biostudies-literatureTan LGuangdong Provincial Key Laboratory IRADSGuangDong Basic and Applied Basic Research FoundationScientific Research Launch Project of Sun Yat-Sen Memorial HospitalNational Natural Science Foundation of ChinaYunfang YuGuangdong Science and Technology Department2411https://www.ebi.ac.uk/biostudies/studies/S-EPMC12294065biostudies-literatureUnknown17(14)<b>Background:</b> 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. <b>Method:</b> 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. <b>Result:</b> We systematically characterized the immune microenvironmenCancersIntegration of Single-Cell Analysis and Bulk RNA Sequencing Data Using Multi-Level Attention Graph Neural Network for Precise Prognostic Stratification in Thyroid Cancer.PMC122940652022B1212010006GuangDong Basic and Applied Basic Research Foundation82404087UICR0600008-62024B12120300022025A1515011563SYSQH-II-2024-07Tan LPeng XLai ZLin ROuyang WHuang ZLong MChen YZhang CWang ZYu YfalseIntegration of Single-Cell Analysis and Bulk RNA Sequencing Data Using Multi-Level Attention Graph Neural Network for Precise Prognostic Stratification in Thyroid Cancer.<b>Background:</b> 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. <b>Method:</b> 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. <b>Result:</b> We systematically characterized the immune microenvironmen2025-01-01T00:00:00Z2025 Jul2026-06-30T03:26:11.204Z2025-08-13T03:04:36.608ZS-EPMC122940654072329210.3390/cancers17142411