Abstract
Cervical cancer is one of the most common gynecological cancers in women and its molecular pathogenesis and disease progression are yet fully understood. Finding new biomarkers is important to detect early diagnosis of cervical cancer to reduce its incidence and mortality rates among women. We have selected 3 microarray gene expression datasets (GSE67522, GSE138080, and GSE75132) and then analysed up-regulated and down-regulated genes in cervical cancer. The hub genes related to this disease were identified from constructed protein-protein interaction network. The statistical significance of key genes was validated with experimental data obtained from TCGA cervical cancer patients. We identified 18 differentially expressed genes from 3 microarray datasets. These genes were highly associated with DNA replication and cell proliferation pathways. Network analysis revealed that CDKN2A as a biomarker for cervical cancer prognosis. The expression and interactions of this gene were analysed with bioinformatic tools. Results of this study showed that CDKN2A has significant interactions with transcription factors, signalling molecules, and miRNAs. In-silico analysis of microarray data can pave the way to predict CDKN2A as a gene target for the diagnosis of cervical cancer. Early diagnosis of this disease would decrease morbidity and mortality among females worldwide.
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