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Background <p>This study aims to identify potential biomarkers in the buffy coat of drug-resistant epilepsy (DRE) patients with mesial temporal lobe epilepsy and to elucidate associated pathways.</p> Methods <p>A comprehensive non-targeted metabolomic and Gene Expression Omnibus (GEO) datasets analysis was first performed on buffy coat from DRE patients and non-epilepsy (CON) patients. Potential enriched biomarkers and pathways were integrated with gene expression profiles from GEO datasets to identify robust biomarkers.</p> Results <p>In the DRE group, there were 15 patients (10 males and 5 females), with an average age of (37.67 ± 15.53) years. In the CON group, there were 10 patients (7 males and 3 females), with an average age of (51.60 ± 18.20) years. A total of 27 potential biomarkers were identified, including 7 down-regulated and 8 up-regulated. Additionally, 9 potential pathways related to DRE were identified. Notably, purine metabolism, tryptophan metabolism and aminoacyl-tRNA metabolism were closely related to DRE. Purine metabolism was up-regulated, while aminoacyl-tRNA and tryptophan metabolism were down-regulated.</p> Conclusions <p>The integration of metabolomic data with GEO datasets analysis offers a new strategy to identify robust biomarkers and pathways. The findings obtained from the buffy coat analysis offer potential insights for the diagnosis and treatment of DRE.</p>

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Identification and enrichment of potential pathways in the buffy coat of patients with DRE using non-targeted metabolomics integrated with GEO Datasets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月26日

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  • Hailin Zhu,
  • Suyue Zheng,
  • Liyuan Xie,
  • Yi Yun,
  • Patrick Kwan,
  • Ben Rollo,
  • Hui Huang

摘要

Background

This study aims to identify potential biomarkers in the buffy coat of drug-resistant epilepsy (DRE) patients with mesial temporal lobe epilepsy and to elucidate associated pathways.

Methods

A comprehensive non-targeted metabolomic and Gene Expression Omnibus (GEO) datasets analysis was first performed on buffy coat from DRE patients and non-epilepsy (CON) patients. Potential enriched biomarkers and pathways were integrated with gene expression profiles from GEO datasets to identify robust biomarkers.

Results

In the DRE group, there were 15 patients (10 males and 5 females), with an average age of (37.67 ± 15.53) years. In the CON group, there were 10 patients (7 males and 3 females), with an average age of (51.60 ± 18.20) years. A total of 27 potential biomarkers were identified, including 7 down-regulated and 8 up-regulated. Additionally, 9 potential pathways related to DRE were identified. Notably, purine metabolism, tryptophan metabolism and aminoacyl-tRNA metabolism were closely related to DRE. Purine metabolism was up-regulated, while aminoacyl-tRNA and tryptophan metabolism were down-regulated.

Conclusions

The integration of metabolomic data with GEO datasets analysis offers a new strategy to identify robust biomarkers and pathways. The findings obtained from the buffy coat analysis offer potential insights for the diagnosis and treatment of DRE.