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Cancer-Critical Genes II: Tumor Suppressor Genes

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 Genes

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 I: Proto-oncogenes

Cancer-Critical Genes I: Proto-oncogenes

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 I: Proto-oncogenes

Cancer-Critical Genes I: Proto-oncogenes

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 Cell

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 Cell

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...

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Related Experiment Video

Updated: May 21, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level.

Wenting Li¹, Rui Wang, Linfu Bai

¹MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Bioinformatics and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.

BMC Systems Biology

|June 14, 2012

Summary

This summary is machine-generated.

Identifying cancer driver mutations is crucial. This study introduces a network analysis integrating genomes and transcriptomes to pinpoint driver mutations by linking genotype-phenotype correlations across tumor samples.

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

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

Published on: July 22, 2020

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Last Updated: May 21, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

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

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

Published on: July 22, 2020

Area of Science:

Oncology
Genomics
Systems Biology

Background:

Identifying cancer driver mutations among numerous genomic alterations is a significant challenge.
Driver mutations are associated with more cancer phenotypes, suggesting genotype-phenotype correlations can aid identification.

Purpose of the Study:

To develop and validate a novel network analysis method for identifying driver mutations.
To integrate cancer genomes and transcriptomes for enhanced driver mutation detection.

Main Methods:

A network analysis approach was employed, integrating genomic and transcriptomic data from cancer samples.
Genotype-phenotype correlations were analyzed across six solid tumor types.

Main Results:

The method successfully identified significant genotype-phenotype correlations and core modules with enriched somatic mutations and aberrant expression.
Core modules frequently contained known cancer driver mutations, often at central hub genes.
Mutated genes showed cancer-type specificity, with some forming distinct cancer type clusters (neural-derived and adenoma).

Conclusions:

The network analysis approach effectively identifies candidate driver mutations within core modules.
This comprehensive network analysis offers insights into convergent cancer development mechanisms across different organs.