in the cerebrospinal fluid is closely related to the prognosis of human spinal cord injury. lncRNAs , as a type of non-coding RNA with a length exceeding 200 base pairs, are deeply involved in gene expression and a variety of physiological and pathological processes 51 . With the continuous deepening of research on lncRNAs in recent years, numerous scholars have successively detected that lncRNAs in patients with spinal cord injury present abnormal expression phenomena and play an important role in the regulatory mechanism of the occurrence and development of spinal cord injury. In the research on the mouse spinal cord injury model by Ding et al, it was found that the differential change of lncRNA reached its peak one week after injury and gradually decreased after three weeks 52 . This phenomenon strongly implies an inherent connection between lncRNA and the degree of spinal cord injury in mice. In the long term, the combined application strategy of lncRNA and miRNA is highly likely to have significant potential value for the protection and repair of secondary nerve injury after spinal cord injury. Based on the above research background, this study successfully constructed a competing endogenous RNA (ceRNA) network with 258 nodes (covering 7 messenger RNAs, 118 miRNAs, and 133 lncRNAs) and 281 edges. The construction of this network has laid a solid foundation for the subsequent in-depth exploration of the mechanism of spinal cord injury. Spinal cord injury often causes patients to suffer from severe neurological dysfunctions and a significant decline in quality of life. However, at present, the efficacy of conventional clinical drugs in the treatment of spinal cord injury remains unclear. Therefore, the development of highly effective drugs for the treatment of spinal cord injury has become an urgent task 53 . This study has explored the potential drugs targeting diagnostic genes and pointed out a new direction for the subsequent treatment of spinal cord injury. This study identified key genes based on a mouse spinal cord injury model. Although this model has extensive application value in mimicking human pathological mechanisms, it must be acknowledged that species differences may limit the direct clinical translation of the results. For example, biological differences exist between mice and humans in terms of immune regulation, nerve regeneration ability, and gene regulatory networks, which may affect the therapeutic relevance of some gene targets. However, the advantage of choosing the mouse model lies in its ability to obtain high-confidence mechanistic data through strict genetic control and pathological staging, which is difficult to achieve with clinical samples. Notably, the core genes identified in this study, such as Itgb2, Tyrobp, Fcer1g, and Ptprc, highly overlap with the gene pathways reported in previous human spinal cord injury studies, suggesting that some conserved molecular mechanisms may be applicable across species. Future research will verify the functional universality of candidate genes by integrating human single-cell transcriptome data and organoid models, and plans to conduct comparative analysis with the gene expression profiles of clinical cohorts. Despite the species limitations, this study systematically reveals the dynamic regulatory network of spinal cord injury, providing key molecular targets and a theoretical framework for subsequent translational research, which is in line with the step-by-step research paradigm from basic research to clinical application. Conclusion In conclusion, nine hub genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, CD86, and Itgax) could serve as effective markers and therapeutic targets of SCI. The specific mechanisms of SCI and effective treatment measures should be explored and verified, which will aid the development of novel treatments and reduce disease burden. Declarations Data Sharing Statement The original contributions put forward in this study are all included in the article and supplementary materials. For further inquiries, please direct them to the corresponding authors. Ethical Approval Not applicable. Acknowledgments We would like to express our sincere gratitude to all the staff members who participated in the intervention and evaluation work of this study. Their diligent efforts and wholehearted dedication demonstrated during the research process are an important guarantee for the smooth progress of this study, and we pay high tribute to their contributions. Author Contributions All authors of this study have made crucial contributions to the reported work in multiple aspects. During the initial conceptualization stage of the research, they actively contributed ideas, laying a solid foundation for the project. In the specific implementation process, they were fully engaged, ensuring the orderly progress of various tasks. In the data analysis and result interpretation part, they applied their professional knowledge and skills to provide profound insights. Moreover, they comprehensively participated in the writing of the article, from the drafting of the initial manuscript, to the subsequent repeated revisions, and then to the rigorous critical review, all with meticulous attention. All authors unanimously approved the final version of the article and jointly determined the target journal for submission. They also committed to being responsible for all aspects of the research work until the end. Funding: This research was supported by the postdoctoral Foundation of Heilongjiang Province, 2023.11-2015.11（LBH-Z23257）. Disclosure The authors hereby solemnly declare that there is no conflict of interest in the process of this study. References Lin A, Shaaya E, Calvert JS, Parker SR, Borton DA, Fridley JS. A Review of Functional Restoration From Spinal Cord Stimulation in Patients With Spinal Cord Injury. Neurospine. Sep 2022;19(3):703-734. doi:10.14245/ns.2244652.326 Alizadeh A, Dyck SM, Karimi-Abdolrezaee S. 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Upregulation of miR‑128 inhibits neuronal cell apoptosis following spinal cord injury via FasL downregulation by repressing ULK1. Mol Med Rep. Sep 2021;24(3)doi:10.3892/mmr.2021.12306 Blight BJ, Gill AS, Sumsion JS, et al. Cell Adhesion Molecules are Upregulated and May Drive Inflammation in Chronic Rhinosinusitis with Nasal Polyposis. J Asthma Allergy. 2021;14:585-593. doi:10.2147/JAA.S307197 Tigchelaar S, Gupta R, Shannon CP, et al. MicroRNA Biomarkers in Cerebrospinal Fluid and Serum Reflect Injury Severity in Human Acute Traumatic Spinal Cord Injury. J Neurotrauma. Aug 1 2019;36(15):2358-2371. doi:10.1089/neu.2018.6256 Tan YT, Lin JF, Li T, Li JJ, Xu RH, Ju HQ. LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer. Cancer Commun (Lond). Feb 2021;41(2):109-120. doi:10.1002/cac2.12108 Ding Y, Song Z, Liu J. Aberrant LncRNA Expression Profile in a Contusion Spinal Cord Injury Mouse Model. Biomed Res Int. 2016;2016:9249401. doi:10.1155/2016/9249401 Zhao C, Xing Z, Zhang C, Fan Y, Liu H. Nanopharmaceutical-based regenerative medicine: a promising therapeutic strategy for spinal cord injury. J Mater Chem B. Mar 17 2021;9(10):2367-2383. doi:10.1039/d0tb02740e Additional Declarations No competing interests reported. Supplementary Files TableS1Theresultsofthediffferentialexpressedgenes.xlsx TableS2ModulesintheWGCNA.xlsx TableS3Top10genes.xlsx TableS4net.network.xlsx TableS5DGIdb.xlsx 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. 