Abstract
Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.
Highlights
Breast cancer, which affects approximately one of every nine women globally, is one of the most widespread cancers among female malignant tumors [1]
Using the edgeR package, with threshold values of adjusted P-value (FDR)
A total of 6315 DEGs were identified by the six pairwise comparisons of the samples from the luminal A subtype, the luminal B subtype, the her2 positive subtype, and the basal-like subtype of breast cancer (Table 1; Supplementary Figure S1)
Summary
Breast cancer, which affects approximately one of every nine women globally, is one of the most widespread cancers among female malignant tumors [1]. It is well known that breast cancer has high heterogeneity at the molecular level. According to the PAM50 molecular typing model, there are luminal A subtype, luminal B subtype, human epidermal growth factor receptor 2 (her2) positive subtype, and basal-like breast cancer subtypes [2,3]. Breast cancer is a complex disease with changed genetic and molecular characteristics. Whole genomic analysis is one of the most efficient ways of studying the diseases. Many researchers focus on prognostic or therapeutic markers for identification using differential expression analysis. In addition to differential gene expression, gene co-expression networks have been extensively explored for high-throughput sequencing data analysis [4]. One of the most frequently applied gene co-expression analysis methods is Weighted gene co-expression network analysis (WGCNA) which can explore the patterns of co-expressed genes from
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