Meta-Analysis of Cervical Cancer Transcriptome with a Network Approach to Identify Key Genes in the Disease

Cervical cancer is one of the most prevalent cancers among women. Accurate diagnosis and treatment of complex diseases require precise identification of molecular characteristics of the disease. Transcriptome profiles provide valuable information on gene expression of the studied cells. Applying meta-analysis approache along with network-based approaches provides precise and valuable information about studied data, which can be used in developing new diagnostic and therapeutic methods. The aim of this study was meta-analysis investigation of cervical cancer transcriptome using a network approach in order to identify key genes in the disease.
 

In the current study, three data-sets including 189 cervical cancer samples, were selected for cervical cancer. The data sets were analyzed separately and the results were integrated to obtain genes with the same expression pattern. These genes were used to construct gene regulatory network by STRING database. Network parameters (including degree and betweenness centrality), were used to explore key elements in the network, which are generalizable to cervical cancer.
 

In this study, 194 genes with same expression pattern, were identified in the three data-sets. Moreover, the obtained network analysis led to identification of 12 key genes in the gene expression regulatory network. Some of these genes have been previously reported as oncogenes and are involved in cell cycle regulation and DNA repair pathways.
 

According to the results of this study, these genes can be considered as the potential diagnostic and therapeutic markers for the treatment of cervical cancer.