Mechanistic and Biomarker Roles of Cell Adhesion Molecules in Spinal Cord Injury: Evidence from Clinical and Molecular Analyses

Mechanistic and Biomarker Roles of Cell Adhesion Molecules in Spinal Cord Injury: Evidence from Clinical and Molecular Analyses | 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 Article Mechanistic and Biomarker Roles of Cell Adhesion Molecules in Spinal Cord Injury: Evidence from Clinical and Molecular Analyses Hongxia Pan, Mingfu Ding, Fei Xie, Xin Sun, Liyi Huang, Yue Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6676117/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cell adhesion molecules (CAMs) play a crucial role in neural circuit reorganization and tissue remodeling following spinal cord injury (SCI). However, the regulatory mechanisms governing CAMs in SCI pathophysiology remain poorly understood. This study employs transcriptomic analysis and bioinformatics approaches to identify key CAM-related genes and their potential roles in SCI, with further validation in clinical serum samples from SCI and non-SCI individuals to explore their diagnostic and therapeutic relevance. Methods: Publicly available transcriptome datasets (GSE151371 and GSE226238) were analyzed, focusing on 156 CAM-related genes (CAM-RGs). Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the GSE151371 dataset to identify differentially expressed genes (DEGs) and key module genes significantly associated with CAM scores in SCI. Candidate genes were identified by intersecting DEGs with key module genes, followed by feature selection using least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) curve analysis was conducted to assess their diagnostic potential. Functional enrichment analysis, immune cell infiltration assessment, molecular regulatory network construction, and drug repurposing predictions were further performed. Finally, biomarker expression was validated using real-time quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) in serum samples from SCI and non-SCI individuals to confirm the bioinformatics findings. Results: Among 3,582 DEGs and 687 key module genes, 99 candidate genes were identified. LASSO regression refined six feature genes (TLR5,TCF7, MAB21L2, SBK1, SLCO5A1 and ADGRG5), among which TCF7, SBK1, and ADGRG5 were significantly downregulated in SCI. Enrichment analyses linked these biomarkers to immune regulation pathways, with their expression positively correlated with resting natural killer (NK) cells and naïve CD4+ T cells. RT-qPCR validation in clinical serum samples confirmed significant downregulation of SBK1 and ADGRG5 in SCI, consistent with transcriptomic findings. Conclusion: This study identified SBK1 and ADGRG5 as candidate CAM-related biomarkers in SCI through integrative bioinformatics analysis and clinical validation. Their downregulation in SCI and association with immune regulation suggest a potential role in neural repair. These findings provide a new insights into CAM-mediated mechanisms in SCI and may contribute to the development of targeted diagnostic and therapeutic strategies. Biological sciences/Molecular biology Biological sciences/Neuroscience Health sciences/Biomarkers Health sciences/Medical research Health sciences/Neurology Health sciences/Pathogenesis Spinal cord injury Cell adhesion molecules Immune infiltration Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Spinal cord injury (SCI) is a devastating neurological disorder caused by trauma or disease, leading to disruption of the spinal cord's structural and functional integrity. This results in varying degrees of motor, sensory, and autonomic dysfunction[ 1 ], along with a high incidence rate, imposing a substantial burden on patients and healthcare systems[ 1 – 7 ]. SCI predominantly affects young individuals, significantly reducing their quality of life[ 8 , 9 ] and placing considerable physical[ 10 – 12 ], psychological[ 9 ], and financial strain on both patients and their families. Over the past three decades, the global prevalence of SCI has increased from 236 to 1298 cases per million, with an estimated 250,000–500,000 new cases reported annually worldwide[ 13 , 14 ]. In China alone, approximately 759,302 individuals are living with traumatic SCI, with around 66,374 new cases reported each year[ 2 , 15 – 17 ]. Despite significant advances in spinal surgery and rehabilitation care, effective therapeutic interventions remain limited, largely due to the complex pathological mechanisms and the central nervous system (CNS) restricted regenerative capacity[ 18 , 19 ]. SCI primarily disrupts neural circuits through axonal damage and neuronal loss, impairing both motor and sensory functions. While neural plasticity remains a critical mechanism for recovery, the intricate molecular changes following SCI, coupled with inconsistent treatment outcomes, present formidable challenges to effective regeneration. A deeper understanding of the underlying molecular mechanisms is essential for identifying novel therapeutic targets and developing effective treatments[ 19 , 20 ]. Cell-cell interactions are fundamental to CNS structure and function. Although cell-extracellular matrix (ECM) interactions contribute to neural repair, cell-cell communication plays a more central role in neural regeneration. Cell adhesion molecules (CAMs), such as cadherins, neural cell adhesion molecules (NCAM), and L1, are key mediators of these interactions, regulating synapse formation, synaptic plasticity, and astrocyte maturation—processes essential for neural circuit remodeling following SCI[ 21 – 23 ]. Notably, L1 has been identified as a potential therapeutic target for Alzheimer’s disease, underscoring the broader therapeutic potential of CAMs in neurological disorders[ 24 ]. In the spinal cord, CAMs are highly expressed and crucial for synaptic remodeling and neural circuit integrity, both of which are critical for functional recovery after SCI. Despite growing evidence of CAMs' involvement in SCI, their precise regulatory mechanisms remain poorly understood. Elucidating the role of CAMs in SCI may uncover novel therapeutic strategies to enhance neural repair and functional recovery. This study utilizes transcriptomic data and bioinformatics approaches to investigate the role of CAMs in SCI. We aim to identify CAM-related biomarkers, analyze their expression patterns, and explore their functional roles and clinical relevance By integrating molecular and bioinformatics analyses, this study seeks to establish a theoretical framework that advances our understanding of CAM-mediated mechanisms in SCI pathophysiology, ultimately guiding the development of improved diagnostic and therapeutic strategies. The workflow of the present study is shown in Fig. 1 . 2. Materials and methods 2.1 Source of data and bioinformatics analysis 2.1.1 Microarray data acquisition Two transcriptome datasets related to SCI were retrieved from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) : GSE151371 (GPL20301) and GSE226238 (GPL18573) (Table 1 ). The GSE151371 dataset contained 10 control and 38 SCI samples (sample source: blood)[ 25 ]. while the GSE226238 dataset included 9 control and 37 SCI samples (sample source: whole blood)[ 26 ]. Next, 156 CAM-related genes (CAM-RGs) were identified from the literature [ 27 ]. Table 1 Details of data. Dataset Platform Tissue SCI Control group Reference (PMID) GSE151371 GPL20301 Serum 38 10 33512429 GSE226238 GPL18573 Serum 37 9 37221869 2.1.2 Bioinformatics Analysis: CAM-related differentially expressed genes (DEGs) in serum samples from SCI patients were identified using WGCNA combined with differential gene expression, LASSO regression, and enrichment analyses. Key genes were further investigated to validate their molecular-level expression and explore their functional mechanisms. 2.2 Differential expression analysis The GSE151371 dataset was processed using DESeq2 (v 3.1.8) [ 28 ] to detect DEGs, applying a threshold of |log 2 (Fold change)| >1 and a false discovery rate (FDR) < 0.05. To illustrate DEGs, both volcano plots and heat maps were generated using ggplot2 (v 3.4.4) [ 29 ] and Pheatmap (v 1.0.12) [ 30 ], respectively. 2.3 CAMs score calculation The ssGSEA function from the GSVA package (v 1.46.0)[ 31 ] was utilized to calculate the CAM associated scores in GSE151371 dataset, aiming to identify genes linked to CAMs in SCI. The Wilcoxon test was subsequently used to compare the CAM associated scores between SCI and control groups, with a significance threshold set at p < 0.05. 2.4 Weighted gene co-expression network analysis (WGCNA) The Hierarchical Clustering function in the WGCNA package (v 1.71)[ 32 ] was implemented to hierarchically cluster all of the samples in GSE151371 dataset to remove any outlier samples. The appropriate soft threshold was determined by calculating the scale-free fit index and the mean connectivity (converging to 0) using the pick Soft Threshold function (R 2 = 0.8). To categorize the gene modules according to the hybrid dynamic shear tree algorithm's criteria, the minimum number of genes per gene module was set to 100, and merge Cut Height to 0.2. The Spearman correlation coefficients between CAM scores and modules were determined using the psych program (v 2.2.9)[ 33 ], and the genes in the modules with the highest correlation coefficients were chosen as key module genes. Lastly, the gg Venndiagram (v 1.7.3) [ 34 ] was applied to detect overlapping genes for DEGs and key module genes, which were then classified as candidate genes for machine learning. 2.5 Least absolute shrinkage and selection operator (LASSO) regression analysis In accordance with the candidate genes, glmnet (v 4.1-4)[ 35 ] was executed to perform LASSO regression analysis, and the genes whose regression coefficients were not penalized to 0 were designated as feature genes. 2.6 Diagnostic capacity and expression assessment of biomarkers To evaluate the diagnostic efficacy of feature genes for SCI, ROC curves were plotted in GSE151371 dataset using pROC (v 1.18.0)[ 36 ] (area under the ROC curve (AUC) > 0.7). Biomarker expression was validated across SCI and control groups using the Wilcoxon test in GSE151371 and GSE226238 datasets (p < 0.05). The expression box-plot was constructed by the box-plot function. The genes that were significantly differentially expressed in the training and validation sets with consistent expression trends were also selected to be recorded as biomarkers for subsequent analyses. 2.7 Gene set enrichment analysis (GSEA) To investigate the functional pathways implicated in biomarkers, enrichment analysis was conducted in GSE151371 dataset. The Spearman correlation coefficients between biomarkers and remaining genes were calculated independently using psych and sorted in descending order. GSEA was completed using cluster Profiler (v 4.7.1.003)[ 37 ] and org. Hs. eg. db, with c2.cp.kegg.v7.5.1.symbols.gmt from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb ) serving as the background gene set. And GSEA was applied to the ridge plot visualization results using enrich plot (v 1.10.2) [ 38 ]. 2.8 Immune infiltration Using CIBERSORT (v1.03)[ 39 ], the infiltration of 22 immune cells was evaluated in GSE151371 dataset in order to investigate the potential regulatory role of immune cells in SCI. The difference in the percentage of immune cell infiltration between SCI and control groups was then compared with the Wilcoxon test. Additionally, Spearman correlations were computed by psych (|cor| >0.3, p < 0.05) between biomarkers and between biomarkers and differential immune cells, respectively. 2.9 Expression of SCI related pathogenic genes The top 20 SCI related pathogenic genes were acquired by searching the GeneCards database at https://www.genecards.org . The expression of these genes was evaluated, and intergroup differences were estimated in GSE151371 dataset. The Pearson correlations between biomarkers and pathogenic genes associated with SCI were then computed using psych. 2.10 Construction of miRNA-mRNA networks To find miRNAs that might be involved in biomarker regulation, biomarker-based prediction of miRNAs was carried out in the miRTarBase database ( http://mirtarbase.mbc.nctu.edu.tw/index.html ) and the miRwalk database ( http://mirwalk.umm.uni-heidelberg.de/ ) to get experimentally verified miRNAs that might be involved in biomarker regulation. After that, miRNAs from the two databases were employed to build miRNA-mRNA networks, correspondingly. 2.11 Prediction of chemotherapeutic agents targeting biomarkers To gain insight into the medications that might target biomarkers to treat SCI, predictions were generated using the comparative toxicogenomics database database ( http://ctdbase.org/ ). Cytoscape(v 3.7.0)[ 40 ] was implemented to depict the biomarker-drug network. 2.12 Expression analysis of biomarkers Serum samples were obtained from SCI patients at West China Hospital, Sichuan University between September 2024 and November 2024 and stored at − 80°C. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. The study was approved by the Ethical Board of West China Hospital of Sichuan University (Approval NO. 2023-34), and written informed consent was obtained from all participants prior to their inclusion in the research. To validate the biomarkers expression consistency with transcriptomic analysis, blood samples were collected from 10 SCI patients and 10 non-SCI control individuals. Total RNA was extracted using the Trizol reagent kit, followed by reverse transcription into cDNA using the RT Easy™ II kit (FOREGENE). Primers were designed based on NCBI reference sequences, and quantitative PCR (qPCR) was performed to assess the expression of key genes in both groups. β-actin was used as the internal control, and relative gene expression levels were calculated using the 2^−ΔΔCt method. Differential expression between groups was analyzed using an independent t-test, with p < 0.05 considered statistically significant and p < 0.01 highly significant. The primer sequences used in this study are listed in Table 2 . Table 2 The primer sequences were used in this study Genes Forward primer Reverse primer β-actin TTTCCAGCCTTCCTTCCT CAGGTCTTTGCGGATGTC ADGRG5 TGGGAAGAACTCCTGAGCTAC GCTGGTGGAGTTGACGAGAG SBK1 TACAAGGGCACAGGCACAA CTCCTGGGCAAAGACGTAGC 2.13 Statistical analysis All bioinformatic projects were conducted using R (v 4.2.2). The Wilcoxon test was utilized to compare data among groups ( p < 0.05). 3. Results 3.1 In SCI, 687 genes had proven to be substantially linked to CAM scores After differential expression analysis in GSE151371 dataset, 3,582 DEGs were determined between SCI and control groups, with 2,309 increased and 1,109 decreased DEGs in SCI group (Figures 2A and 2B). As a result, determined by CAM-RGs, we noticed that CAM scores were substantially decreased in SCI patients (Figure 2C). Furthermore, a hierarchical clustering of all samples in GSE151371 dataset indicated no outlier samples (Figure 2D). WGCNA was then performed on all samples to locate genes that had a strong association with CAM scores. When R 2 was set to 0.8 and the mean connectivity was zero, the optimal soft threshold was chosen as 22 (Figures 2E and 2F). Next, all genes were divided into ten modules relying on the phylogenetic clustering tree (Figure 2G), with the MEgreen module having the strongest connection with CAM scores (cor = 0.79, p = 3^10 -11 ) (Figure 2H). Therefore, 687 of the genes contained inside were determined as key module genes. 3.2 TCF7, SBK1, and ADGRG5: CAM-Associated Biomarkers Implicated in SCI Pathophysiology The intersection of 687 key module genes with 3,582 DEGs yielded 99 candidate genes (Figure 3A). LASSO regression analysis of the candidate genes revealed that 6 genes (TLR5, TCF7, MAB21L2, SBK1, SLCO5A1, and ADGRG5) were classified as feature genes since their regression coefficients were not penalized to 0 when the Lambda. min was 0.0277 (Figures 3B and 3C). The ROC curves suggested that the AUC values for all 6 feature genes were better than 0.7, indicating that they all had high diagnostic abilities for SCI (Figure 3D). Afterwards, ADGRG5, SBK1, and TCF7 were shown to be significantly reduced in SCI patients in both GSE151371 and GSE226238 datasets (Figures 3E and 3F). Therefore, these 3 feature genes were considered as biomarkers for subsequent analyses. There were strongly positive correlations between these three biomarkers (Figure 3G). 3.3 Drug prediction and construct miRNA-mRNA regulation networks To further understand the molecular regulation and clinical medicinal potential of biomarkers, miRNA and drug prediction of targeted biomarkers were utilized respectively. The miRwalk and miRTarBase database predicted 37 miRNAs that regulated biomarkers. The mRNA-miRNA network containing 40 nodes and 37 edges was constructed based on three biomarkers and 37 predicted miRNAs. Complex regulatory relationships could be seen from this mRNA-miRNA network, such as hsa-miR-4270-SBK1, hsa-miR-493-3p-ADGRG5 and hsa-miR-373-5p-TCF7 (Figure 4A). Subsequently, the CTD database was applied to determine 17 medicines that target three biomarkers, with Benzo(a)pyrene simultaneously targeting all three biomarkers (Figure 4B). 3.4 Enrichment analysis for biomarkers associated with immunomodulation GSEA was applied to deeper understand the biomarkers' functional pathways. TCF7, SBK1, and ADGRG5 had a strong relationship with immune regulation (Figures 5A-5C). As a result, we looked further into immune cell infiltration in SCI. The findings suggested that myeloid cells accounted for the majority of SCI patients (Figure 6A), whereas NK cell resting, neutrophil, memory resting CD4+ T cells, and naïve CD4+ T cells differed considerably between SCI and control groups (Figure 6B). Neutrophils exhibited a much larger proportion than the other distinct immune cells in SCI patients, while the opposite was true for the rest of the differential immune cells. Correlations indicated that SBK1 correlated significantly with CD4 naïve T cells, TCF7 correlated strongly with CD4 naïve T cells as well as resting NK cells, while ADGRG5 correlated most notably with resting NK cells (Figure 6C). 3.5 SCI-related genes and biomarkers The 9 of the 20 genes corresponding to SCI in the GeneCards database demonstrated notable variations between two groups in GSE151371 dataset, with TRPV4 being highly expressed in SCI and AR, ATP7A, BDNF-AS, DARS2, DYNC1H1, IL6, SIGMAR1, SOD1, and VAPB being the reverse (Figure 6D). The correlation of biomarkers with 9 significantly distinct SCI-related genes revealed TCF7, SBK1, and ADGRG5 all showed high positive correlations with DYNC1H1, SIGMAR1, and AR (Figure 6E). 3.6. Experimental validation of biomarkers The expression analysis of biomarkers showed that ADGRG5 and SBK1 were significantly reduced in SCI patients, consistent with the gene database analysis results, as shown in Fig 7A-B. 4. Discussion SCI Pathophysiology and the Role of CAMs SCI involves complex pathophysiological processes, driven by inflammation, oxidative stress, and neuronal death. These processes highlight the need to better understand the molecular mechanisms underlying SCI for effective therapeutic strategies. Neural circuit remodeling and synapse formation are critical for functional recovery, where CAMs play a pivotal role[41, 42]. CAMs regulate axonal guidance, synaptic stabilization, and cellular interactions within the extracellular matrix, which directly influence neuronal connectivity and tissue repair. However, the precise molecular mechanisms of CAMs in these processes remain unclear. This study identified three CAMs-related biomarkers (TCF7, SBK1 and ADGRG5) through bioinformatics analysis of gene expression profiles in SCI patient. Clinical validation using serum samples further confirmed significant dysregulation of SBK1 and ADGRG5, underscoring their potential as diagnostic and therapeutic targets in SCI. Role of CAMs in Immune Inflammation and Secondary Injury in SCI In SCI, CAMs such as ICAM-1 and VCAM-1 are upregulated in endothelial cells and astrocytes, facilitating immune cell recruitment and triggering secondary inflammation[21, 43-45]. The inflammatory response, regulated by immune cells, plays a critical role in early SCI, exacerbating secondary injury through mechanisms such as oxidative stress and excitotoxicity, ultimately leading to neuronal death[46]. Our findings reveal that CAM-related genes (TCF7, SBK1, and ADGRG5) may modulate this immune-inflammatory process, highlighting their significance in SCI pathophysiology. TCF7: Linking Wnt Signaling to Immune and Neural Repair in SCI TCF7 encodes T-cell factor 7, a transcription factor involved in the Wnt/β-catenin signaling pathway[47-49]. Reduced expression of TCF7 has been linked to T-cell dysfunction, impairing both immune regulation and tissue repair at the injury site[50]. Additionally, TCF7 supports neural stem cell proliferation and differentiation through the Wnt signaling pathway, facilitating neurogenesis and repair[51]. In this study, bioinformatics analyses revealed decreased TCF7 expression in SCI patients. However, clinical validation did not show statistically significant differences between SCI and non-SCI groups, possibly due to the localized role of TCF7 in tissue repair and intracellular signal transduction, which may not be adequately reflected in peripheral blood analysis due to the blood-brain barrier[52, 53]. Future studies should investigate cerebrospinal fluid (CSF) as a more direct medium to assess changes in the spinal cord microenvironment. SBK1: A Multifaceted Regulator in SCI Repair and Immune Modulation SBK1, a serine/threonine protein kinase, plays a pivotal role in neural development and signal transduction[54, 55]. GSEA and RT-qPCR analyses revealed its involvement in pathways critical SCI repair, including protein synthesis, ECM remodeling, amino acid metabolism, signal transduction, and immune regulation. SBK1 enhances protein synthesis efficiency and fidelity, which are essential for neuronal survival and axonal regeneration[56]. Its enrichment in glycosaminoglycan (GAG) biosynthesis and ECM metabolism pathways suggests a role in restructuring the ECM, providing structural support and biochemical signals for neural repair[57]. By influencing ECM composition and modulating cell adhesion molecules (e.g., ICAM-1, VCAM-1), SBK1 may regulate immune cell aggregation and migration at the injury site[58, 59]. In the lysine degradation pathway, SBK1 supports energy homeostasis and biosynthesis, vital for ATP production and macromolecule synthesis during neuronal recovery[60]. Moreover, its association with the Notch signaling pathway underscores a role in neuronal differentiation, proliferation, apoptosis, and axonal regeneration, all essential for functional recovery[61, 62]. SBK1’s enrichment in primary immunodeficiency pathways suggests its potential role in immune modulation, influencing immune cell recruitment and activation, thereby impacting the inflammatory cascade and repair processes. Further studies are needed to elucidate its precise mechanisms. In conclusion, SBK1 is deeply involved in pathways essential for SCI repair, including protein synthesis, ECM remodeling, metabolic regulation, immune modulation, and signal transduction. Its ability to regulate cell adhesion and immune responses underscores its therapeutic potential for enhancing recovery after SCI. ADGRG5: Modulating Immune Regulation and Neuroregeneration in SCI ADGRG5, an adhesion G protein-coupled receptor(aGPCR), is critical for cell-cell interactions and signal transduction essential for neuroprotection and regeneration [63] . Its downregulation in SCI may disrupt multiple recovery mechanisms, including immune regulation, DNA repair, protein synthesis, and T-cell function. Although direct evidence evidence of ADGRG5’s involvement in specific immune cell activities, such as T-cell and NK-cell modulation, remains limited, its enrichment in pathways like allograft rejection and primary immunodeficiency suggests a role in modulating inflammatory response intensity[64, 65]. As a member of the aGPCRs family, known for immune system regulation and immune cell interactions, ADGRG5 may similarly contribute to T-cell activation, differentiation, and function via the T cell receptor (TCR) signaling pathway, influencing whether T cells adopt protective or injurious roles post-SCI. Reduced ADGRG5 expression may further impair immune cell development and function, exacerbating inflammation and hindering tissue repair[66]. Additionally, aGPCRs are implicated in DNA damage responses, suggesting ADGRG5 may influence DNA repair mechanisms vital for neuronal survival[67]. Its role in ribosome pathways may also support efficient protein synthesis, essential for cellular repair and neural network reconstruction[68, 69]. These potential roles highlight ADGRG5’s importance in SCI recovery and warrant further investigation. CAMs as Key Biomarkers in Immune Regulation and Neural Repair Post-SCI In this study, we identified TCF7, SBK1, and ADGRG5 as key CAM-related genes significantly enriched in the immunodeficiency pathway in SCI, emphasizing the critical role of cell adhesion in immune regulation and neural repair mechanisms[70]. Both SBK1 and ADGRG5 were significantly downregulated post-SCI, with AUC values of 0.85 and 0.832, respectively, highlighting their potential as diagnostic biomarkers. These findings suggest that CAMs not only mediate immune cell recruitment and adhesion but also influence critical biological processes such as inflammation, neuronal repair, and tissue remodeling, providing a mechanistic link between CAMs and SCI pathology. Initial CD4 T cells, closely associated with TCF7 and SBK1, may contribute to both inflammatory and repair processes[71, 72], underscoring the dual role of T cells in either exacerbating inflammation or promoting recovery. SBK1 and ADGRG5 may regulate CD4 T cell adhesion and migration via CAM-mediated mechanisms, directly influencing immune cell activity at the injury site. Although previous studies reported elevated TRPV4 in SCI[73, 74], no significant differences were observed in this study, possibly due to sample or technical limitations. Our results validate the roles of SBK1 and ADGRG5 in immunomodulation and neural repair, supporting the therapeutic potential of targeting CAM-related immune responses in SCI. Future research should investigate their interplay with T cells to refine their diagnostic and therapeutic applications. CD4+ T Cells in SCI CD4+ T cells play a dual role in SCI recovery, balancing pro-inflammatory and anti-inflammatory responses. Upon activation, naïve CD4+ T cells differentiate into subsets such as Th1, Th2, Th17, and Tregs. Th1 and Th17 cells exacerbate inflammation via pro-inflammatory cytokines (e.g., IFN-γ, IL-17), leading to axonal damage and neuronal death[75-77], while Th2 and Tregs promote tissue repair through anti-inflammatory and neurotrophic effects[78]. Adhesion molecules facilitate T cell recruitment and migration across endothelial barriers[79, 80], with pathways like Fas/FasL playing pivotal roles