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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-6162182","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425498650,"identity":"7420b8b9-61b8-4e86-9640-efdd0a8bdd89","order_by":0,"name":"liu kai","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"liu","middleName":"","lastName":"kai","suffix":""},{"id":425498651,"identity":"b9eb92da-9bb3-4ea1-be60-ec41e77f210a","order_by":1,"name":"weikang Ma","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"weikang","middleName":"","lastName":"Ma","suffix":""},{"id":425498652,"identity":"1ea85ad3-00a3-4d99-995c-dfea28441934","order_by":2,"name":"gang Wen","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"gang","middleName":"","lastName":"Wen","suffix":""},{"id":425498653,"identity":"f1ef3cbe-cdd4-4a74-87fd-1529ecd25cb1","order_by":3,"name":"Xinnan Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYJCCAyCCn5mx8cEHAxs54rVItjc3G84oSDMm3iqDM8fbpHk+HE4kqFK3/Yzh4cIddnkMNxIbpG0MmBMY2A8f3YBPi9mZtITDM88kFzPOSGwwzjFgy2PgSUu7gVfLgeQDh3nbmBObJRIbknMMeIoZJHjM8Gs5/7ABqKU+sQ2o5bCFAZAkqOUG2JbDiT08BxubGQwMiNHyLAGo5XjiDPbGZsYegwRjNoJ+OZ9j/Jm3rTpx/2H25z9+/Pkvx89++BheLZiAjTTlo2AUjIJRMAqwAQDRd1Ar+8PuPwAAAABJRU5ErkJggg==","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xinnan","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-03-05 11:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6162182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6162182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78155464,"identity":"b5fc521a-6d1d-462e-b003-f3a597fd16be","added_by":"auto","created_at":"2025-03-10 12:25:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103847,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis and WGCNA analysis. (A) The volplot of the differential expressed genes between SCI and normal group (B) Heatmap of the top 20 DEGs (C) The scale-free fit index for the soft threshold(β) (D) The mean connectivity for soft threshold powers (E) Hierarchical clustering dendrogram of all genes and module colors (F) Heatmap of associations between the modules and traits of SCI.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/11c7cbad24eb160f1b198c70.png"},{"id":78154551,"identity":"64dbc462-88fb-49f8-821e-d7aba99003a4","added_by":"auto","created_at":"2025-03-10 12:17:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109780,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis for the candidate genes (A) Biological process terms (B) cellular component terms (C) molecular function terms (D) The top 10 pathways from the KEGG database.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/eff541f123c5b75c6832474e.png"},{"id":78154525,"identity":"d1fd3042-80a3-4c4d-9a48-60de40caf351","added_by":"auto","created_at":"2025-03-10 12:17:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":486414,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the Protein-protein interaction network. (A) The PPI network of the overlapped genes by STRING (B) The network of the most significant hub genes extracted from the PPI network.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/6bbcc473ffe58d485f6934ab.png"},{"id":78154534,"identity":"470e9e36-2d9e-4ac6-a6e5-956bd477de41","added_by":"auto","created_at":"2025-03-10 12:17:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":699995,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis for the hub genes and diagnostic efficacy of hub genes in the prediction of SCI progression (A, B) GO enrichment map visualizing the top 10 terms from biological processes (BP), cellular components (CC), and molecular functions (MF). (C) KEGG pathway enrichment map (D, E) ROC curves estimating the diagnostic performance of hub genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/b99c02478df513f698beb916.png"},{"id":78154557,"identity":"718645ff-9c80-457b-9154-411024aa21fc","added_by":"auto","created_at":"2025-03-10 12:17:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":764355,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the expression of hub genes at different times points. (A) TYROBP, (B) PTPRC, (C) ITGB2, (D) MPEG1, (E) CD86, (F)FCER1G, (G) ITGAX, (H) FCGR3 and (I) FCGR1.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/3ca311856ac9b7491aabfa1d.png"},{"id":78154530,"identity":"f5f16ff0-63e0-45a3-ae9a-a2224c0d8adc","added_by":"auto","created_at":"2025-03-10 12:17:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2389615,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA identifies signaling pathways involved in the hub genes. The main signaling pathways that are significantly enriched in high expressions of hub genes (A) CD86, (B) FCGR3, (C) TYROBP, (D) ITGB2, (E) FCGR1, (F) ITGAX, (G)PTPRC, (H)FCER1G and (I) MPEG1.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/be71791cd7f626f747aca6fe.png"},{"id":78155467,"identity":"f70178a1-2115-468b-92f0-9dd3633300c0","added_by":"auto","created_at":"2025-03-10 12:25:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1801240,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune cell infiltration. (A) Distribution of immune cells in the sample; (B) Analysis of the difference of immune cells in the SCI group and normal control group; (C) Correlation analysis of immune cell infiltrations with hub genes. (D) Heatmaps depicting the correlations between distinct immune cell compositions.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/4f47b3d8ebfd50f4931f7708.png"},{"id":78155468,"identity":"c5b90058-f801-448a-aaee-cd2d53961919","added_by":"auto","created_at":"2025-03-10 12:25:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":447691,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes based ceRNA network\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/653b885a748d037eaf8fad37.png"},{"id":78154569,"identity":"9414db67-abfd-4f15-8915-ceee6633601a","added_by":"auto","created_at":"2025-03-10 12:17:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":180063,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene-drug network.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/3dac61b0191555b88e0ff37d.png"},{"id":78155857,"identity":"4086843f-72ad-4172-92f0-4e0182b73ab1","added_by":"auto","created_at":"2025-03-10 12:33:46","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":25463,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagram of 8 hub genes in SCI samples and control samples in the GSE132242 dataset.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/e7dade149eb59f3d1f9a20f2.png"},{"id":78306240,"identity":"60b9a4d2-a02c-4414-a6a3-05582f89192b","added_by":"auto","created_at":"2025-03-12 00:46:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7034832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/d93bc8b1-1501-4e55-97c9-b0bf745c58d3.pdf"},{"id":78154528,"identity":"362d8bd7-8288-4d8e-9c9e-9dbac2203d57","added_by":"auto","created_at":"2025-03-10 12:17:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16401,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1Theresultsofthediffferentialexpressedgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/4eadefc75cbc5a7877e82302.xlsx"},{"id":78155465,"identity":"c883d2f8-81f3-4877-929c-ce4e497cf21a","added_by":"auto","created_at":"2025-03-10 12:25:44","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":225749,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2ModulesintheWGCNA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/e84115818ad3d6042a9f5856.xlsx"},{"id":78154523,"identity":"1041969e-1691-4cb5-8700-62b1e88b7807","added_by":"auto","created_at":"2025-03-10 12:17:44","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9279,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3Top10genes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/17f8ba934c2b13b56f6a8723.xlsx"},{"id":78155470,"identity":"dcb76641-5a22-498b-b4d9-b76f5aebb08a","added_by":"auto","created_at":"2025-03-10 12:25:44","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16358,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4net.network.