Single-cell RNA sequencing and transcriptomic analysis reveal key genes and regulatory mechanisms in sepsis

ABSTRACT

The pathogenesis of sepsis, with a high mortality rate and often poor prognosis, has not been fully elucidated. Therefore, an in-depth study on the pathogenesis of sepsis at the molecular level is essential to identify key sepsis-related genes. The aim of this study was to explore the key genes and potential molecular mechanisms of sepsis using a bioinformatics approach. In addition, key genes with miRNA network correlation analysis and immune infiltration correlation analysis were investigated. The scRNA dataset (GSE167363) and RNA-seq dataset (GSE65682, GSE134347) from GEO database were used for screening out differentially expressed genes using single-cell sequencing and transcriptome sequencing. The analysis of immune infiltration was evaluated by the CIBERSORT method. Key genes and possible mechanisms were identified by WGCNA analysis, GSVA analysis, GSEA enrichment analysis and regulatory network analysis, and miRNA networks associated with key genes were constructed. Nine key genes associated with the development of sepsis, namely IL7R, CD3D, IL32, GPR183, HLA-DPB1, CD81, PEBP1, NCL, and ETS1 were screened, and the specific signaling mechanisms associated with the key genes causing sepsis were predicted. Immune profiling showed immune heterogeneity between control and sepsis samples. A regulatory network of 82 miRNAs, 266 pairs of mRNA-miRNA relationship pairs was also constructed. These nine key genes have the potential to become biomarkers for the diagnosis of sepsis and provide new targets and research directions for the treatment of sepsis.

KEYWORDS:

• Sepsis
• key genes
• miRNA
• immune infiltration

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The datasets included in this study are available from the online public database. The data that support our findings are available from the databases: GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html), Gene Atlas gene mapping database (http://geneatlas.roslin.ed.ac.uk/) and GeneCards database（https://www.genecards.org/）.

Abbreviations

GEO database: Gene Expression Omnibus; NCBI: National Center for Biotechnology Information; TSNE: T-Distributed Stochastic Neighbor Embedding; PCA: Principal Component Analysis; WGCNA: Weighted Gene Co-expression Network Analysis; TOM: Topological Overlap Matrix; GSVA: Gene Set Variance Analysis; GSEA: Gene Set Enrichment Analysis; GWAS: Genome-wide Association Study; NES: Normalized Enrichment Score; Cmap database: Connectivity Map; KEGG: Kyoto Encyclopedia of Genes and Genomes; SNP: Single Nucleotide Polymorphisms; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver-operating characteristic; AUC: Area under the ROC curve; MHC: Major Histocompatibility Complex; PC: principal Component;

Author contributions

Conceptualization, QPM, FSM; writing-original draft pre-paration, QPM, QYM, FSM; writing – review and editing, QPM, QYM, FSM. All authors reviewed and approved the final version of the manuscript. All authors read and approved the final manuscript.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02648725.2023.2196475.

Additional information

Funding

There is no funding to report.

Notes on contributors

Qingping Mo

Qingping Mo is a graduate of Southern Medical University with a master's degree and has received 3 years of residency training in Zhujiang Hospital of Southern Medical University.

Qingying Mo

Qingying Mo received her undergraduate clinical hospital education for 5 years at Shuda College of Hunan Normal University and has now graduated with her undergraduate degree.

Fansen Mo

FanSen Mo received 5 years of undergraduate education in clinical medicine at South China University and has now graduated with his undergraduate degree.