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Updated: Jul 16, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Spatial Transcriptomics in Breast Cancer: Advances and Applications.

Yanni Cao1,2, Kangcheng Xu1, Xiaohui Li1

  • 1School of Artificial Intelligence, Anhui University of Science & Technology, Huainan 232001, China.

Biology
|July 15, 2026
PubMed
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Identification and Prognostic Analysis of Immune-Related Genes Co-Regulated by Key Histone Modifications in Breast Cancer.

Current issues in molecular biology·2026

Spatial transcriptomics (ST) maps gene expression within tissues, advancing breast cancer research by detailing tumor microenvironments and heterogeneity. While promising for personalized treatments, ST requires further development in data integration and resolution.

Area of Science:

  • Genomics
  • Molecular Biology
  • Oncology

Background:

  • Traditional transcriptomics lacks spatial resolution within tissues.
  • Single-cell RNA sequencing (scRNA-seq) provides cellular detail but not spatial context.
  • Spatial transcriptomics (ST) quantifies gene expression at specific spatial coordinates within intact tissues.

Purpose of the Study:

  • To review the development of spatial transcriptomics (ST) technology.
  • To highlight the application value of ST in breast cancer research.
  • To explore ST's role in understanding tumor microenvironment and heterogeneity.

Main Methods:

  • Systematic literature review of ST platforms and applications.
  • Analysis of combined scRNA-seq and ST studies.
Keywords:
breast cancerprecision medicinespatial transcriptomicstumor heterogeneitytumor microenvironment

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  • Review of integrated spatial multi-omics approaches in breast cancer.

    • Main Results:

    • ST characterizes spatial organization of breast cancer cells and tumor microenvironment.
    • ST describes spatial dimensions of tumor heterogeneity and immune cell distribution.
    • ST shows potential for refined subtype classification, prognosis, and personalized treatment strategies.

    Conclusions:

    • ST is a crucial tool for analyzing complex spatial organization in breast tumors.
    • Integration of ST with functional studies and orthogonal omics can improve patient outcomes.
    • Further development is needed to address bottlenecks in data integration, resolution, and standardization.