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/694208f2a07372cb32db0200.xlsx"},{"id":78155855,"identity":"37778760-ff05-4176-8807-a0278459287f","added_by":"auto","created_at":"2025-03-10 12:33:44","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12722,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5DGIdb.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6162182/v1/560f0b613a806c2914c65ab5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of key genes and pathways associated with spinal cord injury","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinal cord injury, as a severely traumatic type of central nervous system damage, is closely associated with significant dysfunction of the autonomic nervous system. It exhibits relatively high morbidity and mortality rates, frequently leading to permanent neurological deficits, which severely disrupt patients' quality of life and impose heavy physical and mental burdens and suffering on them\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.Spinal cord injuries are typically induced by high-intensity external forces, such as traffic accidents, fall injuries, and violent assaults\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Additionally, infections, tumors, spinal degenerative diseases, ischemia-reperfusion injuries, and vascular-related factors can also trigger this condition\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. After injury, patients present abnormal manifestations of sensory, motor, and autonomic nerve functions below the injury level\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Given the irreversible nature of spinal cord injury and the long treatment cycle, the treatment of this disease has become a crucial problem that urgently needs to be overcome in the medical field\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. By 2021, there were 759,302 patients with traumatic spinal cord injury in China, with an annual increase of 66,374 cases, showing a continuously rising trend year by year\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Spinal cord injury can be divided into two major stages in the process: primary injury and secondary injury\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. During the primary injury stage, the spine directly endures external force impacts, thereby resulting in fractures, vertebral body displacement, bone fragments, and spinal cord ligament tears, which cause the destruction of nerve parenchyma, rupture of the axonal network, bleeding, and tissue edema, ultimately leading to the imbalance of the homeostasis of the spinal cord environment\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Secondary injury is a series of cascade reactions triggered on the basis of primary injury, which can last from several weeks to several months. The main characteristics include the infiltration of pro-inflammatory cytokines, an increase in reactive oxygen species, a loss of ion homeostasis, mitochondrial dysfunction, and cell death\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite extensive research efforts on spinal cord injury, no substantial major breakthroughs have been achieved in terms of translation into clinical treatment\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, in-depth exploration of its internal mechanism to identify potential therapeutic targets is of particular importance. Circulating immune cells can effectively reflect tissue damage and the body's immune response and can be conveniently obtained from routine blood samples\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. By regulating the immune-inflammatory response in the microenvironment, it is expected to limit the extent of spinal cord injury and promote the functional recovery process\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, numerous research reports have indicated that immune cell infiltration plays a central role in the healing process of spinal cord injury. Chronic spinal cord injury can impair the normal function of CD8 T cells by up-regulating the expression level of programmed cell death protein 1\u003csup\u003e12\u003c/sup\u003e. γδ T cells are recruited to the spinal cord injury site, thereby exacerbating the inflammatory response and further deteriorating nerve injury. The CCL2/CCR2 signal transduction pathway plays a crucial role in the aggregation of T cells at the spinal cord injury site and holds the potential to be developed into a novel therapeutic target in the future\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.In summary, accurate assessment of the degree of immune cell infiltration and in-depth analysis of the composition of infiltrating immune cells are of indispensable significance for deeply dissecting the molecular mechanism of spinal cord injury and exploring new immunotherapy targets. Therefore, this study is dedicated to searching for relevant biomarkers of spinal cord injury and deeply exploring the specific role and mechanism of immune cell infiltration in the occurrence and development of spinal cord injury.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData sources and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenes and clinical information were downloaded from the GEO dataset (https://www.ncbi.nlm.nih.gov/geo/)\u003csup\u003e14\u003c/sup\u003e, which was employed to identify differentially expressed genes (DEGs) and hub genes related to SCI. The GSE5296 contains gene expression profile data from 96 mice in different groups at different time points. During the procedure, moderate injury was induced at the T8 spinal segment under isoflurane anesthesia. The mice were euthanized at 0.5 h, 4 h, 24 h, 7 days, and 28 days after injury, and tissues were taken from the spinal cord section of 0.4 cm in length from the site of impact and the immediately adjacent rostral and caudal regions, in both SCI group per three mice and control group per two mice. A total of 12 individuals were used for each injury time point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo highlight DEGs, gene expression analysis was conducted between the SCI and control groups using the \u0026ldquo;limma\u0026rdquo; package of R. Genes that met the criteria (|logFC| \u0026gt; 1, p \u0026lt; 0.05) were defined as DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA was used to analyze the gene expression patterns of multiple samples and identify highly analogous expression modules and significant module genes, which are extensively utilized in the exploration of associations between expression profiles and clinical information\u003csup\u003e15\u003c/sup\u003e.The optimal soft threshold was first determined, and based on the topological overlap matrix, hierarchical clustering trees were constructed, allowing genes with similar expression patterns