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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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Related Experiment Video

Updated: Jun 5, 2025

A Preclinical Mouse Model of Osteosarcoma to Define the Extracellular Vesicle-mediated Communication Between Tumor and Mesenchymal Stem Cells
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Exploring osteosarcoma based on the tumor microenvironment.

Ao Wu1, Zhi-Kai Yang2, Peng Kong3

  • 1The First Clinical College of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.

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|December 10, 2024
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Summary

This study developed a precise risk assessment model for osteosarcoma, identifying immune-related genes to predict patient outcomes and analyze immune checkpoints effectively.

Keywords:
immune-related genesimmunization checkpointsimmunotherapyosteosarcomatumor microenvironment

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Area of Science:

  • Oncology
  • Immunology
  • Bioinformatics

Background:

  • Osteosarcoma, a bone cancer from mesenchymal cells, presents challenges with metastasis, drug resistance, disability, and mortality.
  • The tumor microenvironment's (TME) immunological characteristics are crucial for osteosarcoma prognosis and treatment, necessitating sensitive prognostic signatures.

Purpose of the Study:

  • To identify a sensitive prognostic signature for osteosarcoma by analyzing immune characteristics within the TME.
  • To develop and validate a risk assessment model based on immune-related differentially expressed genes (IR-DEGs) for predicting osteosarcoma patient outcomes.

Main Methods:

  • Analysis of 84 osteosarcoma samples from the UCSC Xena database for immune infiltration and classification.
  • Identification of differentially expressed genes (DEGs) and immune-related DEGs (IR-DEGs) using TIMER database and intersection analysis.
  • Construction of a risk model using univariate COX regression and LASSO analysis, followed by survival analysis and immune checkpoint evaluation.

Main Results:

  • The majority of DEGs identified were enriched in the immune domain, highlighting the role of immunity in osteosarcoma.
  • The developed risk assessment model showed significant prognostic distinctions between high-risk and low-risk scoring groups.
  • The model's findings were consistent with previous research and yielded meaningful results in analyzing immune checkpoints.

Conclusions:

  • The developed risk assessment model is precise and dependable for forecasting osteosarcoma outcomes.
  • The model aids in analyzing the immunological characteristics of osteosarcoma, offering potential for improved patient management.