Abstract
INTRODUCTION: Prostate cancer (PCa) ranks as the second most common malignancy in men worldwide. While serum prostate-specific antigen (PSA) is routinely monitored, its low specificity frequently leads to overdiagnosis. Cytokines within the tumor microenvironment (TME) demonstrate strong tumor-progression associations, but their combined predictive utility with PSA for metastasis and chemotherapy response remains undetermined. This study aimed to quantify cross-sectional differences in pre-treatment cytokine levels based on metastatic status, assess their prognostic value for biochemical progression-free survival in metastatic patients, and characterize cytokine profiles from baseline to biochemical recurrence. METHODS: We retrospectively analyzed 328 PCa patients (175 metastatic, 153 non-metastatic), collecting data on age, smoking history, Gleason score, total PSA (TPSA), and cytokines. Metastasis-associated factors were identified by Spearman correlation and logistic regression. Prognostic models were evaluated using ROC curves/AUC analysis. Multi index combination was used to find the best prognostic group.Survival analysis employed Kaplan-Meier methodology, while Cox regression assessed post-chemotherapy PSA rebound predictors. INTRODUCTION: We find that smoking, TPSA, and IL-8 emerged as independent metastasis risk factors. Prognostic indices PRE1 (smoking, TPSA, IL-8) and PRE2 (all significant factors) achieved AUCs of 0.788 and 0.787 respectively, with PRE1 demonstrating superior calibration. The AUC of TPSA+IL-6+IL-8+IL-10 four factor combination was 0.753, and this combination yielded high prognostic performance, and the proportion of metastasis group was significantly higher than that of non-metastasis group. Univariate Cox analysis associated age, TPSA, IL-6, IL-8, and TNF-α with PSA rebound, though multivariate analysis identified no independent predictors. DISCUSSION: These results underscore the immunological relevance of specific cytokines in PCa progression and their potential as complementary biomarkers to PSA for improving risk stratification.