to be selected and categorized into modules. The modules were then tested for correlations with the target clinical traits, and the gene network and core genes were identified. We focused on the modules that were highly correlated with SCI, and the genes of the featured module were ascertained for further research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of candidate genes and functional pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs DEGs involved in the development of SCI warrant extensive study, the intersection of DEGs and focal module genes was retained for further analysis. To explore the biological functions of these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on the obtained genes using the \u0026ldquo;clusterProfiler\u0026rdquo; package in R\u003csup\u003e16\u003c/sup\u003e. The GO analysis was pooled and divided into three sections: biological process, molecular function, and cell component\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of hub genes in the protein\u0026ndash;protein interaction (PPI) network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the STRING database\u003csup\u003e18\u003c/sup\u003e,a functional PPI association network of module genes was generated. Cytoscape was used to visualize the network of genes clustered in the module\u003csup\u003e19\u003c/sup\u003e. Subsequently, the top 10 genes in the gene regulatory network were calculated according to the MMC, MNC, EPC, degree, and closeness method related to algorithms using the CytoHubba plug-in\u003csup\u003e20,21\u003c/sup\u003e. The genes that recurred in each result of the top 10 hub genes were selected as the core genes to further explore the potential mechanism and biological functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance of the hub genes related to SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe performance of the top nine hub genes was measured based on the following three aspects: GO analysis was performed to identify the biological pathways significantly related to the hub genes. The expression levels of the hub genes between the SCI and control groups were compared using the t-test or Wilcoxon rank-sum test. We then evaluated the differences in gene expression at different time points after injury. Moreover, receiver operating characteristic (ROC) curve analysis was performed to describe the discriminative power for each hub gene, and the area under the ROC curve (AUC) was calculated. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) was employed to functionally elucidate the biological significance of characteristic genes\u003csup\u003e22\u003c/sup\u003e. The gene set of \u0026ldquo;c2.cp.kegg.v11.0.symbols\u0026rdquo; from the Molecular Signatures Database (MSigDB, http://software.broadinstitute.org/gsea/msigdb)\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e was selected as the reference set. To obtain the normalized enrichment score for each analysis, 1,000 gene set permutations were carried out. An FDR \u0026lt; 0.05 was considered as significant enrichment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Cell Infiltration Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIBERSORT method was employed to determine the relative proportion of immune cells within the tissue of mice with spinal cord injury (SCI). Simultaneously, the merged expression data were annotated, and the immune cell infiltration was calculated based on the expression profiles of mouse tissues\u003csup\u003e24\u003c/sup\u003e. Subsequently, a comparison was made regarding the relative levels of 25 immune cells between the SCI group and the control group. With the aid of the correlation heatmap generated by the \u0026quot;corrplot\u0026quot; package, the interrelationships among these 25 types of infiltrating immune cells were uncovered. Finally, the \u0026quot;ggstatplot\u0026quot; and \u0026quot;ggplot2\u0026quot; packages were utilized to analyze and visually display the Spearman correlation between the key biomarkers and the immune infiltrating cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eceRNA Network Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe target miRNAs of biomarkers were predicted by employing the ENCORI database (https://starbase.sysu.edu.cn/, with the screening criterion of CLIP - DATA \u0026ge; 1) and the miRWalk database (http://mirwalk.uni - heidelberg.de/, where the screening criterion is a score \u0026ge; 0.95). Subsequently, the lncRNAs that target the miRNAs were predicted using the ENCORI database (with the screening criteria of clipExpands \u0026gt; 1 and degradeExpands \u0026gt; 1). The lncRNA - miRNA - mRNA network was then constructed with the utilization of Cytoscape (version 3.9.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of Potential Drugs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy utilizing the Drug-Gene Interaction Database (https://www.dgidb.org/search interactions), the potential drugs applicable to the treatment of spinal cord injury were predicted. Subsequently, the Cytoscape software (version 3.9.0) was employed to conduct the visual display of the biomarker-compound pairing network, so as to more intuitively present the correlative characteristics and potential action patterns between the two, thereby providing data support and a visual basis for the in-depth exploration of spinal cord injury treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical tests were carried out using R software version 4.0.3. All statistical p-values were two-sided, and a p-value less than 0.05 was considered statistically significant. This criterion serves as a reliable basis and a rigorous guideline for the data analysis throughout the entire research.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression profiles of 54 mice with SCI and 42 control mice were downloaded from the GEO database. Of these genes, 149 upregulated genes were screened at the filter condition (p \u0026lt; 0.05 and |logFC| \u0026gt;1) and were selected for subsequent analysis. The volcano plot and heatmap are shown in Figure 1A and B, and DEGs are listed in Supplemental Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA and key modules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further construct the co-expression network and clarify the correlations between the expression profiles and disease, WGCNA was performed. By calculating the inter-gene correlation coefficients, a suitable soft threshold was selected to make the network close to the scale-free network. After determining a soft threshold value of 12 (Figure 1C-D), a scale-free network was constructed following scale independence and mean connectivity. After hierarchical clustering, nine modules were detected using a dynamic tree cut algorithm. After calculating the dissimilarity of module eigengenes and setting a cut-off height of 0.2, corresponding to a correlation of 0.8, the modules were merged, and the four key modules (blue, green, turquoise, and gray) were finally retained. As shown in Figure 1E, each branch of the clustering tree represents a DEG, and each color represents a module. The number of genes in each module was 5,401 in the turquoise module, 1,788 in the blue module, 1,643 in the brown module, 1,583 in the yellow module, 816 in the green module, 486 in the red module, 478 in the gray module, 385 in the pink module, and 478 in the gray module, and two genes were not assigned to any