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Related Concept Videos

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

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-oncogenes01:33

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...

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Related Experiment Video

Updated: Jun 12, 2026

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
09:33

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Published on: August 25, 2023

A Bayesian-Based Integrative Bioinformatics Analysis Nominates Oncogenic Drivers in Neuroblastoma.

Lin Xu1,2, Arhanti Sadanand1, Evan W Neczypor3

  • 1Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Clinical and Translational Science
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

Researchers identified 47 potential cancer-driving genes in neuroblastoma using a new algorithm. These oncogenes reveal new therapeutic targets and vulnerabilities in this common childhood cancer.

Keywords:
Bayes theoremDNA copy number variationsNeuroblastomaN‐Myc proto‐oncogene proteinoncogenes

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Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

Area of Science:

  • Oncology
  • Genetics
  • Bioinformatics

Background:

  • Neuroblastoma, a common pediatric cancer, presents challenges in identifying actionable oncogenic drivers.
  • Targeting specific oncogenes is crucial for developing effective neuroblastoma therapies.

Purpose of the Study:

  • To nominate novel, potentially targetable oncogenic drivers in neuroblastoma using an integrative bioinformatics approach.
  • To validate identified oncogenes as therapeutic targets and assess their correlation with patient survival.

Main Methods:

  • Applied the iExCN Bayesian algorithm for integrative analysis of expression and copy-number data from 195 neuroblastoma specimens.
  • Utilized CRISPR/Cas9 screening and RNA knockdown to validate gene dependencies in neuroblastoma cell lines.
  • Confirmed gene dependencies using DepMap data and analyzed survival trends in neuroblastoma patient cohorts.

Main Results:

  • The iExCN algorithm identified 47 candidate oncogenes, including known drivers (ALK, MDMY, MYCN, XPO1) and novel candidates.
  • CRISPR screening revealed cell line-specific and shared vulnerabilities associated with iExCN genes.
  • DepMap analysis confirmed significant dependency scores for iExCN genes across multiple neuroblastoma cell lines.
  • A trend toward worse survival was observed in patients with copy number gains in more iExCN genes.

Conclusions:

  • The study uncovers multiple, potentially targetable oncogenic drivers in neuroblastoma.
  • The iExCN algorithm demonstrates a powerful, generalizable approach for discovering disease genes and therapeutic targets in cancer.
  • Findings provide a foundation for developing new targeted therapies for neuroblastoma.