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 [32]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.

   Keywords: infantile hemangiomas, pyroptosis, bioinformatics, immune
   cell infiltration, regression

Introduction

   Infantile hemangioma (IH) is the most common benign tumour in
   infants.[33]^1 IHs have unique clinical characteristics and can be
   divided into three phases: proliferation, quiescence and
   involution.[34]^2 IHs usually arise within the first few weeks of life
   and proliferate for 4–18 months of age. The involution phase starts at
   approximately 12 months of age, when the hemangioma tissue is replaced
   by adipose and fibrous tissue.[35]^1^,[36]^3 Exploring the mechanism
   underlying IH progression from proliferation to involution is very
   important for the development of new therapeutic targets.

   The mechanism underlying spontaneous IH regression is unclear. In the
   involution phase, increased apoptosis and decreased Bcl-2 expression
   are observed, indicating that programmed cell death (PCD) is involved
   in this process.[37]^4^,[38]^5 Pyroptosis is a type of PCD that is
   distinct from apoptosis, and it is characterized by cell swelling,
   cytolysis and the release of inflammatory factors. The two pathways of
   pyroptosis include the caspase-1-dependent classical pathway and the
   caspase-4/5/11-mediated nonclassical pathway.[39]^6 These pyroptosis
   pathways activate gasdermin D (GSDM) proteins to translocate to the
   cell membrane and form pores. Subsequently, the cell continuously
   swells until the membrane ruptures, which is accompanied by the release
   of inflammatory cytokines such as IL-1β and IL-18 into the
   extracellular space, which recruit more inflammatory cells to
   aggregate, inducing a severe inflammatory response.[40]^7 Pyroptosis is
   widely involved in tumours,[41]^8 metabolic diseases,[42]^9 and
   inflammatory diseases.[43]^10 However, few studies have investigated
   the role of pyroptosis in hemangiomas, and the mechanism that links
   pyroptosis and IH regression still needs to be elucidated.

   Bioinformatics analysis is a methodology that includes processing,
   analysing, and interpreting biological data by integrating methods and
   technologies from multiple fields, such as computer science,
   statistics, and biology. Previous studies have used bioinformatics to
   identify potential genes involved in the pathogenesis of infantile
   hemangiomas.[44]^11–13 In this study, we used various bioinformatic
   tools to investigate the relationships between pyroptosis-related genes
   (PRGs) and IHs. We identified 14 PRGs related to IH regression
   (IH-PRGs) from the PubMed database and performed a functional
   enrichment analysis of these genes to further understand their role in
   IH regression. The hub genes from the IH-PRGs were subsequently
   screened with PPI network. The diagnostic and predictive values of the
   hub-IH-PRGs were evaluated, and their correlations with IH immune cell
   infiltration were analysed. This study on the role of pyroptosis in IH
   regression has increased our understanding of IH pathogenesis and
   provided new insights for its treatment.

Methods

Data Processing and Differential Expression Gene Analysis

   [45]Figure 1 shows the technical roadmap used for this study. The R
   package GEOquery was used to download [46]GSE127487 array data from the
   GEO database. The chip was sequenced on the [47]GPL10558 (Illumina
   HumanHT-12 V4.0 expression beam) platform. The samples were obtained
   from 23 samples of hemangioma tissue from Homo sapiens and 5 samples of
   normal skin tissue.[48]^14 Among these samples, 5 samples of hemangioma
   tissue were from patients who were treated with propranolol. We
   selected 6 samples of untreated proliferation-phase hemangioma (PIH)
   tissue and 6 samples of untreated involution-phase hemangioma (IIH)
   tissue for this study.

Figure 1.

   [49]Figure 1
   [50]Open in a new tab

   Diagram of the study work flow.

