ArticleOncology letters2018
Integrated analysis of microarray data to identify the genes critical for the rupture of intracranial aneurysm.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- A single-cell atlas of intracranial aneurysm progression identifies a dysfunctional VSMC subpopulation associated with rupture risk.BMC neuroscience · 2026Article
- Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.Frontiers in immunology · 2024Article
- Review
- Interleukin Enhancer Binding Factor 2 Regulates Cell Viability and Apoptosis of Human Brain Vascular Smooth Muscle Cells.Journal of molecular neuroscience : MN · 2021Article
- Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracranial aneurysm (IA) is a localized dilation of the blood vessel. The present study was designed to explore the mechanisms of rupture of IA. GSE13353 (including 11 ruptured and 8 unruptured IA samples) and GSE15629 (including 8 ruptured and 6 unruptured IA samples) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) identified using limma and MetaDE packages were merged, and a protein-protein interaction (PPI) network analysis was performed using Cytoscape software. Pathway enrichment analysis was performed for the nodes of the PPI network using the fisher algorithm. The 100 most prominent genes in the network were designated candidate genes and a hierarchical clustering analysis was performed. The tune.svm function of e1071 package was used to construct a support vector machine (SVM) classifier, and the Candidate Cancer Gene Database was applied to analyze the characterization of gene-associated cancer. Furthermore, the genes involved in the SVM classifier were assessed via principal component analysis (PCA). In the ruptured samples, 1,292 DEGs and 1,029 DEGs separately were identified by limma and MetaDE packages. The 100 most prominent genes in the network included fibronectin 1 (FN1), amyloid β (A4) precursor protein (APP), nuclear RNA export factor 1 (NXF1) and signal transducer and activator of transcription 3 (STAT3). Pathway enrichment analysis identified that toll-like receptor 3 (TLR3) was enriched in the Toll-like receptor signaling pathway. A total of 15 genes (including FN1) were used to construct the SVM classifier. NXF1 was identified to be associated with Nervous System Cancer. PCA revealed that APP, NXF1 and STAT3 were the 3 principal components. TLR3, FN1, APP, NXF1 and STAT3 may affect the rupture of IA.
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