Saghapour Ehsan, Yue Zongliang, Sharma Rahul, Kumar Sidharth, Sembay Zhandos, Willey Christopher D, Chen Jake Y
Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
Health Outcome Research and Policy Department, Harrison College of Pharmacy, Auburn University, AL, US.
bioRxiv. 2024 Apr 2:2024.04.01.587278. doi: 10.1101/2024.04.01.587278.
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.
本研究介绍了基因地形知识图谱表示法（GTKM），这是一种在癌症研究中可视化基因表达数据的新方法。GTKM利用蛋白质-蛋白质相互作用，在二维等高线图上以图形方式显示差异表达基因（DEG），与传统热图方法相比，能更细致入微地理解基因相互作用和表达模式。该研究通过对胶质母细胞瘤（GBM）数据集的四个案例研究证明了GTKM的实用性，重点关注生存分析、亚型识别、IDH1突变分析以及不同肿瘤细胞系的药物敏感性。此外，还开发了一个原型网站来展示这些发现，表明该方法对各种癌症类型具有适应性。研究表明，GTKM能有效识别与GBM中不同临床结果相关的基因模式，其图谱有助于识别对预测生存至关重要的亚基因特征模式。该方法有望在精准医学方面取得重大进展，为理解复杂的基因相互作用和识别癌症治疗中的潜在治疗靶点提供强大工具。
