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Related Concept Videos

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Cancer

Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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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.
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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.
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Updated: May 9, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
07:47

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

Cancer markers: integratively annotated classification.

M Orsini1, A Travaglione, E Capobianco

  • 1CRS4 Bioinformatics Laboratory, Polaris, Pula (CA), Italy.

Gene
|August 10, 2013
PubMed
Summary
This summary is machine-generated.

This study proposes a new method for classifying cancer markers by integrating multiple data sources. This approach aids in identifying diagnostic and prognostic markers for colorectal cancer, improving clinical applications.

Keywords:
AEAdenocarcinoma versus NormalAdenoma versus CarcinomaAdenoma versus NormalAmino acid derivative processArrayExpressCFClinical factorsClinical re-annotationColon carcinoma versus NormalD-caD-cbE-cExposed versus Non-exposedGEOGene Expression OmnibusMarker classificationNOINon-empty intersectionsP-caP-cbP-ccS-cStage II recurrence versus Stage II recurrence freeTCGAThe Cancer Genome AtlasTranslational cancer genomicsTreated versus Non-treatedacdp

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

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Translational cancer genomics integrates experimental data with omics and clinical records.
  • Data retrieval from diverse sources is challenging due to heterogeneity and redundancy.
  • In silico marker identification is complex but crucial for clinical validation.

Purpose of the Study:

  • To propose a cancer marker classification method using publicly available gene expression data.
  • To integrate prediction power from multiple annotation sources for enhanced accuracy.
  • To classify colorectal cancer markers into diagnostic, prognostic, susceptibility, and risk categories.

Main Methods:

  • Leveraging publicly available gene expression datasets.
  • Developing a computational framework for integrating multiple annotation sources.
  • Applying the framework to functional annotation of colorectal cancer markers.

Main Results:

  • Successfully classified colorectal cancer markers.
  • Identified markers belonging to diagnostic, prognostic, susceptibility, and risk factor categories.
  • Demonstrated the feasibility of integrating diverse data for marker classification.

Conclusions:

  • The proposed method offers a robust approach to cancer marker classification.
  • Integrating multiple annotation sources improves the prediction power for clinical applications.
  • This classification aids in understanding colorectal cancer pathogenesis and developing targeted therapies.