Unraveling the Mechanisms Behind Infantile Hemangioma Regression: A Bioinformatics Study
Infantile hemangioma (IH) is the most common benign tumor in infants, characterized by unique clinical phases of proliferation, quiescence, and involution. Understanding the processes driving the regression of IHs is crucial for developing effective treatments. This study delves into the mechanisms underlying IH regression, focusing on pyroptosis and immune cell involvement.
Introduction to Infantile Hemangioma
Infantile hemangiomas typically develop within the first few weeks of life and proliferate for 4 to 18 months. The involution phase begins around 12 months, leading to the replacement of hemangioma tissue with adipose and fibrous tissue. Although the spontaneous regression of IHs is well-documented, the underlying mechanisms remain unclear.
Recent research suggests that programmed cell death (PCD) and, specifically, pyroptosis play a vital role in IH involution. Pyroptosis is a form of PCD distinct from apoptosis, characterized by cell swelling, cytolysis, and inflammatory factor release. This study investigates the role of pyroptosis in IH regression by analyzing gene expression patterns.
Methods: A Comprehensive Bioinformatics Analysis
Data Processing and Differential Expression Gene Analysis
The study utilized gene array data from the GEO database, specifically GSE127487, which included samples from 23 IH tissue samples and 5 normal skin tissue samples. Among these, 6 samples each from untreated proliferation-phase IH (PIH) and involution-phase IH (IIH) were analyzed. Figure 1 outlines the technical roadmap used for this study.
Functional and Pathway Enrichment Analyses
To explore functional and pathway enrichments in IH progression-related genes (IH-PRGs), Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using the clusterProfiler package in R. Figures 5 and 6 present the results of these analyses.
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Figure 5 GO and KEGG enrichment analyses. (A) GO enrichment analysis showing enriched BP, CC, and MF terms. (B–D) Chord plot of top 10 enriched BP, CC, and MF terms. (E) KEGG pathway enrichment analysis. (F) Chord plot of top 10 enriched KEGG pathways. |
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Figure 6 GSEA–GO and KEGG analyses. Profiles of enriched pathways include formation of the cornified envelope, keratinization, and various cell cycle checkpoints. |
Gene Set Enrichment Analysis
Gene set enrichment analysis (GSEA) was used to evaluate the distribution patterns of genes associated with PIH and IIH. Using predefined gene sets, GSEA identified biological pathways impacted by gene expression changes. A p-value less than 0.05 was considered statistically significant.
Protein-Protein Interaction Network and Hub Gene Identification
The STRING database was used to construct protein-protein interaction networks. Cytoscape software visualized these interactions, and CytoHubba identified key hub genes within these networks. Four genes—IL-6, EGFR, IRF-1, and IL-32—showed strong cooperative relationships (Figure 7).
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Figure 7 Protein-protein interaction network showing key hub genes IL-6, EGFR, IRF-1, and IL-32. |
Interaction Networks Between IH-PRGs and miRNAs and Transcription Factors
Interactions between IH-PRGs and microRNAs (miRNAs) and transcription factors (TFs) were analyzed using TarBase v8.0 and ENCODE databases. The resulting networks illustrated the regulatory relationships between these genes (Figure 8).
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Figure 8 Interaction networks of IH-PRGs with miRNAs and TFs, highlighting the top differentially expressed genes IRF1, EGFR, IL-6, MELK, and TRIM. |
Hub Gene Expression Analysis and ROC Validation
The expression levels of identified hub genes in PIH and IIH groups were compared using box plots. The ROC curve was used to evaluate the diagnostic and predictive value of these genes, with an area under the ROC curve (AUC) greater than 0.7 indicating accurate prediction potential (Figure 9).
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Figure 9 Box plots and ROC curves of hub genes IL-6, EGFR, IRF-1, and IL-32, indicating significant differences in expression and strong diagnostic value. |
Immune Cell Infiltration Analysis
Single-sample gene set enrichment analysis (ssGSEA) was performed to assess immune cell infiltration levels in PIH and IIH tissues. Significant differences in immune cell proportions were observed, and correlations between hub genes and immune cell infiltration were calculated (Figure 10).
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Figure 10 Differences in immune cell infiltration (A) and correlation between hub genes and immune cells (B), highlighting significant correlations with IL-6 and IL-32. |
Results: Key Findings
The study identified 1554 differentially expressed genes (DEGs), of which 731 were upregulated and 823 were downregulated. Venn analysis intersected these DEGs with IH-PRGs, yielding 14 overlapping genes.
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Figure 2 Normalized box plots of normalized signal intensities in PIH (red) and IIH (blue) groups. |
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Figure 3 Volcano plot (A) and heatmaps (B) of DEGs, highlighting differentially expressed and significant genes. |
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Figure 4 Venn diagram identifying 14 overlapping genes as IH-PRGs. |
Discussion
The study identified IL-6, EGFR, IRF-1, and IL-32 as hub genes that could serve as potential diagnostic and therapeutic targets for IHs. These genes play critical roles in the immune and inflammatory responses relevant to IH regression.
IL-6, a key cytokine, was found to be involved in promoting hemangioma endothelial cell proliferation and later in regression. IL-6 inhibits gasdermin E and gasdermin D-mediated pyroptosis, suggesting its role in regulating cell death pathways.
EGFR, a surface membrane receptor, is associated with inhibiting various tumor cell functions. Targeting EGFR could potentially induce pyroptosis, offering a new approach to treating hemangiomas.
IRF-1, a transcription factor, is involved in the Hippo-YAP signaling pathway, which promotes hemangioma cell proliferation. Additionally, IRF-1 influences PANoptosis pathways.
IL-32, an inflammatory cytokine, modulates multiple cytokine release and plays a crucial role in inflammatory diseases. It activates the NLRP3 pathway to regulate pyroptosis.
Conclusion
This research provides valuable insights into the molecular mechanisms underlying IH pathogenesis, with a focus on pyroptosis and immune cell infiltration. The identification of IL-6, EGFR, IRF-1, and IL-32 as key genes offers promising avenues for diagnosing and treating IHs. While these findings enhance our understanding of IH progression, further experimental validation is necessary to confirm these results.
The limitations of this study include the need for additional experimental research and the unclear role of pyroptosis in IH regression. Nonetheless, these findings underscore the importance of continued exploration in this area.
By pointing to potential therapeutic targets, this study may ultimately improve patient care for infants with IHs.
Call to Action
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