in T cell adhesion and apoptosis[81]. In this study, the significant correlation of SBK1, ADGRG5, and TCF7 with CD4+ naïve T cells suggests these genes regulate T cell activation, differentiation, and immune microenvironment dynamics post-SCI. The dysregulated expression of these genes may influence the adhesion and migratory behavior of CD4+ T cells, modulating the delicate balance between inflammation and repair. Furthermore, the high diagnostic potential of SBK1 and ADGRG5, as indicated by their AUC values (0.85 and 0.832), underscores their relevance as molecular targets for therapeutic intervention in SCI. NK Cells in SCI NK cells are emerging as important contributors to SCI repair, often acting in synergy with T cells. This study revealed a significant association between ADGRG5 and TCF7 with resting NK cells, highlighting the role of adhesion molecules in regulating immune cell migration, retention, and function. Downregulation of ADGRG5 may impair the recruitment and retention of both NK cells and T cells at the injury site, disrupting inflammatory regulation and tissue repair processes[82]. The interactions between T cells and NK cells, mediated through adhesion molecules, are crucial in shaping the immune microenvironment post-SCI. Together, SBK1, ADGRG5, and TCF7 appear to coordinate immune response and repair mechanisms by influencing the differentiation, adhesion, and functional behavior of these immune cells. Future studies should investigate the adhesion-regulating roles of these genes to clarify their therapeutic potential in restoring immune balance and promoting recovery after SCI. This study identified SBK1 and ADGRG5 as key biomarkers associated with cell adhesion mechanisms, emphasizing their crucial roles in the immunoregulation and repair processes of SCI. These genes play essential roles in immune cell migration and activation, modulation of inflammatory responses, DNA repair, protein synthesis, secondary injury mitigation, and neuronal regeneration. Notably, their significant downregulation in SCI and high diagnostic accuracy, as evidenced by robust AUC values, highlight their potential as molecular targets for SCI diagnosis and therapy. Despite these promising findings, limitations should be acknowledged. First, the relatively small sample size reduces the statistical power and generalizability of the results. Second, the precise mechanisms by which SBK1 and ADGRG5 regulate cell adhesion and immune cell interactions, particularly their crosstalk with T cells and NK cells, remain to be fully elucidated. Future studies should involve larger patient cohorts and investigate these biomarkers in CSF or localized spinal cord tissues to gain deeper insights into their roles within the SCI microenvironment. 5. Conclusion In conclusion, this study underscores the significant involvement of SBK1 and ADGRG5 in SCI immunoregulation and repair through cell adhesion mechanisms. Their strong diagnostic potential suggests they may serve as valuable biomarkers and therapeutic targets for SCI. Further investigations are warranted to validate these findings and explore their mechanistic roles in SCI pathophysiology. Declarations Data availability statement The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. Author contributions PHX drafted and edited the manuscript, DMF and SX assisted with the implementation of this work, XF contributed to the methodology, while HLY, YY and WQ were responsible for the review and editing of the manuscript. All authors have read and approved the final version of the manuscript. Funding This study was supported by the National Key Research and Development Program of China, (Grant No.2023YFC3603800 and 2023YFC3603801), the National Natural Science Foundation of China, (Grant No.82172534 and 82372574) and 1.3.5 Project for Disciplines of Excellence, West china Hospital, Sichuan University(Grant No. ZYJC21038). Ethics statement This study was approved by the Ethical Board of West China Hospital of Sichuan University(Approval NO. 2023-34). Written informed consent was obtained from all participants prior to their inclusion in the research. Consent for publication Not applicable. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Collaborators, G.B.D.S.C.I., Global, regional, and national burden of spinal cord injury, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol, 2023. 22 (11): p. 1026-1047. Hu, S., P. Wang, Y. Dong, and F. Li, Incidence, prevalence and disability of spinal cord injury in China from 1990 to 2019: a systematic analysis of the Global Burden of Disease Study 2019. Eur Spine J, 2023. 32 (2): p. 590-600. Mitton, C., F. Dionne, N. Fallah, and V.K. Noonan, An Economic Analysis of the Association Among Secondary Health Conditions, Health Care Costs, and Quality of Life for Persons With Spinal Cord Injury. Top Spinal Cord Inj Rehabil, 2023. 29 (3): p. 80-88. Ding, W., et al., Spinal Cord Injury: The Global Incidence, Prevalence, and Disability From the Global Burden of Disease Study 2019. Spine (Phila Pa 1976), 2022. 47 (21): p. 1532-1540. McDaid, D., et al., Understanding and modelling the economic impact of spinal cord injuries in the United Kingdom. Spinal Cord, 2019. 57 (9): p. 778-788. Gupta, S., M.A. McColl, K. Smith, and S.J.T. Guilcher, Prescription medication cost, insurance coverage, and cost-related nonadherence among people with spinal cord injury in Canada. Spinal Cord, 2020. 58 (5): p. 587-595. Shiferaw, W.S., T.Y. Akalu, H. Mulugeta, and Y.A. Aynalem, The global burden of pressure ulcers among patients with spinal cord injury: a systematic review and meta-analysis. BMC Musculoskelet Disord, 2020. 21 (1): p. 334. Chang, F., et al., Quality of life of adults with chronic spinal cord injury in mainland china: A cross-sectional study. J Rehabil Med, 2020. 52 (5): p. jrm00058. Borg, D.N., et al., The Effect of Health Service Use, Unmet Need, and Service Obstacles on Quality of Life and Psychological Well-Being in the First Year After Discharge From Spinal Cord Injury Rehabilitation. Arch Phys Med Rehabil, 2020. 101 (7): p. 1162-1169. Loftus, C.J., et al., Bladder management is the top health concern among adults with a spinal cord injury. Neurourol Urodyn, 2024. 43 (2): p. 449-458. Hunt, C., et al., Prevalence of chronic pain after spinal cord injury: a systematic review and meta-analysis. Reg Anesth Pain Med, 2021. 46 (4): p. 328-336. Sangari, S. and M.A. Perez, Prevalence of spasticity in humans with spinal cord injury with different injury severity. J Neurophysiol, 2022. 128 (3): p. 470-479. Golestani, A., et al., Epidemiology of Traumatic Spinal Cord Injury in Developing Countries from 2009 to 2020: A Systematic Review and Meta-Analysis. Neuroepidemiology, 2022. 56 (4): p. 219-239. Jazayeri, S.B., et al., Incidence of traumatic spinal cord injury worldwide: A systematic review, data integration, and update. World Neurosurg X, 2023. 18 : p. 100171. Hao, D., et al., Trends of epidemiological characteristics of traumatic spinal cord injury in China, 2009-2018. Eur Spine J, 2021. 30 (10): p. 3115-3127. Jiang, B., et al., Prevalence, Incidence, and External Causes of Traumatic Spinal Cord Injury in China: A Nationally Representative Cross-Sectional Survey. Front Neurol, 2021. 12 : p. 784647. Zhou, H., et al., Epidemiological and clinical features, treatment status, and economic burden of traumatic spinal cord injury in China: a hospital-based retrospective study. Neural Regen Res, 2024. 19 (5): p. 1126-1133. Hu, X., et al., Spinal cord injury: molecular mechanisms and therapeutic interventions. Signal Transduct Target Ther, 2023. 8 (1): p. 245. Anjum, A., et al., Spinal Cord Injury: Pathophysiology, Multimolecular Interactions, and Underlying Recovery Mechanisms. Int J Mol Sci, 2020. 21 (20). McDonald, J.W. and C. Sadowsky, Spinal-cord injury. Lancet, 2002. 359 (9304): p. 417-25. Chooi, W.H. and S.Y. Chew, Modulation of cell-cell interactions for neural tissue engineering: Potential therapeutic applications of cell adhesion molecules in nerve regeneration. Biomaterials, 2019. 197 : p. 327-344. Tan, C.X. and C. Eroglu, Cell adhesion molecules regulating astrocyte-neuron interactions. Curr Opin Neurobiol, 2021. 69 : p. 170-177. Saint-Martin, M. and Y. Goda, Astrocyte-synapse interactions and cell adhesion molecules. FEBS J, 2023. 290 (14): p. 3512-3526. Hu, J., S.L. Lin, and M. Schachner, A fragment of cell adhesion molecule L1 reduces amyloid-beta plaques in a mouse model of Alzheimer's disease. Cell Death Dis, 2022. 13 (1): p. 48. Kyritsis, N., et al., Diagnostic blood RNA profiles for human acute spinal cord injury. J Exp Med, 2021. 218 (3). Morrison, D., et al., Profiling Immunological Phenotypes in Individuals During the First Year After Traumatic Spinal Cord Injury: A Longitudinal Analysis. J Neurotrauma, 2023. 40 (23-24): p. 2621-2637. Sun, R., Y. Gao, and F. Shen, Identification of subtypes of hepatocellular carcinoma and screening of prognostic molecular diagnostic markers based on cell adhesion molecule related genes. Front Genet, 2022. 13 : p. 1042540. Love, M.I., W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014. 15 (12): p. 550. Gustavsson, E.K., et al., ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics, 2022. 38 (15): p. 3844-3846. Shi, Y., et al., Crosstalk of ferroptosis regulators and tumor immunity in pancreatic adenocarcinoma: novel perspective to mRNA vaccines and personalized immunotherapy. Apoptosis, 2023. 28 (9-10): p. 1423-1435. Wang, B., et al., Identification and validation of chromatin regulator-related signatures as a novel prognostic model for low-grade gliomas using translational bioinformatics. Life Sci, 2024. 336 : p. 122312. Tommasini, D. and B.L. Fogel, multiWGCNA: an R package for deep mining gene co-expression networks in multi-trait expression data. BMC Bioinformatics, 2023. 24 (1): p. 115. Eden, S.K., C. Li, and B.E. Shepherd, Nonparametric estimation of Spearman's rank correlation with bivariate survival data. Biometrics, 2022. 78 (2): p. 421-434. Gao, C.H., G. Yu, and P. Cai, ggVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram. Front Genet, 2021. 12 : p. 706907. Friedman, J., T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw, 2010. 33 (1): p. 1-22. Robin, X., et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011. 12 : p. 77. Yu, G., L.G. Wang, Y. Han, and Q.Y. He, clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012. 16 (5): p. 284-7. Wang, L., et al., Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma. Front Immunol, 2022. 13 : p. 989286. Chen, B., et al., Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol, 2018. 1711 : p. 243-259. Shannon, P., et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13 (11): p. 2498-504. Sreepada, A., M. Tiwari, and K. Pal, Adhesion G protein-coupled receptor gluing action guides tissue development and disease. J Mol Med (Berl), 2022. 100 (10): p. 1355-1372. Higgins JPT, T.J., Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 . . 2023 August 2023 [cited 234; 127-146]. Available from: https://training.cochrane.org/handbook/current. Manu, D.R., et al., Astrocyte Involvement in Blood-Brain Barrier Function: A Critical Update Highlighting Novel, Complex, Neurovascular Interactions. Int J Mol Sci, 2023. 24 (24). Kim, H., et al., Reactive astrocytes transduce inflammation in a blood-brain barrier model through a TNF-STAT3 signaling axis and secretion of alpha 1-antichymotrypsin. Nat Commun, 2022. 13 (1): p. 6581. Montague-Cardoso, K., et al., Changes in vascular permeability in the spinal cord contribute to chemotherapy-induced neuropathic pain. Brain Behav Immun, 2020. 83 : p. 248-259. Ding, Y. and Q. Chen, The NF-kappaB Pathway: a Focus on Inflammatory Responses in Spinal Cord Injury. Mol Neurobiol, 2023. 60 (9): p. 5292-5308. Zhang, H., et al., TCF7 knockdown inhibits the imatinib resistance of chronic myeloid leukemia K562/G01 cells by neutralizing the Wnt/beta‑catenin/TCF7/ABC transporter signaling axis. Oncol Rep, 2021. 45 (2): p. 557-568. Frohlich, J., K. Rose, and A. Hecht, Transcriptional activity mediated by beta-CATENIN and TCF/LEF family members is completely dispensable for survival and propagation of multiple human colorectal cancer cell lines. Sci Rep, 2023. 13 (1): p. 287. Xu, X., et al., Clinical Significance of Transcription Factor 7 (TCF7) as a Prognostic Factor in Gastric Cancer. Med Sci Monit, 2019. 25 : p. 3957-3963. Yu, S., et al., The TCF-1 and LEF-1 transcription factors have cooperative and opposing roles in T cell development and malignancy. Immunity, 2012. 37 (5): p. 813-26. Sharma, M. and K. Pruitt, Wnt Pathway: An Integral Hub for Developmental and Oncogenic Signaling Networks. Int J Mol Sci, 2020. 21 (21). Ding, J., et al., Therapeutic blood-brain barrier modulation and stroke treatment by a bioengineered FZD(4)-selective WNT surrogate in mice. Nat Commun, 2023. 14 (1): p. 2947. Sabbagh, M.F. and J. Nathans, A genome-wide view of the de-differentiation of central nervous system endothelial cells in culture. Elife, 2020. 9 . Lee, U., et al., Robust differentiation of human pluripotent stem cells into mural progenitor cells via transient activation of NKX3.1. Nat Commun, 2024. 15 (1): p. 8392. Antao, A.M., S. Ramakrishna, and K.S. Kim, The Role of Nkx3.1 in Cancers and Stemness. Int J Stem Cells, 2021. 14 (2): p. 168-179. Jiang, P. and Z. Wang, [Progress on axon regeneration in model organisms]. Zhejiang Da Xue Xue Bao Yi Xue Ban, 2020. 49 (4): p. 500-507. Di Francesco, D., et al., Regenerative Potential of A Bovine ECM-Derived Hydrogel for Biomedical Applications. Biomolecules, 2022. 