ArticleNPJ precision oncology2025
Comprehensive single-cell RNA analysis reveals intertumoral microenvironment heterogeneity and hub niche of carcinogenesis in thyroid cancer.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development and validation of a predictive model for thyroid nodule malignancy risk based on intralesional and perilesional ultrasound radiomic features.Gland surgery · 2026Article
- Multi-omics, machine learning, and molecular simulation identify CD44 as a candidate target in endocrine-disrupting chemical-associated thyroid cancer progression.Molecular diversity · 2026Article
- ERK Signaling in Thyroid Cancer: Lineage Suppression, Epigenetic Reprogramming, and Theranostic Reversal.Cancers · 2026Review
- The tumor immune microenvironment of thyroid cancer and colorectal cancer: cellular crosstalk and therapeutic implications.Frontiers in immunology · 2026Review
- Cell-state transitions and microenvironmental remodeling in thyroid cancer progression revealed by single-cell and spatial transcriptomics.Frontiers in immunology · 2026Review
- SHAP-interpretable machine learning integrating exposures and multi-omics reveals immune alterations and biomarkers in COPD-lung cancer comorbidity.Frontiers in immunology · 2026Article
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Authors and funding
9 authors.
Funding
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Abstract
Thyroid cancer, the most common endocrine malignancy, is characterized by a unique and complex tumor microenvironment (TME). To unravel the high tumor heterogeneity and molecular mechanisms driving cancer progression, we performed single-cell RNA sequencing (scRNA-seq) analysis, enabling a comprehensive exploration of cellular diversity and molecular dynamics at single-cell resolution. We employed Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and subsequent identification of cellular clusters. Differential gene expression analysis across subclusters was conducted using the FindAllMarkers function, while the DoHeatmap function was utilized to visualize the distribution of differentially expressed genes. The AUCell algorithm was applied to evaluate pathway enrichment within specific cell subtypes. To decipher cellular communication networks, we integrated the CellChat and NicheNet algorithms, which revealed intricate intercellular signaling interactions. Finally, multiplex immunohistochemistry (mIHC) was performed to validate key cellular interactions identified in silico. By analyzing 405,077 single cells from 50 thyroid cancer samples (including papillary, anaplastic, and metastatic tumors) and 14 normal thyroid tissues, we identified four major cellular subpopulations through unbiased clustering based on gene expression patterns and representative cellular markers. The TME was found to encompass diverse immune, endothelial, and mesenchymal cell subtypes, including novel populations such as CD4 + HSPA1A + T cells. Functional pathway enrichment analysis highlighted the roles of abundant cell types in tumor progression. Cell-cell communication analysis uncovered potential immunotherapeutic targets and revealed critical crosstalk among hub niche cells, including APOE+ macrophages, EMT-like cancer-associated fibroblasts (CAFs), and RBP7+ endothelial cells. These findings were further validated by multiplex immunohistochemistry, confirming the spatial organization and interactions of these cell populations within the TME. Our study provides a comprehensive single-cell transcriptomic atlas of thyroid cancer, offering profound insights into tumor heterogeneity, the functional roles of key niche cells, and potential biomarkers for anticancer therapy. These findings not only enhance our understanding of thyroid cancer biology but also pave the way for the development of novel therapeutic strategies targeting the TME.
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Registered trials
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