Functional Enrichment Analysis of Rare Mutations in Patients with Brain Arteriovenous Malformations

Elena V. Zholdybayeva, Ayazhan Bekbayeva, Karashash Menlibayeva, Алуа Гусмаулемова, Botakoz Kurentay, Bekbolat Tynysbekov, Almas Auganov, Ilyas A. Akhmetollayev, Chingiz Nurimanov

Biomedicines, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/biomedicines13061451
Data confidence 85 high
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Abstract

Background/Objectives: Brain arteriovenous malformations (bAVMs) are rare vascular anomalies characterized by direct connections between arteries and veins, bypassing the capillary network. This study aimed to identify potential genetic factors contributing to the development of sporadic bAVMs. Methods: Three patients (AVM1–3) from Kazakhstan who underwent microsurgical resection at the National Centre for Neurosurgery (NCN) in Astana, Kazakhstan, were analyzed. Brain AVMs were diagnosed using magnetic resonance imaging (MRI). Genomic DNA was isolated from whole venous blood samples, and whole-exome sequencing was performed on the NovaSeq 6000 platform (Illumina). Variants were filtered according to standard bioinformatics protocols, and candidate gene prioritization was conducted using the ToppGene tool. Results: In silico analysis further revealed candidate genes likely associated with lesion development, including COL3A1, CTNNB1, LAMA1, NPHP3, SLIT2, SLIT3, SMO, MAPK3, LRRK2, TTN, ERBB2, PARD3, and OBSL1. It is essential to focus on the genetic variants affecting the following prioritized genes: ERBB2, SLIT3, SMO, MAPK3, and TTN. Mutations in these genes were predicted to be “damaging”. Most of these genes are involved in signaling pathways that control vasculogenesis and angiogenesis. Conclusions: Defects in genes associated with ciliary structure and function may be critical to the pathogenesis of brain AVMs. These findings provide valuable insights into the molecular underpinnings of bAVM development, emphasizing key biological pathways and potential candidate genes. Further research is needed to establish robust correlations between specific genetic mutations and clinical phenotypes, which could ultimately inform the development of improved diagnostic, therapeutic, and prognostic approaches.