空间转录组数据在癌症研究中发挥着至关重要的作用，提供了对肿瘤组织内基因表达的空间组织的细致了解。揭示基因表达的空间动态可以揭示肿瘤异质性的关键见解，并有助于识别潜在的治疗靶点。然而，在许多大规模癌症研究中，空间转录组学数据有限，批量 RNA-seq 和相应的全切片图像 (WSI) 数据更为常见（例如 TCGA 项目）。为了解决这一差距，迫切需要开发能够根据现有 WSI 和批量 RNA-seq 数据以近细胞（点）水平分辨率估计基因表达的方法。这种方法对于重新分析广泛的队列研究和发现在初始评估中被忽视的新生物标志物至关重要。在这项研究中，我们提出了 STGAT（空间转录组图注意网络），这是一种利用图注意网络（GAT）来辨别点之间的空间依赖性的新方法。 STGAT 基于空间转录组数据进行训练，旨在以点级分辨率估计基因表达谱，并预测每个点是否代表肿瘤组织或非肿瘤组织，特别是在仅可获得 WSI 和批量 RNA-seq 数据的患者样本中。对两个乳腺癌空间转录组数据集的综合测试表明，STGAT 在准确预测基因表达方面优于现有方法。使用 TCGA 乳腺癌数据集进行的进一步分析表明，从仅肿瘤点估计的基因表达（由 STGAT 预测）为乳腺癌亚型和肿瘤分期预测提供了更准确的分子特征，并且还可以提高患者的生存率和无病生存率分析。可用性：代码可在 https://github.com/compbiolabucf/STGAT 获取。© 作者 2024。由牛津大学出版社出版。

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.© The Author(s) 2024. Published by Oxford University Press.