Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

• Published in: Hereditas 2019-06, Vol.156 (1), p.20-20, Article 20
• Main Authors: Cai, Yun, Mei, Jie, Xiao, Zhuang, Xu, Bujie, Jiang, Xiaozheng, Zhang, Yongjie, Zhu, Yichao
• Format: Article
• Language: English
• Subjects: Algorithms; Bioinformatic analysis; Biomarkers; Biomarkers, Tumor; Breast cancer; breast neoplasms; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - mortality; Breast Neoplasms - pathology; breasts; Cancer therapies; Clinical medicine; Computational Biology - methods; computer simulation; disease diagnosis; Female; Gastric cancer; Gene expression; Gene Expression Profiling; gene expression regulation; Gene Expression Regulation, Neoplastic; genes; Genomes; Humans; Kinases; Metastases; Metastasis; Neoplasm Metastasis; neoplasm progression; Neoplasm Staging; patients; Prognosis; Prostate cancer; Protein interaction; Protein Interaction Mapping; Protein Interaction Maps; protein-protein interactions; Reproducibility of Results; risk; ROC Curve; Stem cells; Transcriptome; WGCNA
• ISSN: 1601-5223; 0018-0661; 1601-5223
• DOI: 10.1186/s41065-019-0096-6

Breast cancer is one of the most common endocrine cancers among females worldwide. Distant metastasis of breast cancer is causing an increasing number of breast cancer-related deaths. However, the potential mechanisms of metastasis and candidate biomarkers remain to be further explored. The gene expression profiles of GSE102484 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screen for the most potent gene modules associated with the metastatic risk of breast cancer, and a total of 12 modules were identified based on the analysis. In the most significant module (R  = 0.68), 21 network hub genes (MM > 0.90) were retained for further analyses. Next, protein-protein interaction (PPI) networks were used to further explore the biomarkers with the most interactions in gene modules. According to the PPI networks, five hub genes ( , and ) were identified as key genes associated with breast cancer progression. Furthermore, the prognostic value and differential expression of these genes were validated based on data from The Cancer Genome Atlas (TCGA) and Kaplan-Meier (KM) Plotter. Receiver operating characteristic (ROC) curve analysis revealed that the mRNA expression levels of these five hub genes showed excellent diagnostic value for breast cancer and adjacent tissues. Moreover, these five hub genes were significantly associated with worse distant metastasis-free survival (DMFS) in the patient cohort based on KM Plotter. Five hub genes ( , , , , and ) associated with the risk of distant metastasis were extracted for further research, which might be used as biomarkers to predict distant metastasis of breast cancer.