Analyses of BMAL1 and PER2 Oscillations in a Model of Breast Cancer Progression Reveal Changes With Malignancy

每2 昼夜节律 乳腺癌 癌症 恶性肿瘤 生物 疾病 生物钟 句号(音乐) 内科学 肿瘤科 癌症研究 医学 内分泌学 时钟 物理 声学
作者
Hui-Hsien Lin,Maan Qraitem,Yue Lian,Stephanie R. Taylor,Michelle E. Farkas
出处
期刊:Integrative Cancer Therapies [SAGE]
卷期号:18 被引量:23
标识
DOI:10.1177/1534735419836494
摘要

From an epidemiological standpoint, disruptions to circadian rhythms have been shown to contribute to the development of various disease pathologies, including breast cancer. However, it is unclear how altered circadian rhythms are related to malignant transformations at the molecular level. In this article, a series of isogenic breast cancer cells representing disease progression was used to investigate the expression patterns of core circadian clock proteins BMAL1 and PER2. Our model is indicative of 4 stages of breast cancer and includes the following cells: MCF10A (non-malignant), MCF10AT.Cl2 (pre-malignant), MCF10Ca1h (well-differentiated, malignant), and MCF10Ca1a (poorly differentiated, malignant). While studies of circadian rhythms in cancer typically use low-resolution reverse transcription polymerase chain reaction assays, we also employed luciferase reporters BMAL1:Luc and PER2:Luc in real-time luminometry experiments. We found that across all 4 cancer stages, PER2 showed relatively stable oscillations compared with BMAL1. Period estimation using both wavelet-based and damped-sine-fitting methods showed that the periods are distributed over a wide circadian range and there is no clear progression in mean period as cancer severity progresses. Additionally, we used the K-nearest neighbors algorithm to classify the recordings according to cancer line, and found that cancer stages were largely differentiated from one another. Taken together, our data support that there are circadian discrepancies between normal and malignant cells, but it is difficult and insufficient to singularly use period evaluations to differentiate them. Future studies should employ other progressive disease models to determine whether these findings are representative across cancer types or are specific to this series.

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