   To identify the differential expressed genes between PIHs and IIHs, the
   limma package of R software was used. A box plot was constructed to
   check the normalization of the samples. We defined differentially
   expressed genes (DEGs) as those with a |logFC|>1 and a P value<0.05.
   Through a PubMed search for research related to cell apoptosis, a total
   of 356 genes (pyroptosis related genes, PRGs) related to cell apoptosis
   were retrieved from the GeneCards database, AmiGO2 database, and MSigDB
   database.[51]^15 The differentially expressed genes associated with
   pyroptosis in IH regression (IH-PRGS) were obtained by intersecting the
   DEGs with the PRGs.

Functional Enrichment Analysis

   To explore the functional and pathway enrichment of IH-PRGs, Gene
   Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes
   (KEGG) pathway enrichment analyses were performed with the
   clusterProfiler package in R. GO terms and pathways with P values<0.05
   were considered significant.

Gene Set Enrichment Analysis

   Gene set enrichment analysis (GSEA) was employed to evaluate the
   distribution pattern of genes within a predetermined set of a gene
   table This is accomplished by sorting genes on the basis of their
   correlation with genotypes to determine their impact on phenotypes. We
   obtained the gene sets “c5.go.all.v2022.1.Hs.symbols.gmt” and
   “c2.cp.all.v2022.1.Hs.symbols.gmt” from MsigDB and used the
   clusterProfiler package in R to perform GSEA, where a p value less than
   0.05 was considered statistically significant.

Protein‒protein Interaction Network Construction and Hub Genes Identification

   We used the STRING database to construct a protein‒protein interaction
   network with the IH-PRGs. Cytoscape 3.6.1 was used to visualize the
   results. We adopted CytoHubba, a Cytoscape plug-in, to screen the hub
   genes (hub-IH-PRGs).

Construction of Interaction Networks Between IH-PRGs and Related miRNAs and
Transcription Factors

   The TarBase v8.0 and ENCODE databases were used to identify miRNAs and
   TFs associated with IH-PRGs. The target IH-PRG-miRNA and IH-PRG-TF
   networks were subsequently visualized with Cytoscape software.

Expression Analysis and ROC Validation of the Hub-IH-PRGs

   We analysed the expression levels of the hub-IH-PRGs in the PIH and IIH
   groups, and the results are presented as a box plot. The ROC curve is
   used to evaluate the diagnostic and predictive value of these genes,
   where an area under the ROC curve (AUC) value greater than 0.7 is
   considered accurate for prediction purposes.

Immune Cell Infiltration Analysis and Correlation Analysis of the Hub-IH-PRGs

   We used single-sample gene set enrichment analysis (ssGSEA) to analyse
   the proportion of immune cells in each sample to identify significantly
   differentially enriched immune cells between the PIH and IIH groups.
   Finally, we calculated the Pearson correlation coefficient between the
   expression levels of the hub-IH-DEGs and the significantly enriched
   immune cells.

Statistical Analysis

   All the data analyses were conducted with R software version 1.2. For
   the comparison of two consecutive variables, the statistical
   significance of normally distributed variables was estimated with an
   independent Student’s t test, and the differences between nonnormally
   distributed variables were analysed with the Mann‒Whitney U-test (ie,
   the Wilcoxon rank sum test). Bonferroni adjustment was used for
   multiple testing correction.

Results

Differential Expression Gene Analysis

   Normalized box plots ([52]Figure 2) revealed that the signal intensity
   of each sample in the two groups was approximately at the median level,
   indicating a good degree of sample normalization. On the basis of the
   screening criteria, 1554 DEGs were identified, of which 731 were
   upregulated and 823 were downregulated. A volcano plot and heatmaps of
   the data are displayed in [53]Figure 3A and [54]B. The intersection of
   PRGs with DEGs was selected after Venn analysis ([55]Figure 4), and 14
   overlapping genes were identified as IH-PRGs.

Figure 2.

   [56]Figure 2
   [57]Open in a new tab

   Normalized box plots of the [58]GSE127487 dataset samples. Blue
   represents the IIH group, and red represents the PIH group. The
   abscissa in the figure represents the sample number, and the ordinate
   represents the chip signal intensity.

Figure 3.

   [59]Figure 3
   [60]Open in a new tab

   Differentially expressed genes (DEGs). (A) Volcano plot of the DEGs
   between IIHs and PIHs. The abscissa is the log2-fold change, the
   ordinate is -log10 (p value), red nodes represent upregulated DEGs,
   blue nodes represent downregulated DEGs, and grey nodes represent genes
   that are not significantly differentially expressed. (B) Heatmaps of
   the DEGs. The horizontal axis indicates the sample number, the vertical
   axis indicates the respective DEGs, red represents high gene
   expression, blue represents low gene expression, red bars indicate PIH
   tissue, and blue bars indicate IIH tissue.

Figure 4.

   [61]Figure 4
   [62]Open in a new tab

   Venn diagrams of the differentially expressed genes (DEGs) and
   pyroptosis-related genes (PRGs). The pink circles represent the DEGs
   between PIHs and IIHs, and the blue circles represent the genes related
   to pyroptosis. Fourteen genes that were common to the IH
   regression-associated DEGs and pyroptosis-related genes were
   identified: IL6, MIR20B, DPEP1, IL13RA2, PCSK9, GBP5, MALAT1, EGFR,
   CLEC5A, MELK, IL32, IRF1, ADORA3, and TRIM24.

Functional Enrichment Analysis of IH-PRGs

   Subsequently, GO functional enrichment analysis was performed on the
   IH-PRGs ([63]Figure 5A). The differentially expressed IH-PRGs were
   associated with biological processes, such as the cellular responses to
   chemical stimuli, response to stress, response to chemical, cellular
   response to organic substance, apoptotic process, cell promotion,
   programmed cell death, regulation of apoptotic process, cellular
   response to oxidative stress, regulation of programmed cell death, etc.
   ([64]Figure 5B). Additionally, the differentially expressed IH-PRGs are
   enriched in cellular components, such as the interleukin-6 receptor
   complex, multiple bodies, internal vessel, extracellular space,
   pericrystalline fibres, plasma membrane part, extracellular region
   part, extrinsic component of the external side of the plasma membrane,
   endolysosome membrane, cell surface, endolysosome, etc. ([65]Figure
   5C). In terms of molecular function, these genes are enriched in virus
   receptor activity, hijacked molecular function, very low-density
   lipoprotein particle binding, metal dipeptidase activity, signalling
   receptor binding, oestrogen response element binding, very low-density
   lipoprotein particle receptor binding, protein tyrosine kinase
   activity, molecular function regulator, and receptor regulator activity
   ([66]Figure 5D). The KEGG annotation of the IH-PRGs revealed that they
   were enriched in the JAK-STAT signalling pathway, pertussis,
   cytokine–cytokine receptor interaction, EGFR tyrosine kinase inhibitor
   resistance, C-type lectin receptor signalling pathway, HIF-1 signalling
   pathway, TNF signalling pathway, and FoxO signalling-related pathways
   ([67]Figure 5E and [68]F).

Figure 5.

   [69]Figure 5
   [70]Open in a new tab

   GO and KEGG enrichment analyses. (A) GO enrichment analysis. The
   ordinate indicates the -log(p value), the abscissa represents the
   enriched GO terms, and the bar colours indicate the BP (red), CC
   (green) and MF (blue) GO terms. (B–D) Chord plot showing the top 10
   enriched BP (B), CC (C) and MF (D) GO terms. (E) KEGG pathway
   enrichment analysis. The abscissa indicates the size of the -log10(p
   value), and the ordinate indicates the pathway name. (F) Chord plot
   showing the top 10 enriched KEGG pathways.

Gene Set Enrichment Analysis

   The GSEA results revealed that the genes associated with PIHs and IIHs
   were primarily involved in nuclear chromosome segregation, mitotic
   nuclear division, meiotic cell cycle process, mitotic cell cycle phase
   transition, epidermis development, skin development, regulation of
   antigen processing and presentation, and antigen processing and
   presentation ([71]Figure 6A). The biological pathways impacted by gene
   expression in the dataset included the cornified envelope,
   keratinization, type I hemidesmosome assembly, apoptotic cleavage of
   cell adhesion proteins, cell cycle checkpoints, cell cycle mitosis,
   mitotic metaphase and anaphase, mitotic prometaphase, Rho GTPases
   activate formins, and ATM signalling in development and disease
   ([72]Figure 6B–F).

Figure 6.

   [73]Figure 6
   [74]Open in a new tab

   GSEA–GO and KEGG analyses of the [75]GSE127487 dataset. (A) GSEA–GO
   analysis of the [76]GSE127487 dataset. In the distribution curves, the
   y-axis represents the gene set, the x-axis represents the log fold
   change (FC) distribution of the core molecules in each gene set, and
   the shape of the peak represents the logFC of the core molecules in the
   gene set. The curve peak height corresponds to the position where the
   logFC of most molecules in this group is concentrated. If the
   normalized enrichment score (NES) of the corresponding gene set is
   negative, the peak of the gene set is generally to the left of zero; if
   the NES of the corresponding gene set is positive, the peak of the gene
   set is generally to the right of zero. (B–F) GSEA–KEGG analysis of
   [77]GSE127487 dataset showing that the enriched pathways were formation
   of the cornified envelope, keratinization, type I hemidesmosome
   assembly, apoptotic cleavage of cell adhesion proteins, cell cycle
   checkpoints, cell cycle mitosis, mitotic metaphase, and anaphase,
   mitotic prometaphase, Rho GTPases activate formins, and ATM signalling
   during development and disease.

PPI Network Construction and Hub Gene Identification

   There were a total of 14 IH-PRGs and 7 PPI pairs in the constructed PPI
   networks. The four genes with the strongest cooperative relationships
   were IL6, EGFR, IRF1, and IL32 ([78]Figure 7).

Figure 7.

   [79]Figure 7
   [80]Open in a new tab

   Protein–protein interaction (PPI) network. (A) PPI network of
   IH-associated pyroptosis-related genes (IH-PRGs) constructed with the
   STRING database. Each network node represents a protein, and the lines
   represent protein–protein associations. (B) Visualization produced in R
   software of the PPI network depicting the protein interactions of
   IH-PRGs from the table provided by the STRING database. The 4 genes
   with the highest number of interactions are shown.

Analysis of IH-PRGs and Related miRNAs and Transcription Factors

   We constructed an IH-PRG-miRNA interaction network consisting of 12
   genes and 188 miRNAs ([81]Figure 8A). The top five differentially
   expressed IH-PRGs were IRF1 (targeted by 73 miRNAs), EGFR (targeted by
   56 miRNAs), IL6 (targeted by 40 miRNAs), MELK (targeted by 32 miRNAs),
   and TRIM (targeted by 24 miRNAs). The IH-PRG-TF interaction network
   consisted of 11 genes and 245 TFs ([82]Figure 8A). The top five IH-PRGs
   were MALAT1 (regulated by 197 TFs), IRF1 (regulated by 66 TFs), TRIM24
   (regulated by 26 TFs), and IL32 (regulated by 24TFs) ([83]Figure 8B).

Figure 8.

   [84]Figure 8
   [85]Open in a new tab

   Correlations between IH-associated pyroptosis-related genes (IH-PRGs)
   and miRNAs and transcription factors (TFs). (A) IH-PRG-miRNA network.
   The blue nodes represent miRNAs, and the red nodes represent IH-PRGs.
   (B) IH-PRG-TF network. The green nodes represent TFs, and the red nodes
   represent IH-PRGs.

Hub-IH-PRG Expression Analysis and ROC Validation

   The expression levels of the hub-IH-PRGs in the [86]GSE127487 dataset
   are presented as a box plot ([87]Figure 9A). In the [88]GSE127487
   dataset, there was a statistically significant difference in the
   expression levels of IL6, EGFR, IRF1, and IL32 (p<0.05). The expression
   levels of IL6 and EGFR were lower in PIHs than in IIHs, whereas the
   expression levels of IRF1 and IL32 were greater in PIHs than in IIHs.
   The ROC curve revealed that all four genes have diagnostic value for
   PIH (AUC>0.7) ([89]Figure 9B).