12 (9). Pickett, J.R., Y. Wu, L.F. Zacchi, and H.T. Ta, Targeting endothelial vascular cell adhesion molecule-1 in atherosclerosis: drug discovery and development of vascular cell adhesion molecule-1-directed novel therapeutics. Cardiovasc Res, 2023. 119 (13): p. 2278-2293. Bui, T.M., H.L. Wiesolek, and R. Sumagin, ICAM-1: A master regulator of cellular responses in inflammation, injury resolution, and tumorigenesis. J Leukoc Biol, 2020. 108 (3): p. 787-799. Guo, Y., X. Luo, and W. Guo, The impact of amino acid metabolism on adult neurogenesis. Biochem Soc Trans, 2023. 51 (1): p. 233-244. Qian, D., et al., Blocking Notch signal pathway suppresses the activation of neurotoxic A1 astrocytes after spinal cord injury. Cell Cycle, 2019. 18 (21): p. 3010-3029. Jian, Y.P., et al., MicroRNA-34a suppresses neuronal apoptosis and alleviates microglia inflammation by negatively targeting the Notch pathway in spinal cord injury. Eur Rev Med Pharmacol Sci, 2020. 24 (3): p. 1420-1427. Vizurraga, A., et al., Mechanisms of adhesion G protein-coupled receptor activation. J Biol Chem, 2020. 295 (41): p. 14065-14083. Palacios, D., et al., The G Protein-Coupled Receptor GPR56 Is an Inhibitory Checkpoint for NK Cell Migration. J Immunol, 2024. 213 (9): p. 1349-1357. Tseng, W.Y., M. Stacey, and H.H. Lin, Role of Adhesion G Protein-Coupled Receptors in Immune Dysfunction and Disorder. Int J Mol Sci, 2023. 24 (6). Kubo, F., et al., Loss of the adhesion G-protein coupled receptor ADGRF5 in mice induces airway inflammation and the expression of CCL2 in lung endothelial cells. Respir Res, 2019. 20 (1): p. 11. Leysen, H., et al., G Protein-Coupled Receptor Systems as Crucial Regulators of DNA Damage Response Processes. Int J Mol Sci, 2018. 19 (10). Fusco, C.M., et al., Neuronal ribosomes exhibit dynamic and context-dependent exchange of ribosomal proteins. Nat Commun, 2021. 12 (1): p. 6127. Slomnicki, L.P., et al., Requirement of Neuronal Ribosome Synthesis for Growth and Maintenance of the Dendritic Tree. J Biol Chem, 2016. 291 (11): p. 5721-5739. Man, H., et al., [Jisuikang formula promotes spinal cord injury repair in rats by activating the YAP/PKM2 signaling axis in astrocytes]. Nan Fang Yi Ke Da Xue Xue Bao, 2024. 44 (4): p. 636-643. Escobar, G., D. Mangani, and A.C. Anderson, T cell factor 1: A master regulator of the T cell response in disease. Sci Immunol, 2020. 5 (53). Hirahara, K., et al., The Role of CD4(+) Resident Memory T Cells in Local Immunity in the Mucosal Tissue - Protection Versus Pathology. Front Immunol, 2021. 12 : p. 616309. Kumar, H., et al., Elevated TRPV4 Levels Contribute to Endothelial Damage and Scarring in Experimental Spinal Cord Injury. J Neurosci, 2020. 40 (9): p. 1943-1955. Gao, Z.S., et al., Berberine-loaded M2 macrophage-derived exosomes for spinal cord injury therapy. Acta Biomater, 2021. 126 : p. 211-223. Shi, Y., et al., Th17 cells and inflammation in neurological disorders: Possible mechanisms of action. Front Immunol, 2022. 13 : p. 932152. Weng, W.T., et al., 4-Ethylguaiacol modulates neuroinflammation and Th1/Th17 differentiation to ameliorate disease severity in experimental autoimmune encephalomyelitis. J Neuroinflammation, 2021. 18 (1): p. 110. Milne, S.M., et al., Myelin oligodendrocyte glycoprotein reactive Th17 cells drive Janus Kinase 1 dependent transcriptional reprogramming in astrocytes and alter cell surface cytokine receptor profiles during experimental autoimmune encephalomyelitis. Sci Rep, 2024. 14 (1): p. 13146. Wang, S., et al., Small Extracellular Vesicles Derived from Altered Peptide Ligand-Loaded Dendritic Cell Act as A Therapeutic Vaccine for Spinal Cord Injury Through Eliciting CD4(+) T cell-Mediated Neuroprotective Immunity. Adv Sci (Weinh), 2024. 11 (3): p. e2304648. Lund, M.C., et al., The Inflammatory Response after Moderate Contusion Spinal Cord Injury: A Time Study. Biology (Basel), 2022. 11 (6). Wang, D., et al., Soluble CD146, a cerebrospinal fluid marker for neuroinflammation, promotes blood-brain barrier dysfunction. Theranostics, 2020. 10 (1): p. 231-246. Zhang, Z., K.K. Yung, and J.K. Ko, Therapeutic Intervention in Cancer by Isoliquiritigenin from Licorice: A Natural Antioxidant and Redox Regulator. Antioxidants (Basel), 2022. 11 (7). Feng, Y., et al., Natural killer cell deficiency experiences higher risk of sepsis after critical intracerebral hemorrhage. Int J Immunopathol Pharmacol, 2021. 35 : p. 20587384211056495. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6676117","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485067183,"identity":"f2c23939-ae32-4714-9c49-bb673456e450","order_by":0,"name":"Hongxia Pan","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hongxia","middleName":"","lastName":"Pan","suffix":""},{"id":485067184,"identity":"3f5eadc4-c307-4572-854e-1a38d9eb446b","order_by":1,"name":"Mingfu Ding","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Mingfu","middleName":"","lastName":"Ding","suffix":""},{"id":485067185,"identity":"bd32ac24-5469-4289-80e9-d718648ab603","order_by":2,"name":"Fei Xie","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xie","suffix":""},{"id":485067186,"identity":"df638547-9f64-438a-81c2-36fde94b0c1c","order_by":3,"name":"Xin Sun","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Sun","suffix":""},{"id":485067187,"identity":"b4efa6cd-d0c6-4f04-91ab-5780b390b179","order_by":4,"name":"Liyi Huang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Liyi","middleName":"","lastName":"Huang","suffix":""},{"id":485067188,"identity":"a63eae65-b3bc-466b-889e-b2f5f512dd04","order_by":5,"name":"Yue Yang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yang","suffix":""},{"id":485067189,"identity":"81098705-a5cf-416c-a46b-a7b6fed86eb2","order_by":6,"name":"Quan Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFACxgYQKQfhsJGgxZgULRCQ2EC0FoPjzY23eWrupM9vP2PA8KHsMAP/7AYCWs4cbLbmOfYsd8OZHAPGGecOM0jcOYBfi9mNxDZpHrbDuRskeAyYedsOMxhIJBDQcv8hUMu/w+nyM4Ba/hKl5QZjmzTQ8ASGG0AtjMRosT+T2Gw5t++w4YYzaQUHe86l80jcIKBFsv34wxtvvh2Wl28/vPHBjzJrOf4ZBLSAgASMcQCIeQirR9YyCkbBKBgFowArAACpkEOH16d6yAAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Quan","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-05-16 01:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6676117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6676117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86790358,"identity":"3c4735b1-7ef7-428b-9aa4-7c480a849cfa","added_by":"auto","created_at":"2025-07-15 14:47:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94673,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Bioinformatics Retrieval and Analysis Process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/2b10e82a6432ff9a279a716e.png"},{"id":86791285,"identity":"366b7b73-0c78-41b0-aa68-5fcd67250333","added_by":"auto","created_at":"2025-07-15 14:55:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":704224,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DEGs and WGCNA. (A) Volcano plot of DEGs from GSE151371. (B) Heat map of DEGs from GSE151371. (C) Difference in CAMs-RG expression. (D) Sample clustering for outliers detection. (E) Scale independence. (F) Mean connectivity. (G) Cluster dendragram. (H) Module-CAMs relationships.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/841c1fd57ddf109d7c2b6892.png"},{"id":86790360,"identity":"5e7406df-6161-4297-b8a6-e241dc951f01","added_by":"auto","created_at":"2025-07-15 14:47:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":415007,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/75c380a23271a95319c13f7f.png"},{"id":86793048,"identity":"b6182f26-0882-46f6-a04d-4e983c583ec9","added_by":"auto","created_at":"2025-07-15 15:11:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191275,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/ea6ace3cbbe3e052629bb0f7.png"},{"id":86790359,"identity":"0fe32917-e448-404d-b46b-e4faf66ed62d","added_by":"auto","created_at":"2025-07-15 14:47:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":393721,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/9057aaaabfdf49caf69a6340.png"},{"id":86790369,"identity":"63d0a15e-62ae-4e4b-92b1-cd00ac38b247","added_by":"auto","created_at":"2025-07-15 14:47:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":763555,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/a2fd3b043c57cdffb377df2e.png"},{"id":86790371,"identity":"1f7be9ca-1c6f-4b23-9892-6a53a342ee5a","added_by":"auto","created_at":"2025-07-15 14:47:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":38698,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/fee42dcc3f345e0cf0b20ef1.png"},{"id":88315832,"identity":"af2a7455-925c-48d5-8dc3-97468fb4b27e","added_by":"auto","created_at":"2025-08-05 07:53:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3798892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6676117/v1/ca12aa79-7588-4b6f-a1a7-41d68df6ed34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanistic and Biomarker Roles of Cell Adhesion Molecules in Spinal Cord Injury: Evidence from Clinical and Molecular Analyses","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSpinal cord injury (SCI) is a devastating neurological disorder caused by trauma or disease, leading to disruption of the spinal cord's structural and functional integrity. This results in varying degrees of motor, sensory, and autonomic dysfunction[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], along with a high incidence rate, imposing a substantial burden on patients and healthcare systems[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. SCI predominantly affects young individuals, significantly reducing their quality of life[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and placing considerable physical[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], psychological[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and financial strain on both patients and their families. Over the past three decades, the global prevalence of SCI has increased from 236 to 1298 cases per million, with an estimated 250,000\u0026ndash;500,000 new cases reported annually worldwide[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In China alone, approximately 759,302 individuals are living with traumatic SCI, with around 66,374 new cases reported each year[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite significant advances in spinal surgery and rehabilitation care, effective therapeutic interventions remain limited, largely due to the complex pathological mechanisms and the central nervous system (CNS) restricted regenerative capacity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSCI primarily disrupts neural circuits through axonal damage and neuronal loss, impairing both motor and sensory functions. While neural plasticity remains a critical mechanism for recovery, the intricate molecular changes following SCI, coupled with inconsistent treatment outcomes, present formidable challenges to effective regeneration. A deeper understanding of the underlying molecular mechanisms is essential for identifying novel therapeutic targets and developing effective treatments[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCell-cell interactions are fundamental to CNS structure and function. Although cell-extracellular matrix (ECM) interactions contribute to neural repair, cell-cell communication plays a more central role in neural regeneration. Cell adhesion molecules (CAMs), such as cadherins, neural cell adhesion molecules (NCAM), and L1, are key mediators of these interactions, regulating synapse formation, synaptic plasticity, and astrocyte maturation\u0026mdash;processes essential for neural circuit remodeling following SCI[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Notably, L1 has been identified as a potential therapeutic target for Alzheimer\u0026rsquo;s disease, underscoring the broader therapeutic potential of CAMs in neurological disorders[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the spinal cord, CAMs are highly expressed and crucial for synaptic remodeling and neural circuit integrity, both of which are critical for functional recovery after SCI. Despite growing evidence of CAMs' involvement in SCI, their precise regulatory mechanisms remain poorly understood. Elucidating the role of CAMs in SCI may uncover novel therapeutic strategies to enhance neural repair and functional recovery.\u003c/p\u003e\u003cp\u003eThis study utilizes transcriptomic data and bioinformatics approaches to investigate the role of CAMs in SCI. We aim to identify CAM-related biomarkers, analyze their expression patterns, and explore their functional roles and clinical relevance By integrating molecular and bioinformatics analyses, this study seeks to establish a theoretical framework that advances our understanding of CAM-mediated mechanisms in SCI pathophysiology, ultimately guiding the development of improved diagnostic and therapeutic strategies.\u003c/p\u003e\u003cp\u003eThe workflow of the present study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Source of data and bioinformatics analysis\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 Microarray data acquisition\u003c/h2\u003e\u003cp\u003eTwo transcriptome datasets related to SCI were retrieved from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e: GSE151371 (GPL20301) and GSE226238 (GPL18573) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The GSE151371 dataset contained 10 control and 38 SCI samples (sample source: blood)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. while the GSE226238 dataset included 9 control and 37 SCI samples (sample source: whole blood)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Next, 156 CAM-related genes (CAM-RGs) were identified from the literature [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of data.