Integrated single-cell and bulk RNA sequencing reveals T-cell heterogeneity and identifies an immune-related prognostic signature in thyroid carcinoma

Patients with thyroid carcinoma (TC) exhibit prognostic heterogeneity, and the conventional TNM staging system is inadequate for accurately predicting individual recurrence and survival. There is therefore a pressing need for novel molecular stratification tools that incorporate the heterogeneity of the tumour immune microenvironment. This study aimed to develop a clinical prognostic model based on key immune cell subpopulations, providing a robust framework for patient risk stratification and precision oncology for TC. Integrating single-cell RNA-sequencing with bulk transcriptomics, we employed MiloR and pseudotime analysis to identify key critical cell clusters. Using univariate Cox and LASSO regression, two risk models were constructed from T-cell-related genes and validated via ROC curves, Kaplan-Meier survival curves and nomograms, among other methods. Our exploratory analysis identifies T cells as a potentially key population in TC, as they exhibit the most pronounced abundance shifts between tumor and normal tissues. An 8-gene signature was established, showing robust prognostic performance in training and testing cohorts, and high-risk patients had significantly poorer overall survival (OS). High-risk scores were enriched in pathways related to IL-17 signaling, TGF-beta, hypoxia, and EMT, suggesting links to invasion and immune evasion. Immune analysis revealed higher stromal scores and reduced infiltration of CD8 + T and NK cells in high-risk patients, indicating an immune-imbalanced state. To complement the OS model, we also constructed an eight-gene progression-free interval (PFI) model, which demonstrated favorable predictive performance. Both the OS and PFI models were preliminarily validated in an external cohort of 15 TC samples profiled by Oxford Nanopore Technologies (ONT) sequencing. The eight‑gene OS and PFI models developed in this study overcome the limitation of conventional staging systems in inadequately capturing immune heterogeneity, providing a quantitative basis for individualized prognostic assessment and intensified surveillance strategies in TC.