Identification of key autophagy-related genes and pathways in spinal cord injury

Spinal cord injury (SCI) can cause a range of functional impairments, and patients with SCI have limited potential for functional recovery. Previous studies have demonstrated that autophagy plays a role in the pathological process of SCI, but the specific mechanism of autophagy in this context remains unclear. Therefore, we explored the role of autophagy in SCI by identifying key autophagy-related genes and pathways. This study utilized the GSE132242 expression profile dataset, which consists of four control samples and four SCI samples; autophagy-related genes were sourced from GeneCards. R software was used to screen differentially expressed genes (DEGs) in the GSE132242 dataset, which were then intersected with autophagy-related genes to identify autophagy-related DEGs in SCI. Subsequently, the expression levels of these genes were confirmed and analyzed with gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein–protein interaction (PPI) analysis was conducted to identify interaction genes, and the resulting network was visualized with Cytoscape. The MCODE plug-in was used to build gene cluster modules, and the cytoHubba plug-in was applied to screen for hub genes. Finally, the GSE5296 dataset was used to verify the reliability of the hub genes. We screened 129 autophagy-related DEGs, including 126 up-regulated and 3 down-regulated genes. GO and KEGG pathway enrichment analysis showed that these 129 genes were mainly involved in the process of cell apoptosis, angiogenesis, IL-1 production, and inflammatory reactions, the TNF signaling pathway and the p53 signaling pathway. PPI identified 10 hub genes, including CCL2, TGFB1, PTGS2, FN1, HGF, MYC, IGF1, CD44, CXCR4, and SERPINEL1. The GSE5296 dataset revealed that the control group exhibited lower expression levels than the SCI group, although only CD44 and TGFB1 showed significant differences. This study identified 129 autophagy-related genes that might play a role in SCI. CD44 and TGFB1 were identified as potentially important genes in the autophagy process after SCI. These findings provide new targets for future research and offer new perspectives on the pathogenesis of SCI.


Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of autophagy-related DEGs in SCI
The GO function is divided into three categories: biological processes (BP), cellular components (CC), and molecular functions (MF), while the KEGG pathways explain the primary functions of genes at the molecular level.The "cluster profiler" and "GOplot" R software packages were used to perform GO and KEGG pathway enrichment analyses and visualizations for all autophagy-related genes, and the adjusted P-value, was considered the threshold for significant enrichment.The results of the top 10 enriched categories were visualized with bubble charts and bar charts.

Construction of the protein-protein interaction (PPI) network and identification of central genes and key modules
To elucidate the functional interactions between proteins, the Search Tool for Recurring Instances of Neighbouring Genes (STRING, https:// cn.string-db.org/) and Cytoscape software (https:// cytos cape.org/) were utilized.Specifically, the "Betweenness" algorithm in the "CytoNCA" plug-in was used to analyze all genes and construct a circular PPI network based on the "Betweenness" score.Subsequently, the "MCODE" plug-in was used to cluster the gene network and identify key sub-network modules.Lastly, the "CytoHubb" plug-in was employed to screen the top 10 hub genes using a variety of built-in algorithms.

Statistical analysis
In this study, the SPSS 26.0 version was utilized for statistical analysis.First, the Shapiro-Wilk method was applied to test whether the data conformed to a normal distribution.For data conforming to a normal distribution, we employed an independent sample Student's t-test to compare the gene expression levels of samples.For nonnormal distribution data, we used the Mann-Whitney U test.P-value less than 0.05 was considered statistically significant.

GO and KEGG analyses of autophagy-related DEGs in SCI
To determine the biological processes and KEGG pathways of the 129 autophagy-related DEGs, we conducted GO annotation and KEGG enrichment analysis using the "clusterProfiler" R software package.We identified 674 significantly enriched GO biological process terms and selected the top 10 terms from BP, CC, and MF for visualization.The BP terms were mainly associated with the apical processes, positive regulation of gene expression, and angiogenesis.The CC terms were mainly enriched in the cytoplasm, nucleus, and cytosol.By contrast, the MF terms were mainly enriched in protein binding, authentic protein binding, and macromolecular complex binding (Fig. 4a,b, Tables 1, 2, and 3).In the KEGG enrichment analysis, the 129 autophagy-related DEGs were significantly enriched in 93 KEGG pathway terms, including the TNF signaling pathway, apoptosis, and the p53 signaling pathway (Fig. 4c,d, Table 4).

