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

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...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Single Cell Analysis of the Tumor Microenvironment Landscape Across the Disease Spectrum of Multiple Myeloma.

Blood·2026
Same author

NHE6-driven endosome-autophagy axis confers proteasome inhibitor resistance in multiple myeloma.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026
Same author

Leaflet Modification Technique With UNICORN in Failed Aortic Bioprosthesis: Procedural Step-by-Step, Best Practice, and Troubleshooting.

JACC. Cardiovascular interventions·2026
Same author

BenchTop Validation of UNICORN Leaflet Modification Technique in Redo-Transcatheter Aortic Valve Replacement.

JACC. Cardiovascular interventions·2026
Same author

Chordal-Zone Free Externalization of Retrograde Wire to Facilitate Base-to-Tip LAMPOON.

JACC. Cardiovascular interventions·2026
Same author

Clinicopathologic features of systemic ALK-negative anaplastic large cell lymphoma with TP53 deletion.

American journal of clinical pathology·2026

Related Experiment Video

Updated: May 9, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Correlation analysis connects cancer subtypes.

Pei Lin1, Zhongxi Huang

  • 1Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.

Plos One
|July 18, 2013
PubMed
Summary
This summary is machine-generated.

This study reveals molecular similarities across cancer subtypes, linking similar phenotypes and outcomes to shared transcriptional and pathway profiles. Notably, complement and coagulation cascades are frequently dysregulated, suggesting potential for immune-based cancer therapies.

Related Experiment Videos

Last Updated: May 9, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Cancer subtypes exhibit diverse molecular characteristics.
  • Understanding molecular commonality across different cancer types is crucial for developing targeted therapies.
  • Previous studies have focused on single-tissue analyses, limiting cross-cancer insights.

Purpose of the Study:

  • To perform a cross-tissue comparative analysis of molecular commonality between subtypes of six major cancers.
  • To identify shared transcriptional and pathway profiles correlating with similar phenotypes or clinical outcomes.
  • To explore pathway dysregulation across multiple cancer subtypes using Gene Set Enrichment Analysis.

Main Methods:

  • Comparative analysis of molecular subtypes across ovarian, breast, liver, brain, lung, and nasopharyngeal cancers.
  • Transcriptional and pathway profile correlation analysis.
  • Gene Set Enrichment Analysis (GSEA) for pathway dysregulation detection.

Main Results:

  • Molecular subtypes with similar phenotypes or clinical outcomes showed correlated transcriptional and pathway profiles.
  • Gene Set Enrichment Analysis identified significant pathway dysregulation across multiple cancer subtypes.
  • The 'complement and coagulation cascades' pathway was found to be dysregulated in eleven subtypes across five tissues.

Conclusions:

  • Shared molecular characteristics exist between cancer subtypes of different tissues, linked to phenotype and clinical outcomes.
  • Pathway dysregulation is a common feature across diverse cancer subtypes.
  • The frequently dysregulated 'complement and coagulation cascades' pathway presents a potential target for personalized immune-based cancer therapies.