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Related Experiment Video

Updated: Sep 25, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Transcriptomic Signatures in Colorectal Cancer Progression.

Pavel Ershov1, Stanislav Poyarkov1, Yulia Konstantinova2

  • 1Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia.

Current Molecular Medicine
|May 1, 2022
PubMed
Summary

This review identifies 28 core hub-genes critical for colorectal cancer (CRC) progression, offering insights into signaling pathways and potential therapeutic targets for this metastatic disease.

Keywords:
Transcriptomicscolorectal cancerdifferential expressionhub-genesnetworksprognostic valueprotein-protein interactions

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Area of Science:

  • Bioinformatics and Computational Biology
  • Molecular Oncology
  • Genomics and Transcriptomics

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer mortality with high metastatic potential.
  • Understanding CRC pathogenesis and identifying biomarkers are crucial for clinical advancement.

Approach:

  • Systematic review and analysis of bioinformatics and experimental data.
  • Identification and consolidation of hub-genes associated with CRC progression.
  • Network analysis to elucidate upstream regulatory mechanisms.

Key Points:

  • 301 hub-genes identified across 40 studies, with a core set of 28 significant genes.
  • Two distinct clusters identified: chemokine signaling and cell cycle regulation.
  • Specific hub-genes (e.g., BGN, TIMP1, CCNB1) linked to patient survival and CRC metastasis.
  • Upstream regulators like RELA, STAT3, and FOXM1 identified through network analysis.

Conclusions:

  • Provides fundamental insights into CRC pathogenicity and molecular mechanisms.
  • Highlights potential cellular targets for optimizing therapeutic interventions.
  • Supports the development of transcriptomics-based prognostic and predictive biomarkers for CRC.