Journal of Translational Medicine (Jul 2025)

A dynamic molecular landscape in colorectal cancer progression at single-cell resolution

  • Jianwen Sheng,
  • Nianshuang Li,
  • Shuai Li,
  • Yuman Ye,
  • Yuanhang Liao,
  • Yupeng Zhang,
  • Huan Chen,
  • Mengxian Tu,
  • Guping Zhong,
  • Chaoliang Xiong,
  • Pan Zheng,
  • Yuting Lei,
  • Zekun He,
  • Xingxing He,
  • Yao Zhang,
  • Lei Wang,
  • Xiaolin Gao,
  • Yin Zhu,
  • Jianping Liu,
  • Huizhen Fan

DOI
https://doi.org/10.1186/s12967-025-06785-9
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 26

Abstract

Read online

Abstract Background Precursor lesions like polyps and adenomas in the colon commonly precede colorectal cancer (CRC), advancing through the “normal-polyp-adenoma-carcinoma” sequence towards malignancy. Yet, the cellular heterogeneity and molecular mechanisms involved in CRC development remain inadequately characterized. Methods To understand the molecular mechanisms driving the onset and progression of CRC, we conducted a comprehensive analysis of ten clinical colorectal samples representing sequential pathological stages using single-cell RNA sequencing (scRNA-seq). Validation was performed through immunofluorescence and immunohistochemistry analyses in a separate human colorectal tissue cohort. Additional verification was carried out using bioinformatics analyses of public TCGA and GEO datasets. Results Our comprehensive analyses not only reveal the cellular diversity and transcriptomic differences throughout disease progression but also highlight the importance of leveraging ligand–receptor gene expression to distinguish various cell subtypes. Subsequent examination and validation with a larger sample cohort uncover the specific involvement of ligand–receptor genes, transcription factors, immunoglobulin genes, and heat shock genes in regulating immune responses and microenvironment changes during colorectal tumorigenesis. Conclusions Our extensive transcriptome dataset provides valuable insights and acts as a fundamental resource to deepen our understanding of the complex molecular landscape in CRC. This dataset facilitates improved diagnostic accuracy and the creation of more personalized therapeutic approaches. Graphical Abstract

Keywords