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Background <p>This study employs a comprehensive approach using Genome-Wide Association Studies (GWAS), protein–protein interaction networks, gene co-expression networks, gene interaction networks, and centrality analysis to explore genetic and network interactions related to glioma and Alzheimer's disease.</p> Methods <p>Through detailed analysis of glioma single-cell data, we found that gene expression patterns are closely related to cell types and states. Principal Component Analysis (PCA) and dimensionality reduction techniques like UMAP and t-SNE reveal cell population heterogeneity and potential subgroups. This research also involved building machine learning models to classify glioma and assessing their performances, as well as a model that can best classify each type..</p> Results <p>We investigated these cell interaction networks along with NRG signaling networks for glioma to discern cell–cell communication and signaling events. The SPP1 signaling pathway and gene expression analysis further triage the specific genes mediating the interactive role in glioma cells.</p> Conclusion <p>This study presented a comprehensive view of gene expression, cell cell interactions and signaling networks in glioma, which might be a crucial piece to understand glioma complexity and usher in new therapeutic strategies across medical divisions.</p>

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Multidimensional analysis reveals gene expression, cell interactions, and signaling networks in glioma and Alzheimer’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月19日

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  • Meiyi Wang,
  • Jie Yu,
  • Dongfang Ge,
  • Zhiwen Geng,
  • Chengqun Wei,
  • Dan Xu

摘要

Background

This study employs a comprehensive approach using Genome-Wide Association Studies (GWAS), protein–protein interaction networks, gene co-expression networks, gene interaction networks, and centrality analysis to explore genetic and network interactions related to glioma and Alzheimer's disease.

Methods

Through detailed analysis of glioma single-cell data, we found that gene expression patterns are closely related to cell types and states. Principal Component Analysis (PCA) and dimensionality reduction techniques like UMAP and t-SNE reveal cell population heterogeneity and potential subgroups. This research also involved building machine learning models to classify glioma and assessing their performances, as well as a model that can best classify each type..

Results

We investigated these cell interaction networks along with NRG signaling networks for glioma to discern cell–cell communication and signaling events. The SPP1 signaling pathway and gene expression analysis further triage the specific genes mediating the interactive role in glioma cells.

Conclusion

This study presented a comprehensive view of gene expression, cell cell interactions and signaling networks in glioma, which might be a crucial piece to understand glioma complexity and usher in new therapeutic strategies across medical divisions.