ArticleClinical, cosmetic and investigational dermatology2025
Integrative Bioinformatics Analysis of Pyroptosis-Related Genes and Analysis of Immune Cell Infiltration in Infantile Hemangioma Regression.
Article in Clinical, cosmetic and investigational dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Integrative Bioinformatics Analysis and Machine Learning Reveal TSPAN7 Regulates the Involution of Infantile Hemangioma via NK Cells.Clinical, cosmetic and investigational dermatology · 2026Article
- Immune microenvironment of infantile hemangioma: cellular dynamics, molecular networks, and emerging immunomodulatory strategies.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Infantile hemangiomas (IHs) are characterized by spontaneous regression, and their pathogenesis involves immune cell infiltration and programmed cell death. The molecular pathways and mechanisms involved in pyroptosis in IHs are still unclear. This study aimed to identify genes related to pyroptosis in IH regression by bioinformatics methods and to explore the effects of these pyroptosis-related genes (PRGs) on disease pathology and immune cell infiltration. Methods: The microarray dataset GSE127487 was assessed to identify differentially expressed genes (DEGs) between proliferation-phase IH (PIHs) and involution-phase IH (IIHs). The DEGs that overlapped with PRGs were considered IH-PRGs. The IH-PRGs were validated and subjected to functional enrichment analysis and Genomes pathway analyses. Gene set enrichment analysis (GSEA) was also performed to analyse the biological significance of the DEGs. The NetworkAnalyst database was used to analyse the correlation network of IH-PRGs and miRNAs as well as that of IH-PRGs and transcription factors. The STRING online database and Cytoscape were used to identify the hub-IH-PRGs. Additionally, a single-sample GSEA algorithm was applied to assess immune cell infiltration in IHs, and correlation analysis was performed between the hub-IH-PRGs and infiltrating immune cells. Results: Fourteen IH-PRGs were identified. IL6, EGFR, IRF1 and IL32 were identified as hub-IH-PRGs and displayed excellent diagnostic performance. Immune cell infiltration analysis revealed notable differences in CD8+ T cells, Tgd cells and Th17 cells between PIHs and IIHs. IL-6 was significantly positively correlated with Th17 cell infiltration and significantly negatively correlated with Tgd cell infiltration; EGFR was negatively correlated with Tgd cell infiltration; and IRF1 and IL32 were significantly negatively correlated with Th17 cell infiltration. Conclusion: Four PRGs, namely, IL6, EGFR, IRF1 and IL32, may play a significant role in IH regression. This study provides insights into the molecular mechanisms underlying IH pathogenesis, highlighting the importance of pyroptosis and immune cell infiltration.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.