An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Sepideh Dashti,Soudeh Ghafouri-Fard,Mohammad Taheri

doi:10.1038/s41598-020-76024-2

Sepideh Dashti, Soudeh Ghafouri-Fard + Show 1 more

Open Access

https://doi.org/10.1038/s41598-020-76024-2

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Abstract

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

Highlights

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades
After correction, removing the batch effects and performing data normalization, 887 differentially expressed genes (DEGs) including 730 upregulated and 157 downregulated DEGs were screened between breast cancer and normal samples from GSE65194 and GSE45827 according to |logFC|> 2 and false discovery rate (FDR) < 0.01 as cut-off criteria
We used a bioinformatics strategy to identify key genes and signaling pathways in breast cancer pathogenesis with a focus on the role of long non-coding RNAs (lncRNAs) and their interactions with protein-coding genes. Such interactions can be assessed using experimental approaches which are costly and laborious. Bioinformatics methods for such purpose fall into two groups: strategies that use sequence, structural data and physicochemical features, and methods that are based on network construction

Summary

Introduction

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. Hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. Among the recently appreciated genes in this regard are long non-coding RNAs (lncRNAs)[6] These transcripts are involved in the regulation of fundamental cell survival pathways and have functional interactions with proteins and other non-coding RNAs that participate in the pathogenesis of breast c ancer[7]. Identification of such networks is an important step towards design of targeted therapies in breast cancer.

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