module (Supplemental Table 2). According to the correlation coefficient between the eigengene network and clinical traits, the turquoise module showed high adjacency to SCI (r = 0.47, p \u0026lt; 0.001, Figure 1F), and was selected as the biologically meaningful module for further study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate genes and functional analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results obtained from the differential analysis and WGCNA, the DEG highly associated with SCI were emphasized, so DEGs and genes that belonged to the turquoise module were filtered as candidate genes. GO analysis showed that in the biological process category, the candidate genes were mainly enriched in pathways related to phagocytosis, such as positive regulation of phagocytosis, myeloid leukocyte activation, and positive regulation of cytokine production (Figure 2A). The membrane raft, membrane microdomain, and NADPH oxidase complex genes were enriched in the cellular component category (Figure 2B). The genes in the molecular function process were involved in immunoglobulin binding, immune receptor activity, and pattern recognition receptor activity (Figure 2C). Moreover, KEGG analysis of DEGs demonstrated that 24 pathways were enriched, and the candidates were involved in osteoclast differentiation, phagosome, neutrophil extracellular trap formation, B cell receptor signaling pathway, and FcgR-mediated phagocytosis (Figure 2D). The threshold for the functional analysis was a p-value of \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of the hub genes in the PPI network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PPI network was constructed based on the genes included in the critical turquoise module, which contained 97 nodes and 906 edges in the network (Figure 3A). Using the CytoHubba plug-in in Cytoscape, the top 10 genes were confirmed, and the nine overlapping genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, CD86, and Itgax) were maintained as core genes by the integrated sorting of algorithms, and the network was visualized (Supplemental Table 3, Figure 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnderlying mechanism and differences in hub genes between the groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in figure 4A-B, based on GO pathway enrichment analysis, immune-related pathways, especially leukocyte-mediated immunity, are significantly enriched, and membrane transport is a common feature of the nine genes. Through KEGG enrichment analysis, it was found that key genes were potentially associated with immune response and inflammatory signals (Figure 4C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistinguishing ability and validation of the hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC analysis was performed to further investigate the roles of the hub genes. As illustrated in Figure 4D、E, the AUC of the time-dependent ROC curve was \u0026gt;0.7, which indicates the good discriminative performance of the nine hub genes. The most accurate variable, as determined by the AUC, was CD86 (AUC = 0.898), followed by Ptprc (AUC = 0.869) and Fcgr3 (AUC = 0.847). The combined AUC of these nine hub genes is 0.936.(95%CI=0.887-0.976) As shown in Figure 5, the expression level varied markedly during the short-term period after injury, but the difference was not significant (p \u0026gt; 0.05). At \u0026ge;24 h after the induction of SCI, the expression levels significantly differed between the two groups, and we also observed that the expression of hub genes in the SCI group increased over time and then stabilized after 7 days.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignaling Pathways Involved in hub Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough GSEA analysis, we evaluated the signal pathways involved in hub genes. Our results show that CD86 (Figure 6A), FCGR3 (Figure 6B), TYROBP (Figure 6C), ITGB2 (Figure 6D), FCGR1 (Figure 6E), ITGAX (Figure 6F), PTPRC (Figure 6G) FCER1G (Figure 6H) and MPEG1 (Figure 6I) are all positively correlated with complement and coagulation cascade, NF-\u0026kappa;B signal pathway and extracellular matrix-receptor interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune cell infiltration in SCI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in figure 7A, we use the CIBERSORT algorithm to calculate the proportion of immune cell infiltration in each sample. Figure 7B shows the proportion of immune cell infiltration in SCI and the control group, respectively. Compared with the control group, the activated proportion of T cells CD8 Naive，M0 macrophages\u0026nbsp;and DC active cell in SCI group was significantly higher. Compared with the SCI group, the activated proportion of Neutrophils cell,\u0026nbsp;Monocyte cell, T cells CD4 memory, Plasma cell and Th17 cells in the control group increased significantly. The results showed that there were significant differences in immune microenvironment between the SCI group and the control group. Then the correlation between immune cells was further estimated, and the results showed that plasma cells were positively correlated with mast cells and neutrophils. There was a significant negative correlation between M0 macrophages and neutrophils, initial B cells and plasma cells (Figure. 7D). In order to understand the value of Hub gene in immunity, we analyzed the correlation between 9 Hub genes and immune cell infiltration (Figure 7C). The results showed that the hub genes were positively correlated with resting NK cells, M1 and M2 macrophages, monocytes and resting dendritic cells, and negatively correlated with initial B cells, T cell helper follicular cells, plasma cells, activated NK cells and M0 macrophages, suggesting that Hub gene may be involved in the immune process of SCI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic genes based ceRNA network\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo deeply analyze the regulatory mechanisms of biomarkers, this research initiated the construction of a ceRNA regulatory network, with the relevant illustration shown in Figure 8. The construction process began by using the miRWalk and ENCORI databases to predict microRNAs (miRNAs) associated with biomarkers. Through this step, 126 pairs of messenger RNA (mRNA)-miRNA associations were successfully obtained. Subsequently, based on the 118 miRNAs acquired above, a search for related long non-coding RNAs (lncRNAs) was carried out in the ENCORI database, and finally 155 pairs of miRNA-lncRNA combinations were determined. After completing the above preliminary data preparation work, the ceRNA network architecture was established with the assistance of Cytoscape software. This network encompasses 258 nodes, specifically including 7 mRNAs, 118 miRNAs, and 133 lncRNAs, and the interconnections among the nodes form 281 edges. (Supplemental Table 4) The successful construction of this network lays a solid foundation for further exploring the role of biomarkers under the ceRNA regulatory mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential drugs targeting the diagnostic genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the process of exploring therapeutic drugs for spinal cord injury, this study focused on the discovery of potential drugs. By conducting a comprehensive search in the DGIdb database for specific biomarkers, it aimed to obtain drug information with targeted therapeutic effects. From the search results, among the numerous potential drugs discovered, the numbers of drugs targeting different biomarkers varied. Specifically, there were a total of 9 drugs targeting CD86, while the numbers of drugs targeting FCER1G, ITGAX, ITGB2, and PTPRC were 3, 3, 36, and 18 respectively, and the relevant data were clearly presented in Figure 9.Based on the association information between the above drugs and the corresponding biomarkers, a network system composed of messenger RNA (mRNA) and drugs was further constructed. This network encompassed 69 nodes, including 5 mRNAs and 64 drugs, and the interconnections among the nodes formed 69 edges.(Supplemental Table 5）It is worth noting that in the field of clinical trials for spinal cord injury, it has been found through practical verification that 31 of the drugs discovered above have been proven to have definite therapeutic effectiveness. It provided an important basis for drug selection and research in the clinical treatment of spinal cord injury.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross‑validation of external datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the precision of the research results, we carried out cross-validation using the GSE 132242 dataset, with the focus on detecting and analyzing the expression levels of nine key genes. This dataset was sourced from the GEO database, and after downloading, we immediately initiated an in-depth analysis.Upon comparison, it was found that among these nine key genes, except for Mpeg1, the expression level characteristics of the remaining eight genes in the GSE 132242 dataset were generally consistent with those in the GSE 5296 dataset. Specifically, in terms of expression levels, the control group was significantly lower than the spinal cord injury (SCI) group, and these eight key genes exhibited significant differences between the two groups. (Figure 10.)\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSpinal cord injury, as a highly destructive neurological disorder, can lead to abnormalities in body movement function, sensory ability, and autonomic nerve function below the injury level, which has an extremely severe negative impact on the quality of life of patients\u003csup\u003e4\u003c/sup\u003e. The pathological process can generally be divided into two stages: primary injury and secondary injury. The primary injury refers to the initial mechanical damage, while the secondary injury includes a series of changes mainly characterized by inflammatory responses and apoptosis\u003csup\u003e25\u003c/sup\u003e. At the same time, current research findings show that the infiltration of immune cells in the spinal cord injury area plays a non-negligible role in the occurrence and development process of this disorder\u003csup\u003e26\u003c/sup\u003e. Based on this, this study is committed to exploring the biomarkers associated with spinal cord injury and deeply analyzing the role and mechanism of immune cell infiltration therein.\u003c/p\u003e\n\u003cp\u003eDuring this research process, in order to deeply explore the potential genes and signaling pathways involved in the pathogenesis of spinal cord injury, we adopted a variety of expression analysis methods and combined with the weighted gene co-expression network analysis (WGCNA) technique. Through the intersection analysis of differentially expressed genes (DEGs) and module genes that show a high correlation with spinal cord injury, a series of candidate genes were successfully screened out\u003csup\u003e27\u003c/sup\u003e. Through comparative analysis, 149 differentially expressed genes were identified between the spinal cord injury group and the control group. Further research found that nine key hub genes are mainly concentrated in immune-related, phagocytosis-related, and inflammatory signal transduction pathways. These genes play a crucial role in maintaining and regulating the physiological functions of the body. After more in-depth verification experiments, we finally determined that these hub genes are the key molecular targets involved in the pathogenesis of spinal cord injury, providing an important theoretical basis for further exploring the mechanism of spinal cord injury and developing targeted treatment strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the DEGs that were found to be upregulated after SCI, CCL3 was found to be the most highly upregulated. CCL3 is an important member of the CC chemokine family and contributes to the mediation of acute inflammation\u003csup\u003e28\u003c/sup\u003e. CCL3 is reportedly involved in the inflammatory response and secondary damage after SCI, and could be considered a possible target to maintain the immune response for functional recovery\u003csup\u003e29\u003c/sup\u003e. Among the nine hub genes identified (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, CD86, and Itgax), Itgb2 was at the top of the ranking according to the MCC algorithm. Itgb2 encodes a protein called CD18 (integrin beta chain-2), which is a member of the integrin family\u003csup\u003e30\u003c/sup\u003e. Itgb2 plays a critical role as a membrane receptor in cell adhesion and cell recognition and is involved in many biological processes, including embryogenesis, hemostasis, and immune response\u003csup\u003e31\u003c/sup\u003e. In our study, Itgb2 was determined to be associated with the progression of SCI, which is consistent with the findings of previous studies. Further, Mpeg1, which encodes Perforin-2, was found to be highly expressed in macrophages and other phagocytes. Perforin-2 acts in the phagosome and breaks down the phagocytosed cells\u003csup\u003e32\u003c/sup\u003e,but the exact mechanism underling intracellular transport and delivery to phagosomes remains unclear.\u0026nbsp;Tyrobp has been developed as a prognostic biomarker of gastric cancer and clear cell renal cell carcinoma\u003csup\u003e33\u003c/sup\u003e. A previous \u0026nbsp;study reported a clear reduction in Tyrobp gene expression after injury to sensory neurons\u003csup\u003e34\u003c/sup\u003e, which has been confirmed as an essential component of adult microglial functionality and ongoing neuropathic pain. Fcgr1, which encodes the proteins CD64, belongs to the leukocyte IgG receptor family, and Fcgr1 is the only high-affinity receptor for the Fc region of immunoglobulin gamma\u003csup\u003e35\u003c/sup\u003e. Further, Negishi-Koga et al. \u0026nbsp; reported that the intensity of Frcg signaling regulates the level of osteoclastogenesis36. CD86 is a costimulatory molecule and also a protein marker of M1-type macrophages. Related studies have shown that macrophages can undergo M1-type or M2-type polarization after spinal cord injury, thus exerting different functions, which is crucial for the tissue immune response and homeostasis repair\u003csup\u003e37\u003c/sup\u003e. The protein encoded by protein tyrosine phosphatase, receptor type C (Ptprc) belongs to the protein tyrosine phosphatase (PTP) family, and it exerts its effects by directly interacting with the components of the antigen receptor complex or activating various Src family kinases necessary for antigen receptor signaling\u003csup\u003e38\u003c/sup\u003e. This study indicates an association between Ptprc and oxidative stress after SCI. The integrin \u0026alpha;X encoded by Itgax is a cell surface protein that is involved in the adhesion between cells and the extracellular matrix, as well as cell migration and other processes\u0026nbsp;\u003csup\u003e39\u003c/sup\u003e. After SCI, Itgax may play a role in the migration of immune cells to the injury site, as well as the migration and differentiation of neural stem cells. The receptors encoded by Fcgr1 and Fcgr3 are expressed on immune cells and play a key role in spinal cord injury (SCI)\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e. Fcgr1 can activate immune cells to trigger inflammation, is expressed in nerve cells, promotes nerve cell survival and axonal regeneration through signaling pathways, and mediates the activation of microglia to affect the repair microenvironment\u003csup\u003e41\u003c/sup\u003e; Fcgr3 can recruit immune cells and regulate polarization, interact with the complement system to reduce damage, and affect neuronal signaling and connection reconstruction in synaptic plasticity\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eExpression of all nine hub genes was significantly higher in the SCI group than in the control group at \u0026ge;24 h after injury. For the nine hub genes, CD86 achieved the highest AUC, and the AUC of the other eight genes ranged from 0.7 to 0.9, indicating the strong predictive accuracy of all genes, particularly CD86. CD86, which is extensively expressed on dendritic cells, monocytes, and activated B lymphocytes, was the main co-factor in the induction of T lymphocyte proliferation and interleukin-2 production\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSCI not only causes a huge psychological burden and severe limb dysfunction but also activates vascular, inflammatory, and immune processes. This study found a potential association between key genes and immune response and inflammatory signals. Inflammation plays an important role in the cascade of secondary injury of SCI. During SCI, injured cells release injury-related molecular patterns, activate pattern recognition receptors expressed by immune cells, and promote recruitment of resident and peripheral immune cells in the lesion site\u003csup\u003e44\u003c/sup\u003e . These cells can secrete a large number of pro-inflammatory cytokines, such as interleukin-1 (IL-1), IL-6 and tumor necrosis factor-\u0026alpha; (TNF-\u0026alpha;), which further aggravate the inflammatory response and seriously damage the microenvironment of nerve regeneration, resulting in the aggravation of SCI\u003csup\u003e45\u003c/sup\u003e. Therefore, effectively inhibiting the excessive inflammatory response of SCI can promote the recovery of neurological function and tissue regeneration, and improve the prognosis of SCI. Cytokines are an important part of innate immune response. There is a bidirectional interaction between the neurological and immune systems\u003csup\u003e46\u003c/sup\u003e. After SCI, due to the loss of microglia, an astrocytic fibrous scar gradually develops, leading to enhanced immune infiltration, neuronal necrosis, recovery of damage function, and severely impacted tissue \u0026nbsp;regeneration\u003csup\u003e47\u003c/sup\u003e. In order to more accurately evaluate the effect of immune cell infiltration in SCI, we used CIBERSORT to analyze immune cell infiltration through mouse tissue expression profile. The immunocyte infiltration of M0 macrophages, immature CD-8 T cells and DC-activated cells increased significantly, indicating that it may be related to the occurrence and progression of SCI. In addition, the immune cell infiltration at different time points after spinal cord injury may have the characteristics of dynamic changes. Studies have shown that after SCI, macrophage M1 phenotypic polarization increases, inflammatory response intensifies, damaging neuronal cells\u003csup\u003e48\u003c/sup\u003e .our study also found that there is a correlation between immune cells and key genes. According to one study, Itgb2 can form various adhesion interactions with mononuclear cells, macrophages and granulocytes and mediate the uptake of complement coated particles\u003csup\u003e49\u003c/sup\u003e. Given that the nine hub genes identified in this study were mainly enriched in immune-related pathways and were of significance to modulation after injury, approaches targeting immune factors or proinflammatory cytokines may provide important insights in terms of therapeutic development.\u003c/p\u003e\n\u003cp\u003eWithin the scope of this research, in addition to the previously mentioned key hub genes, the non-coding RNAs closely related to these genes have also become the focus of investigation. A considerable number of previous studies have demonstrated that non-coding RNAs play an extremely crucial regulatory role in the entire process of the occurrence and development of spinal cord injury. Among them, differentially expressed miRNAs have been verified to have a close association with the pathological responses induced by spinal cord injury. Tigchelaar\u003csup\u003e50\u003c/sup\u003e\u0026nbsp; et al carried out sequencing work on the blood and cerebrospinal fluid of 39 patients with spinal cord injury and 5 healthy controls. The research results indicated that in the cerebrospinal fluid of the spinal cord injury patient group, as many as 190 miRNAs exhibited differential expression states, while there were 19 in the serum. Moreover, 24 hours after the occurrence of injury, the miRNA expression profile in the cerebrospinal fluid could accurately determine whether the ASIA grade had improved. From this, it can be inferred that the level of miRNAs in the cerebrospinal fluid is closely related to the prognosis of human spinal cord injury. \u0026nbsp;lncRNAs , as a type of non-coding RNA with a length exceeding 200 base pairs, are deeply involved in gene expression and a variety of physiological and pathological processes\u003csup\u003e51\u003c/sup\u003e. With the continuous deepening of research on lncRNAs in recent years, numerous scholars have successively detected that lncRNAs in patients with spinal cord injury present abnormal expression phenomena and play an important role in the regulatory mechanism of the occurrence and development of spinal cord injury. In the research on the mouse spinal cord injury model by Ding et al, it was found that the differential change of lncRNA reached its peak one week after injury and gradually decreased after three weeks\u003csup\u003e52\u003c/sup\u003e. This phenomenon strongly implies an inherent connection between lncRNA and the degree of spinal cord injury in mice. In the long term, the combined application strategy of lncRNA and miRNA is highly likely to have significant potential value for the protection and repair of secondary nerve injury after spinal cord injury. Based on the above research background, this study successfully constructed a competing endogenous RNA (ceRNA) network with 258 nodes (covering 7 messenger RNAs, 118 miRNAs, and 133 lncRNAs) and 281 edges. The construction of this network has laid a solid foundation for the subsequent in-depth exploration of the mechanism of spinal cord injury.\u003c/p\u003e\n\u003cp\u003eSpinal cord injury often causes patients to suffer from severe neurological dysfunctions and a significant decline in quality of life. However, at present, the efficacy of conventional clinical drugs in the treatment of spinal cord injury remains unclear. Therefore, the development of highly effective drugs for the treatment of spinal cord injury has become an urgent task\u003csup\u003e53\u003c/sup\u003e. This study has explored the potential drugs targeting diagnostic genes and pointed out a new direction for the subsequent treatment of spinal cord injury.