Identification of key genes associated with cervical cancer based on bioinformatics analysis
Xinmeng Yang,
Mengsi Zhou,
Yingying Luan
et al.
Abstract:Background
Cervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis.
Methods
GSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING webs… Show more
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