Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Tumor Microenvironment02:17

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...
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Glioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score.

Mediators of inflammation·2026
Same author

Mitochondria Pathway Signature Predicts Prognosis and Therapeutic Response and Identifies REXO2 as a Crucial Regulator in Breast Cancer.

Mediators of inflammation·2026
Same author

Single-Cell Transcriptomics Reveals Biomarkers for NK Cell Dysfunction in Endometriosis-Associated Immune Dysregulation.

Mediators of inflammation·2026
Same author

Comprehensive Single-Cell Characterization of LDL in the Ovarian Cancer Microenvironment and Its Prognostic Implications.

Mediators of inflammation·2026
Same author

Single-Cell Transcriptomic Analysis Reveals an Inflammatory Antigen-Presenting Macrophages Subtype Drive Vitiligo Pathogenesis Through STAT1-Mediated Dual Mechanisms.

Mediators of inflammation·2026
Same author

Integrative Genomic and Single-Cell Insights Into Efferocytosis-Mediated Immune Regulation in Clear Cell Renal Cell Carcinoma.

Mediators of inflammation·2026

Related Experiment Video

Updated: Jul 21, 2026

Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma
06:15

Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma

Published on: October 27, 2014

27.8K

Glioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score.

Hongyi Zhou1, Xi Yang1, Wen Zhao2

  • 1Department of Anus and Intestine Surgery, The Affiliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang, 315040, China, nbu.edu.cn.

Mediators of Inflammation
|January 7, 2026
PubMed
Summary

This study developed a novel glioblastoma (GBM) prognostic model using cancer-associated fibroblast (CAF) genes. The model accurately predicts patient survival and response to treatment, offering new avenues for personalized GBM therapy.

Keywords:
cancer-associated fibroblastsglioblastomaimmunotherapyprognosistumor immune microenvironment

More Related Videos

Translational Orthotopic Models of Glioblastoma Multiforme
07:37

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

3.6K
Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

846

Related Experiment Videos

Last Updated: Jul 21, 2026

Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma
06:15

Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma

Published on: October 27, 2014

27.8K
Translational Orthotopic Models of Glioblastoma Multiforme
07:37

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

3.6K
Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

846

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Cancer-associated fibroblasts (CAFs) are crucial in the tumor microenvironment but underexplored in glioblastoma (GBM).
  • Systematic characterization of CAFs is needed for precise GBM patient stratification and treatment optimization.

Purpose of the Study:

  • To develop and validate a prognostic model for glioblastoma (GBM) based on cancer-associated fibroblast (CAF) related genes.
  • To provide insights into the role of CAFs in GBM progression, immunity, and therapeutic response.

Main Methods:

  • Single-cell RNA sequencing (scRNA-seq) data analysis using the Seurat package to identify CAF phenotypes and key prognostic genes in GBM.
  • Construction and validation of a CAF-based risk score model and a nomogram integrating clinicopathological features.
  • Multi-dimensional analyses including gene mutation, pathway enrichment, immune infiltration, immunotherapy response, and drug sensitivity.

Main Results:

  • Six CAF-related genes (FAM241B, LSM2, IGFBP2, LOXL1, OSMR, STOX1) were identified as significant prognostic markers for GBM.
  • The CAF-based risk score model demonstrated robust prognostic performance and served as an independent predictor of overall survival.
  • The nomogram integrating the risk score and clinical features improved predictive accuracy and reliability, correlating with immune cell infiltration and predicting treatment outcomes.

Conclusions:

  • A novel GBM risk profiling framework and nomogram accurately predict prognosis and stratify patients.
  • The findings offer valuable insights into CAF roles in GBM, aiding in tumor mutation deciphering, immune landscape mapping, and drug/immunotherapy prediction.
  • This framework has the potential to significantly enhance personalized treatment strategies and improve patient outcomes in GBM.