Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC. Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 cervical cancer samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and KEGG pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and HPA database to exhibit the differences in gene and protein levels of candidate genes. Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNMT1, CHAF1B, CHAF1A, MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA databases and qRT-PCR confirmed that these genes were highly expressed in CC tissues. Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and KNTC1, as potential diagnostic and prognostic biomarkers of CC.

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