融合病理图像与基因表达数据，发现胶质母细胞瘤异质性的关键基因。
Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing Data
- 用全切片图像与RNA测序联合建模，挖掘肿瘤异质性
- 识别出与不同进展模式相关的特异性基因谱
- 为治疗靶点发现提供新思路，适合肿瘤生物信息学研究者
胶质母细胞瘤是一种高度侵袭性的脑癌，进展迅速且预后极差。尽管治疗手段有所进步，但驱动其恶性行为的遗传机制仍不明确。本研究采用多模态深度学习方法，结合全切片图像与RNA测序数据，分析胶质母细胞瘤异质性。通过整合病理图像与RNA-seq数据，并引入新型RNA-seq编码策略，我们识别出与不同疾病进展模式相关的特定基因表达特征。这些发现揭示了胶质母细胞瘤异质性的潜在遗传机制，为治疗干预提供了新靶点。代码与数据下载地址：https://github.com/ma3oun/gbheterogeneity。
原文摘要 · Abstract (English)
Glioblastoma is a highly aggressive form of brain cancer characterized by rapid progression and poor prognosis. Despite advances in treatment, the underlying genetic mechanisms driving this aggressiveness remain poorly understood. In this study, we employed multimodal deep learning approaches to investigate glioblastoma heterogeneity using joint image/RNA-seq analysis. Our results reveal novel genes associated with glioblastoma. By leveraging a combination of whole-slide images and RNA-seq, as well as introducing novel methods to encode RNA-seq data, we identified specific genetic profiles that may explain different patterns of glioblastoma progression. These findings provide new insights into the genetic mechanisms underlying glioblastoma heterogeneity and highlight potential targets for therapeutic intervention. Code and data downloading instructions are available at: https://github.com/ma3oun/gbheterogeneity.
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