Identification of CDK2 as a key apoptotic gene for predicting cervical cancer prognosis using bioinformatics and...

This study aimed to identify apoptosis - related genes with diagnostic and prognostic value in cervical cancer (CC) using integrated bioinformatics and machine learning approaches.

Gene expression datasets were obtained from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), with GSE192897 used as the training set. A total of 451 differentially expressed genes (DEGs) were identified, including 221 upregulated and 230 downregulated genes. Eleven apoptosis - related upregulated DEGs were selected for further analysis using three machine learning algorithms: random forest, logistic regression, and support vector machine. Validation was performed using GSE192897, GSE166466, and TCGA-CESC datasets.

Among the evaluated genes, cyclin-dependent kinase 2 (CDK2) consistently achieved an AUC > 0.8 in all three validation datasets and had a weighted sum rank > 10, meeting stringent selection criteria. In a CC mouse model, CDK2 expression was significantly elevated and positively correlated with squamous cell carcinoma antigen, carcinoembryonic antigen, vascular endothelial growth factor, and heparanase. siRNA-mediated knockdown of CDK2 reduced cell proliferation and migration while promoting apoptosis. Mice with high CDK2 expression showed significantly lower 4-week survival rates, indicating poor prognosis.

This study identified CDK2 as a key apoptosis - related gene with strong diagnostic and prognostic value in cervical cancer. CDK2 promotes tumor progression and is associated with poor survival, suggesting its potential as a biomarker and therapeutic target for personalized treatment strategies in CC.