Objective: Cervical cancer is the fourth most common cancer in women worldwide. Therefore, it is necessary to research and propose new diagnostic, prognostic, and therapeutic biomarkers that decrease the high incidence and mortality rates. In this study, we performed bioinformatic analysis using a public dataset to investigate genes as potential diagnostic and prognostic biomarkers.

Materials and Methods: Differentially expressed genes (DEGs) were identified using GSE63678 and GSE7410 datasets in the GEO2R database. Validation of DEGs was performed in Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas (HPA) databases. Enrichment, survival, and Receiver Operating Characteristic (ROC) analyses were performed in Enrichr, Kaplan Meier Plotter, and easyROC software, respectively. Expression according to FIGO stages and copy number were analyzed in GEPIA and cBioportal databases. Methylation was analyzed in the DiseaseMeth database.

Results: We identified 485 DEGs involved in several pathways, including central carbon metabolism. The high Solute carrier family 2, facilitated glucose transporter member 1 (SLC2A1), L-lactate dehydrogenase A chain (LDHA), and Hexokinase-2 (HK2) expression. Also, the low Fibroblast growth factor receptor 2 (FGFR2) expression correlates with poor survival. LDHA and FGFR2 methylation are associated with cervical cancer.

Conclusions: Our results suggested that SLC2A1, LDHA, HK2, and FGFR2 expression could be useful as prognostic biomarkers, while the SLC2A1 and HK2 expression could be good diagnostic biomarkers.