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Machine learning algorithms assisted identification of post-stroke depression associated biological features.

Xintong Zhang1, Xiangyu Wang2, Shuwei Wang3

  • 1Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

Frontiers in Neuroscience
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

Researchers identified two key genes, SDHD and FERMT3, as potential biomarkers for post-stroke depression (PSD). These findings may enable earlier diagnosis and prevention of PSD, improving patient recovery.

Keywords:
GEOWGCNAmachine learning algorithmsmetabolismpost-stroke depression

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Area of Science:

  • Genomics and Bioinformatics
  • Neuroscience
  • Metabolomics

Background:

  • Post-stroke depression (PSD) is a common complication impacting stroke patient recovery.
  • Current diagnostic and therapeutic strategies for PSD lack effective, metabolism-related biomarkers.
  • Stroke involves dynamic metabolic and hemodynamic changes, highlighting the need for metabolism-focused research.

Purpose of the Study:

  • To discover metabolism-related diagnostic and therapeutic biomarkers for post-stroke depression (PSD).
  • To identify reliable molecular targets for improving PSD diagnosis and treatment.

Main Methods:

  • Utilized gene expression datasets (GSE140275, GSE122709, GSE180470) from the GEO database.
  • Applied differential gene expression (DEG) analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms (LASSO, random forest).
  • Validated candidate genes in independent datasets and constructed a nomogram model for PSD diagnosis.

Main Results:

  • Identified 557 metabolism-associated candidate hub genes.
  • Selected two signature genes, SDHD and FERMT3, with significant roles in depression.
  • The nomogram model demonstrated good diagnostic accuracy (AUC for SDHD: 0.896, FERMT3: 0.964) and correlated with depression severity (HAMD scores).

Conclusions:

  • SDHD and FERMT3 are identified as valuable diagnostic and therapeutic biomarkers for post-stroke depression (PSD).
  • These biomarkers offer potential for earlier diagnosis and prevention of PSD.
  • The findings contribute to understanding the metabolic underpinnings of PSD and offer new avenues for clinical intervention.