Identification of common diagnostic genes and molecular pathways in endometriosis and systemic lupus erythematosus by machine learning approach and in vitro experiment
2025

Identifying Genes Linked to Endometriosis and Lupus

Sample size: 121 publication 10 minutes Evidence: moderate

Author Information

Author(s): Yang Pusheng, Zhu Yiping, Miao Yaxin, Wang Tao, Liu Wenwen, Zhang Jiaxin, Ge Beilei, Sun Jing

Primary Institution: Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University

Hypothesis

What are the common diagnostic genes and molecular pathways in endometriosis and systemic lupus erythematosus?

Conclusion

The study identified four co-diagnostic genes that may help in the early diagnosis and targeted therapy for endometriosis and systemic lupus erythematosus.

Supporting Evidence

  • Four co-diagnostic genes (PMP22, QSOX1, REV3L, SP110) were identified for endometriosis and SLE.
  • The AUC value of the four co-diagnostic genes was higher than 0.85 in both cohorts.
  • Functional enrichment analysis showed significant pathways related to DNA replication and cell adhesion.
  • Tributyrin was found to act on the four co-diagnostic genes simultaneously.

Takeaway

Researchers found some genes that are important for both endometriosis and lupus, which could help doctors diagnose and treat these diseases better.

Methodology

The study used GEO datasets to identify differentially expressed genes and constructed prediction signatures using LASSO logistic regression analysis.

Potential Biases

The study may have biases due to reliance on existing datasets and lack of diverse clinical samples.

Limitations

The study was based on public databases with a small sample size for clinical validation.

Participant Demographics

The study involved women with endometriosis and systemic lupus erythematosus, primarily of reproductive age.

Statistical Information

P-Value

p<0.05

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.7150/ijms.101754

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