The cyclin D1-CDK4 oncogenic interactome enables identification of potential novel oncogenes and clinical prognosis

Siwanon Jirawatnotai1, Samanta Sharma, Wojciech Michowski

  • 1a Department of Pharmacology; Faculty of Medicine Siriraj Hospital ; Mahidol University ; Bangkok , Thailand.

Insights

Researchers mapped the protein interactions of cyclin D1 and CDK4 in breast cancer cells. They identified BAIAPL21 as a potential oncogene and developed an Aggregate Expression Score (AES) for cancer prognosis.

Area of Science:

  • Oncology
  • Molecular Biology
  • Cancer Research

Background:

  • Overexpression of cyclin D1 and CDK4 is common in human cancers.
  • Understanding protein interactions is crucial for identifying cancer drivers and developing new therapies.

Purpose of the Study:

  • To construct the cyclin D1 and CDK4 protein interaction network in MCF7 breast cancer cells.
  • To identify novel CDK4 interactors and potential oncogenes.
  • To assess the prognostic value of cyclin D1 interactome expression in human cancers.

Main Methods:

  • Protein-protein interaction network construction using MCF7 cells.
  • Identification and validation of novel protein partners.
  • Integrative analysis of interactome data with somatic copy number alterations.
  • Assessment of BAIAPL21 oncogenic potential through overexpression studies.
  • Development and validation of an Aggregate Expression Score (AES).

Main Results:

  • Novel CDK4 protein partners, including FKBP5, were identified, with FKBP5 maintaining CDK4 levels.
  • BAIAPL21 was identified as a potential oncogene, showing increased expression in tumors and enhancing cancer cell growth and tumor formation in vivo.
  • The Aggregate Expression Score (AES) demonstrated prognostic value in ER-positive breast cancers.

Conclusions:

  • Protein interactome analysis is a valuable strategy for discovering novel oncogenes and improving cancer prognosis.
  • BAIAPL21 represents a potential therapeutic target and biomarker.
  • The AES provides a novel tool for predicting patient outcomes in specific breast cancer subtypes.

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