Identification of hub genes as potential diagnostic biomarkers for cervical cancer: A bioinformatic approach

Cervical cancer remains a prevalent malignancy with rising incidence, primarily due to sexual transmission, persistent HPV infection, and delayed screening. Identifying new biomarkers is critical for improved diagnosis, prognosis, and treatment of cervical cancer. This study utilized integrated bioinformatics to identify potential biomarkers by analysing gene expression data from the GEO database.

Four GEO microarray datasets (GSE7410, GSE7803, GSE52903, GSE67522) were analysed using GEO2R to identify DEGs with an adjusted p-value <0.05. Common DEGs were visualized using Venn diagrams. Protein-protein interaction network was constructed using STRING to identify hub genes. Gene Ontology (GO) and KEGG pathway analyses were performed to investigate biological functions and pathways. The Human Protein Atlas (HPA) was used for in silico validation of protein expression via immunohistochemistry. Kaplan-Meier survival analysis was performed to determine the prognostic value of hub genes.

Analysis revealed 684 common DEGs across the datasets (446 upregulated, 238 downregulated). The top 20 upregulated DEGs from GSE67522 were used for heatmap construction and PPI analysis, leading to the identification of nine key hub genes. GO and KEGG analyses showed that six of these were significantly involved in cell cycle regulation and tumorigenic pathways. These hub genes were validated for their protein expression through HPA data.

Six hub genes (CCNB2, AURKA, CDC20, CDT1, CENPF, and KIF2C) were identified as potential biomarkers for cervical cancer management.

These findings provide valuable insight into the molecular profiles of genes that play significant roles in cervical cancer for translational outcomes in diagnosis.