Research Paper Volume 15, Issue 13 pp 6346—6360
Identification of key biomarkers in ischemic stroke: single-cell sequencing and weighted co-expression network analysis
- 1 Department of Emergency Medicine, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian 223300, People’s Republic of China
- 2 Department of Respiratory Medicine, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian 223300, People’s Republic of China
Received: April 24, 2023 Accepted: June 19, 2023 Published: July 6, 2023
https://doi.org/10.18632/aging.204855How to Cite
Copyright: © 2023 Tao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Purpose: At present, there is a lack of accurate early diagnostic markers for ischemic stroke.
Methods: By using dimensionality reduction cluster analysis, differential expression analysis, weighted co-expression network analysis, protein-protein interaction network analysis, cell heterogeneity and key pathogenic genes were identified in ischemic stroke. Immunomicroenvironment analysis was used to explore the immune landscape and immune associations of key genes in ischemic stroke. The analysis platform we use is R software (version 4.0.5). PCR experiments were used to verify the expression of key genes.
Results: Single cell sequencing data in ischemic stroke can be annotated as fibroblast cells, pre-B cell CD34, neutrophils cells, bone marrow (BM), keratinocytes, macrophage, neurons and mesenchymal stem cells (MSC). By the intersection of differential expression analysis and WGCNA analysis, 385 genes were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these genes were highly correlated with multiple functions and pathways. Protein-protein interaction network analysis revealed that MRPS11 and MRPS12 were key genes, both of which were down-regulated in ischemic stroke. The Pseudo-time series analysis found that the expression of MRPS12 decreased gradually with the differentiation of pre-B cell CD34 cells in ischemic stroke, suggesting that the downregulation of MRPS12 expression may play an important role in ischemic stroke. At last, PCR showed that MRPS11 and MRPS12 were significantly down-regulated in peripheral blood of patients with ischemic stroke.
Conclusions: Our study provides a reference for the study of pathogenesis and key targets of ischemic stroke.