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

Abstract

BackgroundCervical 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 MethodsWe 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.ResultsWe 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.ConclusionsThe VMTRG‐based prognostic model demonstrates reliable clinical prognostic value and enhances understanding of vesicle‐mediated transport mechanisms in CC.