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Related Experiment Video

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Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
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Machine learning integration of single-cell and bulk transcriptomics identifies fibroblast-driven prognostic markers

Ning Zhang1, Ruiyan Liu1, Siya Wu1

  • 1Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China; College of Public Health, Zhengzhou University, Zhengzhou, China.

Biomolecules & Biomedicine
|April 23, 2025
PubMed
Summary
This summary is machine-generated.

This study used single-cell RNA sequencing to identify a seven-gene signature from colorectal cancer (CRC) fibroblasts. This signature predicts patient prognosis and guides personalized therapy strategies.

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Area of Science:

  • Oncology
  • Genomics
  • Immunology

Background:

  • Single-cell RNA sequencing (scRNA-seq) reveals tumor microenvironment (TME) complexity in colorectal cancer (CRC).
  • Translating scRNA-seq insights into clinical biomarkers and therapies for CRC is challenging.

Purpose of the Study:

  • To comprehensively analyze the CRC TME using scRNA-seq.
  • To identify fibroblast-derived prognostic biomarkers for CRC risk stratification.
  • To explore potential therapeutic strategies based on identified signatures.

Main Methods:

  • scRNA-seq analysis of 306 CRC samples (448,255 cells).
  • Construction of intercellular communication networks to identify TME regulatory hubs.
  • Machine learning integration for developing a seven-gene prognostic signature.
  • Survival analysis and validation in independent datasets (TCGA-COAD, GSE17536).

Main Results:

  • Fibroblasts identified as central regulatory hubs in the CRC TME.
  • A robust seven-gene fibroblast-related prognostic signature developed and validated.
  • Signature associated with immune cell infiltration, immune function, and drug sensitivity (camptothecin, irinotecan).

Conclusions:

  • Fibroblast-derived signatures hold significant prognostic value in colorectal cancer.
  • The identified signature can aid in risk stratification and personalized treatment development.
  • Findings contribute to advancing precision oncology for CRC patients.