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

Aifang Zhong; Feichi Wang; Yang Zhou; Ning Ding; Guifang Yang; Xiangping Chai

doi:10.1016/j.wneu.2023.08.043

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

World Neurosurg. 2023 Nov:179:e166-e186. doi: 10.1016/j.wneu.2023.08.043. Epub 2023 Aug 18.

Authors

Aifang Zhong¹, Feichi Wang², Yang Zhou², Ning Ding², Guifang Yang², Xiangping Chai³

Affiliations

¹ Department 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. Electronic address: 208202133@csu.edu.cn.
² Department 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.
³ Department 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. Electronic address: chaixiangping@csu.edu.cn.

PMID: 37597661
DOI: 10.1016/j.wneu.2023.08.043

Abstract

Background: The determination of biological mechanisms and biomarkers related to intracranial aneurysm (IA) rupture is of utmost significance for the development of effective preventive and therapeutic strategies in the clinical field.

Methods: GSE122897 and GSE13353 datasets were downloaded from Gene Expression Omnibus. Data extracted from GSE122897 were used for analyzing differential gene expression, and consensus clustering was performed to identify stable molecular subtypes. Clinical characteristics were compared between subgroups, and fast gene set enrichment analysis and weighted gene coexpression network analysis were performed. Hub genes were identified via least absolute shrinkage and selection operator analysis. Predictive models were constructed based on hub genes using the Light Gradient Boosting Machine, eXtreme Gradient Boosting, and logistic regression algorithm. Immune cell infiltration in IA samples was analyzed using Microenvironment Cell Population counter, CIBERSORT, and xCell algorithm. The correlation between hub genes and immune cells was analyzed. The predictive model and immune cell infiltration were validated using data from the GSE13353 dataset.

Results: A total of 43 IA samples were classified into 2 subgroups based on gene expression profiles. Subgroup I had a higher risk of rupture, while 70% of subgroup II remained unruptured. In subgroup I, specific genes were associated with inflammation and immunity, and weighted gene coexpression network analysis revealed that the black module genes were linked to IA rupture. We identified 4 hub genes (spermine synthase, macrophage receptor with collagenous structure, zymogen granule protein 16B, and LIM and calponin-homology domains 1), which constructed predictive models with good diagnostic performance in differentiating between ruptured and unruptured IA samples. Monocytic lineage was found to be a significant factor in IA rupture, and the 4 hub genes were linked to monocytic lineage (P < 0.05).

Conclusions: We reveal a new molecular subtype that can reflect the actual pathological state of IA rupture, and our predictive models constructed by machine learning algorithms can efficiently predict IA rupture.

Keywords: Immune processes; Inflammation; Intracranial aneurysms rupture; Machine learning; Molecular subtype.

MeSH terms

Aneurysm, Ruptured* / pathology
Humans
Inflammation
Intracranial Aneurysm* / pathology
Machine Learning
Transcriptome