Bioinformatic Analysis of Key Genes and Pathways Related to Keloids

Non-coding RNAs in hypertrophic scars and keloids: Current research and clinical relevance: A review

Hypertrophic scars (HS) and keloids (KD) are lesions that develop as a result of excessive fibroblast proliferation and collagen deposition in response to dermal injury, leading to dysregulation of the inflammatory, proliferative, and remodeling phases during wound healing. HS and KD affect up to 90 % of the population and are associated with lower quality of life, physical health, and mental status in patients. Efficient targeted treatment represents a significant challenge, primarily due to our limited understanding of their underlying pathogenesis. Non-coding RNAs (ncRNAs), which constitute a significant portion of the human transcriptome with minimal or no protein-coding capacity, have been implicated in various cellular physiologies and pathologies and may serve as diagnostic indicators or therapeutic targets. NcRNAs have been found to be aberrantly expressed and regulated in HS and KD. This review provides a summary of the expression profiles and molecular mechanisms of three common ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), in HS and KD. It also discusses their potential as biomarkers for the diagnosis and treatment of these diseases and provides novel insights into epigenetic-based diagnosis and treatment strategies for HS and KD.

[Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning]

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Identification and characterization of bone/cartilage-associated signatures in common fibrotic skin diseases

Background: Fibrotic skin diseases are characterized by excessive accumulation of the extracellular matrix (ECM) and activation of fibroblasts, leading to a global healthcare burden. However, effective treatments of fibrotic skin diseases remain limited, and their pathological mechanisms require further investigation. This study aims to investigate the common biomarkers and therapeutic targets in two major fibrotic skin diseases, namely, keloid and systemic sclerosis (SSc), by bioinformatics analysis.

Methods: The keloid (GSE92566) and SSc (GSE95065) datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then constructed a protein–protein interaction (PPI) network for the identification of hub genes. We explored the possibility of further functional enrichment analysis of hub genes on the Metascape, GeneMANIA, and TissueNexus platforms. Transcription factor (TF)–hub gene and miRNA–hub gene networks were established using NetworkAnalyst. We fixed GSE90051 and GSE76855 as the external validation datasets. Student’s t-test and receiver operating characteristic (ROC) curve were used for candidate hub gene validation. Hub gene expression was assessed in vitro by quantitative real-time PCR.

Results: A total of 157 overlapping DEGs (ODEGs) were retrieved from the GSE92566 and GSE95065 datasets, and five hub genes (COL11A1, COL5A2, ASPN, COL10A1, and COMP) were identified and validated. Functional studies revealed that hub genes were predominantly enriched in bone/cartilage-related and collagen-related processes. FOXC1 and miR-335-5p were predicted to be master regulators at both transcriptional and post‐transcriptional levels.

Conclusion: COL11A1, COL5A2, ASPN, COL10A1, and COMP may help understand the pathological mechanism of the major fibrotic skin diseases; moreover, FOXC1 and miR-355-5p could build a regulatory network in keloid and SSc.

Bioinformatics analysis and identification of dysregulated POSTN in the pathogenesis of keloid

Keloid is a benign fibro-proliferative dermal tumour formed by an abnormal scarring response to injury and characterised by excessive collagen accumulation and invasive growth. The pathophysiology of keloids is complex, and the treatment for keloids is still an unmet medical need. Here, we investigated the transcriptional gene that influences keloid development by comparing keloid, non-lesioned keloid skin and normal skin as well as keloid fibroblast and normal fibroblast (GSE83286, GSE92566, GSE44270). Based on the analysis, 146 up-regulated genes and 48 down-regulated genes were found in keloid tissue compared with normal skin and keloid no-lesioned skin. Eleven genes were further identified by overlapping the DEGs from keloid tissue described previously with DEGs in keloid fibroblast. The overlapped genes included PRR16, SFRP2, EDIL3, GERM1, POSTN, PDE3A, GALNT5, F2RL2, EYA4, ZFHX4, and AIM2. POSTN is the most crucial node in PPI network, which mainly correlate to collagen-related genes. Moreover, siRNA knockdown identified POSTN is a crucial regulatory gene that regulates keloid fibroblast migration and collagen I, collagen III expression level. In conclusion, our study identified 11 hub genes that play crucial role in keloid formation and provided insights for POSTN to be the therapeutic target for keloid through bioinformatic analysis of three datasets. Additionally, our results would support the development of future therapeutic strategies.