Lv Qing, Liu Yansong, Huang Hu, Zhu Mingjie, Wu Junqiang, Meng Dong
Department of Breast Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China.
Department of Breast Surgery, Tumor Hospital of Mudanjiang City, Mudanjiang, Heilongjiang, People's Republic of China.
Onco Targets Ther. 2020 Jun 12;13:5541-5550. doi: 10.2147/OTT.S255300. eCollection 2020.
Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognosis, and the pathogenesis of this life-threatening disease is yet to be fully elucidated. This study aims to identify key genes of IBC, which could be potential diagnostic or therapeutic targets.
Four datasets GSE5847, GSE22597, GSE23720, and GSE45581 were downloaded from the Gene Expression Omnibus (GEO) and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential bio-functions of the differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed for functional modules analysis and hub genes identification, and TCGA survival analysis and qRT-PCR of clinical samples were used to further explore and validate the effect of hub genes on IBC.
A total of 114 DEGs were identified from the GEO datasets. GO and KEGG analyses showed that the DEGs were mainly enriched in oncogenesis and cell adhesion. From the PPI network, we screened out five hub genes, including , and . Survival analysis and expression validation verified the robustness of the hub genes.
The present study provides new insight into the understanding of IBC pathogenesis and the identified hub genes may serve as potential targets for diagnosis and treatment.
炎性乳腺癌(IBC)是一种预后较差的罕见乳腺癌类型,这种危及生命的疾病的发病机制尚未完全阐明。本研究旨在鉴定IBC的关键基因,这些基因可能是潜在的诊断或治疗靶点。
从基因表达综合数据库(GEO)下载了四个数据集GSE5847、GSE22597、GSE23720和GSE45581,并进行了差异表达分析。进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析,以了解差异表达基因(DEG)的潜在生物功能。构建蛋白质-蛋白质相互作用(PPI)网络用于功能模块分析和枢纽基因鉴定,并使用TCGA生存分析和临床样本的qRT-PCR进一步探索和验证枢纽基因对IBC的影响。
从GEO数据集中共鉴定出114个DEG。GO和KEGG分析表明,DEG主要富集于肿瘤发生和细胞粘附。从PPI网络中,我们筛选出五个枢纽基因,包括 , 和 。生存分析和表达验证证实了枢纽基因的稳健性。
本研究为理解IBC发病机制提供了新的见解,鉴定出的枢纽基因可能作为诊断和治疗的潜在靶点。