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDataset\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTissue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControl group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference (PMID)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE151371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL20301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33512429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE226238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL18573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37221869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 Bioinformatics Analysis:\u003c/h2\u003e\u003cp\u003eCAM-related differentially expressed genes (DEGs) in serum samples from SCI patients were identified using WGCNA combined with differential gene expression, LASSO regression, and enrichment analyses. Key genes were further investigated to validate their molecular-level expression and explore their functional mechanisms.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e\u003cp\u003eThe GSE151371 dataset was processed using DESeq2 (v 3.1.8) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] to detect DEGs, applying a threshold of |log\u003csub\u003e2\u003c/sub\u003e (Fold change)| \u0026gt;1 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To illustrate DEGs, both volcano plots and heat maps were generated using ggplot2 (v 3.4.4) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and Pheatmap (v 1.0.12) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 CAMs score calculation\u003c/h2\u003e\u003cp\u003eThe ssGSEA function from the GSVA package (v 1.46.0)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was utilized to calculate the CAM associated scores in GSE151371 dataset, aiming to identify genes linked to CAMs in SCI. The Wilcoxon test was subsequently used to compare the CAM associated scores between SCI and control groups, with a significance threshold set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Weighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e\u003cp\u003eThe Hierarchical Clustering function in the WGCNA package (v 1.71)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was implemented to hierarchically cluster all of the samples in GSE151371 dataset to remove any outlier samples. The appropriate soft threshold was determined by calculating the scale-free fit index and the mean connectivity (converging to 0) using the pick Soft Threshold function (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.8). To categorize the gene modules according to the hybrid dynamic shear tree algorithm's criteria, the minimum number of genes per gene module was set to 100, and merge Cut Height to 0.2. The Spearman correlation coefficients between CAM scores and modules were determined using the psych program (v 2.2.9)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and the genes in the modules with the highest correlation coefficients were chosen as key module genes. Lastly, the gg Venndiagram (v 1.7.3) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] was applied to detect overlapping genes for DEGs and key module genes, which were then classified as candidate genes for machine learning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Least absolute shrinkage and selection operator (LASSO) regression analysis\u003c/h2\u003e\u003cp\u003eIn accordance with the candidate genes, glmnet (v 4.1-4)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was executed to perform LASSO regression analysis, and the genes whose regression coefficients were not penalized to 0 were designated as feature genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Diagnostic capacity and expression assessment of biomarkers\u003c/h2\u003e\u003cp\u003eTo evaluate the diagnostic efficacy of feature genes for SCI, ROC curves were plotted in GSE151371 dataset using pROC (v 1.18.0)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (area under the ROC curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.7). Biomarker expression was validated across SCI and control groups using the Wilcoxon test in GSE151371 and GSE226238 datasets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The expression box-plot was constructed by the box-plot function. The genes that were significantly differentially expressed in the training and validation sets with consistent expression trends were also selected to be recorded as biomarkers for subsequent analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Gene set enrichment analysis (GSEA)\u003c/h2\u003e\u003cp\u003eTo investigate the functional pathways implicated in biomarkers, enrichment analysis was conducted in GSE151371 dataset. The Spearman correlation coefficients between biomarkers and remaining genes were calculated independently using psych and sorted in descending order. GSEA was completed using cluster Profiler (v 4.7.1.003)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and org. Hs. eg. db, with c2.cp.kegg.v7.5.1.symbols.gmt from the Molecular Signatures Database (MSigDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e serving as the background gene set. And GSEA was applied to the ridge plot visualization results using enrich plot (v 1.10.2) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Immune infiltration\u003c/h2\u003e\u003cp\u003eUsing CIBERSORT (v1.03)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], the infiltration of 22 immune cells was evaluated in GSE151371 dataset in order to investigate the potential regulatory role of immune cells in SCI. The difference in the percentage of immune cell infiltration between SCI and control groups was then compared with the Wilcoxon test. Additionally, Spearman correlations were computed by psych (|cor| \u0026gt;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between biomarkers and between biomarkers and differential immune cells, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Expression of SCI related pathogenic genes\u003c/h2\u003e\u003cp\u003eThe top 20 SCI related pathogenic genes were acquired by searching the GeneCards database at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org\u003c/span\u003e\u003cspan address=\"https://www.genecards.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The expression of these genes was evaluated, and intergroup differences were estimated in GSE151371 dataset. The Pearson correlations between biomarkers and pathogenic genes associated with SCI were then computed using psych.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Construction of miRNA-mRNA networks\u003c/h2\u003e\u003cp\u003eTo find miRNAs that might be involved in biomarker regulation, biomarker-based prediction of miRNAs was carried out in the miRTarBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirtarbase.mbc.nctu.edu.tw/index.html\u003c/span\u003e\u003cspan address=\"http://mirtarbase.mbc.nctu.edu.tw/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e and the miRwalk database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e to get experimentally verified miRNAs that might be involved in biomarker regulation. After that, miRNAs from the two databases were employed to build miRNA-mRNA networks, correspondingly.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Prediction of chemotherapeutic agents targeting biomarkers\u003c/h2\u003e\u003cp\u003eTo gain insight into the medications that might target biomarkers to treat SCI, predictions were generated using the comparative toxicogenomics database database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e Cytoscape(v 3.7.0)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was implemented to depict the biomarker-drug network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Expression analysis of biomarkers\u003c/h2\u003e\u003cp\u003eSerum samples were obtained from SCI patients at West China Hospital, Sichuan University between September 2024 and November 2024 and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. The study was approved by the Ethical Board of West China Hospital of Sichuan University (Approval NO. 2023-34), and written informed consent was obtained from all participants prior to their inclusion in the research.\u003c/p\u003e\u003cp\u003eTo validate the biomarkers expression consistency with transcriptomic analysis, blood samples were collected from 10 SCI patients and 10 non-SCI control individuals. Total RNA was extracted using the Trizol reagent kit, followed by reverse transcription into cDNA using the RT Easy\u0026trade; II kit (FOREGENE). Primers were designed based on NCBI reference sequences, and quantitative PCR (qPCR) was performed to assess the expression of key genes in both groups. β-actin was used as the internal control, and relative gene expression levels were calculated using the 2^\u0026minus;ΔΔCt method. Differential expression between groups was analyzed using an independent t-test, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 highly significant. The primer sequences used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe primer sequences were used in this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward primer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReverse primer\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ-actin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTTTCCAGCCTTCCTTCCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCAGGTCTTTGCGGATGTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADGRG5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGGGAAGAACTCCTGAGCTAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCTGGTGGAGTTGACGAGAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTACAAGGGCACAGGCACAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTCCTGGGCAAAGACGTAGC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll bioinformatic projects were conducted using R (v 4.2.2). The Wilcoxon test was utilized to compare data among groups ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;In SCI, 687 genes had proven to be substantially linked to CAM scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter differential expression analysis in GSE151371 dataset, 3,582 DEGs were determined between SCI and control groups, with 2,309 increased and 1,109 decreased DEGs in SCI group (Figures 2A and 2B). As a result, determined by CAM-RGs, we noticed that CAM scores were substantially decreased in SCI patients (Figure 2C). Furthermore, a hierarchical clustering of all samples in GSE151371 dataset indicated no outlier samples (Figure 2D). WGCNA was then performed on all samples to locate genes that had a strong association with CAM scores. When R\u003csup\u003e2\u003c/sup\u003e was set to 0.8 and the mean connectivity was zero, the optimal soft threshold was chosen as 22 (Figures 2E and 2F). Next, all genes were divided into ten modules relying on the phylogenetic clustering tree (Figure 2G), with the MEgreen module having the strongest connection with CAM scores (cor = 0.79, p = 3^10\u003csup\u003e-11\u003c/sup\u003e) (Figure 2H). Therefore, 687 of the genes contained inside were determined as key module genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 TCF7, SBK1, and ADGRG5: CAM-Associated Biomarkers Implicated in SCI Pathophysiology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intersection of 687 key module genes with 3,582 DEGs yielded 99 candidate genes (Figure 3A). LASSO regression analysis of the candidate genes revealed that 6 genes (TLR5, TCF7, MAB21L2, SBK1, SLCO5A1, and ADGRG5) were classified as feature genes since their regression coefficients were not penalized to 0 when the Lambda. min was 0.0277 (Figures 3B and 3C). The ROC curves suggested that the AUC values for all 6 feature genes were better than 0.7, indicating that they all had high diagnostic abilities for SCI (Figure 3D). Afterwards, ADGRG5, SBK1, and TCF7 were shown to be significantly reduced in SCI patients in both GSE151371 and GSE226238 datasets (Figures 3E and 3F). Therefore, these 3 feature genes were considered as biomarkers for subsequent analyses. There were strongly positive correlations between these three biomarkers (Figure 3G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Drug prediction and construct miRNA-mRNA regulation networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further understand the molecular regulation and clinical medicinal potential of biomarkers, miRNA and drug prediction of targeted biomarkers were utilized respectively. The miRwalk and miRTarBase database predicted 37 miRNAs that regulated biomarkers. The mRNA-miRNA network containing 40 nodes and 37 edges was constructed based on three biomarkers and 37 predicted miRNAs. Complex regulatory relationships could be seen from this mRNA-miRNA network, such as hsa-miR-4270-SBK1, hsa-miR-493-3p-ADGRG5 and hsa-miR-373-5p-TCF7 (Figure 4A). Subsequently, the CTD database was applied to determine 17 medicines that target three biomarkers, with Benzo(a)pyrene simultaneously targeting all three biomarkers (Figure 4B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Enrichment analysis for biomarkers associated with immunomodulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA was applied to deeper understand the biomarkers\u0026apos; functional pathways. TCF7, SBK1, and ADGRG5 had a strong relationship with immune regulation (Figures 5A-5C). As a result, we looked further into immune cell infiltration in SCI. The findings suggested that myeloid cells accounted for the majority of SCI patients (Figure 6A), whereas NK cell resting, neutrophil, memory resting CD4+ T cells, and na\u0026iuml;ve CD4+ T cells differed considerably between SCI and control groups (Figure 6B). Neutrophils exhibited a much larger proportion than the other distinct immune cells in SCI patients, while the opposite was true for the rest of the differential immune cells. Correlations indicated that SBK1 correlated significantly with CD4 na\u0026iuml;ve T cells, TCF7 correlated strongly with CD4 na\u0026iuml;ve T cells as well as resting NK cells, while ADGRG5 correlated most notably with resting NK cells (Figure 6C). