Combined with bioinformatics and machine learning, the diagnostic model, Immunological features and subtypes of stage IV endometriosis with infertility were analyzed

Author:

Lin Yong1,Long Yan1,He Jin1,Yi Qinqin1,Wu Jiao1

Affiliation:

1. LUZHOU MATERNAL AND CHILD HEALTH HOSPITAL (LUZHOU SECOND PEOPLE'S HOSPITAL)

Abstract

Abstract

Many studies have shown that endometriosis can lead to infertility in women of reproductive age, but the mechanism is unknown. Our study aims to explore the pathogenesis of stage IV endometriosis with infertility and the role of characteristic genes in this condition. Methods Gene expression profiles were obtained from the GSE120103 dataset retrieved from the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify key modules. Subsequently, minimum absolute contraction, selection operator (LASSO), and random forest machine learning algorithms were employed to screen the characteristic genes of stage IV endometriosis complicated with infertility. The ROC curve and diagnostic model were generated to evaluate the diagnostic efficacy. CIBERSORT was utilized to estimate immune cell infiltration and quantify immune checkpoints. Additionally, we constructed the regulatory network of miRNA and transcription factors.GSEA was utilized to explore the signaling pathways associated with characteristic genes, and potential small molecule compounds were identified through screening the CTD database. Samples from individuals with infertility in stage IV endometriosis were categorized using the consensus clustering method, followed by an examination of the expression and immunological features of different subtypes. Results We identified five characteristic genes (CDY2A, KRT6B, SLC2A2, SRY, MYH7) that predict infertility in stage IV endometriosis. When compared to women of childbearing age with stage IV endometriosis, the immunological features of stage IV endometriosis combined with infertility show significant differences, which are clearly linked to the characteristic genes. Patients can benefit from a gene-based characteristic nomogram. Our study reveals that multiple signaling pathways are strongly associated with infertility in stage IV endometriosis. Furthermore, several small molecule compounds were predicted based on the characteristic genes, and relevant regulatory networks of miRNA and TF were constructed. Stage IV endometriosis combined with infertility is categorized into three subtypes, each showing significantly different immunological characteristics of the characteristic genes. Conclusion This study enhances our understanding of the pathogenesis and immune mechanisms of stage IV endometriosis with infertility. It identifies effective characteristic genes and subtypes, offering valuable insights for treatment. Nevertheless, additional prospective studies and experiments are necessary to validate our findings.

Publisher

Springer Science and Business Media LLC

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