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Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation.
Saghapour, Ehsan; Yue, Zongliang; Sharma, Rahul; Kumar, Sidharth; Sembay, Zhandos; Willey, Christopher D; Chen, Jake Y.
Afiliación
  • Saghapour E; Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
  • Yue Z; Health Outcome Research and Policy Department, Harrison College of Pharmacy, Auburn University, AL, US.
  • Sharma R; Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
  • Kumar S; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, US.
  • Sembay Z; Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
  • Willey CD; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, US.
  • Chen JY; Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
bioRxiv ; 2024 Apr 02.
Article en En | MEDLINE | ID: mdl-38617348
ABSTRACT
This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos