Genes Relevant to Tissue Response to Cancer Therapy Display Diurnal Variation in mRNA Expression in Human Oral Mucosa

Fangyi Gu1, Eduardo Cortes Gomez2, Jianhong Chen1

  • 1Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, US.

Abstract

Insights

This study identified diurnal gene expression variations in oral mucosa, crucial for optimizing cancer chronotherapy timing. These findings suggest potential biomarkers for personalized cancer treatment schedules.

Area of Science:

  • Chronobiology
  • Cancer Therapeutics
  • Molecular Biology

Background:

  • Cancer chronotherapy requires precise treatment timing for individual patients.
  • Oral mucosa is susceptible to damage from cancer therapies, necessitating research into its diurnal variations.
  • Understanding gene expression patterns in oral tissue is critical for optimizing treatment schedules.

Purpose of the Study:

  • To characterize diurnal variations in gene expression within oral mucosal tissue.
  • To identify potential biomarkers for optimizing the timing of cancer therapy.
  • To address the gap in knowledge regarding the best time(s) for treating individual cancer patients.

Main Methods:

  • RNA sequencing (RNA-seq) was performed on oral mucosal samples from 11 healthy volunteers at 6 time points over 24 hours.
  • A cosine-based method was used to estimate individual and average peak gene expression times and amplitudes.
  • Correlations between gene expression peak times and age were analyzed, adjusting for sleep timing.

Main Results:

  • Several genes involved in circadian regulation (PER3, CIART, TEF, PER1, PER2, CRY2, ARNTL) and cell cycle regulation (WEE1) showed significant diurnal variation.
  • Average peak expression times varied by gene, with some showing substantial individual differences (e.g., WEE1).
  • Older individuals exhibited later peak expression times for PER1 and PER3.

Conclusions:

  • Oral mucosal gene expression exhibits diurnal variations relevant to cancer treatment response.
  • Identified genes (PER3, CIART, TEF, PER1, PER2, CRY2, ARNTL, WEE1) are potential candidates for developing biomarkers.
  • These biomarkers could enable non-invasive optimization of individual cancer therapy timing.

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