Abstract
BACKGROUND: There is a close correlation between HPV infection and systemic immune status. The purpose of this study was to determine which lymphocytes in peripheral blood influence human papillomavirus (HPV) infection and to identify whether peripheral blood lymphocyte (PBL) subsets could be used as biomarkers to predict HPV clearance in the short term. METHODS: This study involved 716 women undergoing colposcopy from 2019 to 2021. Logistic and Cox regression were used to analyze the association of PBLs with HPV infection and clearance. Using Cox regression, bidirectional stepwise regression and the Akaike information criterion (AIC), lymphocyte prediction models were developed, with the C-index assessing performance. ROC analysis determined optimal cutoff values, and their accuracy for HPV clearance risk stratification was evaluated via Kaplan‒Meier and time-dependent ROC. Bootstrap resampling validated the model and cutoff values. RESULTS: Lower CD4 + T cells were associated with a higher risk of HPV, high-risk HPV, HPV18 and HPV52 infections, with corresponding ORs (95% CI) of 1.58 (1.16-2.15), 1.71 (1.23-2.36), 2.37 (1.12-5.02), and 3.67 (1.78-7.54), respectively. PBL subsets mainly affect the natural clearance of HPV, but their impact on postoperative HPV outcomes is not significant (P > 0.05). Lower T-cell and CD8 + T-cell counts, as well as a higher NK cell count, are unfavorable factors for natural HPV clearance (P < 0.05). The optimal cutoff values determined by the PBL prognostic model (T-cell percentage: 67.39%, NK cell percentage: 22.65%, CD8 + T-cell model risk score: 0.95) can effectively divide the population into high-risk and low-risk groups, accurately predicting the natural clearance of HPV. After internal validation with bootstrap resampling, the above conclusions still hold. CONCLUSIONS: CD4 + T cells were important determinants of HPV infection. T cells, NK cells, and CD8 + T cells can serve as potential biomarkers for predicting natural HPV clearance, which can aid in patient risk stratification, individualized treatment, and follow-up management.