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 SCI-related genes and biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 9 of the 20 genes corresponding to SCI in the GeneCards database demonstrated notable variations between two groups in GSE151371 dataset, with TRPV4 being highly expressed in SCI and AR, ATP7A, BDNF-AS, DARS2, DYNC1H1, IL6, SIGMAR1, SOD1, and VAPB being the reverse (Figure 6D). The correlation of biomarkers with 9 significantly distinct SCI-related genes revealed TCF7, SBK1, and ADGRG5 all showed high positive correlations with DYNC1H1, SIGMAR1, and AR (Figure 6E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. Experimental validation of biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression analysis of biomarkers showed that ADGRG5 and SBK1 were significantly reduced in SCI patients, consistent with the gene database analysis results, as shown in Fig 7A-B.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003eSCI Pathophysiology and the Role of CAMs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSCI involves complex pathophysiological processes, driven by inflammation, oxidative stress, and neuronal death. These processes highlight the need to better understand the molecular mechanisms underlying SCI for effective therapeutic strategies. Neural circuit remodeling and synapse formation are critical for functional recovery, where CAMs play a pivotal role[41, 42]. CAMs regulate axonal guidance, synaptic stabilization, and cellular interactions within the extracellular matrix, which directly influence neuronal connectivity and tissue repair. However, the precise molecular mechanisms of CAMs in these processes remain unclear. This study identified three CAMs-related biomarkers (TCF7, SBK1 and ADGRG5) through bioinformatics analysis of gene expression profiles in SCI patient. Clinical validation using serum samples further confirmed significant dysregulation of SBK1 and ADGRG5, underscoring their potential as diagnostic and therapeutic targets in SCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of CAMs in Immune Inflammation and Secondary Injury in SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn SCI, CAMs such as ICAM-1 and VCAM-1 are upregulated in endothelial cells and astrocytes, facilitating immune cell recruitment and triggering secondary inflammation[21, 43-45]. The inflammatory response, regulated by immune cells, plays a critical role in early SCI, exacerbating secondary injury through mechanisms such as oxidative stress and excitotoxicity, ultimately leading to neuronal death[46]. Our findings reveal that CAM-related genes (TCF7, SBK1, and ADGRG5) may modulate this immune-inflammatory process, highlighting their significance in SCI pathophysiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTCF7: Linking Wnt Signaling to Immune and Neural Repair in SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTCF7 encodes T-cell factor 7, a transcription factor involved in the Wnt/\u0026beta;-catenin signaling pathway[47-49]. Reduced expression of TCF7 has been linked to T-cell dysfunction, impairing both immune regulation and tissue repair at the injury site[50]. Additionally, TCF7 supports neural stem cell proliferation and differentiation through the Wnt signaling pathway, facilitating neurogenesis and repair[51]. In this study, bioinformatics analyses revealed decreased TCF7 expression in SCI patients. However, clinical validation did not show statistically significant differences between SCI and non-SCI groups, possibly due to the localized role of TCF7 in tissue repair and intracellular signal transduction, which may not be adequately reflected in peripheral blood analysis due to the blood-brain barrier[52, 53]. Future studies should investigate cerebrospinal fluid (CSF) as a more direct medium to assess changes in the spinal cord microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBK1: A Multifaceted Regulator in SCI Repair and Immune Modulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSBK1, a serine/threonine protein kinase, plays a pivotal role in neural development and signal transduction[54, 55]. GSEA and RT-qPCR analyses revealed its involvement in pathways critical SCI repair, including protein synthesis, ECM remodeling, amino acid metabolism, signal transduction, and immune regulation. SBK1 enhances protein synthesis efficiency and fidelity, which are essential for neuronal survival and axonal regeneration[56]. Its enrichment in glycosaminoglycan (GAG) biosynthesis and ECM metabolism pathways suggests a role in restructuring the ECM, providing structural support and biochemical signals for neural repair[57]. By influencing ECM composition and modulating cell adhesion molecules (e.g., ICAM-1, VCAM-1), SBK1 may regulate immune cell aggregation and migration at the injury site[58, 59]. In the lysine degradation pathway, SBK1 supports energy homeostasis and biosynthesis, vital for ATP production and macromolecule synthesis during neuronal recovery[60]. Moreover, its association with the Notch signaling pathway underscores a role in neuronal differentiation, proliferation, apoptosis, and axonal regeneration, all essential for functional recovery[61, 62]. SBK1\u0026rsquo;s enrichment in primary immunodeficiency pathways suggests its potential role in immune modulation, influencing immune cell recruitment and activation, thereby impacting the inflammatory cascade and repair processes. Further studies are needed to elucidate its precise mechanisms. \u0026nbsp; In conclusion, SBK1 is deeply involved in pathways essential for SCI repair, including protein synthesis, ECM remodeling, metabolic regulation, immune modulation, and signal transduction. Its ability to regulate cell adhesion and immune responses underscores its therapeutic potential for enhancing recovery after SCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADGRG5: Modulating Immune Regulation and Neuroregeneration in SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADGRG5, an adhesion G protein-coupled receptor(aGPCR), is critical for cell-cell interactions and signal transduction essential for neuroprotection and regeneration\u003c/strong\u003e[63]\u003cstrong\u003e. Its downregulation in SCI may disrupt multiple recovery mechanisms, including immune regulation, DNA repair, protein synthesis, and T-cell function.\u003c/strong\u003e Although direct evidence evidence of ADGRG5\u0026rsquo;s involvement in specific immune cell activities, such as T-cell and NK-cell modulation, remains limited, its enrichment in pathways like allograft rejection and primary immunodeficiency suggests a role in modulating inflammatory response intensity[64, 65]. As a member of the aGPCRs family, known for immune system regulation and immune cell interactions, ADGRG5 may similarly contribute to T-cell activation, differentiation, and function via the T cell receptor (TCR) signaling pathway, influencing whether T cells adopt protective or injurious roles post-SCI. Reduced ADGRG5 expression may further impair immune cell development and function, exacerbating inflammation and hindering tissue repair[66]. Additionally, aGPCRs are implicated in DNA damage responses, suggesting ADGRG5 may influence DNA repair mechanisms vital for neuronal survival[67]. Its role in ribosome pathways may also support efficient protein synthesis, essential for cellular repair and neural network reconstruction[68, 69]. These potential roles highlight ADGRG5\u0026rsquo;s importance in SCI recovery and warrant further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAMs as Key Biomarkers in Immune Regulation and Neural Repair Post-SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we identified TCF7, SBK1, and ADGRG5 as key CAM-related genes significantly enriched in the immunodeficiency pathway in SCI, emphasizing the critical role of cell adhesion in immune regulation and neural repair mechanisms[70]. Both SBK1 and ADGRG5 were significantly downregulated post-SCI, with AUC values of 0.85 and 0.832, respectively, highlighting their potential as diagnostic biomarkers. These findings suggest that CAMs not only mediate immune cell recruitment and adhesion but also influence critical biological processes such as inflammation, neuronal repair, and tissue remodeling, providing a mechanistic link between CAMs and SCI pathology. Initial CD4 T cells, closely associated with TCF7 and SBK1, may contribute to both inflammatory and repair processes[71, 72], underscoring the dual role of T cells in either exacerbating inflammation or promoting recovery. SBK1 and ADGRG5 may regulate CD4 T cell adhesion and migration via CAM-mediated mechanisms, directly influencing immune cell activity at the injury site. Although previous studies reported elevated TRPV4 in SCI[73, 74], no significant differences were observed in this study, possibly due to sample or technical limitations. Our results validate the roles of SBK1 and ADGRG5 in immunomodulation and neural repair, supporting the therapeutic potential of targeting CAM-related immune responses in SCI. Future research should investigate their interplay with T cells to refine their diagnostic and therapeutic applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCD4+ T Cells in SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCD4+ T cells play a dual role in SCI recovery, balancing pro-inflammatory and anti-inflammatory responses. Upon activation, na\u0026iuml;ve CD4+ T cells differentiate into subsets such as Th1, Th2, Th17, and Tregs. Th1 and Th17 cells exacerbate inflammation via pro-inflammatory cytokines (e.g., IFN-\u0026gamma;, IL-17), leading to axonal damage and neuronal death[75-77], while Th2 and Tregs promote tissue repair through anti-inflammatory and neurotrophic effects[78]. Adhesion molecules facilitate T cell recruitment and migration across endothelial barriers[79, 80], with pathways like Fas/FasL playing pivotal roles in T cell adhesion and apoptosis[81]. In this study, the significant correlation of SBK1, ADGRG5, and TCF7 with CD4+ na\u0026iuml;ve T cells suggests these genes regulate T cell activation, differentiation, and immune microenvironment dynamics post-SCI. The dysregulated expression of these genes may influence the adhesion and migratory behavior of CD4+ T cells, modulating the delicate balance between inflammation and repair. Furthermore, the high diagnostic potential of SBK1 and ADGRG5, as indicated by their AUC values (0.85 and 0.832), underscores their relevance as molecular targets for therapeutic intervention in SCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNK Cells in SCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNK cells are emerging as important contributors to SCI repair, often acting in synergy with T cells. This study revealed a significant association between ADGRG5 and TCF7 with resting NK cells, highlighting the role of adhesion molecules in regulating immune cell migration, retention, and function. Downregulation of ADGRG5 may impair the recruitment and retention of both NK cells and T cells at the injury site, disrupting inflammatory regulation and tissue repair processes[82]. The interactions between T cells and NK cells, mediated through adhesion molecules, are crucial in shaping the immune microenvironment post-SCI. Together, SBK1, ADGRG5, and TCF7 appear to coordinate immune response and repair mechanisms by influencing the differentiation, adhesion, and functional behavior of these immune cells. Future studies should investigate the adhesion-regulating roles of these genes to clarify their therapeutic potential in restoring immune balance and promoting recovery after SCI.\u003c/p\u003e\n\u003cp\u003eThis study identified SBK1 and ADGRG5 as key biomarkers associated with cell adhesion mechanisms, emphasizing their crucial roles in the immunoregulation and repair processes of SCI. These genes play essential roles in immune cell migration and activation, modulation of inflammatory responses, DNA repair, protein synthesis, secondary injury mitigation, and neuronal regeneration. Notably, their significant downregulation in SCI and high diagnostic accuracy, as evidenced by robust AUC values, highlight their potential as molecular targets for SCI diagnosis and therapy.