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Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study

  • Reproductive physiology and disease
  • Published:
Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

Abstract

Objective

The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition.

Methods

We used GEO database data, including 74 non-endometriosis and 74 varying-degree EMs patients. Our approach involved identifying significant gene modules, exploring gene intersections, identifying core genes, and screening for potential EMs biomarkers using weighted gene co-expression network analysis (WGCNA) and various machine learning approaches. We also performed gene set enrichment analysis (GSEA) to understand relevant pathways. This comprehensive approach helps investigate EMs genetics and potential biomarkers.

Results

Nine genes were identified at the intersection, suggesting their involvement in EMs. GSEA linked DEGs to pathways like complement and coagulation cascades, DNA replication, chemokines, apical plasma membrane processes, and diseases such as Hepatitis B, Human T-cell leukemia virus 1 infection, and COVID-19. Five feature genes (FOS, CFD, CCNA1, CA4, CST1) were selected by machine learning for an effective EMs diagnostic nomogram. GSEA indicated their roles in mismatch repair, cell cycle regulation, complement and coagulation cascades, and IL-17 inflammation. Notable differences in immune cell proportions (CD4 T cells, CD8 T cells, DCs, macrophages) were observed between normal and disease groups, suggesting immune involvement.

Conclusions

This study suggests the potential involvement of phase separation-related genes in the pathogenesis of endometriosis (EMs) and identifies promising biomarkers for diagnosis. These findings have implications for further research and the development of new therapeutic strategies for EMs.

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Data availability

All data generated or analyzed during this study are included in this published article and its supplementary materials.

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Author information

Author notes

  1. Qiuyi Liang and Shengmei Yang contributed equally to this work.

Authors and Affiliations

  1. Computational Medicine and Epidemiology Laboratory (CMEL), The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China

    Qiuyi Liang, Meiyi Mai, Xiurong Chen & Xiao Zhu

  2. Obstetrical Department, The Affiliated Hospital of Qingdao University, Qingdao, China

    Shengmei Yang

Authors
  1. Qiuyi Liang
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  4. Xiurong Chen
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  5. Xiao Zhu
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Contributions

XZ conceived the work. QL studied and drafted the manuscript. SY and XZ discussed and edited the manuscript. MM and XC assisted study. XZ checked the statistical and bioinformatic accuracy as an expert in statistics and bioinformatics. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Xiao Zhu.

Ethics declarations

Ethical approval and consent to participate

This is a retrospective study of publicly available databases, and the database developers obtained appropriate ethical review standards. Informed consent forms are not required for patient data extracted from public databases.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Highlights

• Exploring EMs mechanisms: This study delves into the mechanisms of endometriosis (EMs) by investigating the role of phase separation-related genes.

• Identification of candidate genes: The research identifies nine genes potentially linked to EMs, shedding light on their involvement in the condition’s development.

• Pathway insights: Gene set enrichment analysis reveals associations of DEGs with pathways like complement and coagulation cascades and DNA replication, providing a deeper understanding of EMs.

• Machine learning biomarkers: Five feature genes—FOS, CFD, CCNA1, CA4, and CST1—were selected through machine learning, offering potential diagnostic biomarkers.

• Effective diagnostic model: A nomogram-based diagnostic model utilizing FOS, CFD, and CCNA1 demonstrates high accuracy in distinguishing EMs from normal cases.

• Immunological signatures: Significant differences in immune cell proportions between EMs and normal groups suggest potential immune involvement in the disease.

• Clinical implications: These findings open avenues for further research and the development of novel therapeutic strategies for endometriosis.

Supplementary Information

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Supplementary file2 (DOCX 39 KB)

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Liang, Q., Yang, S., Mai, M. et al. Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study. J Assist Reprod Genet (2024). https://doi.org/10.1007/s10815-024-03079-9

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