Construction and Validation of a Novel Prognostic Model Based on Cervical Cancer-Related Genes.

BACKGROUND

Cervical cancer (CC) is the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths in women worldwide, however, the treatment options for advanced CC are limited. Therefore, there is an urgent need in the clinic for reliable prognostic models to guide clinical decision-making.

METHODS

We conducted differential gene expression analysis on cervical cancer samples and normal samples to obtain differentially expressed genes (DEGs). We used WGCNA analysis to identify the most relevant module associated with cervical cancer and intersected with DEGs to obtain cervical cancer-related genes. We then constructed a protein-protein interaction (PPI) network using these genes and identified core genes using the Hubba plugin in Cytoscape software. Subsequently, we built a prognostic model using the identified cervical cancer-related genes in combination with the TCGA database. GSE44001 was used to verify the accuracy of the model. We performed a single-gene survival analysis on the genes involved in model construction.

RESULTS

We obtained 52 cervical cancer-related genes and 22 core genes (DNA2, CEP55, GINS1, RFC4, KIF14, GINS2, MYBL2, KIF4A, RAD54L, KNTC1, SPAG5, MELK, CENPE, MCM2, NCAPH, MCM5, ASPM, HELLS, DTL, FOXM1, TOP2A, CDC45). We successfully constructed a prognostic model using cervical cancer-related genes. The comprehensive analysis showed that the constructed prognostic model could effectively predict the prognosis of cervical cancer patients, with AUC values of 0.858, 0.802, and 0.797 for 1, 3, and 5 years in the training group, respectively. The results were consistent in the validation using the GSE44001 dataset. Single-gene survival analysis showed that APOD was an independent prognostic biomarker for cervical cancer.

CONCLUSION

APOD is a prognostic biomarker for cervical cancer, and the prognostic model constructed by identified cervical cancer-related genes can successfully distinguish the prognosis of patients with cervical cancer.