Comprehensive single-cell analysis reveals cellular heterogeneity and immune interactions in colorectal cancer

Colorectal cancer (CRC) presents considerable therapeutic challenges due to its diverse cellular composition and intricate microenvironment. Our study utilized single-cell RNA sequencing (scRNA-seq) on CRC tissues, examining 18,741 individual cells, which were grouped into six primary cell populations: epithelial, fibroblast, endothelial, T and NK, B, and myeloid. The epithelial cells exhibited notable variations in gene copy numbers. Within T_NK cells, we identified four distinct subsets. CytoTRACE analysis indicated that subtype C3 exhibited lower differentiation potential, whereas subtypes C0 and C1 showed higher differentiation potential. Consistently, Monocle pseudotime trajectory analysis positioned C3 cells at the terminal stage of differentiation, while C0 cells were enriched at the early stage of the developmental trajectory, suggesting functional heterogeneity among T/NK subpopulations. Through functional analyses with GSVA and ssGSEA, subtype C3 displayed the highest inflammation-associated activity scores. Further exploration of transcription factors defined three unique regulatory clusters among T_NK cells, illuminating their gene-regulation networks. We developed a prognostic signature using markers from subtype C3 T_NK cells combined with age-associated genes, revealing a significant correlation with patient survival outcomes. This prognostic model proved effective in categorizing CRC patients according to risk. Additionally, immune profiling employing ESTIMATE, CIBERSORT, and Xcell algorithms underscored the complexity of immune cell populations within CRC tumors. Analysis of tumor mutational burden (TMB) highlighted differential patterns between patient groups and its relationship to prognostic risk levels. Collectively, these insights provide a detailed perspective on CRC cell diversity and immune dynamics, supporting the advancement of targeted and personalized therapeutic interventions.

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Publication Details

Journal
Translational Oncology
Published
2026-04-22
DOI
https://doi.org/10.1016/j.tranon.2026.102761
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Comprehensive single-cell analysis reveals cellular heterogeneity and immune interactions in colorectal cancer

Shaofan Qiu, Song Wang, Rui Kong, Zhenyu Chi
Translational Oncology
Single-cell and spatial transcriptomics
article

Comprehensive single-cell analysis reveals cellular heterogeneity and immune interactions in colorectal cancer

Shaofan Qiu, Song Wang, Rui Kong, Zhenyu Chi
article en

Abstract

Colorectal cancer (CRC) presents considerable therapeutic challenges due to its diverse cellular composition and intricate microenvironment. Our study utilized single-cell RNA sequencing (scRNA-seq) on CRC tissues, examining 18,741 individual cells, which were grouped into six primary cell populations: epithelial, fibroblast, endothelial, T and NK, B, and myeloid. The epithelial cells exhibited notable variations in gene copy numbers. Within T_NK cells, we identified four distinct subsets. CytoTRACE analysis indicated that subtype C3 exhibited lower differentiation potential, whereas subtypes C0 and C1 showed higher differentiation potential. Consistently, Monocle pseudotime trajectory analysis positioned C3 cells at the terminal stage of differentiation, while C0 cells were enriched at the early stage of the developmental trajectory, suggesting functional heterogeneity among T/NK subpopulations. Through functional analyses with GSVA and ssGSEA, subtype C3 displayed the highest inflammation-associated activity scores. Further exploration of transcription factors defined three unique regulatory clusters among T_NK cells, illuminating their gene-regulation networks. We developed a prognostic signature using markers from subtype C3 T_NK cells combined with age-associated genes, revealing a significant correlation with patient survival outcomes. This prognostic model proved effective in categorizing CRC patients according to risk. Additionally, immune profiling employing ESTIMATE, CIBERSORT, and Xcell algorithms underscored the complexity of immune cell populations within CRC tumors. Analysis of tumor mutational burden (TMB) highlighted differential patterns between patient groups and its relationship to prognostic risk levels. Collectively, these insights provide a detailed perspective on CRC cell diversity and immune dynamics, supporting the advancement of targeted and personalized therapeutic interventions.

Translational OncologyVol. 68
Hebei Medical University (CN), Second Hospital of Hebei Medical University (CN), Hejian People's Hospital (CN), First Affiliated Hospital Zhejiang University (CN), Chongqing Medical University (CN)
Hebei Province Medical Science Research Key Project
Openalex Percentile: Top 21%
Single-cell and spatial transcriptomics
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