Identification of Vesicle-Mediated Transport-Related Genes for Predicting Prognosis, Immunotherapy Response, and Drug Screening in Cervical Cancer.

Background: Cervical cancer is one of the most common malignancies among women. Vesicle-mediated transport mechanisms significantly influence tumor cell behavior through intercellular material exchange. However, prognostic significance in CC patients remains underexplored.

Research Design And Methods: We identified differentially expressed vesicle-mediated transport-related genes from TCGA and GeneCards datasets through differential expression analysis. We constructed a prognostic model using Cox regression and LASSO regression, categorized patients into high- and low-risk groups, and validated the model in the GEO data set. A nomogram integrating clinical features and risk scores demonstrated the model's independent prognostic capability. We analyzed tumor immune cell infiltration, immune checkpoints, and predicted immunotherapy responses in the high- and low-risk groups. Finally, we screened potential drugs for targeting CC and conducted drug-sensitivity analysis.

Results: We successfully established a 10-gene prognostic model based on VMTRGs. The low-risk group exhibited favorable prognosis, significant immune cell infiltration, and promising immunotherapy response, whereas the high-risk group showed higher sensitivity to chemotherapeutic agents such as Docetaxel and Paclitaxel. Potential drugs identified for targeting CC patients included Megestrol acetate, Lenvatinib, Adavosertib, and Barasertib.

Conclusions: The VMTRG-based prognostic model demonstrates reliable clinical prognostic value and enhances understanding of vesicle-mediated transport mechanisms in CC.