Integrated transcriptome and network analysis identifies EZH2/CCNB1/PPARG as prognostic factors in breast cancer - Li, Yalun; Chen, Gang; Zhang, Kun; Cao, Jianqiao; Zhao, Huishan; Cong, Yizi; Qiao, Guangdong (Frontiers in Genetics, 2023)
Breast cancer (BC) represents a cancer that is featured by high morbidity, relapse and mortality rates in women worldwide. Therefore, further exploring its pathogenesis is of great significance. This study aimed to select therapy genes and possible biomarkers to predict BC using bioinformatics methods. To this end, this study collected 21 healthy breasts along with 457 BC tissues in two Gene Expression Omnibus (GEO) datasets, and later identified differentially expressed genes (DEGs). Survival-associated DEGs were screened by adopting Kaplan-Meier curve. Based on Gene Ontology (GO) annotation, survival-associated DEGs were mostly associated with cell division and cellular response to hormone stimulus. The enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathway was mostly correlated with cell cycle and tyrosine metabolism. Using overlapped survival-associated DEGs, a survival-associated PPI network was constructed. Then, PPI analysis revealed three hub genes (EZH2, CCNB1, PPARG) by the degree of connection. By The Cancer Genome Atlas (TCGA)-BRCA dataset and BC tissue samples, these hub genes were confirmed. Through Gene Set Enrichment Analysis (GSEA), the molecular mechanism of the potential therapy and prognostic genes were evaluated. Thus, hub genes showed association with KEGG_CELL_CYCLE and VANTVEER_BREAST_CANCER_POOR_PROGNOSIS genesets. Finally, based on integrated bioinformatics analysis, this study identified three hub genes to be possible prognostic biomarkers and therapeutic targets for BC. These obtained results will contribute to further understanding the underground molecular mechanisms related to the BC occurrence and prognostic outcome. Key words Breast cancer, Bioinformatics, Prognosis biomarker, GEO, TCGA.