Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Ras Gene

The Ras Gene

The Ras-gene-encoded proteins are regulators of signaling pathways controlling cell proliferation, differentiation, or cell survival. The Ras-gene family in humans constitutes three primary members—the HRas, NRas, and KRas. These genes code for four functionally distinct yet closely related proteins—the HRas, NRas, KRas4A, and KRas4B. The involvement of mutant Ras genes in human cancer was first discovered in 1982 and is among the most common causes of human tumorigenesis.
Ras is a...

Small GTPases - Ras and Rho

Small GTPases - Ras and Rho

Ras and Rho are small monomeric GTPases that act downstream of receptor tyrosine kinase (RTK) and regulate various cellular processes. These GTPases switch between active and inactive states by binding to guanine nucleotides.
Three regulatory proteins control their activity:

Abnormal Proliferation

Abnormal Proliferation

Under normal conditions, most adult cells remain in a non-proliferative state unless stimulated by internal or external factors to replace lost cells. Abnormal cell proliferation is a condition in which the cell's growth exceeds and is uncoordinated with normal cells. In such situations, cell division persists in the same excessive manner even after cessation of the stimuli, leading to persistent tumors. The tumor arises from the damaged cells that replicate to pass the damage to the...

You might also read

Related Articles

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

Sort by

Same author

NF1 and SPRED1/2 cooperate through RAS-MAPK-independent functions.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Dual inhibition of GTP-bound (ON) and GDP-bound (OFF) KRAS<sup>G12C</sup> suppresses PI3Kα and leads to potent tumor inhibition.

bioRxiv : the preprint server for biology·2026

Same author

Revisiting RAS family GTPase signaling: effector selectivity and oncogenic bypass.

The Biochemical journal·2026

Same author

Dashboard Knee: Injury Mechanisms, Diagnostic Challenges, and Treatment Outcomes.

Cureus·2026

Same author

The heart of the matter: a personal view of Fred Wittinghofer's contributions to RAS biology and drug design.

Biological chemistry·2026

Same author

Disulfide tethering reveals cryptic pockets in oncogenic KRAS.

Communications chemistry·2026

Search research articles

Related Experiment Video

Updated: Dec 17, 2025

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Published on: March 8, 2020

ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms

Ming Yi¹, Dwight V Nissley², Frank McCormick^2,3

¹NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. yiming@mail.nih.gov.

Scientific Reports

|June 26, 2020

Summary

We developed a computational method to accurately measure Ras dependency using gene expression data. This approach enhances understanding of Ras biology and offers potential clinical applications in cancer research.

More Related Videos

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods

Published on: August 4, 2022

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Related Experiment Videos

Last Updated: Dec 17, 2025

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Published on: March 8, 2020

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods

Published on: August 4, 2022

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Area of Science:

Oncology
Computational Biology
Genomics

Background:

Ras-dependency indexes (RDIs) are crucial for understanding Ras-driven cancers but are difficult to determine and reproduce.
Existing methods for RDI calculation have limitations in application and reproducibility.

Purpose of the Study:

To develop and validate a computational method for deriving RDIs using gene expression data.
To apply computational RDIs to identify pathways associated with Ras dependency and assess their clinical relevance.

Main Methods:

Applied a computational single sample gene set enrichment analysis (ssGSEA) method to derive RDIs from gene expression data.
Validated computational RDIs against experimental data, siRNA studies, and external datasets.
Analyzed The Cancer Genome Atlas (TCGA) patient data to assess pathway enrichment and survival associations.

Main Results:

Computationally derived RDIs showed high agreement with experimental values and correlated well with previous studies.
Identified key pathways, including Fas signaling, distinguishing extreme Ras dependency levels in cell lines.
Demonstrated conserved pathway patterns and significant associations between computational RDIs and patient survival in TCGA data.

Conclusions:

The computational ssGSEA method reliably determines RDIs in both cancer cell lines and patient samples.
Computational RDIs provide valuable insights into Ras biology and potential therapeutic strategies for Ras-dependent cancers.