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.

翻译：胶质母细胞瘤是一种高度侵袭性的脑癌，其特征是进展迅速且预后不良。尽管治疗方法有所进步，但驱动这种侵袭性的潜在遗传机制仍知之甚少。在本研究中，我们采用多模态深度学习方法，通过联合图像/RNA-seq分析来研究胶质母细胞瘤的异质性。我们的结果揭示了与胶质母细胞瘤相关的新基因。通过结合全切片图像和RNA-seq数据，并引入编码RNA-seq数据的新方法，我们识别出可能解释胶质母细胞瘤不同进展模式的特异性遗传谱。这些发现为胶质母细胞瘤异质性的遗传机制提供了新的见解，并突出了潜在的治疗干预靶点。