Comprehensive single-cell RNA analysis reveals intertumoral microenvironment heterogeneity and hub niche of carcinogenesis in thyroid cancer

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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Journal
npj Precision Oncology
Published
2025-11-26
DOI
https://doi.org/10.1038/s41698-025-00924-7
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Comprehensive single-cell RNA analysis reveals intertumoral microenvironment heterogeneity and hub niche of carcinogenesis in thyroid cancer

Nanjun Li, Lu Yin, Haoxiang Xuan, Yunuo Shen et al.
npj Precision Oncology
Single-cell and spatial transcriptomics
article

Comprehensive single-cell RNA analysis reveals intertumoral microenvironment heterogeneity and hub niche of carcinogenesis in thyroid cancer

Nanjun Li, Lu Yin, Haoxiang Xuan, Yunuo Shen, Hao Xu, Ping Wang, Junjie Ma, Shijie Lin, Qianhui Xu
article en

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.

npj Precision OncologyVol. 9(1)
Shanghai Medical College of Fudan University (CN), Sun Yat-sen University (CN), Fudan University (CN), Westlake University (CN), Fudan University Shanghai Cancer Center (CN), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN), Second Affiliated Hospital of Zhejiang University (CN), Zhejiang University (CN)
Openalex Percentile: Top 67%
Single-cell and spatial transcriptomics
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