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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein-protein Interfaces02:04

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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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
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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A novel method to identify gene interaction patterns.

Xinguo Lu1, Fang Liu2, Qiumai Miao2

  • 1College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Road, Changsha, 410082, China. hnluxinguo@126.com.

BMC Genomics
|June 11, 2021
PubMed
Summary
This summary is machine-generated.

This study reveals that most gene functional modules in cancer are overlapping (Dependent-Communities). This new method effectively identifies cancer biomarkers and uncovers mechanisms of carcinogenesis.

Keywords:
Gene communitiesModule networkProtein-protein interaction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene interaction modules are crucial for identifying cancer biomarkers and understanding tumorigenesis.
  • Existing methods often overlook the overlapping characteristics of gene modules.
  • Understanding these overlaps is key to elucidating regulatory relationships and cancer progression.

Purpose of the Study:

  • To investigate the functional overlapping patterns within gene modules.
  • To explore the regulatory relationships of overlapping genes and communities.
  • To advance the understanding of cancer formation and progression.

Main Methods:

  • Analysis of six cancer datasets from The Cancer Genome Atlas.
  • Identification of three types of gene functional modules: Independent-Community, Dependent-Community, and Merged-Community.
  • Comparison with existing methods like Lemon-Tree and K-Means.

Main Results:

  • Dependent-Communities constituted the vast majority (96.5%) of identified gene modules across six cancers.
  • The proposed method identified gene communities enriched in known Gene Ontology (GO) categories with lower p-values compared to Lemon-Tree and K-Means.
  • Identified communities and driver genes significantly distinguished patient survival prognosis and accurately differentiated tumor from normal samples, serving as potential biomarkers.

Conclusions:

  • Overlapping gene modules (Dependent-Communities) are predominant in cancer.
  • The developed method, accounting for module overlaps, outperforms methods that do not.
  • Overlapping genes act as bridges between functional communities, contributing to a comprehensive understanding of carcinogenesis.