Abstract
This study developed a prognostic signature for cervical cancer using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and TISCH database, focusing on cancer-associated fibroblasts (CAFs). Through LASSO Cox regression and integrated bioinformatics analyses, we identified 144 differentially expressed genes (DEGs) related to CAFs, from which an 11-gene CAF-related signature (CAFRSig) was constructed. The CAFRSig effectively stratified patients into high- and low-risk categories, demonstrating significant prognostic capability in predicting overall survival. Gene ontology (GO) and gene set variation analysis (GSVA) linked the DEGs to crucial pathways in tumor malignancy, immune response, and fatty acid metabolism. The immune landscape analysis, utilizing the TIMER platform and CIBERSORT algorithm, revealed a positive correlation between immune cell effector functions and CAFRSig scores, highlighting the model's potential to identify patients likely to respond to immune checkpoint blockade (ICB) therapies. Furthermore, neuropilin 1 (NRP1), a key gene in the CAFRSig, was upregulated in cervical cancer tissues and associated with disease progression and differentiation. The downregulation of NRP1 curbed cell proliferation and influenced the epithelial-mesenchymal transition (EMT), implicating the PI3K/AKT pathway and modulating PD-L1 expression. This comprehensive analysis establishes a robust prognostic signature based on CAF-related genes, offering valuable insights for optimizing therapeutic strategies in cervical cancer management.