\u003c/p\u003e\n\u003cp\u003eThis study identified key genes based on a mouse spinal cord injury model. Although this model has extensive application value in mimicking human pathological mechanisms, it must be acknowledged that species differences may limit the direct clinical translation of the results. For example, biological differences exist between mice and humans in terms of immune regulation, nerve regeneration ability, and gene regulatory networks, which may affect the therapeutic relevance of some gene targets. However, the advantage of choosing the mouse model lies in its ability to obtain high-confidence mechanistic data through strict genetic control and pathological staging, which is difficult to achieve with clinical samples. Notably, the core genes identified in this study, such as Itgb2, Tyrobp, Fcer1g, and Ptprc, highly overlap with the gene pathways reported in previous human spinal cord injury studies, suggesting that some conserved molecular mechanisms may be applicable across species. Future research will verify the functional universality of candidate genes by integrating human single-cell transcriptome data and organoid models, and plans to conduct comparative analysis with the gene expression profiles of clinical cohorts. Despite the species limitations, this study systematically reveals the dynamic regulatory network of spinal cord injury, providing key molecular targets and a theoretical framework for subsequent translational research, which is in line with the step-by-step research paradigm from basic research to clinical application.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, nine hub genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, CD86, and Itgax) could serve as effective markers and therapeutic targets of SCI. The specific mechanisms of SCI and effective treatment measures should be explored and verified, which will aid the development of novel treatments and reduce disease burden.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eData Sharing Statement\u003c/h2\u003e\n\u003cp\u003eThe original contributions put forward in this study are all included in the article and supplementary materials. For further inquiries, please direct them to the corresponding authors.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;We would like to express our sincere gratitude to all the staff members who participated in the intervention and evaluation work of this study. Their diligent efforts and wholehearted dedication demonstrated during the research process are an important guarantee for the smooth progress of this study, and we pay high tribute to their contributions.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors of this study have made crucial contributions to the reported work in multiple aspects. During the initial conceptualization stage of the research, they actively contributed ideas, laying a solid foundation for the project. In the specific implementation process, they were fully engaged, ensuring the orderly progress of various tasks. In the data analysis and result interpretation part, they applied their professional knowledge and skills to provide profound insights. Moreover, they comprehensively participated in the writing of the article, from the drafting of the initial manuscript, to the subsequent repeated revisions, and then to the rigorous critical review, all with meticulous attention. All authors unanimously approved the final version of the article and jointly determined the target journal for submission. They also committed to being responsible for all aspects of the research work until the end.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the postdoctoral Foundation of Heilongjiang Province, 2023.11-2015.11（LBH-Z23257）.\u003c/p\u003e\n\u003ch2\u003eDisclosure\u003c/h2\u003e\n\u003cp\u003eThe authors hereby solemnly declare that there is no conflict of interest in the process of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLin A, Shaaya E, Calvert JS, Parker SR, Borton DA, Fridley JS. A Review of Functional Restoration From Spinal Cord Stimulation in Patients With Spinal Cord Injury. Neurospine. Sep 2022;19(3):703-734. doi:10.14245/ns.2244652.326\u003c/li\u003e\n\u003cli\u003eAlizadeh A, Dyck SM, Karimi-Abdolrezaee S. Traumatic Spinal Cord Injury: An Overview of Pathophysiology, Models and Acute Injury Mechanisms. 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Mar 17 2021;9(10):2367-2383. doi:10.1039/d0tb02740e\u003c/li\u003e\n\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, weighted gene co-expression network analysis, co-expression network, hub gene, Therapeutic target","lastPublishedDoi":"10.21203/rs.3.rs-6162182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6162182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpinal cord injury (SCI) is a devastating traumatic injury, which can lead to severe motor, sensory and autonomic nervous dysfunction. This study is based on the GSE5296 expression profile dataset, including 42 control samples and 54 SCI samples. Differential analysis was used to identify differentially expressed genes between control and SCI mices, and weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression network. A protein-protein interaction network was constructed based on the intersection of differential genes and module genes. Nine hub genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, Cd86 and Itgax) were selected by Cytoscape's comprehensive algorithm based on MMC, MNC, EPC, degree and proximity. In addition, the results of the hub gene were verified, and the Receiver Operating Characteristic (ROC) curve was drawn to evaluate the diagnostic efficiency. Subsequently, the expression levels of these genes were confirmed and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). These hub genes are enriched in immune-related pathways, especially leukocyte-mediated immunity, phagocytosis and membrane transport pathways. Gene Set Enrichment Analysis (GSEA) was used to explore the molecular mechanism of biomarkers. In this study, CIBERSORT was used to identify cell types to evaluate the inflammatory state of SCI. To study the relationship between key markers and infiltrating immune cells. In addition, we also use Cytoscape software to construct the Competing Endogenous RNA (ceRNA) regulatory network of biomarkers and obtain potential targeted drugs in the DGIdb database (Drug-Gene Interaction database). Finally, the reliability of Hub gene was verified by using GSE132242 dataset. These findings provide a new target for future research and a new perspective for the pathogenesis of spinal cord injury.\u003c/p\u003e","manuscriptTitle":"Identification of key genes and pathways associated with spinal cord injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-10 12:17:39","doi":"10.21203/rs.3.rs-6162182/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":"6b5afe87-4605-4f05-b9c9-ad467e6dd851","owner":[],"postedDate":"March 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-12T00:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-10 12:17:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6162182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6162182","identity":"rs-6162182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}