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The Tumor Microenvironment

The Tumor Microenvironment

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...

The Tumor Microenvironment

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Related Experiment Video

Updated: Jun 26, 2026

Isolation of Cells with Morphological and Spatial Information from Oral Submucous Fibrosis Samples by Laser Capture Microdissection

Isolation of Cells with Morphological and Spatial Information from Oral Submucous Fibrosis Samples by Laser Capture Microdissection

Published on: August 11, 2023

Exploring phenotype-associated modules in an oral cavity tumor using an integrated framework.

Zhirong Sun¹, Jie Luo, Yun Zhou

¹Institute of Bioinformatics and Systems Biology, State Key Laboratory of Biomembrane and Membrane Biotechnology and Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, 100084, China. sunzhr@mail.tsinghua.edu.cn

Bioinformatics (Oxford, England)

|February 3, 2009

Summary

This study introduces a novel framework to identify key gene networks in tumors. The approach, using "coherent modules," successfully identified 18 modules linked to oral cavity tumor metastasis.

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Last Updated: Jun 26, 2026

Isolation of Cells with Morphological and Spatial Information from Oral Submucous Fibrosis Samples by Laser Capture Microdissection

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Published on: December 15, 2023

Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

Tumors are complex traits involving multiple genes and biological processes.
Proteins in common biological processes often cluster in protein-protein interaction networks.

Purpose of the Study:

To develop a novel framework for identifying phenotype-relevant areas within biological networks.
To define and optimize 'coherent modules' for analyzing complex diseases like cancer.

Main Methods:

Developed a framework to identify 'coherent modules' in protein-protein interaction networks.
Optimized modules for low inter-module coupling and high intra-module similarity.
Applied the framework to an oral cavity tumor dataset.

Main Results:

Identified 18 significant coherent modules associated with oral cavity tumors.
Modules were found to represent distinct biological processes.
The identified modules showed potential relevance to lymph node metastasis.

Conclusions:

The coherent module approach provides a novel perspective for tumor research.
This framework aids in the prognosis of tumors by identifying key network modules.
The method is extensible and generalizes existing expression data analysis techniques.