Single‐Cell Profiling Reveals Distinct Immune Hallmarks in Untreated Primary Colorectal and Liver Metastasis Cancers

ABSTRACT Background Colorectal cancer (CRC) is one of the most commonly occurring cancers all over the world. Because of the high metastasis rate, the 5‐year survival rate of metastatic CRC is only around 10%. CRC cells trigger the remodeling of the tumor microenvironment (TME), so decoding the TME between metastatic and primary sites becomes crucial. Methods Single‐cell sequencing and analysis were performed on 36 samples from four anatomical locations in nine untreated CRC liver metastasis patients. Cells from primary and metastatic sites were compared, and TMEs were validated. Genes from primary and metastatic sites were compared using a nonparametric test. Results Eleven major cell types are identified, and high heterogeneity of cell distribution among different locations is observed. We reveal several new features in liver metastatic tumors, including CXCL13 + CD4 T cells and regulatory CD4 T cells enriched, exhausted CD8 + T cells (Tex) increased, SPP1+ tumor macrophage increased, IGFBP7+ cancer‐associated fibroblasts in the metastatic TME, and the relative activation of the linoleic acid metabolism pathway. Besides, tumor cells contribute to four distinct biological modules, encompassing apoptosis, epithelial‐mesenchymal transition, the cell cycle, and immune processes. Conclusion Our study contributes new features for the microenvironment of colon cancer and liver metastasis, which may provide unique insights for novel biomarkers and potential treatment strategies.

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Publication Details

Journal
Chronic Diseases and Translational Medicine
Published
2026-05-12
DOI
https://doi.org/10.1002/cdt3.70051
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Single‐Cell Profiling Reveals Distinct Immune Hallmarks in Untreated Primary Colorectal and Liver Metastasis Cancers

Shuangmei Zou, Zhixun Zhao, Tianli Chen, Z.H. Liu et al.
Chronic Diseases and Translational Medicine
Single-cell and spatial transcriptomics
article

Single‐Cell Profiling Reveals Distinct Immune Hallmarks in Untreated Primary Colorectal and Liver Metastasis Cancers

Shuangmei Zou, Zhixun Zhao, Tianli Chen, Z.H. Liu, Haipeng Chen, Gao Y, Hengchang Liu, Xu Guan, Zheng Jiang, Xishan Wang, Ming Yang
article en

Abstract

ABSTRACT Background Colorectal cancer (CRC) is one of the most commonly occurring cancers all over the world. Because of the high metastasis rate, the 5‐year survival rate of metastatic CRC is only around 10%. CRC cells trigger the remodeling of the tumor microenvironment (TME), so decoding the TME between metastatic and primary sites becomes crucial. Methods Single‐cell sequencing and analysis were performed on 36 samples from four anatomical locations in nine untreated CRC liver metastasis patients. Cells from primary and metastatic sites were compared, and TMEs were validated. Genes from primary and metastatic sites were compared using a nonparametric test. Results Eleven major cell types are identified, and high heterogeneity of cell distribution among different locations is observed. We reveal several new features in liver metastatic tumors, including CXCL13 + CD4 T cells and regulatory CD4 T cells enriched, exhausted CD8 + T cells (Tex) increased, SPP1+ tumor macrophage increased, IGFBP7+ cancer‐associated fibroblasts in the metastatic TME, and the relative activation of the linoleic acid metabolism pathway. Besides, tumor cells contribute to four distinct biological modules, encompassing apoptosis, epithelial‐mesenchymal transition, the cell cycle, and immune processes. Conclusion Our study contributes new features for the microenvironment of colon cancer and liver metastasis, which may provide unique insights for novel biomarkers and potential treatment strategies.

Chronic Diseases and Translational Medicine
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), National Cancer Center (US)
Good health and well-being
Openalex Percentile: Top 7%
Single-cell and spatial transcriptomics
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