Glioblastoma (GBM) is a malignant brain tumour, but its subtypes (mesenchymal, classical, and proneural) show different prognoses. Pyroptosis is a programmed cell death relating to tumour progression, but its association with GBM is poorly understood. In this work, we collected 73 GBM samples (the Xiangya GBM cohort) and reported that pyroptosis involves tumour-microglia interaction and tumour response to interferon-gamma. GBM samples were grouped into different subtypes, cluster 1 and cluster 2, based on pyroptosis-related genes. Cluster 1 samples manifested a worse prognosis and had a more complicated immune landscape than cluster 2 samples. Single-cell RNA-seq data analysis supported that cluster 1 samples respond to interferon-gamma more actively. Moreover, the machine learning algorithm screened several potential compounds, including nutlin-3, for cluster 1 samples as a novel treatment. In vitro experiments supported that cluster 1 cell line, T98G, is more sensitive to nutlin-3 than cluster 2 cell line, LN229. Nutlin-3 can trigger oxidative stress by increasing DHCR24 expression. Moreover, pyroptosis-resistant genes were upregulated in LN229, which may participate against nutlin-3. Therefore, we hypothesis that GBM may be able to upregulate pyroptosis resistant related genes to against nutlin-3-triggered cell death. In summary, we conclude that pyroptosis highly associates with GBM progression, tumour immune landscape, and tumour response to nutlin-3.
Keyphrases
- single cell
- rna seq
- nlrp inflammasome
- machine learning
- cell death
- oxidative stress
- data analysis
- stem cells
- genome wide
- dendritic cells
- inflammatory response
- high throughput
- deep learning
- gene expression
- immune response
- spinal cord injury
- risk assessment
- copy number
- subarachnoid hemorrhage
- neuropathic pain
- white matter
- climate change
- combination therapy
- heat shock