Figure 9.

   [90]Figure 9
   [91]Open in a new tab

   Expression of hub-IH-associated pyroptosis-related genes (hub-IH-PRGs).
   (A) Expression levels of the 4 hub-IH-PRGs in the [92]GSE127487
   dataset. Red represents the PIH group, blue represents the IIH group,
   the abscissa represents the hub-IH-PRGs, and the ordinate represents
   the gene expression value. (B) Receiver operating characteristic (ROC)
   curve prediction of the hub-IH-PRGs in the [93]GSE127487 dataset.
   *p<0.05, ** p<0.01 compared with control.

Immune Cell Infiltration Analysis

   Tdg cells were significantly more abundant in PIH tissue than in IIH
   tissue, whereas Th17 and CD8 T cells were significantly less abundant
   in PIH tissue than in IIH tissue ([94]Figure 10A). Further analysis of
   the correlation between the hub-IH-PRGs and significantly different
   infiltrating immune cells revealed a significant positive correlation
   between IL-6 and Th17 cell infiltration and a significant negative
   correlation with Tgd cell infiltration; EGFR was negatively correlated
   with Tgd cell infiltration; and IRF1 and IL32 were significantly
   negatively correlated with Th17 cell infiltration ([95]Figure 10B).

Figure 10.

   [96]Figure 10
   [97]Open in a new tab

   Analysis of immune cell infiltration. (A) Differences in the enrichment
   of 24 immune cells between the PIH and IIH groups. Red represents the
   PIH group, blue represents the IIH group, the abscissa represents the
   immune cells, and the ordinate represents the score. (B) Heatmap of the
   correlation analysis between the hub-IH-PRGs and infiltrating immune
   cells. Blue indicates a negative correlation with immune cells, red
   indicates a positive correlation with immune cells, the horizontal axis
   indicates hub-IH-PRGs, and the vertical axis indicates immune cells.
   *p<0.05, ** p<0.01 compared with control.

Discussion

   IH is characterized by spontaneous regression, but the mechanism
   underlying IH regression is unclear.[98]^1^,[99]^16 PCD is involved in
   the spontaneous regression of infantile hemangiomas.[100]^4^,[101]^5 As
   a new type of programmed cell death, the relationship between
   pyroptosis and spontaneous regression of hemangiomas has not been
   documented. This is the first study to report the correlation between
   pyroptosis-related genes and the regression of infantile hemangiomas.

   Fourteen genes that were differentially expressed between hemangiomas
   in the proliferation and involution stages and related to pyroptosis
   were identified genes that contribute to the regression of hemangiomas.
   GO enrichment analysis revealed that these IH-PRGs were enriched mainly
   in the terms “cell response to stimuli”, “inflammatory response”,
   “immune response”, and “PCD”. KEGG analysis revealed that these IH-PRGs
   were enriched mainly in the following signalling pathways: JAK-STAT,
   C-type lectin receptor signalling pathway, HIF-1 signalling pathway,
   TNF signalling pathway, FoxO signalling pathway, and other signalling
   pathways. The JAK-STAT pathway is extensively involved in stem cell
   maintenance and IH proliferation. The downregulation of the JAK-STAT
   pathway can lead to the depletion of hemangioma stem cells, which is an
   important reason for the regression of IHs.[102]^17–19 Propranolol can
   promote the regression of IHs by inhibiting the JAK-STAT
   pathway.[103]^20 However, JAK-STAT signalling regulates pyroptosis and
   participates in multiple disease processes in a bidirectional manner,
   facilitating pyroptosis in certain pathological
   states[104]^21^,[105]^22 while inhibiting it in other
   states.[106]^23^,[107]^24

