文章：

识别耐药性获得中的关键调控基因：乳腺癌单细胞转录组拟时序轨迹建模

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

原文发布日期：15 May 2024

DOI: 10.3390/cancers16101884

类型: Article

开放获取: 是

英文摘要：

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such asRPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

摘要翻译： 

单细胞RNA测序技术为在单细胞层面解析癌症耐药机制提供了重要见解。然而，在分子系统水平理解动态细胞状态转变仍存在局限，需要采用系统生物学研究方法。本研究提出一种整合数学模型与拟时序分析的方法，利用他莫昔芬处理的乳腺癌细胞系MCF-7的时间序列单细胞转录组数据进行分析。单细胞分析识别出五个特征鲜明的亚群，包括他莫昔芬敏感组与耐药组。通过单基因数学模型，我们在每个亚群约6000个基因中发现560-680个呈现多稳态表达特征的基因，其中包含RPS6KB1等雌激素受体阳性乳腺癌细胞存活关键基因。分岔分析揭示了这些基因的调控机制，并将其映射至与细胞存活及转移相关通路的分子网络中。本建模方法系统识别出获得性耐药的关键调控基因，深化了对乳腺癌潜在药物靶点的理解。

原文链接：

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome