Hyperbaric Oxygen Treatment on Keloid Tumor Immune Gene Expression

Introduction

Keloids represent a complex fibroproliferative skin disorder characterized by tumor-like features, excessive collagen deposition, and recurrence following injury. The pathogenesis of keloids involves multifactorial interactions, including genetic predisposition, dysregulated inflammation, immune dysfunction, and oncogenic signaling pathways. Prior studies have identified genetic loci on chromosomes 2q23 and 7p11 in familial keloid cases, along with elevated levels of inflammatory cytokines such as IL-1, IL-6, TNF-α, IL-10, IL-4, and IL-13. Immune cell involvement, particularly macrophages and T lymphocytes, has been implicated in keloid progression. Additionally, tumor-associated pathways like STAT3 signaling contribute to keloid pathogenesis, highlighting overlaps with oncological mechanisms.

Hyperbaric oxygen therapy (HBOT), a treatment involving 100% oxygen inhalation at elevated atmospheric pressures, has shown clinical benefits in reducing keloid recurrence rates post-surgery and alleviating symptoms like pruritus and pain. Preclinical studies suggest HBOT modulates inflammatory responses, epithelial-mesenchymal transition (EMT), and cytokine expression in keloids. However, the molecular mechanisms underlying HBOT’s effects on tumor immune gene networks and immune cell dynamics remain poorly understood. This study investigates HBOT-induced alterations in immune-related gene expression and immune cell infiltration patterns in keloid tissues, aiming to elucidate its therapeutic mechanisms.

Methods

Patient Enrollment and HBOT Protocol

Twelve keloid patients (aged 22–47 years) with chest keloids were enrolled between February and April 2021. Participants were divided into two groups: the HBOT group (HK, n = 6) received four preoperative HBOT sessions (once daily), while the non-HBOT group (K, n = 6) served as controls. HBOT was administered in a hyperbaric chamber pressurized to 2.0 ATA over 30 minutes, followed by 60 minutes of 100% oxygen inhalation via face mask. Surgical excision of keloid tissues occurred within 24 hours after the final HBOT session.

Tissue Processing and Gene Expression Analysis

Keloid specimens were analyzed using the Oncomine Immune Response Research Assay (Thermo Fisher), which profiles 395 immune- and tumor-related genes across 36 functional categories. RNA sequencing data were processed using R package software (v3.4.3) for differential gene expression analysis, with thresholds set at P 1.5. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified enriched biological processes and signaling pathways.

Protein Interaction Networks and Hub Gene Identification

STRING (v11.5) and Cytoscape (v3.7.2) were used to construct protein-protein interaction (PPI) networks from differentially expressed genes (DEGs). CytoHubba identified top hub genes based on network centrality. Quantitative PCR (qPCR) validated hub gene expression using primers listed in Table 1.

Immune Cell Infiltration Analysis

The CIBERSORT algorithm deconvoluted gene expression data to estimate immune cell proportions. Immunohistochemistry (IHC) verified infiltration levels of CD4+ T cells, CD8+ T cells, and CD1a+ dendritic cells using antibodies from ProteinTech (CD4: 67786-1-Ig; CD8: 66868-1-Ig; CD1a: 17325-1-AP). ImageJ quantified positively stained cells.

Statistical Analysis

SPSS (v22.0) performed Student’s t-tests, with P < 0.05 considered significant.

Results

Histopathological Changes Post-HBOT

Hematoxylin-eosin (H&E) staining revealed reduced perivascular inflammatory cell infiltration in HBOT-treated keloids (HK group) compared to controls (K group) (Figure 1A vs. 1B).

Differential Gene Expression and Functional Annotation

Principal component analysis (PCA) segregated HK and K groups, confirming distinct transcriptional profiles (Figure 2A). A total of 395 DEGs were identified: 178 upregulated and 217 downregulated in the HK group. GO analysis highlighted enrichment in T-cell activation (P = 1.22×10⁻⁷), lymphocyte activation regulation (P = 3.65×10⁻⁷), cytokine activity (P = 1.45×10⁻¹²), and plasma membrane externality (P = 2.31×10⁻⁹) (Figure 3A–C). KEGG pathways associated with viral infections (influenza A, cytomegalovirus, Kaposi sarcoma-associated herpesvirus) were significantly altered (Figure 3D).

Hub Gene Network and Validation

The PPI network identified ten hub genes: ITGAM, IL-4, IL-6, IL-2, PTPRC, CD86, TGF, CD80, CTLA4, and IL-10 (Figure 2B–C). qPCR confirmed significant downregulation of CD80 (FC = −2.1, P = 0.008), IL-4 (FC = −1.8, P = 0.013), ITGAM (FC = −2.3, P = 0.004), and PTPRC (FC = −1.7, P = 0.021) in the HK group (Figure 4A–D). IL-10 and IL-2 showed nonsignificant upregulation, while CTLA4, IL-6, CD86, and TGF exhibited nonsignificant downregulation.

Immune Cell Infiltration Dynamics

CIBERSORT analysis revealed decreased CD8+ T-cell infiltration (P = 0.032) and increased resting dendritic cells (P = 0.041) in the HK group. Activated memory CD4+ T cells were significantly elevated (P = 0.018) (Figure 5A–C). IHC corroborated higher CD4+ T-cell infiltration in the HK group (P = 0.025), whereas CD8+ T cells and CD1a+ dendritic cells showed no significant differences (Figure 6A–C).

Discussion

HBOT Modulates Immune Gene Networks in Keloids

This study demonstrates that HBOT reprograms keloid immune gene expression, particularly downregulating T-cell co-stimulatory molecules (CD80, CD86), integrin signaling (ITGAM), and Th2 cytokines (IL-4). Reduced CD80 and CD86 expression aligns with prior reports showing HBOT suppresses dendritic cell co-stimulatory markers, dampening T-cell activation. ITGAM (CD11b) downregulation suggests impaired leukocyte adhesion and migration, potentially mitigating inflammation. The Th2-skewed cytokine milieu in keloids, driven by IL-4 and IL-13, is attenuated post-HBOT, consistent with antifibrotic effects observed in other fibrotic disorders.

CD4+ T Cells as Key Mediators of HBOT Effects

CIBERSORT and IHC data highlight activated memory CD4+ T cells as pivotal responders to HBOT. While CD8+ T cells decreased, the expansion of CD4+ subsets implies a shift toward regulatory or memory phenotypes. Elevated IL-10 (though nonsignificant) parallels studies where HBOT enhances anti-inflammatory cytokines, counteracting keloid fibroblast proliferation via TGF-β/Smad inhibition. The interplay between HBOT, CD4+ T cells, and cytokine networks warrants further exploration.

Clinical and Mechanistic Implications

HBOT’s ability to reduce inflammatory infiltration and modulate immune gene expression supports its adjunctive use in keloid management. By targeting co-stimulatory pathways (CD80/CD86-CD28/CTLA4) and integrin-mediated inflammation (ITGAM), HBOT may disrupt keloid progression. However, the short HBOT duration (four sessions) and small sample size limit mechanistic depth. Long-term HBOT regimens and single-cell sequencing could clarify temporal dynamics and cellular heterogeneity.

Conclusion

HBOT exerts multifaceted effects on keloid pathophysiology by downregulating proinflammatory and immune-activating genes (CD80, IL-4, ITGAM, PTPRC) and altering immune cell infiltration, particularly enhancing CD4+ T-cell activity. These findings position HBOT as a promising immunomodulatory therapy for keloids, warranting larger trials to optimize protocols and validate biomarkers.

https://doi.org/10.1097/CM9.0000000000001780

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