Abstract
Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets. Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan–Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer. Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.
Highlights
Breast cancer is the most common non-cutaneous malignancy in American women, which has an estimated 268,600 new cases and 41,760 deaths in 2019, representing 30% of all new cancer cases and 15% of cancer-related deaths (Gradishar et al, 2018; Siegel et al, 2019)
A total of 321 differentially expressed genes (DEGs) including 203 up-regulated (Figure 1G) and 118 down-regulated genes (Figure 1H) identified in the discovery phase were confirmed in the the Cancer Genome Atlas (TCGA) dataset, resulting in an 89.2% consistency between the discovery and validation analysis
We have discussed that high expression of six hub genes is involved in the development of breast cancer and associated with worse overall survival (OS), suggesting that these hub genes may serve as potential prognostic biomarkers and therapeutic targets for breast cancer
Summary
Breast cancer is the most common non-cutaneous malignancy in American women, which has an estimated 268,600 new cases and 41,760 deaths in 2019, representing 30% of all new cancer cases and 15% of cancer-related deaths (Gradishar et al, 2018; Siegel et al, 2019). A recent meta-analysis with a large cohort of TNBC cases has subclassified TNBC into at least four subtypes: basal-like immune-activated (BLIA), basal-like immunesuppressed (BLIS), luminal androgen receptor (LAR), and mesenchymal (MES) tumor (Lehmann et al, 2011; Burstein et al, 2015). This subclassification is further supported by the Cancer Genome Atlas (TCGA) Program through the analysis of mRNA, miRNA, DNA, and epigenetic profiles (Cancer Genome Atlas Network, 2012). Accumulating evidences have supported the hypothesis that these breast cancer subgroups share similar activated or repressed genes and common signaling pathways (Adamo et al, 2011; Dey et al, 2017). We aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets
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