Immune Cell Infiltration and Genetic Drivers in Endometriosis Development

New potential biomarkers pave the way for personalized treatments in endometriosis

Key Points

Highlight

• CHMP4C (charged multivesicular body protein 4C) and wKAT2B (lysine acetyltransferase2B) are identified as key genes linked to endometriosis pathogenesis, particularly through their role in regulating immune cell infiltration.

Importance

• The study identifies key genes and immune mechanisms in the development of endometriosis offering potential biomarkers for improved diagnosis and targeted treatments.

What’s done here?

• Researchers from China analyzed gene expression datasets from the Gene Expression Omnibus (GEO) using bioinformatics tools and machine learning.
• Differentially expressed genes and hub genes (CHMP4C and KAT2B) associated with endometriosis were identified.
• The findings were validated using qRT-PCR on tissue samples.
• Endometriosis samples were divided into two clusters based on hub gene expression, and immune cell infiltration patterns and immune checkpoint gene expression were analyzed.

Key Results

• CHMP4C and KAT2B were significantly downregulated in ectopic tissues compared to normal tissues.
• Two endometriosis clusters showed distinct immune profiles, with one cluster exhibiting higher immune cell infiltration and checkpoint gene expression.
• CHMP4C was linked to B cell activity, suggesting a role in promoting inflammation.
• KAT2B was associated with oxidative phosphorylation.
• Higher expression of immune checkpoint genes in one cluster suggested a potential for immune-targeted therapies.

Strengths and Limitations

• Strengths included integrated bioinformatics, machine learning, and experimental validation for robust findings.
• The small sample size for qRT-PCR validation was the limitation of the study.

Lay Summary

The development of endometriosis is closely linked to immune cell infiltration, where immune cells and their secreted cytokines play a key role in driving inflammation and the formation of lesions. Studies highlight increased neutrophil infiltration in ectopic tissues, which release IL-8, further exacerbating inflammation. Alongside genetic and epigenetic changes, immune dysregulation contributes to the progression of endometriosis, emphasizing the importance of understanding immune mechanisms in its development.

Li et al. from Jinan, China, conducted a study aimed at identifying key genes and immune mechanisms involved in endometriosis pathogenesis by integrating bioinformatics analysis, machine learning, and experimental validation. Their goal was to uncover potential biomarkers and immune cell infiltration patterns associated with the progression of endometriosis. The study was published in the January 2025 issue of the journal Scientific Reports.

The researchers analyzed gene expression datasets from the Gene Expression Omnibus (GEO) to identify key modules and genes associated with endometriosis using weighted gene co-expression network analysis (WGCNA). Machine learning techniques were employed to screen hub genes, followed by validation using qRT-PCR.

The study revealed that the hub genes CHMP4C and KAT2B were differentially expressed in ectopic tissues compared to normal tissues. Furthermore, qRT-PCR confirmed lower expression of CHMP4C and KAT2B in endometriotic tissues, linking them to immune regulation in endometriosis pathogenesis. CHMP4C was found to interact with B cells, which are increased in endometriosis patients, suggesting that CHMP4C might promote inflammation by affecting B cell activity. Similarly, KAT2B was found to be involved in oxidative phosphorylation, a process implicated in the development of endometriosis. The researchers also discovered that one of the endometriosis clusters had higher levels of immune checkpoint genes, indicating that these patients might benefit from treatments targeting these immune pathways.

This study provides new insights into how immune cells and specific genes like CHMP4C and KAT2B contribute to endometriosis, and findings could lead to better methods for classifying the disease and developing targeted treatments, particularly for patients with high levels of immune cell activity.

Research Source: https://pubmed.ncbi.nlm.nih.gov/39843899/