Abstract
BACKGROUND: Human papillomavirus (HPV) infection, especially high-risk types like HPV16 and HPV18, is a primary cause of cervical cancer. The p53 gene influences cellular response to DNA damage and has a functional polymorphism (rs1042522, p.Arg72Pro) that affects susceptibility to degradation by HPV E6 protein. This study aims to analyze the relationship among p53 genotypes, high-risk HPV infection, and hematological parameters in cervical cancer development and to develop a predictive model. METHODS: This retrospective cross-sectional study collected cervical cancer specimens and brush samples from patients at the First People's Hospital of Changzhou between January 2020 and August 2024. HPV types and p53 genotyping were performed using PCR. Inflammatory markers like neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and platelet-to-lymphocyte ratio (PLR) were calculated. Statistical analyses including logistic regression and LASSO were used to construct a predictive model. RESULTS: The study included 147 female patients with cervical cancer and controls. HPV16 and HPV18 had high infection rates. In the log-additive model, each additional p53 C allele reduced the risk by 48% (OR = 0.52, 95% CI: 0.27-0.98, P = 0.038). Significant interactions were found between p53 genotypes and HPV18 infection on cervical cancer risk (P = 0.026). Cervical cancer patients showed reduced red blood cell count and hemoglobin. The predictive model, including p53 genotype, HPV16, HPV18, and hematological parameters, had an AUC of 0.920 (95% CI: 0.875-0.965). CONCLUSION: The study identified significant differences in p53 genotypes, HPV infection, and hematological parameters between cervical cancer patients and controls. The predictive model demonstrated high discriminatory ability for cervical cancer risk assessment. The interaction between HPV18 and p53 genotypes suggests a potential protective effect of the p53 C allele. Larger studies are needed to validate these findings.