Endometriosis (EM) is a chronic and painful condition characterized by the presence of endometrial glands and stroma outside the uterine cavity. This disease is thought to be estrogen-dependent and often results in inflammation, adhesions, pain, and infertility, with common sites of occurrence being the pelvic peritoneum and ovaries. It affects approximately 5–10% of women of reproductive age, with 50–80% of affected individuals experiencing pelvic pain, and half of these women also facing infertility challenges. Clinically, EM is categorized into peritoneal (superficial), ovarian, and deep adenomyosis externa types.
Currently, the gold standard for diagnosing EM involves laparoscopy combined with histological examination. However, this method carries certain risks, such as trauma, adhesion formation, and reduced fertility. Additionally, it does not provide an effective means for early detection. On average, the delay between the onset of symptoms and diagnosis is about 6.7 years, with some cases extending to 12 years, resulting in a missed window for optimal treatment. This highlights the urgent need for non-invasive biomarkers to facilitate early diagnosis.
Retrograde menstruation has long been considered a key hypothesis in understanding the etiology of EM, suggesting that endometrial cells may be carried to the peritoneal cavity via lymphatic or blood vessels. Despite these insights, the precise molecular mechanisms and causes of EM remain unclear. A genetic and epigenetic theory proposes that exposure to abnormal conditions such as inflammation, oxidative stress, and immune cytokines may lead to genetic or epigenetic changes in ectopic endometrial cells. For example, it has been found that inflammation can enhance the expression of progesterone receptor (PR)-C, which in turn may aggravate the condition by antagonizing the anti-inflammatory PR-B. Other studies have shown that proteins like apolipoprotein E, peroxisome proliferator-activated receptor γ, and phospholipase A2 group II/V are significantly upregulated in ectopic tissue compared to eutopic tissue, indicating their potential roles in EM. Additionally, long non-coding RNAs (lncRNAs) have been implicated in the onset and progression of EM.
Despite these findings, much of the research has focused on individual genetic or epigenetic alterations, which has limited the understanding of the complex interactions between different genes in EM. To gain a more comprehensive view, bioinformatics methods have been employed to compare gene expression profiles in EM patients and healthy individuals. This approach aims to identify more candidate genes and better understand the relationships between them.
Immune cell infiltration (ICI) plays a critical role in the pathogenesis of EM. Numerous studies have shown that immune cells and their cytokine products promote inflammation, contributing to the lesions seen in EM. For instance, neutrophil infiltration in early ectopic tissues leads to the secretion of IL-8, which induces further inflammation. These findings suggest a close relationship between the immune system and the progression of EM, making ICI a significant factor in understanding the disease’s development.
Bioinformatics tools have become invaluable in the search for new EM biomarkers. In this study, gene expression data from the Gene Expression Omnibus (GEO) database were analyzed, and differentially expressed genes (DEGs) were identified using the “limma” package in R. To explore gene co-expression patterns, the weighted gene co-expression network analysis (WGCNA) was applied, revealing gene modules associated with EM. Through machine learning techniques like Lasso, Random Forest, and Support Vector Machine (SVM), a gene signature consisting of CHMP4C and KAT2B was established. This 2rG gene signature was validated by qRT-PCR and further used for unsupervised clustering of EM samples, which revealed two distinct EM clusters. Consensus clustering based on DEGs was also performed to verify these results.
Finally, to explore the relationship between the identified biomarkers and EM, immune cell infiltration characteristics of each cluster were analyzed. The correlation between the two key genes in the 2rG signature and immune cell infiltration was further validated through qRT-PCR, providing deeper insights into the immune mechanisms underlying EM.
These findings highlight the potential of bioinformatics and immune profiling in the early diagnosis and understanding of endometriosis, offering new avenues for targeted treatments and non-invasive diagnostic methods in the future.
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