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

336
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...
336
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

7.4K
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...
7.4K
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

8.8K
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...
8.8K
Tumor Progression02:07

Tumor Progression

6.3K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
6.3K
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

11.8K
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...
11.8K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.7K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
5.7K
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Cgpa: Multi-context Insights From The Cancer Gene Prognosis Atlas.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Cgpa: Multi-context Insights From The Cancer Gene Prognosis Atlas.

Related Experiment Video

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

7.5K

CGPA: multi-context insights from the cancer gene prognosis atlas.

Biwei Cao1, Xiaoqing Yu1, Gullermo Gonzalez1

  • 1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.

Biorxiv : the Preprint Server for Biology
|August 2, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The Cancer Gene Prognosis Atlas (CGPA) platform overcomes limitations of univariable analysis for cancer gene prognosis. It enables comprehensive, customized analysis for enhanced biomarker discovery and validation.

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.9K
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

Related Experiment Videos

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

7.5K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.9K
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

Area of Science:

  • * Oncology
  • * Bioinformatics
  • * Computational Biology

Background:

  • * Traditional univariable survival analysis inadequately captures the prognostic potential of genes in cancer transcriptomic data.
  • * This limitation leads to the omission of potentially significant prognostic markers, termed univariable missed-opportunity prognostic (UMOP) genes.
  • * Complex prognostic implications, especially with multiple covariates and thresholds, necessitate advanced analytical approaches.

Purpose of the Study:

  • * To introduce the Cancer Gene Prognosis Atlas (CGPA) as a user-friendly platform for in-depth, customized prognostic analysis of cancer genes.
  • * To enhance gene-centric biomarker research by facilitating the exploration of gene pairs, gene-hallmark relationships, and composite biological mechanisms.
  • * To support multi-gene panel assessment and the discovery of prognostic gene modules using curated immunotherapy data.

Main Methods:

  • * Development of an interactive, web-based platform (CGPA) for customized prognostic analysis of cancer gene expression data.
  • * Integration of capabilities for exploring gene-pair and gene-hallmark associations, and analyzing multi-gene panels.
  • * Inclusion of a portal for identifying prognostic gene modules from cancer immunotherapy datasets.

Main Results:

  • * CGPA provides an accessible interface for researchers, regardless of statistical expertise, to investigate gene prognostic landscapes.
  • * The platform supports data-driven exploration of complex biological mechanisms, such as synthetic lethality and immunosuppression.
  • * CGPA facilitates both mechanism-to-machine analysis and the discovery of prognostic gene modules.

Conclusions:

  • * CGPA significantly advances cancer gene biomarker research by offering comprehensive and customizable prognostic analysis tools.
  • * The platform empowers researchers to precisely investigate gene prognostic values, enhancing biomarker discovery and validation.
  • * CGPA integrates mechanistic insights with data-driven strategies for a synergistic approach to understanding cancer prognosis.