Integration of Single-Cell Analysis and Bulk RNA Sequencing Data Using Multi-Level Attention Graph Neural Network for Precise Prognostic Stratification in Thyroid Cancer.

ABSTRACT: 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 microenvironmen

SUBMITTER: Tan L 

PROVIDER: S-EPMC12294065 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature