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<p>Endometriosis (EMs) is a chronic disease affecting millions of women worldwide, yet its pathogenesis remains unclear, and current diagnostic methods are limited. This study based on the EMs dataset from Gene Expression Omnibus (GEO), key genes related to cell apoptosis in EMs were identified through methods such as differential expression analysis and machine learning. Furthermore, analyses including nomogram construction, immune infiltration analysis, and drug prediction were performed based on these key genes. Three apoptosis-related genes—FAS, PRKAR2B and CSF2RB were identified as key genes. The nomogram constructed based on these key genes has good predictive performance. Immune infiltration analysis revealed associations between CSF2RB and activated B cells and immature dendritic cells, while FAS correlated with myeloid-derived suppressor cells (MDSCs). Additionally, potential therapeutic agents targeting these genes were identified. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis revealed that FAS and CSF2RB expression levels were significantly downregulated in the EMs group compared to controls (<i>P</i> &lt; 0.05). In conclusion, FAS, PRKAR2B and CSF2RB are promising diagnostic biomarkers for EMs and are associated with specific immune cell populations, offering potential targets for future therapeutic interventions.</p>

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Analysis of diagnostic apoptosis-related biomarkers and immune cell infiltration characteristics in endometriosis by integrating bioinformatics and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Xiulan Weng,
  • Jingxuan Ye,
  • Wenyu Lin,
  • Dingjie Wang,
  • Jinsong Yi,
  • Zhenhong Wang,
  • Pengming Sun

摘要

Endometriosis (EMs) is a chronic disease affecting millions of women worldwide, yet its pathogenesis remains unclear, and current diagnostic methods are limited. This study based on the EMs dataset from Gene Expression Omnibus (GEO), key genes related to cell apoptosis in EMs were identified through methods such as differential expression analysis and machine learning. Furthermore, analyses including nomogram construction, immune infiltration analysis, and drug prediction were performed based on these key genes. Three apoptosis-related genes—FAS, PRKAR2B and CSF2RB were identified as key genes. The nomogram constructed based on these key genes has good predictive performance. Immune infiltration analysis revealed associations between CSF2RB and activated B cells and immature dendritic cells, while FAS correlated with myeloid-derived suppressor cells (MDSCs). Additionally, potential therapeutic agents targeting these genes were identified. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis revealed that FAS and CSF2RB expression levels were significantly downregulated in the EMs group compared to controls (P < 0.05). In conclusion, FAS, PRKAR2B and CSF2RB are promising diagnostic biomarkers for EMs and are associated with specific immune cell populations, offering potential targets for future therapeutic interventions.