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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Related Experiment Video

Updated: Apr 28, 2026

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis
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Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

Yangyang Xie1, Kai Yu2

  • 1Pharmacy Department, The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, 325000, China.

Current Medicinal Chemistry
|February 16, 2024
PubMed
Summary

This study identified 8 hub genes for glaucoma using machine learning and established a diagnostic model. These findings offer new insights into glaucoma pathogenesis and potential therapeutic targets.

Keywords:
GlaucomaLASSO regression modelWGCNAdiagnostic model.random forest modelsupport vector machines model

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Area of Science:

  • Genomics and bioinformatics
  • Ophthalmology
  • Computational biology

Background:

  • Glaucoma is a leading cause of blindness due to elevated intraocular pressure damaging the optic nerve.
  • Current diagnostic methods for glaucoma lack molecular specificity.
  • Understanding the molecular mechanisms of glaucoma is crucial for developing effective treatments.

Purpose of the Study:

  • To identify key genes (hub genes) associated with glaucoma using multiple machine learning algorithms.
  • To elucidate the molecular mechanisms and gene regulatory networks underlying glaucoma.
  • To establish a molecular diagnostic model and explore potential drug-gene-disease networks for glaucoma.

Main Methods:

  • Utilized microarray data (GSE9944) from the Gene Expression Omnibus database.
  • Applied Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machines (SVM) for feature selection.
  • Performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify glaucoma-related genes.
  • Integrated results from four methods to determine overlapping hub genes and constructed a diagnostic model.
  • Conducted molecular docking and CIBERSORT analysis for gene-drug interactions and immune cell infiltration.

Main Results:

  • Identified 8 hub genes: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158, and GSN.
  • Developed a molecular diagnostic model for glaucoma with an area under the curve of 1.
  • GSN gene showed potential regulatory roles in T cell populations (CD4 naive and Tregs).
  • Constructed gene-drug networks to identify potential therapeutic agents for glaucoma.

Conclusions:

  • Successfully identified 8 critical hub genes and a highly accurate molecular diagnostic model for glaucoma.
  • The study provides a foundation for further research into glaucoma pathogenesis and therapeutic strategies.
  • The identified hub genes and networks offer potential targets for novel glaucoma treatments.