Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis

  • J Transl Med. 2026 May 23;24(1):940. doi: 10.1186/s12967-026-08266-z.
Yu-Feng Huang  1 ,  Kun-Long Wang  2
Affiliations
  • 1. The First Clinical Medical School, Shanxi Medical University, Taiyuan, China. [email protected].
  • 2. The First Clinical Medical School, Shanxi Medical University, Taiyuan, China.
Abstract

Background: Gliomas constitute 80% of malignant brain Tumors, with Glioblastoma (GBM) being the most aggressive subtype. The single-cell-level mechanisms underlying gliomagenesis are poorly understood, hindering therapeutic development. We combine genome-wide association studies (GWAS) with bulk tissue and single-cell multi-omics to prioritize genetically supported candidate genes and to explore potential cell-type-specific mechanisms relevant to gliomagenesis.

Methods: We integrated the largest Glioma GWAS with brain-specific multi-omics to prioritize genetically supported candidate genes using two broad categories of prioritized methods. Biological enrichment, differential gene expression, and CRISPR/miRNA were used to assess target enrichment and druggability. By integrating single-cell multi-omics data (genomics, transcriptomics, epigenomics), we investigated GBM-relevant cells, tumor microenvironment (TME) interactions, and cell-type-specific mechanisms in glioblastomagenesis. Additionally, phenome-wide association studies (PheWAS) and drug repurposing analyses were conducted to annotate genetic pleiotropy and enhance drug repositioning.

Results: We prioritized 11 high-confidence and 47 putatively causal genes, most of which are druggable. Astrocytes and oligodendrocyte precursor cells (OPCs) were implicated as GBM-relevant cell populations, with significantly increased TME cell communication between these populations and neurons. We further identified 14 putative cell-type-specific effects related to glioblastomagenesis, including three high-confidence genes (EGFR in astrocytes, CDKN2A in OPCs, and JAK1 in excitatory neurons). Most effects (85.7%, 12/14) were associated with non-GBM-relevant cell cells, encompassing both glial and neural cells.

Conclusions: This study systematically identifies genetically supported candidate genes in gliomagenesis and their cell-type-specific effects. These findings provide a resource for future mechanistic investigation and may help inform the development of more precise therapeutic hypotheses.

Keywords
Cell-type-specific causal genes; Genome-wide association studies; Glioblastomagenesis; Single-cell multi-omics.
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