Integration of single‐cell and bulk transcriptomics reveals immune‐related signatures in keloid

Abstract

Background Keloid is a pathological dermatological condition that manifests as an overgrowth scar secondary to skin trauma. This study endeavored to excavate immune‐related signatures of keloid based on single‐cell RNA (scRNA) sequencing data and bulk RNA sequencing data. Method The keloid‐relevant scRNA sequencing dataset GSE163973 and bulk RNA sequencing dataset GSE113619 were mined from the GEO database. The “Seurat” R package was utilized for data quality control, cell clustering, and investigation of marker genes of each cell cluster. The “SingleR” package helped match the marker genes of the corresponding cluster to specific cell types. Moreover, the R package “Monocle” was deployed for pseudotemporal ordering analysis, and the “clusterProfiler” was applied for functional and pathway enrichment analysis. The immune‐related signatures were then identified, and potential targeted drugs were predicted via the DGIdb database. Verification of the immune‐related signatures in clinical validation samples was implemented by RT‐qPCR. Results Totally 23 cell clusters were screened and classified into 10 cell types based on the scRNA sequencing data. The keloid group had a significantly higher endothelial cell proportion than the control group. As enrichment analysis was applied in both differentially expressed genes (DEGs) of scRNA and bulk RNA sequencing data, we found they were enriched in multiple common immune‐related pathways and biological processes. Meanwhile, we acquired three immune‐related signatures (VCAM1, CALCRL, and HLA‐DPB1) by intersecting the above DEGs with immune‐related genes (IRGs). Then, we predicted 16 drugs potentially targeting the biomarkers through the DGIdb database. Finally, the outcome of RT‐qPCR of clinical validation samples further verified the results. Conclusion In conclusion, we analyzed the cell types and functional differences in the keloid through scRNA and bulk RNA sequencing data. We identified three immune‐related signatures (VCAM1, CALCRL, and HLA‐DPB1) in keloid, providing a basis for further in‐depth investigation of the molecular mechanisms of keloid and exploration of therapeutic targets.

Revisiting roles of mast cells and neural cells in keloid: exploring their connection to disease activity

“…By unraveling the cellular heterogeneity within keloids through scRNA-seq analyses of AK, IK, and MS samples, we identified the potential role of MCs and NCs in the context of fibrotic skin disease. Most previous studies have proposed various therapeutic targets for keloid treatment, they have not taken the impact of phenotypic differences associated with disease activity status into consideration (19)(20)(21). However, a few studies have defined keloid activity by describing changes in keloid size.…”

“…Additionally, the differential susceptibility of individuals from various ethnic backgrounds, with dark-skinned populations exhibiting higher rates, indicates the role of environmental factors in conjunction with genetic predisposition (16)(17)(18). In recent years, there has been a significant advance in understanding molecular mechanisms underlying keloid pathogenesis (2,(19)(20)(21)(22)(23). While these advances have shed light on the proliferation of mesenchymal fibroblasts (FBs) driven by fibrogenic growth factors such as TGF-b signaling and their association with the pathological accumulation of extracellular matrix components, the broader landscape of keloid development remains complex (3,9,24,25).…”

To construct a prognostic model and identify target gene of macrophage polarization by machine learning for predicting immune responses in osteosarcoma and pan- cancer

“…We utilized FindAllMarkers tools to identify differential genes between a cluster and all other clusters, with |log2 (fold change) | > 1 and an adjusted P-value of 0.01. Then, cell types were annotated through "SingleR" package (31). The "commpath" package was used to analyze the relationship between TAMs and ICI therapy (32).…”

Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers

“…The application of machine learning and bioinformatics to analyse microarray data has become widespread, aiming to identify important genes and offer insights for disease prevention and treatment [11]. Singlecell RNA sequencing (scRNA-seq) analysis is a neoteric technique that can identify cell types and subtypes associated with various diseases, allowing for the study of intergroup gene expression and differences in cellular development [12,13]. DFU causes multiple cellular functional impairments, including those in macrophages, endothelial cells, keratinocytes, and epidermal cells [14].…”