Cervical cancer (CC) is a common gynecological disease that seriously threatens women's health. This study aims to explore key genes and pathways related to CC prognosis through bioinformatics, providing new insights for further treatment of CC. CC patient data were analyzed from the public databases. The enrichment analyses explored the roles and pathways of CC-related differentially expressed genes (DEGs). A prognostic key gene was identified using Venn diagrams and subjected to survival analysis. Gene Set Enrichment Analysis (GSEA) was employed to investigate the potential pathways of key genes. Correlations between the key gene and clinical features were examined. The function of the key gene was validated through immunohistochemistry, flow cytometry, transwell, MTT, and Western blot assays in vitro and in vivo. Our research identified 2459 upregulated genes among DEGs between healthy and tumor cervical tissues. These DEGs were primarily enriched in the PI3K-AKT and MAPK pathways. Moreover, EFNA1 was recognized as a key prognostic gene in CC, with elevated expression compared to normal tissue. A negative correlation between EFNA1 levels and patient survival rates was corroborated by Kaplan-Meier analysis. Furthermore, EFNA1 expression correlated with the cancer stage and was linked to antigen presentation, folate synthesis, and IL-17 signaling. Knockdown of EFNA1 enhanced apoptosis and reduced migration, invasion, and proliferation in vitro and in vivo, inhibiting EMT and MAPK pathways. This study revealed the key signaling pathways in CC progression and identified EFNA1 as a crucial prognostic biomarker, potentially impacting CC treatment.