23 November 2019 : Clinical Research
Identification of Hub Genes Using Co-Expression Network Analysis in Breast Cancer as a Tool to Predict Different StagesYun Fu1ACE*, Qu-Zhi Zhou2BCD, Xiao-Lei Zhang3BC, Zhen-Zhen Wang4BF, Peng Wang1CDF
Med Sci Monit 2019; 25:8873-8890
BACKGROUND: Breast cancer has a high mortality rate and is the most common cancer of women worldwide. Our gene co-expression network analysis identified the genes closely related to the pathological stage of breast cancer.
MATERIAL AND METHODS: We performed weighted gene co-expression network analysis (WGCNA) from the Gene Expression Omnibus (GEO) database, and performed pathway enrichment analysis on genes from significant modules.
RESULTS: A non-metastatic sample (374) of breast cancer from GSE102484 was used to construct the gene co-expression network. All 49 hub genes have been shown to be upregulated, and 19 of the 49 hub genes are significantly upregulated in breast cancer tissue. The roles of the genes CASC5, CKAP2L, FAM83D, KIF18B, KIF23, SKA1, GINS1, CDCA5, and MCM6 in breast cancer are unclear, so in order to better reveal the staging of breast cancer markers, it is necessary to study those hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that 49 hub genes were enriched to sister chromatid cohesion, spindle midzone, microtubule motor activity, cell cycle, and something else. Additionally, there is an independent data set – GSE20685 – for module preservation analysis, survival analysis, and gene validation.
CONCLUSIONS: This study identified 49 hub genes that were associated with pathologic stage of breast cancer, 19 of which were significantly upregulated in breast cancer. Risk stratification, therapeutic decision making, and prognosis predication might be improved by our study results. This study provides new insights into biomarkers of breast cancer, which might influence the future direction of breast cancer research.
Keywords: Biological Markers, Genes, abl, Triple Negative Breast Neoplasms, Biomarkers, Tumor, Breast Neoplasms, China, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, gene ontology, Gene Regulatory Networks, Neoplasm Staging, Prognosis, Survival Analysis
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