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The Tumor Microenvironment

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Artificial Intelligence-driven spatial transcriptomics in OSCC: Mapping the tumor microenvironment and personalizing

Soundharya Manogaran¹, Ramya Ramadoss², Suganya Panneer Selvam²

¹Department of Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India.

Journal of Oral Biology and Craniofacial Research

|November 24, 2025

Summary

Spatial transcriptomics (ST) reveals gene expression patterns in oral squamous cell carcinoma (OSCC) tissues. This technology, combined with AI, enhances OSCC diagnosis and treatment strategies.

Keywords:

Artificial intelligence Oral squamous cell carcinoma (OSCC)Precision oncology Spatial transcriptomics Tumor microenvironment

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Published on: September 13, 2022

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (ST) provides high-resolution gene expression data within tissue architecture.
The tumor microenvironment (TME) in oral squamous cell carcinoma (OSCC) is complex and benefits from spatial analysis.
Conventional methods like bulk RNA sequencing obscure crucial spatial heterogeneity.

Purpose of the Study:

To explore the application of ST in understanding OSCC.
To identify spatially distinct gene signatures and prognostic markers in OSCC.
To investigate the synergy of ST with AI for improved OSCC analysis.

Main Methods:

Utilized various ST platforms (e.g., Visium, Slide-seqV2, MERFISH, GeoMx DSP, CosMx SMI, Stereo-seq).
Analyzed gene expression patterns in the context of tumor architecture.
Integrated ST data with artificial intelligence (AI) and machine learning algorithms.

Main Results:

Identified spatially distinct gene signatures specific to OSCC subtypes.
Discovered novel prognostic markers through spatial gene expression analysis.
AI integration enabled automated feature extraction, clustering, and predictive modeling.

Conclusions:

ST offers unprecedented insights into OSCC's spatial organization and TME.
The combination of ST and AI holds significant potential for precision pathology in OSCC.
This synergy can revolutionize OSCC diagnosis, risk assessment, and personalized therapy.