PURPOSE: Cervical carcinoma is the second most prevalent and the fifth most deadly malignancy seen in women worldwide. Dysregulated activation of EGF ErbB system has been implicated in diverse types of human cancer; however, it is elusive how it is regulated in human cervical cancer cells. We herein aimed to explore the mechanisms of cervical carcinoma response to epidermal growth factor (EGF), with a view of the pathways activated by EGF. METHODS: Using the GSE6783 affymetrix microarray data accessible from gene expression omnibus database, we first identified the differentially expressed genes between EGF-stimulated and -unstimulated samples. Then we constructed a regulation network and identified the network motifs. We also performed biological process and pathway enrichment analyses to functionally classify the genes in the regulation network. RESULTS: A total of 11 network motifs were identified in the regulation network. EGF treatment could increase the risk of cancer via dysregulation of cancer-related pathways and immune response pathways. CONCLUSIONS: Network motif analysis is useful in mining the useful information underlying the network. We hope our work could serve as a basis for further experimentation.
PURPOSE:Cervical carcinoma is the second most prevalent and the fifth most deadly malignancy seen in women worldwide. Dysregulated activation of EGF ErbB system has been implicated in diverse types of humancancer; however, it is elusive how it is regulated in human cervical cancer cells. We herein aimed to explore the mechanisms of cervical carcinoma response to epidermal growth factor (EGF), with a view of the pathways activated by EGF. METHODS: Using the GSE6783 affymetrix microarray data accessible from gene expression omnibus database, we first identified the differentially expressed genes between EGF-stimulated and -unstimulated samples. Then we constructed a regulation network and identified the network motifs. We also performed biological process and pathway enrichment analyses to functionally classify the genes in the regulation network. RESULTS: A total of 11 network motifs were identified in the regulation network. EGF treatment could increase the risk of cancer via dysregulation of cancer-related pathways and immune response pathways. CONCLUSIONS: Network motif analysis is useful in mining the useful information underlying the network. We hope our work could serve as a basis for further experimentation.