\u003c/p\u003e\n\u003cp\u003eDespite these promising findings, limitations should be acknowledged. First, the relatively small sample size reduces the statistical power and generalizability of the results. Second, the precise mechanisms by which SBK1 and ADGRG5 regulate cell adhesion and immune cell interactions, particularly their crosstalk with T cells and NK cells, remain to be fully elucidated. Future studies should involve larger patient cohorts and investigate these biomarkers in CSF or localized spinal cord tissues to gain deeper insights into their roles within the SCI microenvironment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study underscores the significant involvement of SBK1 and ADGRG5 in SCI immunoregulation and repair through cell adhesion mechanisms. Their strong diagnostic potential suggests they may serve as valuable biomarkers and therapeutic targets for SCI. Further investigations are warranted to validate these findings and explore their mechanistic roles in SCI pathophysiology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePHX drafted and edited the manuscript, DMF and SX assisted with the implementation of this work, XF contributed to the methodology, while HLY, YY and WQ were responsible for the review and editing of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Program of China, (Grant No.2023YFC3603800 and 2023YFC3603801), the National Natural Science Foundation of China, (Grant No.82172534 and 82372574) and 1.3.5 Project for Disciplines of Excellence, West china Hospital, Sichuan University(Grant No. ZYJC21038).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethical Board of West China Hospital of Sichuan University(Approval NO. 2023-34). Written informed consent was obtained from all participants prior to their inclusion in the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators, G.B.D.S.C.I., \u003cem\u003eGlobal, regional, and national burden of spinal cord injury, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.\u003c/em\u003e Lancet Neurol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(11): p. 1026-1047.\u003c/li\u003e\n\u003cli\u003eHu, S., P. Wang, Y. Dong, and F. Li, \u003cem\u003eIncidence, prevalence and disability of spinal cord injury in China from 1990 to 2019: a systematic analysis of the Global Burden of Disease Study 2019.\u003c/em\u003e Eur Spine J, 2023. \u003cstrong\u003e32\u003c/strong\u003e(2): p. 590-600.\u003c/li\u003e\n\u003cli\u003eMitton, C., F. Dionne, N. Fallah, and V.K. Noonan, \u003cem\u003eAn Economic Analysis of the Association Among Secondary Health Conditions, Health Care Costs, and Quality of Life for Persons With Spinal Cord Injury.\u003c/em\u003e Top Spinal Cord Inj Rehabil, 2023. \u003cstrong\u003e29\u003c/strong\u003e(3): p. 80-88.\u003c/li\u003e\n\u003cli\u003eDing, W., et al., \u003cem\u003eSpinal Cord Injury: The Global Incidence, Prevalence, and Disability From the Global Burden of Disease Study 2019.\u003c/em\u003e Spine (Phila Pa 1976), 2022. \u003cstrong\u003e47\u003c/strong\u003e(21): p. 1532-1540.\u003c/li\u003e\n\u003cli\u003eMcDaid, D., et al., \u003cem\u003eUnderstanding and modelling the economic impact of spinal cord injuries in the United Kingdom.\u003c/em\u003e Spinal Cord, 2019. \u003cstrong\u003e57\u003c/strong\u003e(9): p. 778-788.\u003c/li\u003e\n\u003cli\u003eGupta, S., M.A. McColl, K. Smith, and S.J.T. Guilcher, \u003cem\u003ePrescription medication cost, insurance coverage, and cost-related nonadherence among people with spinal cord injury in Canada.\u003c/em\u003e Spinal Cord, 2020. \u003cstrong\u003e58\u003c/strong\u003e(5): p. 587-595.\u003c/li\u003e\n\u003cli\u003eShiferaw, W.S., T.Y. Akalu, H. Mulugeta, and Y.A. Aynalem, \u003cem\u003eThe global burden of pressure ulcers among patients with spinal cord injury: a systematic review and meta-analysis.\u003c/em\u003e BMC Musculoskelet Disord, 2020. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 334.\u003c/li\u003e\n\u003cli\u003eChang, F., et al., \u003cem\u003eQuality of life of adults with chronic spinal cord injury in mainland china: A cross-sectional study.\u003c/em\u003e J Rehabil Med, 2020. \u003cstrong\u003e52\u003c/strong\u003e(5): p. jrm00058.\u003c/li\u003e\n\u003cli\u003eBorg, D.N., et al., \u003cem\u003eThe Effect of Health Service Use, Unmet Need, and Service Obstacles on Quality of Life and Psychological Well-Being in the First Year After Discharge From Spinal Cord Injury Rehabilitation.\u003c/em\u003e Arch Phys Med Rehabil, 2020. \u003cstrong\u003e101\u003c/strong\u003e(7): p. 1162-1169.\u003c/li\u003e\n\u003cli\u003eLoftus, C.J., et al., \u003cem\u003eBladder management is the top health concern among adults with a spinal cord injury.\u003c/em\u003e Neurourol Urodyn, 2024. \u003cstrong\u003e43\u003c/strong\u003e(2): p. 449-458.\u003c/li\u003e\n\u003cli\u003eHunt, C., et al., \u003cem\u003ePrevalence of chronic pain after spinal cord injury: a systematic review and meta-analysis.\u003c/em\u003e Reg Anesth Pain Med, 2021. \u003cstrong\u003e46\u003c/strong\u003e(4): p. 328-336.\u003c/li\u003e\n\u003cli\u003eSangari, S. and M.A. Perez, \u003cem\u003ePrevalence of spasticity in humans with spinal cord injury with different injury severity.\u003c/em\u003e J Neurophysiol, 2022. \u003cstrong\u003e128\u003c/strong\u003e(3): p. 470-479.\u003c/li\u003e\n\u003cli\u003eGolestani, A., et al., \u003cem\u003eEpidemiology of Traumatic Spinal Cord Injury in Developing Countries from 2009 to 2020: A Systematic Review and Meta-Analysis.\u003c/em\u003e Neuroepidemiology, 2022. \u003cstrong\u003e56\u003c/strong\u003e(4): p. 219-239.\u003c/li\u003e\n\u003cli\u003eJazayeri, S.B., et al., \u003cem\u003eIncidence of traumatic spinal cord injury worldwide: A systematic review, data integration, and update.\u003c/em\u003e World Neurosurg X, 2023. \u003cstrong\u003e18\u003c/strong\u003e: p. 100171.\u003c/li\u003e\n\u003cli\u003eHao, D., et al., \u003cem\u003eTrends of epidemiological characteristics of traumatic spinal cord injury in China, 2009-2018.\u003c/em\u003e Eur Spine J, 2021. \u003cstrong\u003e30\u003c/strong\u003e(10): p. 3115-3127.\u003c/li\u003e\n\u003cli\u003eJiang, B., et al., \u003cem\u003ePrevalence, Incidence, and External Causes of Traumatic Spinal Cord Injury in China: A Nationally Representative Cross-Sectional Survey.\u003c/em\u003e Front Neurol, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 784647.\u003c/li\u003e\n\u003cli\u003eZhou, H., et al., \u003cem\u003eEpidemiological and clinical features, treatment status, and economic burden of traumatic spinal cord injury in China: a hospital-based retrospective study.\u003c/em\u003e Neural Regen Res, 2024. \u003cstrong\u003e19\u003c/strong\u003e(5): p. 1126-1133.\u003c/li\u003e\n\u003cli\u003eHu, X., et al., \u003cem\u003eSpinal cord injury: molecular mechanisms and therapeutic interventions.\u003c/em\u003e Signal Transduct Target Ther, 2023. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 245.\u003c/li\u003e\n\u003cli\u003eAnjum, A., et al., \u003cem\u003eSpinal Cord Injury: Pathophysiology, Multimolecular Interactions, and Underlying Recovery Mechanisms.\u003c/em\u003e Int J Mol Sci, 2020. \u003cstrong\u003e21\u003c/strong\u003e(20).\u003c/li\u003e\n\u003cli\u003eMcDonald, J.W. and C. Sadowsky, \u003cem\u003eSpinal-cord injury.\u003c/em\u003e Lancet, 2002. \u003cstrong\u003e359\u003c/strong\u003e(9304): p. 417-25.\u003c/li\u003e\n\u003cli\u003eChooi, W.H. and S.Y. Chew, \u003cem\u003eModulation of cell-cell interactions for neural tissue engineering: Potential therapeutic applications of cell adhesion molecules in nerve regeneration.\u003c/em\u003e Biomaterials, 2019. \u003cstrong\u003e197\u003c/strong\u003e: p. 327-344.\u003c/li\u003e\n\u003cli\u003eTan, C.X. and C. Eroglu, \u003cem\u003eCell adhesion molecules regulating astrocyte-neuron interactions.\u003c/em\u003e Curr Opin Neurobiol, 2021. \u003cstrong\u003e69\u003c/strong\u003e: p. 170-177.\u003c/li\u003e\n\u003cli\u003eSaint-Martin, M. and Y. Goda, \u003cem\u003eAstrocyte-synapse interactions and cell adhesion molecules.\u003c/em\u003e FEBS J, 2023. \u003cstrong\u003e290\u003c/strong\u003e(14): p. 3512-3526.\u003c/li\u003e\n\u003cli\u003eHu, J., S.L. Lin, and M. Schachner, \u003cem\u003eA fragment of cell adhesion molecule L1 reduces amyloid-beta plaques in a mouse model of Alzheimer\u0026apos;s disease.\u003c/em\u003e Cell Death Dis, 2022. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 48.\u003c/li\u003e\n\u003cli\u003eKyritsis, N., et al., \u003cem\u003eDiagnostic blood RNA profiles for human acute spinal cord injury.\u003c/em\u003e J Exp Med, 2021. \u003cstrong\u003e218\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eMorrison, D., et al., \u003cem\u003eProfiling Immunological Phenotypes in Individuals During the First Year After Traumatic Spinal Cord Injury: A Longitudinal Analysis.\u003c/em\u003e J Neurotrauma, 2023. \u003cstrong\u003e40\u003c/strong\u003e(23-24): p. 2621-2637.\u003c/li\u003e\n\u003cli\u003eSun, R., Y. Gao, and F. Shen, \u003cem\u003eIdentification of subtypes of hepatocellular carcinoma and screening of prognostic molecular diagnostic markers based on cell adhesion molecule related genes.\u003c/em\u003e Front Genet, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 1042540.\u003c/li\u003e\n\u003cli\u003eLove, M.I., W. Huber, and S. Anders, \u003cem\u003eModerated estimation of fold change and dispersion for RNA-seq data with DESeq2.\u003c/em\u003e Genome Biol, 2014. \u003cstrong\u003e15\u003c/strong\u003e(12): p. 550.\u003c/li\u003e\n\u003cli\u003eGustavsson, E.K., et al., \u003cem\u003eggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2.\u003c/em\u003e Bioinformatics, 2022. \u003cstrong\u003e38\u003c/strong\u003e(15): p. 3844-3846.\u003c/li\u003e\n\u003cli\u003eShi, Y., et al., \u003cem\u003eCrosstalk of ferroptosis regulators and tumor immunity in pancreatic adenocarcinoma: novel perspective to mRNA vaccines and personalized immunotherapy.\u003c/em\u003e Apoptosis, 2023. \u003cstrong\u003e28\u003c/strong\u003e(9-10): p. 1423-1435.\u003c/li\u003e\n\u003cli\u003eWang, B., et al., \u003cem\u003eIdentification and validation of chromatin regulator-related signatures as a novel prognostic model for low-grade gliomas using translational bioinformatics.\u003c/em\u003e Life Sci, 2024. \u003cstrong\u003e336\u003c/strong\u003e: p. 122312.\u003c/li\u003e\n\u003cli\u003eTommasini, D. and B.L. Fogel, \u003cem\u003emultiWGCNA: an R package for deep mining gene co-expression networks in multi-trait expression data.\u003c/em\u003e BMC Bioinformatics, 2023. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 115.\u003c/li\u003e\n\u003cli\u003eEden, S.K., C. Li, and B.E. Shepherd, \u003cem\u003eNonparametric estimation of Spearman\u0026apos;s rank correlation with bivariate survival data.\u003c/em\u003e Biometrics, 2022. \u003cstrong\u003e78\u003c/strong\u003e(2): p. 421-434.\u003c/li\u003e\n\u003cli\u003eGao, C.H., G. Yu, and P. Cai, \u003cem\u003eggVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram.\u003c/em\u003e Front Genet, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 706907.\u003c/li\u003e\n\u003cli\u003eFriedman, J., T. Hastie, and R. Tibshirani, \u003cem\u003eRegularization Paths for Generalized Linear Models via Coordinate Descent.\u003c/em\u003e J Stat Softw, 2010. \u003cstrong\u003e33\u003c/strong\u003e(1): p. 1-22.\u003c/li\u003e\n\u003cli\u003eRobin, X., et al., \u003cem\u003epROC: an open-source package for R and S+ to analyze and compare ROC curves.\u003c/em\u003e BMC Bioinformatics, 2011. \u003cstrong\u003e12\u003c/strong\u003e: p. 77.\u003c/li\u003e\n\u003cli\u003eYu, G., L.G. Wang, Y. Han, and Q.Y. He, \u003cem\u003eclusterProfiler: an R package for comparing biological themes among gene clusters.\u003c/em\u003e OMICS, 2012. \u003cstrong\u003e16\u003c/strong\u003e(5): p. 284-7.\u003c/li\u003e\n\u003cli\u003eWang, L., et al., \u003cem\u003eCuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma.\u003c/em\u003e Front Immunol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 989286.\u003c/li\u003e\n\u003cli\u003eChen, B., et al., \u003cem\u003eProfiling Tumor Infiltrating Immune Cells with CIBERSORT.\u003c/em\u003e Methods Mol Biol, 2018. \u003cstrong\u003e1711\u003c/strong\u003e: p. 243-259.\u003c/li\u003e\n\u003cli\u003eShannon, P., et al., \u003cem\u003eCytoscape: a software environment for integrated models of biomolecular interaction networks.\u003c/em\u003e Genome Res, 2003. \u003cstrong\u003e13\u003c/strong\u003e(11): p. 2498-504.\u003c/li\u003e\n\u003cli\u003eSreepada, A., M. Tiwari, and K. Pal, \u003cem\u003eAdhesion G protein-coupled receptor gluing action guides tissue development and disease.\u003c/em\u003e J Mol Med (Berl), 2022. \u003cstrong\u003e100\u003c/strong\u003e(10): p. 1355-1372.\u003c/li\u003e\n\u003cli\u003eHiggins JPT, T.J., Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors).\u003cem\u003e Cochrane Handbook for Systematic Reviews of Interventions version 6.4 . \u003c/em\u003e. 2023 August 2023 [cited 234; 127-146]. Available from: https://training.cochrane.org/handbook/current.\u003c/li\u003e\n\u003cli\u003eManu, D.R., et al., \u003cem\u003eAstrocyte Involvement in Blood-Brain Barrier Function: A Critical Update Highlighting Novel, Complex, Neurovascular Interactions.\u003c/em\u003e Int J Mol Sci, 2023. \u003cstrong\u003e24\u003c/strong\u003e(24).\u003c/li\u003e\n\u003cli\u003eKim, H., et al., \u003cem\u003eReactive astrocytes transduce inflammation in a blood-brain barrier model through a TNF-STAT3 signaling axis and secretion of alpha 1-antichymotrypsin.\u003c/em\u003e Nat Commun, 2022. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 6581.\u003c/li\u003e\n\u003cli\u003eMontague-Cardoso, K., et al., \u003cem\u003eChanges in vascular permeability in the spinal cord contribute to chemotherapy-induced neuropathic pain.\u003c/em\u003e Brain Behav Immun, 2020. \u003cstrong\u003e83\u003c/strong\u003e: p. 248-259.\u003c/li\u003e\n\u003cli\u003eDing, Y. and Q. Chen, \u003cem\u003eThe NF-kappaB Pathway: a Focus on Inflammatory Responses in Spinal Cord Injury.