Expression dynamics of periodic transcripts during cancer cell cycle progression and their correlation with

Chun-Xiao Li1,2, Jin-Song Wang1, Wen-Na Wang2

  • 1State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

Military Medical Research
|December 18, 2022
PubMed
Abstract

Insights

This study reveals periodic transcript patterns during the cell cycle, identifying specific alternative splicing variants that better predict cancer drug sensitivity than standard gene levels.

Area of Science:

  • Molecular Biology
  • Genomics
  • Cancer Research

Background:

  • The cell cycle is regulated by transcriptional mechanisms, with alternative splicing expanding this regulatory network.
  • Understanding cell cycle transcript isoform expression is crucial but currently unclear.
  • Current cancer therapies targeting cell cycle checkpoints lack evaluation at the alternative splicing transcript level.

Purpose of the Study:

  • To explore alternative splicing patterns during cell cycle progression.
  • To identify cell cycle-related transcripts and their correlation with drug sensitivity.
  • To evaluate the potential of alternative splicing transcripts as biomarkers for drug response.

Main Methods:

  • Performed synchronized transcriptomic assays in colon and breast cancer cell lines.
  • Developed a novel algorithm to identify periodic transcript patterns.
  • Utilized drug sensitivity and transcript datasets to assess gene and isoform correlations with drug response.
  • Conducted cytotoxicity assays to validate findings for specific transcripts.

Main Results:

  • Established high-resolution transcriptome datasets for synchronized cell cycle samples.
  • Identified thousands of periodically expressed transcripts across multiple cancer cell lines.
  • Found that many alternative splicing transcripts correlated better with drug sensitivity than their corresponding genes.
  • Demonstrated that specific transcripts, like ENST00000547281 for CDK4, are more associated with resistance to CDK4/6 inhibitors.

Conclusions:

  • Identified a panel of cell cycle-related periodic transcripts.
  • Demonstrated that transcript isoform levels offer distinct predictive values for drug sensitivity compared to gene levels.
  • Provided novel insights into alternative splicing's role in drug development and evaluation for cancer therapies.

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