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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: May 13, 2026

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR
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Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Published on: January 22, 2014

Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis.

Mingyuan Liu1, Xiaojun Hou, Ping Zhang

  • 1Department of Neurology, Changhai Hospital, 168 Changhai Road, Shanghai 200433, China.

Molecular Biology Reports
|March 5, 2013
PubMed
Summary
This summary is machine-generated.

This study identifies 1,297 differentially coexpressed genes in multiple sclerosis (MS), revealing key immune and neurological functions. Transcription factors like IKZF1 and BACH1 may be crucial in MS pathogenesis.

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Last Updated: May 13, 2026

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Detection of MicroRNAs in Microglia by Real-time PCR in Normal CNS and During Neuroinflammation
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Detection of MicroRNAs in Microglia by Real-time PCR in Normal CNS and During Neuroinflammation

Published on: July 23, 2012

Area of Science:

  • Neuroimmunology
  • Genomics
  • Bioinformatics

Background:

  • Multiple sclerosis (MS) is a leading cause of neurological disability in young adults.
  • Genetic variants are implicated in MS pathogenesis, but molecular mechanisms require further elucidation.
  • Gene expression profiling offers insights into disease complexity.

Purpose of the Study:

  • To analyze the molecular mechanisms underlying multiple sclerosis (MS).
  • To identify key genes and regulatory factors involved in MS pathogenesis.
  • To leverage bioinformatics to understand gene expression alterations in MS.

Main Methods:

  • Downloaded gene expression data for MS from the Gene Expression Omnibus (GEO).
  • Utilized differentially coexpressed genes (DCGs) analysis and bioinformatics tools.
  • Applied the regulatory impact factor (RIF) algorithm to assess transcription factor roles.

Main Results:

  • Identified 1,297 differentially coexpressed genes (DCGs) between MS patients and healthy controls.
  • Functional annotation linked DCGs to immune and neurological functions.
  • IKZF1, BACH1, CEBPB, EGR1, and FOS were identified as potential key regulatory transcription factors.

Conclusions:

  • Confirmed multiple molecular alterations in the pathogenesis of MS.
  • Highlighted the potential of identified genes and transcription factors as prognostic markers.
  • Emphasized the role of bioinformatics in unraveling complex disease mechanisms.