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Background and aims <p>To identify novel biomarkers and therapeutic targets for polycystic ovary syndrome (PCOS) using integrated bioinformatics approaches.</p> Methods <p>We integrated two microarray datasets (GSE34526, <i>n</i> = 10; GSE137684, <i>n</i> = 12) and validated findings in an independent RNA-seq dataset (GSE168404, <i>n </i>= 10). We employed weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and three machine learning algorithms to investigate differentially expressed genes (DEGs) and module genes.</p> Results <p>We retrieved gene expression datasets GSE34526 and GSE137684, utilizing the limma package to identify DEGs between PCOS and control subjects. WGCNA revealed 122 upregulated and 431 downregulated genes across 11 distinct modules, with the darkslateblue module containing 143 genes showing the highest Pearson correlation coefficient. Enrichment analyses indicated significant associations with pathways related to lipid metabolism, glucose metabolism, neutrophil regulation, and various immune functions. These findings were validated in an in vitro PCOS cell model.</p> Conclusions <p>Our study highlights SEC14L5 as a key differentially expressed gene in PCOS, providing a promising target for clinical research and treatment of PCOS patients.</p>

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Integrated weighted gene co-expression network analysis and machine learning analysis identifies SEC14L5 as a potential biomarker for polycystic ovary syndrome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Zhe Wang,
  • Fei Yu,
  • Pei He,
  • Rui Hua,
  • Yinghe Zhao,
  • Hongchuan Tan,
  • Song Quan,
  • Mian Liu

摘要

Background and aims

To identify novel biomarkers and therapeutic targets for polycystic ovary syndrome (PCOS) using integrated bioinformatics approaches.

Methods

We integrated two microarray datasets (GSE34526, n = 10; GSE137684, n = 12) and validated findings in an independent RNA-seq dataset (GSE168404, n = 10). We employed weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and three machine learning algorithms to investigate differentially expressed genes (DEGs) and module genes.

Results

We retrieved gene expression datasets GSE34526 and GSE137684, utilizing the limma package to identify DEGs between PCOS and control subjects. WGCNA revealed 122 upregulated and 431 downregulated genes across 11 distinct modules, with the darkslateblue module containing 143 genes showing the highest Pearson correlation coefficient. Enrichment analyses indicated significant associations with pathways related to lipid metabolism, glucose metabolism, neutrophil regulation, and various immune functions. These findings were validated in an in vitro PCOS cell model.

Conclusions

Our study highlights SEC14L5 as a key differentially expressed gene in PCOS, providing a promising target for clinical research and treatment of PCOS patients.