   We identified IL-6, EGFR, IRF1, and IL32 as hub genes that have high
   diagnostic value for regression-stage hemangiomas and may be potential
   targets for the staging, diagnosis, and treatment of hemangiomas. IL-6
   is an important cytokine that regulates immune and inflammatory
   responses.[108]^25^,[109]^26 Compared with that in normal skin tissues,
   the expression of IL-6 mRNA in hemangioma tissues is increased, and
   IL-6 is thought to promote the proliferation of hemangioma endothelial
   cells.[110]^19^,[111]^27^,[112]^28 However, serum IL-6 levels are
   increased in patients with nonprogressive hemangiomas compared with
   those with progressive hemangiomas,[113]^29 and tissue IL-6 mRNA
   expression in proliferation-phase IHs is lower than that in the
   degeneration-phase IHs,[114]^27 indicating that IL-6 is involved in
   hemangioma regression. IL-6 inhibits gasdermin E (GSDME)- and gasdermin
   D (GSDMD)-mediated pyroptosis.[115]^30 EGFR is a member of the ErbB
   receptor family that is located on the surface of cell membranes. EGFR
   binds to EGF or TGF-α and is associated with the inhibition of tumour
   cell proliferation, angiogenesis, invasion, metastasis, and
   apoptosis.[116]^31 EGFR-targeted inhibitors can induce strong
   pyroptosis in lung cancer, which is an important biological mechanism
   for the treatment of lung cancer.[117]^32 IRF-1 is a transcription
   factor that can be expressed through IFN-γ, which activates the
   JAK/STAT1 pathway. Mono-(2-ethylhexyl) phthalate increases the
   expression of IRF-1, which subsequently increases Hippo–YAP
   signalling-mediated hemangioma cell proliferation.[118]^33 Moreover,
   IRF1 is an upstream regulator of PANoptosis, and molecules in the IRF1
   pathway could be targeted in the treatment of diseases associated with
   aberrant inflammatory cell death.[119]^34 IL-32 is an inflammatory
   cytokine that promotes the release of various cytokines and plays an
   important role in the regulation of inflammatory diseases and the
   tumour immune microenvironment.[120]^35 IL-32 can regulate pyroptosis
   by activating the NOD1/2/TLR4/NLRP3 pathway.[121]^36

   A comparison of the infiltration of immune cells in PIH and IIH tissues
   via single-sample GSEA revealed that the numbers of CD8^+ T cells and
   Th17 cells in IIH tissues were significantly greater than those in PIH
   tissues, whereas the number of Tdg cells in IIH tissues was
   significantly lower. Interestingly, the expression of the hub genes in
   the regression stage was consistent with that of the differentially
   abundant infiltrating immune cells. 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. IH involution might
   be an immune-mediated process.[122]^37 CD8^+ T cells are specifically
   recruited to hemangioma tissue and induce apoptosis in tumour cells.
   Moreover, pyroptosis promotes the infiltration of CD8^+ T cells into
   tumour tissue. IL6 can induce initial CD4^+ T cells to differentiate
   into Th17 cells through the JAK-STAT signalling pathway, while Th17
   cells secrete IL-17 and IFN-γ, which promote CD8^+ T cell activation.
   Activated CTLs induce pyroptosis through the granule exocytosis pathway
   and play an important role in antitumour immunity.[123]^38^,[124]^39
   The main T cells in the skin, γδ T cells, participate in the
   development of the microenvironment. Cytotoxicity is the main
   biological effect of γδ T cells. Additionally, γδ T cells can induce
   pyroptosis.[125]^40

   This study has several limitations. Despite performing a multifaceted
   bioinformatics analysis, further experimental research is needed to
   confirm the present findings. In addition, the specific mechanism
   underlying pyroptosis and immune cell infiltration during the
   regression of IHs remains unclear. The role of pyroptosis in the
   regression of IHs needs further exploration.

   In summary, four PRGs, namely, IL6, EGFR, IRF1 and IL32, may play
   significant roles in the regression of IHs. This study provides
   insights into the molecular mechanisms underlying IH pathogenesis,
   highlighting the importance of pyroptosis and immune cell infiltration.
   The identification of diagnostic markers and potential therapeutic
   targets may improve the management of IHs.

Acknowledgments