基于公共数据集的三阴性乳腺癌转录组学分析

1)浙江工业大学药学院,浙江杭州310014; 2)义乌市中心医院药剂科,浙江义乌322099

转录组学; 三阴性乳腺癌; 公共数据库; 基因芯片; 生物信息学分析; 病理标志物

Transcriptome analysis of triple negative breast cancer based on public database
GONG Junjie1,2, WANG Ping1, and XU Zijin1

1)College of Pharmacy, Zhejiang University of Technology, Hangzhou 310014, Zhejiang Province, P.R.China;2)Department of Pharmacy, Yiwu Central Hospital,Yiwu 322099, Zhejiang Province, P.R.China

transcriptomics; triple negative breast cancer; public database; gene chip; bioinformatics analysis; pathological marker

DOI: 10.3724/SP.J.1249.2021.05517

备注

三阴性乳腺癌(triple negative breast cancer, TNBC)是乳腺癌中恶性程度较高的一种病理分型,因其病理呈现雌激素受体、孕激素受体和人类表皮生长因子受体2均为阴性的特殊性,使得目前药物治疗效果不甚理想,探究与TNBC病变过程相关的病理标志物是当前乳腺癌研究领域的热点之一.通过高通量基因表达(gene expression omnibus, GEO)数据库获取TNBC相关的基因集,并进行主成分分析、差异基因筛选、基因功能和信号通路的富集分析、蛋白互作网络构建以及加权基因共表达网络分析等转录组学分析.结果表明,TNBC和非TNBC具有明显不同的基因表达模式,这些差异基因主要富集于表皮生长和分化、乳腺上皮细胞生长和发育等相关生物学过程,还富集于转录因子结合相关分子功能与雌激素信号转导相关通路.整合蛋白互作网络和共表达网络的结果,可以预测TFF1、 FOXA1、 AGR2和AGR3可能是涉及TNBC病理表型的关键基因.研究结果为揭示TNBC潜在的病理标志物以及由此衍生的疾病诊疗靶标或分子调控机制,提供一定的理论研究依据.
Triple negative breast cancer(TNBC)is a pathological type with high malignancy in breast cancer. Because the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 appear histologically negative, the treatment effect of the existing drug is not ideal. Exploring the TNBC related pathological markers is one of hot spots in the research field of breast cancer. TNBC related gene sets are obtained from gene expression omnibus(GEO)database for transcriptome analysis, including principal component analysis, screening differentially expressed genes(DEGs), gene function enrichment analysis and signal pathway enrichment analysis, protein interaction network construction, weighted gene coexpression network analysis(WGCNA), etc. The results show that there are significantly differentially expressed genes(DEGs)between TNBC and non TNBC population. These DEGs are mainly enriched in the biological processes related to epidermal growth and differentiation, growth and development of mammary epithelial cells, and in molecular functions related to transcription factor binding and estrogen signal transduction pathways. The results of protein interaction network and weighted gene coexpression network analysis are integrated and TFF1, FOXA1, AGR2 and AGR3 are predicted as the key genes involved in the pathological phenotype of TNBC. The study of transcriptome analysis based on TNBC gene set in public database may provide certain theoretical research basis to reveal the potential biomarkers for pathogenesis of TNBC, and the derived disease diagnosis and therapeutic targets or molecular regulation mechanisms.
·