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Related Experiment Video

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Machine Learning-Derived Diverse Regulated Cell Death Patterns for Therapeutic Target Identification in Glaucoma.

Qianxue Mou1, Gaigai Li2, Sifei Xiang1

  • 1Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China.

International Journal of General Medicine
|December 10, 2025
PubMed
Summary

This study identified key genes involved in regulated cell death (RCD) pathways in glaucoma. These findings highlight potential new targets for diagnosing and treating this leading cause of irreversible vision loss.

Keywords:
WGCNAimmune cell infiltrationmachine learningretinal ganglion cell

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Area of Science:

  • Ophthalmology
  • Genetics
  • Immunology

Background:

  • Glaucoma is a primary cause of irreversible blindness globally.
  • Understanding its molecular underpinnings is crucial for developing effective interventions.

Purpose of the Study:

  • To identify molecular mechanisms and regulatory networks of hub genes in human glaucoma.
  • To discover promising targets for glaucoma detection and treatment.

Main Methods:

  • Utilized Gene Expression Omnibus datasets (GSE758, GSE2378, GSE9944).
  • Identified differentially expressed genes (DEGs) related to regulated cell death (RCD).
  • Employed Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning to find hub genes, followed by GSEA and molecular docking.

Main Results:

  • Identified 358 RCD-related DEGs, emphasizing the immune response in glaucoma pathogenesis.
  • Discovered 33 hub genes, including PLEC, DLGAP4, and GPI, with diagnostic and therapeutic potential.
  • PLEC emerged as a promising candidate gene linked to glaucomatous neurodegeneration and potential drug targets.

Conclusions:

  • Developed a machine-learning framework to refine molecular subtypes and identify druggable genes in glaucoma.
  • These findings offer novel molecular targets for glaucoma diagnosis and treatment strategies.