{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tan L"],"funding":["Guangdong Provincial Key Laboratory IRADS","GuangDong Basic and Applied Basic Research Foundation","Scientific Research Launch Project of Sun Yat-Sen Memorial Hospital","National Natural Science Foundation of China","Yunfang Yu","Guangdong Science and Technology Department"],"pagination":["2411"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12294065"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(14)"],"pubmed_abstract":["<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 microenvironmen"],"journal":["Cancers"],"pubmed_title":["Integration of Single-Cell Analysis and Bulk RNA Sequencing Data Using Multi-Level Attention Graph Neural Network for Precise Prognostic Stratification in Thyroid Cancer."],"pmcid":["PMC12294065"],"funding_grant_id":["2022B1212010006","GuangDong Basic and Applied Basic Research Foundation","82404087","UICR0600008-6","2024B1212030002","2025A1515011563","SYSQH-II-2024-07"],"pubmed_authors":["Tan L","Peng X","Lai Z","Lin R","Ouyang W","Huang Z","Long M","Chen Y","Zhang C","Wang Z","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"Integration of Single-Cell Analysis and Bulk RNA Sequencing Data Using Multi-Level Attention Graph Neural Network for Precise Prognostic Stratification in Thyroid Cancer.","description":"<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 microenvironmen","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-06-30T03:26:11.204Z","creation":"2025-08-13T03:04:36.608Z"},"accession":"S-EPMC12294065","cross_references":{"pubmed":["40723292"],"doi":["10.3390/cancers17142411"]}}