\u003c/em\u003e Mol Neurobiol, 2023. \u003cstrong\u003e60\u003c/strong\u003e(9): p. 5292-5308.\u003c/li\u003e\n\u003cli\u003eZhang, H., et al., \u003cem\u003eTCF7 knockdown inhibits the imatinib resistance of chronic myeloid leukemia K562/G01 cells by neutralizing the Wnt/beta‑catenin/TCF7/ABC transporter signaling axis.\u003c/em\u003e Oncol Rep, 2021. \u003cstrong\u003e45\u003c/strong\u003e(2): p. 557-568.\u003c/li\u003e\n\u003cli\u003eFrohlich, J., K. Rose, and A. Hecht, \u003cem\u003eTranscriptional activity mediated by beta-CATENIN and TCF/LEF family members is completely dispensable for survival and propagation of multiple human colorectal cancer cell lines.\u003c/em\u003e Sci Rep, 2023. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 287.\u003c/li\u003e\n\u003cli\u003eXu, X., et al., \u003cem\u003eClinical Significance of Transcription Factor 7 (TCF7) as a Prognostic Factor in Gastric Cancer.\u003c/em\u003e Med Sci Monit, 2019. \u003cstrong\u003e25\u003c/strong\u003e: p. 3957-3963.\u003c/li\u003e\n\u003cli\u003eYu, S., et al., \u003cem\u003eThe TCF-1 and LEF-1 transcription factors have cooperative and opposing roles in T cell development and malignancy.\u003c/em\u003e Immunity, 2012. \u003cstrong\u003e37\u003c/strong\u003e(5): p. 813-26.\u003c/li\u003e\n\u003cli\u003eSharma, M. and K. Pruitt, \u003cem\u003eWnt Pathway: An Integral Hub for Developmental and Oncogenic Signaling Networks.\u003c/em\u003e Int J Mol Sci, 2020. \u003cstrong\u003e21\u003c/strong\u003e(21).\u003c/li\u003e\n\u003cli\u003eDing, J., et al., \u003cem\u003eTherapeutic blood-brain barrier modulation and stroke treatment by a bioengineered FZD(4)-selective WNT surrogate in mice.\u003c/em\u003e Nat Commun, 2023. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 2947.\u003c/li\u003e\n\u003cli\u003eSabbagh, M.F. and J. Nathans, \u003cem\u003eA genome-wide view of the de-differentiation of central nervous system endothelial cells in culture.\u003c/em\u003e Elife, 2020. \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLee, U., et al., \u003cem\u003eRobust differentiation of human pluripotent stem cells into mural progenitor cells via transient activation of NKX3.1.\u003c/em\u003e Nat Commun, 2024. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 8392.\u003c/li\u003e\n\u003cli\u003eAntao, A.M., S. Ramakrishna, and K.S. Kim, \u003cem\u003eThe Role of Nkx3.1 in Cancers and Stemness.\u003c/em\u003e Int J Stem Cells, 2021. \u003cstrong\u003e14\u003c/strong\u003e(2): p. 168-179.\u003c/li\u003e\n\u003cli\u003eJiang, P. and Z. Wang, \u003cem\u003e[Progress on axon regeneration in model organisms].\u003c/em\u003e Zhejiang Da Xue Xue Bao Yi Xue Ban, 2020. \u003cstrong\u003e49\u003c/strong\u003e(4): p. 500-507.\u003c/li\u003e\n\u003cli\u003eDi Francesco, D., et al., \u003cem\u003eRegenerative Potential of A Bovine ECM-Derived Hydrogel for Biomedical Applications.\u003c/em\u003e Biomolecules, 2022. \u003cstrong\u003e12\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003ePickett, J.R., Y. Wu, L.F. Zacchi, and H.T. Ta, \u003cem\u003eTargeting endothelial vascular cell adhesion molecule-1 in atherosclerosis: drug discovery and development of vascular cell adhesion molecule-1-directed novel therapeutics.\u003c/em\u003e Cardiovasc Res, 2023. \u003cstrong\u003e119\u003c/strong\u003e(13): p. 2278-2293.\u003c/li\u003e\n\u003cli\u003eBui, T.M., H.L. Wiesolek, and R. Sumagin, \u003cem\u003eICAM-1: A master regulator of cellular responses in inflammation, injury resolution, and tumorigenesis.\u003c/em\u003e J Leukoc Biol, 2020. \u003cstrong\u003e108\u003c/strong\u003e(3): p. 787-799.\u003c/li\u003e\n\u003cli\u003eGuo, Y., X. Luo, and W. Guo, \u003cem\u003eThe impact of amino acid metabolism on adult neurogenesis.\u003c/em\u003e Biochem Soc Trans, 2023. \u003cstrong\u003e51\u003c/strong\u003e(1): p. 233-244.\u003c/li\u003e\n\u003cli\u003eQian, D., et al., \u003cem\u003eBlocking Notch signal pathway suppresses the activation of neurotoxic A1 astrocytes after spinal cord injury.\u003c/em\u003e Cell Cycle, 2019. \u003cstrong\u003e18\u003c/strong\u003e(21): p. 3010-3029.\u003c/li\u003e\n\u003cli\u003eJian, Y.P., et al., \u003cem\u003eMicroRNA-34a suppresses neuronal apoptosis and alleviates microglia inflammation by negatively targeting the Notch pathway in spinal cord injury.\u003c/em\u003e Eur Rev Med Pharmacol Sci, 2020. \u003cstrong\u003e24\u003c/strong\u003e(3): p. 1420-1427.\u003c/li\u003e\n\u003cli\u003eVizurraga, A., et al., \u003cem\u003eMechanisms of adhesion G protein-coupled receptor activation.\u003c/em\u003e J Biol Chem, 2020. \u003cstrong\u003e295\u003c/strong\u003e(41): p. 14065-14083.\u003c/li\u003e\n\u003cli\u003ePalacios, D., et al., \u003cem\u003eThe G Protein-Coupled Receptor GPR56 Is an Inhibitory Checkpoint for NK Cell Migration.\u003c/em\u003e J Immunol, 2024. \u003cstrong\u003e213\u003c/strong\u003e(9): p. 1349-1357.\u003c/li\u003e\n\u003cli\u003eTseng, W.Y., M. Stacey, and H.H. Lin, \u003cem\u003eRole of Adhesion G Protein-Coupled Receptors in Immune Dysfunction and Disorder.\u003c/em\u003e Int J Mol Sci, 2023. \u003cstrong\u003e24\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003eKubo, F., et al., \u003cem\u003eLoss of the adhesion G-protein coupled receptor ADGRF5 in mice induces airway inflammation and the expression of CCL2 in lung endothelial cells.\u003c/em\u003e Respir Res, 2019. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 11.\u003c/li\u003e\n\u003cli\u003eLeysen, H., et al., \u003cem\u003eG Protein-Coupled Receptor Systems as Crucial Regulators of DNA Damage Response Processes.\u003c/em\u003e Int J Mol Sci, 2018. \u003cstrong\u003e19\u003c/strong\u003e(10).\u003c/li\u003e\n\u003cli\u003eFusco, C.M., et al., \u003cem\u003eNeuronal ribosomes exhibit dynamic and context-dependent exchange of ribosomal proteins.\u003c/em\u003e Nat Commun, 2021. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 6127.\u003c/li\u003e\n\u003cli\u003eSlomnicki, L.P., et al., \u003cem\u003eRequirement of Neuronal Ribosome Synthesis for Growth and Maintenance of the Dendritic Tree.\u003c/em\u003e J Biol Chem, 2016. \u003cstrong\u003e291\u003c/strong\u003e(11): p. 5721-5739.\u003c/li\u003e\n\u003cli\u003eMan, H., et al., \u003cem\u003e[Jisuikang formula promotes spinal cord injury repair in rats by activating the YAP/PKM2 signaling axis in astrocytes].\u003c/em\u003e Nan Fang Yi Ke Da Xue Xue Bao, 2024. \u003cstrong\u003e44\u003c/strong\u003e(4): p. 636-643.\u003c/li\u003e\n\u003cli\u003eEscobar, G., D. Mangani, and A.C. Anderson, \u003cem\u003eT cell factor 1: A master regulator of the T cell response in disease.\u003c/em\u003e Sci Immunol, 2020. \u003cstrong\u003e5\u003c/strong\u003e(53).\u003c/li\u003e\n\u003cli\u003eHirahara, K., et al., \u003cem\u003eThe Role of CD4(+) Resident Memory T Cells in Local Immunity in the Mucosal Tissue - Protection Versus Pathology.\u003c/em\u003e Front Immunol, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 616309.\u003c/li\u003e\n\u003cli\u003eKumar, H., et al., \u003cem\u003eElevated TRPV4 Levels Contribute to Endothelial Damage and Scarring in Experimental Spinal Cord Injury.\u003c/em\u003e J Neurosci, 2020. \u003cstrong\u003e40\u003c/strong\u003e(9): p. 1943-1955.\u003c/li\u003e\n\u003cli\u003eGao, Z.S., et al., \u003cem\u003eBerberine-loaded M2 macrophage-derived exosomes for spinal cord injury therapy.\u003c/em\u003e Acta Biomater, 2021. \u003cstrong\u003e126\u003c/strong\u003e: p. 211-223.\u003c/li\u003e\n\u003cli\u003eShi, Y., et al., \u003cem\u003eTh17 cells and inflammation in neurological disorders: Possible mechanisms of action.\u003c/em\u003e Front Immunol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 932152.\u003c/li\u003e\n\u003cli\u003eWeng, W.T., et al., \u003cem\u003e4-Ethylguaiacol modulates neuroinflammation and Th1/Th17 differentiation to ameliorate disease severity in experimental autoimmune encephalomyelitis.\u003c/em\u003e J Neuroinflammation, 2021. \u003cstrong\u003e18\u003c/strong\u003e(1): p. 110.\u003c/li\u003e\n\u003cli\u003eMilne, S.M., et al., \u003cem\u003eMyelin oligodendrocyte glycoprotein reactive Th17 cells drive Janus Kinase 1 dependent transcriptional reprogramming in astrocytes and alter cell surface cytokine receptor profiles during experimental autoimmune encephalomyelitis.\u003c/em\u003e Sci Rep, 2024. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 13146.\u003c/li\u003e\n\u003cli\u003eWang, S., et al., \u003cem\u003eSmall Extracellular Vesicles Derived from Altered Peptide Ligand-Loaded Dendritic Cell Act as A Therapeutic Vaccine for Spinal Cord Injury Through Eliciting CD4(+) T cell-Mediated Neuroprotective Immunity.\u003c/em\u003e Adv Sci (Weinh), 2024. \u003cstrong\u003e11\u003c/strong\u003e(3): p. e2304648.\u003c/li\u003e\n\u003cli\u003eLund, M.C., et al., \u003cem\u003eThe Inflammatory Response after Moderate Contusion Spinal Cord Injury: A Time Study.\u003c/em\u003e Biology (Basel), 2022. \u003cstrong\u003e11\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003eWang, D., et al., \u003cem\u003eSoluble CD146, a cerebrospinal fluid marker for neuroinflammation, promotes blood-brain barrier dysfunction.\u003c/em\u003e Theranostics, 2020. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 231-246.\u003c/li\u003e\n\u003cli\u003eZhang, Z., K.K. Yung, and J.K. Ko, \u003cem\u003eTherapeutic Intervention in Cancer by Isoliquiritigenin from Licorice: A Natural Antioxidant and Redox Regulator.\u003c/em\u003e Antioxidants (Basel), 2022. \u003cstrong\u003e11\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eFeng, Y., et al., \u003cem\u003eNatural killer cell deficiency experiences higher risk of sepsis after critical intracerebral hemorrhage.\u003c/em\u003e Int J Immunopathol Pharmacol, 2021. \u003cstrong\u003e35\u003c/strong\u003e: p. 20587384211056495.\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, Cell adhesion molecules, Immune infiltration, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6676117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6676117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCell adhesion molecules (CAMs) play a crucial role in neural circuit reorganization and tissue remodeling following spinal cord injury (SCI). However, the regulatory mechanisms governing CAMs in SCI pathophysiology remain poorly understood. This study employs transcriptomic analysis and bioinformatics approaches to identify key CAM-related genes and their potential roles in SCI, with further validation in clinical serum samples from SCI and non-SCI individuals to explore their diagnostic and therapeutic relevance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ePublicly available transcriptome datasets (GSE151371 and GSE226238) were analyzed, focusing on 156 CAM-related genes (CAM-RGs). Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the GSE151371 dataset to identify differentially expressed genes (DEGs) and key module genes significantly associated with CAM scores in SCI. Candidate genes were identified by intersecting DEGs with key module genes, followed by feature selection using least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) curve analysis was conducted to assess their diagnostic potential. Functional enrichment analysis, immune cell infiltration assessment, molecular regulatory network construction, and drug repurposing predictions were further performed. Finally, biomarker expression was validated using real-time quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) in serum samples from SCI and non-SCI individuals to confirm the bioinformatics findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 3,582 DEGs and 687 key module genes, 99 candidate genes were identified. LASSO regression refined six feature genes (TLR5,TCF7, MAB21L2, SBK1, SLCO5A1 and ADGRG5), among which TCF7, SBK1, and ADGRG5 were significantly downregulated in SCI. Enrichment analyses linked these biomarkers to immune regulation pathways, with their expression positively correlated with resting natural killer (NK) cells and naïve CD4+ T cells. RT-qPCR validation in clinical serum samples confirmed significant downregulation of SBK1 and ADGRG5 in SCI, consistent with transcriptomic findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study identified SBK1 and ADGRG5 as candidate CAM-related biomarkers in SCI through integrative bioinformatics analysis and clinical validation. Their downregulation in SCI and association with immune regulation suggest a potential role in neural repair. These findings provide a new insights into CAM-mediated mechanisms in SCI and may contribute to the development of targeted diagnostic and therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Mechanistic and Biomarker Roles of Cell Adhesion Molecules in Spinal Cord Injury: Evidence from Clinical and Molecular Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 14:47:32","doi":"10.21203/rs.3.rs-6676117/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":"203362f2-3015-4a21-aad6-ff0ce017a942","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51490152,"name":"Biological sciences/Molecular biology"},{"id":51490153,"name":"Biological sciences/Neuroscience"},{"id":51490154,"name":"Health sciences/Biomarkers"},{"id":51490155,"name":"Health sciences/Medical research"},{"id":51490156,"name":"Health sciences/Neurology"},{"id":51490157,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2025-08-05T07:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 14:47:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6676117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6676117","identity":"rs-6676117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}