| Objective:To investigate whether the Neutrophil to Apolipoprotein A1 ratio(NAR)in peripheral blood is a risk factor for the incidence of Prostate cancer(PCa),and to evaluate its application value in the initial diagnosis of PCa in order to obtain hematological indicators that can effectively predict the incidence of PCa.Methods:Collect information on all patients who underwent prostate biopsy at Henan University People’s Hospital from January 2019 to October 2022,and gather the following clinical data: age,medical history,results of serum prostate-specific antigen(PSA)tests,counts of neutrophils、lymphocytes and monocytes,values of Apolipoprotein A1(Apo A1),High-density lipoprotein(HDL),Low-density lipoprotein(LDL),and Cystatin C(Cys C),histopathological results,and Gleason scores.Only patients with PSA levels between 4-20 ng/m L were included.NAR,NLR,and LMR were defined as "Neutrophil count/Apo A1","Neutrophil count/lymphocyte count",and "Lymphocyte count/Monocyte count",respectively.All of the above peripheral blood markers were obtained within two weeks before biopsy.Patients with other malignant tumors and hematological diseases,recent local infection or systemic infection and related treatment,atypical acinar hyperplasia,prostatic intraepithelial neoplasia,ductal carcinoma,ductal adenocarcinoma and other pathological types and related clinical data are excluded.Based on the pathological results,the patients were divided into the experimental group(PCa group)and the control group(benign prostatic hyperplasia(BPH)group).Statistical methods were used to analyze and evaluate the predictive value of related indicators in the diagnosis of PCa.Results:1.From the baseline data,it can be concluded that the neutrophil count,NAR,NLR,LMR and PSA in the PCa group were higher than those in the BPH group,and all were statistically significant(P<0.05);Apo A1,HDL and LDL in the PCa group were lower than those in the BPH group and were statistically significant(P<0.05).2.After univariate analysis,it was concluded that neutrophil count,lymphocyte count,Apo A1,NAR,NLR,LMR,HDL,LDL,Cys C,and PSA were associated with the incidence of PCa and were risk factors for PCa,all of which were statistically significant(P<0.05).3.After multivariate analysis,NAR and PSA were independent risk factors for PCa [OR(95%CI)=1.557(1.263-1.920),1.098(1.028-1.174)],both of which were statistically significant(P<0.05);LDL and Cys C were protective factors of PCa [OR(95%CI)= 0.677(0.461-0.994),0.264(0.088-0.793)],all were statistically significant(P<0.05).4.It can be seen from the ROC curve,The cut-off value of NAR was 4.31,the area under the ROC curve was 0.696,the sensitivity was 0.764,and the specificity was 0.567;the optimal cut-off value for LDL was2.255,the area under the ROC curve was 0.589,the sensitivity was 0.547,the specificity was 0.640;the optimal cut-off value for Cys C was 1.055,the area under the ROC curve was 0.538,the sensitivity was 0.679,and the specificity was 0.457;the optimal cut-off value for PSA was 9.935,the area under the ROC curve was 0.653,the sensitivity was 0.594,and the specificity was 0.701;NAR+PSA had an area under the ROC curve of 0.710,a sensitivity of 0.774 and a specificity of 0.610;NAR+Cys C had an area under the ROC curve of 0.724,a sensitivity of 0.764 and a specificity of 0.652;the area under the ROC curve of PSA+LDL was 0.668,with a sensitivity of 0.528 and a specificity of 0.799;and PSA+ The area under the ROC curve of Cys C was 0.665,the sensitivity was 0.500,and the specificity was 0.793;LDL+Cys C had an area under the ROC curve of 0.607,with a sensitivity of 0.811 and specificity of 0.378;NAR+PSA+Cys C had an area of0.728 with a sensitivity of 0.717 and a specificity of 0.720;the area under the ROC curve of PSA+LDL+Cys C was 0.687,with a sensitivity of 0.547 and a specificity of 0.774;NAR+PSA+LDL+ Cys C has an area under the ROC curve of 0.740,a sensitivity of 0.708,and a specificity of 0.713.5.Through the comparison of ROC curves,it can be seen that the overall prediction model(NAR+PSA+LDL+Cys C)has the greatest diagnostic value for PCa;Through the nomogram,calibration curve and decision curve,it can be seen that the overall prediction model(NAR+PSA+LDL+Cys C)has good clinical application value.6.There were significant differences in NAR levels under different PSA interval groups,different Gleason score groups,and different risk groups(P<0.05).Conclusions:1.NAR is significantly related to the risk of PCa,and it is an independent risk factor of PCa.When NAR>4.31,the risk of prostate cancer is higher,which can be used as a reference diagnostic index before prostate biopsy;2.The inclusion of NAR into the prediction model can increase the diagnostic efficiency of the model,and the NAR+PSA+LDL+Cys C joint prediction model has the highest diagnostic efficiency,which can be used as a diagnostic prediction model for PCa,and has high clinical use value;3.NAR level is positively correlated with Gleason score classification and PCa risk grouping,which is of certain value in evaluating the prognosis of PCa. |
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