Abstract
Breast cancer is the most frequently diagnosed malignancy among women, and triple-negative breast cancer (TNBC) is a highly aggressive subtype. Increasing evidence has shown that lncRNAs are involved in tumor growth, cell-cycle, and apoptosis through interactions with miRNAs or mRNAs. However, there is still limited data on ceRNAs involved in the molecular mechanisms underlying TNBC. In this study, we applied the weighted gene co-expression network analysis to the existing microarray mRNA and lncRNA expression data obtained from the breast tissues of TNBC patients to find the hub genes and lncRNAs involved in TNBC. Functional enrichment was performed on the module that correlated with Ki-67 status the most (Turquoise module). The hub genes in the Turquoise module were found to be associated with DNA repair, cell proliferation, and the p53 signaling pathway. We performed co-expression analysis of the protein-coding and lncRNA hub genes in the Turquoise module. Analysis of the RNA-seq data obtained from The Cancer Genome Atlas database revealed that the protein-coding genes and lncRNAs that were co-expressed were also differentially expressed in the TNBC tissues compared with the normal mammary tissues. On the basis of establishing the ceRNA network, two mRNAs (RAD51AP1 and TYMS) were found to be correlated with overall survival in TNBC. These results suggest that TNBC-specific mRNA and lncRNAs may participate in a complex ceRNA network, which represents a potential therapeutic target for the treatment of TNBC.
Highlights
Breast cancer is the fifth leading cause of death and most frequently diagnosed malignancy in women worldwide[1]
By the dynamic tree cut method, 3 co-expressed gene modules were identified in the aggregate, and each module was marked by a different color (Fig. 3B)
Each module contained a group of Long noncoding RNAs (lncRNAs) that were coordinately expressed and had a high topological overlap matrix (TOM), and they potentially involved in similar biological processes
Summary
Breast cancer is the fifth leading cause of death and most frequently diagnosed malignancy in women worldwide[1] It is characterized by at least four different clinically relevant molecular subtypes: Luminal A, Luminal B, her2-enriched type, and triple negative breast cancer (TNBC)[2]. WGCNA can be used to study biological networks based on genetic correlations It identifies modules (clusters) of highly correlated genes[14]. WGCNA can identify candidate biomarkers and therapeutic targets for different types of cancer[15,16,17,18]. We applied WGCNA in combination with functional enrichment analysis to the available TNBC mRNA and lncRNA expression data to identify the hub genes, including lncRNAs involved in TNBC.
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