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Molecular Subtypes and Machine Learning-Based Predictive Models for Intracranial Aneurysm Rupture.

Aifang Zhong1, Feichi Wang1, Yang Zhou1

  • 1Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Trauma Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.

World Neurosurgery
|August 19, 2023
PubMed
Summary

Researchers identified a new molecular subtype of intracranial aneurysm (IA) rupture. Machine learning models using four key genes can predict IA rupture risk, aiding in prevention and treatment.

Keywords:
Immune processesInflammationIntracranial aneurysms ruptureMachine learningMolecular subtype

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding biological mechanisms and biomarkers for intracranial aneurysm (IA) rupture is crucial for developing effective clinical strategies.
  • Current methods for predicting IA rupture risk require improvement.

Purpose of the Study:

  • To identify novel molecular subtypes of IA rupture.
  • To develop accurate predictive models for IA rupture using machine learning.
  • To investigate the role of immune cell infiltration in IA rupture.

Main Methods:

  • Differential gene expression analysis and consensus clustering on IA datasets (GSE122897, GSE13353).
  • Weighted gene coexpression network analysis and LASSO regression to identify hub genes.
  • Machine learning algorithms (Light GBM, XGBoost, logistic regression) for predictive model construction.
  • Analysis of immune cell infiltration and its correlation with hub genes.

Main Results:

  • Two distinct molecular subtypes of IA were identified, with one subgroup exhibiting a higher rupture risk.
  • Four hub genes (spermine synthase, macrophage receptor with collagenous structure, zymogen granule protein 16B, LIM and calponin-homology domains 1) were identified and formed predictive models with high diagnostic performance.
  • Monocytic lineage was significantly associated with IA rupture, and the identified hub genes correlated with this lineage.

Conclusions:

  • A novel molecular subtype reflecting IA rupture pathology was discovered.
  • Machine learning-based predictive models demonstrate high efficiency in predicting IA rupture.
  • These findings offer potential for improved diagnostic and therapeutic approaches for IA.