Background:The prognosis management of thyroid cancer remains a significant challenge. This study highlights the critical role of T cells in the tumor microenvironment and aims to improve prognostic precision by integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data, providing a more comprehensive view of tumor biology at the single-cell level.Method:15 thyroid cancer scRNA-seq samples were analyzed from GEO and 489 patients from TCGA. A multi-level attention graph neural network (MLA-GNN) model was applied to integrate T-cell-related differentially expressed genes (DEGs) for predicting disease-free survival (DFS). Patients were divided into training and validation cohorts in an 8:2 ratio.Result:We systematically characterized the immune microenvironment of metastatic thyroid cancer by using single-cell transcriptomics and identified the important role of T-cell subtypes in the development of thyroid cancer. T-cell-based DEGS between tumor tissues and normal tissues were also identified. Subsequently, T-cell-based risk signatures were selected for establishing a risk model using MLA-GNN. Finally, our MLA-GNN-based model demonstrated an excellent ability to predict the DFS of thyroid cancer patients (1-year AUC: 0.965, 3-years AUC: 0.979, and 5-years AUC: 0.949 in training groups, and 1-year AUC: 0.879, 3-years AUC: 0.804, and 5-years AUC: 0.804 in validation groups).Conclusions:Risk features based on T-cell genes have demonstrated the effectiveness in predicting the prognosis of thyroid cancer. By conducting a comprehensive characterization of T-cell features, we aim to enhance our understanding of the tumor’s response to immunotherapy and uncover new strategies for the treatment of cancer.
背景：甲状腺癌的预后管理仍是一项重大挑战。本研究聚焦T细胞在肿瘤微环境中的关键作用，旨在通过整合批量RNA测序与单细胞RNA测序数据，在单细胞层面提供更全面的肿瘤生物学视角，从而提升预后判断的精准度。 方法：从GEO数据库获取15例甲状腺癌单细胞RNA测序样本，并纳入TCGA数据库中489例患者数据。应用多层次注意力图神经网络模型，整合T细胞相关差异表达基因构建无病生存期预测模型。按8:2比例将患者划分为训练集与验证集。 结果：通过单细胞转录组学系统解析转移性甲状腺癌的免疫微环境，明确了T细胞亚型在甲状腺癌发展中的重要作用。同时鉴定了肿瘤组织与正常组织间基于T细胞的差异表达基因。随后采用多层次注意力图神经网络筛选基于T细胞的分子特征建立风险模型。最终，该模型展现出优异的甲状腺癌患者无病生存期预测能力（训练集1年、3年、5年AUC值分别为0.965、0.979、0.949；验证集对应AUC值为0.879、0.804、0.804）。 结论：基于T细胞基因的风险特征能有效预测甲状腺癌预后。通过对T细胞特征进行系统性解析，本研究有望深化对肿瘤免疫治疗应答机制的理解，为癌症治疗开辟新策略。
肿瘤(癌症)患者之家