Abstract
INTRODUCTION: The occurrence of Spinal cord injury (SCI) brings economic burden and social burden to individuals, families and society, and the complications after SCI greatly affect the rehabilitation and treatment of patients in the later stage.This study focused on the potential biomarkers that co-exist in SCI and sarcopenia, with the expectation to diagnose and prognose patients in the acute phase and rehabilitation phase using comprehensive data analysis. METHODS: The datasets used in this study were downloaded from Gene Expression Omnibus (GEO) database. Firstly, the datasets were analyzed with the "DEseq2" and "Limma" R package to identify differentially expressed genes (DEGs), which were then visualized using volcano plots. The SCI and sarcopenia DEGs that overlapped were used to construct a protein-protein interaction (PPI) network. Three algorithms were used to obtain a list of the top 10 hub genes. Next, validation of the hub genes was performed using three datasets. According to the results, the top hub genes were DCN, FSTL1, and COL12A1, which subsequently underwent were Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. We also assessed immune cell infiltration with the CIBERSORT algorithm to explore the immune cell landscape. The correlations between the hub genes and age and body mass index were investigated. To illustrate the biological mechanisms of the hub genes more clearly, a single-cell RNA-seq dataset was assessed to determine gene expression when muscle injury occurred. According to our analysis and the role in muscle, we chose the fibro/adipogenic progenitors (FAPs) cluster in the next step of the analysis. In the sub cluster analysis, we use the "Monocle" package to perform the trajectory analysis in different injury time points and different cell states. RESULTS: A total of 144 overlapped genes were obtained from two datasets. Following PPI network analysis and validation, we finally identified three hub-genes (DCN, FSTL1, and COL12A1), which were significantly altered in sarcopenic SCI patients both before and after rehabilitation training. The three hub genes were also significantly expressed in the FAPs clusters. Furthermore, following injury, the expression of the hub genes changed with the time points, changing in FAPs cluster. DISCUSSION: Our study provides comprehensive insights into how muscle changes after SCI are associated with sarcopenia by moving from RNA-seq to RNA-SEQ, including Immune infiltration landscape, pesudotime change and so on. The three hub genes identified in this study could be used to distinguish the sarcopenia state at the genomic level. Additionally, they may also play a prognostic role in evaluating the efficiency of rehabilitation training.