Construction of the PPI network and identification of central genes and key modules
To investigate the interactions between the 129 autophagy-related DEGs, we performed a PPI analysis using the STRING database and visualized the results with Cytoscape.First, we used the "CytoNCA" plug-in to build a PPI network based on the Betweenness score (Fig. 5a).In addition, we used the MCODE plug-in to identify important gene cluster modules and identified three clusters.Cluster 1 contained 39 nodes and 271 edges, with a score of 14.263.Cluster 2 contained 21 nodes and 73 edges, with a score of 7.300, while cluster 3 contained seven nodes and 10 edges, with a score of 3.333 (Fig. 5b-d).To identify central genes, we used the CytoHubba plug-in, which identified 10 central genes: CCL2, TGFB1, PTGS2, FN1, HGF, MYC, IGF1, CD44, CXCR4, and SERPINE1 (Fig. 5e).

Cross-validation of external datasets
To verify the accuracy of our results, we conducted cross-validation with the GSE5296 dataset to examine the expression levels of the 10 key genes.We downloaded this dataset from the GEO database and selected samples with the same damage time as those in the GSE132242 dataset for analysis.Except for SERPINE1, the expression levels of the nine remaining key genes were similar to those in the GSE132242 dataset.Specifically, the control group exhibited lower expression levels than the SCI group, although only CD44 and TGFB1 showed significant differences.The expression levels of the remaining seven key genes did not show a significant difference between the two groups (Fig. 6).

Discussion
The results of the GO analysis revealed that the 129 autophagy-related DEGs mainly functioned in the cytoplasm, nucleus, and cytosol.Their molecular functions were related to protein binding and macromolecular complex binding.These genes were involved in apoptosis, angiogenesis, and the regulation of IL-1β production and the inflammatory response.Additionally, the KEGG enrichment analysis demonstrated that these genes were mainly enriched in the TNF signaling pathway, apoptosis, and the p53 signaling pathway.The TNF and the p53 signaling pathways are mainly associated with the neuroinflammatory reaction and the apoptosis of cells and neurons in SCI.These series of analysis results confirmed that autophagy played a crucial role in SCI by regulating inflammatory response and apoptosis and might also impact angiogenesis.p53 is a crucial regulator of apoptosis, and many apoptosis-related molecules exert their effects through p53, resulting in a complex process 10 .A prior study has identified p53 and Bax-dependent cell apoptosis induced by DNA damage as the main cause of spinal motor neuron death after nerve avulsion 11 .Another study has reported that p53-mediated spinal cord mitochondrial apoptosis induced   www.nature.com/scientificreports/by DNA damage is an essential mechanism of cell death after SCI 12 .Moreover, p53 is involved in cell survival and axon growth, indicating that it is a critical factor influencing functional recovery after SCI and plays a vital regulatory role in neurite outgrowth 13 .Additionally, SIRT1 may also inhibit SCI cell apoptosis by regulating the p53 signaling pathway 14 .
In recent years, as research on Traditional Chinese Medicine has expanded, scholars have discovered that Schisandrin B can reduce the inflammatory response, oxidative stress, and apoptosis of in SCI by inhibiting the p53 signaling pathway 15 .Similarly, Buyang Huanwu Decoction treats SCI by regulating the p53 signaling pathway 16 .These findings support the results of our study, indicating that the p53 signaling pathway plays an essential role in SCI.
This study found that IGF1 was enriched in the p53 signaling pathway.Previous research has demonstrated that IGF1 inhibited autophagy by activating the PI3K/Akt/mTOR signaling pathway, which promoted functional  www.nature.com/scientificreports/recovery after SCI in rats.However, it is unclear whether IGF1 regulates autophagy through the p53 signaling pathway and contributes to the pathogenesis of SCI; therefore, further investigation is necessary 17 .
It is well-established that TNF plays a pivotal role in the inflammatory response following SCI by inducing cytokine and chemokine expression 18 .Another study has observed that the TNF signaling pathway remained activated throughout the course of SCI, with stronger activation during the early stages 19 .On the first day after the injury, a combination of TNF, recombinant IL-6, and IL-1 at the lesion site led to the recruitment and activation of microglia and macrophages.However, by the fourth day, TNF administration reduced the activation of microglia and the size of the lesion area, suggesting that TNF plays different roles at different time points after SCI 20 .
This study found that two hub genes (CCL2 and PTGS2) were found to be enriched in the TNF signaling pathway.CCL2 is an important chemokine that regulates autophagy and responds to various physiological and pathophysiological stimuli by activating autophagy.The inhibition of CCL2 expression and the PI3K/Akt/mTOR signaling pathway can activate autophagy, effectively reducing neuronal apoptosis after SCI 21 .In another study, macrophage migration inhibitory factor inhibitors improved the motor function of rats' hind limbs by reducing the microglia and macrophages recruited by CCL2 at the injury site 22 .Recent studies based on bioinformatics analysis have demonstrated that PTGS2 is related to iron death and immune infiltration after SCI 23,24 .Our research suggests that PTGS2 affects the functional recovery of mice after SCI by regulating autophagy, but the specific mechanism of action requires further investigation.Moreover, the GO and PPI analyses revealed that several genes might contribute significantly to the pathophysiology of SCI.For example, autophagy-related DEGs identified through GO analysis could regulate the production of IL-1β, the main mediator of inflammation, which plays a harmful role in SCI.Inhibiting IL-1β can have protective effects in SCI 25 .The up-regulation of CD44 after SCI contributes to cell adhesion and glial cell attraction, promoting SCI injury repair 26 .The SDF-1/CXCR4 interaction recruits exogenous mesenchymal stem cells into injured spinal cord tissue, which may enhance nerve regeneration.Furthermore, the CXCR4 signaling pathway is involved in the migration of Schwann cells from the peripheral nervous system to the central nervous system after SCI, improving motor function 27,28 .HGF is endogenously produced in the spinal cord of rats after SCI 29 and gradually increases during the first week after injury, remaining at a high level in the short term.HGF helps reduce the extent of SCI and improve functional recovery by exhibiting anti-inflammatory, anti-apoptotic, angiogenic, antifibrotic, and neurogenic properties in transplanted neural stem cells (NSCs) 30 .TGF-β is significantly upregulated by microglia and macrophages at the epicenter, rostral, and caudal areas after SCI and plays a crucial role in regulating nerve regeneration 31 .It modulates neurite growth, promotes glial scar formation, and interacts with immune cells to mediate inflammation and the immune response induced by nerve injury 32 .The formation of the glial scar is attributed to chondroitin sulfate proteoglycan (CSPG).Up-regulated TGF-β after SCI can inhibit the autophagic flux, enhance the secretion of CSPG, and impair nerve regeneration.Targeted inhibition of TGF-β can restore the autophagic flux, reduce the formation of the glial scar, and the recovery of spinal cord function 33 .
This study has several limitations that need to be acknowledged.First, we only used the latest dataset for our analysis, resulting in a limited sample size and possible deviations in our results.Second, the validation dataset was published in July 2006, and errors due to technical reasons are unavoidable.Additionally, different SCI operation methods may lead to different results.Third, further study of the potential mechanism of the selected hub genes is limited because of a lack of in vivo and in vitro experiments.

Conclusion
We screened 129 autophagy-related DEGs through bioinformatics analysis and identified vital pathways related to these genes.Additionally, we identified 10 hub genes including CCL2, TGFB1, PTGS2, FN1, HGF, MYC, IGF1, CD44, CXCR4, and SERPINE1.After multiple validations, the results suggested that CD44 and TGFB1 as potential research and treatment targets for autophagy after SCI.The follow-up study will experimentally verify the results of this study.

Figure 1 .
Figure 1.Differentially expressed genes (DEGs) in spinal cord injury (SCI) samples and control samples.(a) Correlation analysis of four SCI samples and four control samples.(b) Volcano map showing that 1525 genes were up-regulated, and 370 were down-regulated.(c) Heat map showing that the up-regulated and downregulated genes.

Figure 3 .
Figure3.Spearman correlation analysis of autophagy-related genes that were differentially expressed in the top 50 between the SCI samples and the control samples.The results revealed various correlations among the 50 genes.FAS and CSTB showed the strongest positive correlation (Cor = 1.00).Several negatively correlated gene pairs were found among these 50 genes, including IL-24 and PCNA, VAMP8 and PNF1, and TUBA8 and IGFBP3, of these, IL-24 and CD44 had the strongest negative correlation (Cor = − 0.98).

Figure 4 .
Figure 4. Top 10 terms in gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment map.(a,b) GO enrichment map visualizing the top 10 terms from biological processes (BP), cellular components (CC), and molecular functions (MF).The BP terms were mainly associated with the apical process, positive regulation of gene expression, and angiogenesis.The CC terms were mainly enriched in the cytoplasm, nucleus, and cytosol.The MF terms were mainly enriched in protein binding, authentic protein binding, and macromolecular complex binding.(c,d) KEGG pathway enrichment map showing that the 129 autophagy-related DEGs were significantly enriched in 93 KEGG pathway terms, including the tumor necrosis factor (TNF) signaling pathway, apoptosis, and the p53 signaling pathway.

Figure 6 .
Figure 6.Violin Plot of 10 hub genes in SCI samples and control samples in the GSE5296 dataset.Violin Plot showing that the control group exhibited lower expression levels than the SCI group, although only CD44 and TGFB1 showed significant differences.

Table 1 .
Overview of the top 10 GO-BP terms.

Table 2 .
Overview of the top 10 GO-CC terms.

Table 3 .
Overview of the top 10 GO-MF terms.

Table 4 .
Overview of the top 10 KEGG pathways.