Abstract
BACKGROUND: Cervical cancer (CC) remains a prevalent malignancy with significant mortality among women, highlighting the urgent need for reliable prognostic tools. While chemokines have emerged as pivotal regulators in tumor progression, their potential in constructing prognostic models for CC remains underexplored. This study aimed to develop a chemokine-related gene signature for outcome prediction and therapeutic guidance in CC patients. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) cervical cancer cohort were analyzed to identify differentially expressed chemokine-related genes. Prognostic genes were screened through univariate Cox, multivariate Cox, and LASSO regression analyses, followed by the development of a risk stratification model. The model's clinical relevance was evaluated by assessing its correlations with clinicopathological features, immune profiles, pathway enrichment, and therapeutic responses. A nomogram integrating risk scores and clinical parameters was constructed for survival prediction. RESULTS: A nine-gene signature (CCL17, CXCL8, TNF, FOXP3, CXCL1, CCL20, ITGA5, CXCL3, CCR7) was established as an independent prognostic indicator. Kaplan-Meier analysis revealed significantly shorter overall survival (OS) and progression-free survival (PFS) in high-risk patients compared to low-risk counterparts (P < 0.05). Multivariate Cox regression confirmed the signature's independence from conventional clinical variables (P < 0.05). The nomogram demonstrated robust predictive accuracy, with 1-, 3-, and 5-year survival AUC values of 0.805, 0.729, and 0.710, respectively. Distinct immune cell infiltration patterns were observed between risk groups, with low-risk patients exhibiting enhanced potential for immunotherapy and chemotherapy responsiveness. CONCLUSION: This study presents a clinically applicable prognostic model based on chemokine-related genes, providing insights for risk stratification and therapeutic decision-making in CC. Further validation through multicenter cohorts and mechanistic investigations of the identified genes are warranted to advance precision oncology strategies.