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Related Experiment Video

Updated: Sep 11, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial transcriptomics and scRNA-seq: decoding tumor complexity and constructing prognostic models in colorectal

Wei Song1, Yatao Wang1, Min Zhou2

  • 1Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.

Human Genomics
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study reveals distinct tumor cell subtypes in colorectal cancer (CRC), with specific subtypes linked to advanced stages and treatment efficacy. A novel 13-gene prognostic signature (PS) predicts patient outcomes and immune response.

Keywords:
Colorectal cancerPersonalized therapySingle-cell RNA sequencingSpatial transcriptomicsTumor heterogeneity

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Area of Science:

  • Oncology
  • Genomics
  • Translational Medicine

Background:

  • Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics advance understanding of tumor microenvironment and cellular heterogeneity.
  • This heterogeneity is crucial for developing personalized therapies for colorectal cancer (CRC).

Purpose of the Study:

  • To explore tumor cell heterogeneity in CRC.
  • To evaluate the prognostic value of these heterogeneous cells in various therapeutic contexts.
  • To understand the impact of tumor cell heterogeneity on CRC progression.

Main Methods:

  • Employed scRNA-seq and spatial transcriptomics to analyze CRC tumor cell heterogeneity and spatial distribution.
  • Utilized a machine learning algorithm (StepCox backward) to develop a prognostic signature.

Main Results:

  • Identified nine distinct tumor cell subtypes, with MLXIPL+ neoplasm prevalent in advanced CRC.
  • MLXIPL+ neoplasm correlated with chemotherapy and targeted therapy efficacy and was located in the tumor core.
  • Developed a 13-gene prognostic signature (PS); low PS scores indicated higher immune infiltration and better treatment response.

Conclusions:

  • Tumor cell heterogeneity plays a critical role in CRC progression and treatment response.
  • Personalized therapeutic strategies targeting specific tumor subpopulations are needed.
  • The developed PS has significant clinical value for predicting CRC patient prognosis.