本研究旨在评估血清生化指标，包括碱性磷酸酶（AKP），乳酸脱氢酶（LDH），α-L-岩藻糖苷酶（AFU），血清唾液酸（SA）和纤维蛋白原（FIB），在PSA值在4-20 ng/mL之间的前列腺癌（PCa）和临床意义明显的前列腺癌（CSPCa）患者中的预测价值。本研究回顾性分析了2022年3月至2022年7月期间在我院接受前列腺活检的408名符合条件的患者的临床数据。CSPCa定义为“Gleason 评分组≥2”。为了分析PCa/CSPCa与血清生化指标之间的关联，进行了单变量逻辑回归和多变量逻辑回归。基于多变量逻辑回归模型，我们构建了模型，并比较了曲线下面积（AUC）。我们生成了关于PCa的直观图，ROC曲线，DCA曲线和校准曲线。总体而言，我们研究了271例患有PCa（包括155例患有CSPCa）和137例非PCa患者。患有PCa的患者更有可能饮酒，总PSA（TPSA）值更高，游离PSA（FPSA）和游离/总PSA（f/T）值更低。与非CSPCa组相比，CSPCa组的TPSA值更高，f/T值更低。单变量逻辑回归分析没有显示出显著结果。然而，当所有因素都纳入多因素逻辑回归分析时，AKP，AFU，SA，TPSA和FPSA均保持显著。这一发现表明，在控制其他因素（包括潜在的可能被低估的混杂效应）的影响后，暴露因素对结局有独立的影响。通过ROC曲线，我们发现SA和TPSA水平是更强的预测因子。相反，使用年龄、AFU、FIB和FPSA对PCa和CSPCa的预测价值不显著。在我们的研究中，血清生化指标是PSA值在4-20 ng/mL之间的患者的潜在预测工具，此外，我们研究结论中还指出，新的血清生化指标SA在诊断PCa和CSPCa时也是有用的。版权所有 © 2023 Sun和Yan.

This study aimed to evaluate the predictive value of the serum biochemical index, including alkaline phosphatase (AKP), lactate dehydrogenase (LDH), α-L-fucosidase (AFU), serum sialic acid (SA), and fibrinogen (FIB), for prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in patients with a prostate-specific antigen (PSA) value between 4 and 20 ng/mL.This study retrospectively examined the clinical data of 408 eligible patients who underwent prostate biopsies in our hospital between March 2015 and July 2022. CSPCa was defined as a "Gleason grade group of≥2". For analyzing the association between PCa/CSPCa and serum biochemical index, univariable logistic regression and multivariable logistic regression were conducted. Based on the multivariable logistic regression model, we constructed models and compared the area under the curve (AUC). We generated the nomogram, the ROC curve, the DCA curve, and the calibration curve for PCa.Overall, we studied 271 patients with PCa (including 155 patients with CSPCa) and 137 non-PCa patients. Patients with PCa were more likely to consume alcohol, have higher total PSA (TPSA) values, and have lower free PSA (FPSA) and free/total PSA (f/T) values. There were higher TPSA values and lower f/T values in the CSPCa group when compared with the non-CSPCa group. The univariate logistic regression analyses did not show significant results. However, AKP, AFU, SA, TPSA, and FPSA all retain significant significance when all factors are included in multifactor logistic regression analysis. This finding suggests that the exposure factor exhibited an independent effect on the outcome after controlling for other factors, including the potential confounding effects that may have been underestimated. Through ROC curves, we found that SA and TPSA levels are more powerful predictors. In contrast, there is a lack of excellent predictive value for PCA and CSPCa using Age, AFU, FIB, and FPSA.In our study, serum biochemical index is a potential prediction tool for PCa and CSPCa for patients with PSA values between 4 and 20 ng/mL. Additionally, the new serum biochemical index SA is also useful when diagnosing PCa and CSPCa, as we conclude in our study.Copyright © 2023 Sun and Yan.