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

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

11.3K
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...
11.3K
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

7.2K
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...
7.2K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

13.3K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
13.3K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.1K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.1K
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

4.6K
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...
4.6K
Mechanisms of Retrovirus-induced Cancers01:51

Mechanisms of Retrovirus-induced Cancers

4.9K
Retroviruses are RNA viruses that have been shown to cause cancers in diverse species, including chickens, mice, cats, and monkeys. The RNA genomes of these viruses are first reverse-transcribed into single and then double-stranded DNA (dsDNA) copies. This dsDNA called proviral DNA then integrates into the host genome. Subsequently, the host cell transcribes the proviral DNA in concert with the chromosomal DNA. This leads to the production of viral RNA and proteins that assemble at the host...
4.9K

You might also read

Related Articles

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

Sort by
Same author

A synergistic interaction between PRMT5 and LSD1 inhibitors in AML.

Science advances·2026
Same author

Reply to: Improving the Clinical Interpretability of Functional Drug Screens: A Suggestion for Standardized Clinical Decision Thresholds in Quadratic Phenotypic Optimization Platform.

JCO precision oncology·2025
Same author

Peripheral T-Cell Lymphoma Management in East and Southeast Asia: Real-World Challenges and Aspirations of the Asian Lymphoma Study Group.

JCO global oncology·2025
Same author

Priming with DNMT Inhibitors Potentiates PD-1 Immunotherapy by Triggering Viral Mimicry in Relapsed/Refractory NK/T-cell Lymphoma.

Cancer discovery·2025
Same author

Genome-wide in vivo CRISPR screens identify GATOR1 complex as a tumor suppressor in Myc-driven lymphoma.

Nature communications·2025
Same author

Charting New Paths in Cancer Research: Insights from the Frontiers in Cancer Science Conference 2024.

Cancer research·2025

Related Experiment Video

Updated: May 9, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

6.5K

Single-cell Resolved Oncogene Co-expression: From Principles to Clinical Impact.

Shruti Sridhar1, Allison S Y Chan1, Anand D Jeyasekharan1,2,3,4

  • 1Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.

Blood Cancer Discovery
|April 28, 2025
PubMed
Summary

Studying oncogene co-expression at the single-cell level offers valuable biological insights and clinical applications. Advancements in scalable methods, AI, and mathematical models are crucial for realizing its full potential.

More Related Videos

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

53
A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells
10:13

A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

Published on: July 3, 2013

11.0K

Related Experiment Videos

Last Updated: May 9, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

6.5K
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

53
A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells
10:13

A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

Published on: July 3, 2013

11.0K

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Oncogene co-expression patterns are critical in cancer development.
  • Understanding these patterns at a single-cell level provides higher resolution than bulk analysis.
  • Current methods may lack the scalability and analytical sophistication for comprehensive study.

Purpose of the Study:

  • To explore the concept and utility of single-cell oncogene co-expression analysis.
  • To discuss the clinical and biological implications of this research approach.
  • To highlight key technological and analytical advancements needed for clinical integration.

Main Methods:

  • Review of current literature on oncogene co-expression studies.
  • Conceptual framework for single-cell resolution analysis.
  • Discussion on the role of scalable technologies and computational models.

Main Results:

  • Single-cell resolution offers unprecedented detail into oncogene interactions.
  • Co-expression patterns can reveal novel therapeutic targets and biomarkers.
  • Integration of AI and advanced quantification models is essential for clinical translation.

Conclusions:

  • Single-cell oncogene co-expression analysis holds significant promise for advancing cancer research.
  • Scalable methods, robust quantification, and AI are key enablers for clinical utility.
  • This approach can lead to more personalized and effective cancer diagnostics and therapeutics.