[Difference between prostate cancer patients' Gleason scores from preoperative biopsy and those from postoperative pathology].

OBJECTIVE: To evaluate the consistency of the Gleason scores of PCa patients based on preoperative biopsy with those from postoperative pathology, identify the possible factors influencing results of scoring, and construct a risk scoring model. METHODS: We collected the demographic and clinical data on the patients with PCa confirmed by preoperative prostate biopsy or postoperative pathology and treated by radical prostatectomy within 6 months after diagnosis. Using paired sample t-test, we identified the difference between the Gleason scores based on preoperative biopsy and those from postoperative pathology, analyzed the demographic and clinical data on the patients for relevant factors affecting the consistency of the Gleason scores, and calculated and visualized the relative risk values of the factors through Poisson regression. From the continuous variables with statistical significance, we screened independent risk factors for the difference in the Gleason scores by Lasso regression analysis, established a risk scoring model, generated risk coefficients, and evaluated the predictive ability of the model using the ROC curve. Based on the results of imaging examination with statistically significant differences, we constructed a column chart by logistic regression and evaluated the predictive validity of the chart using calibration curves, decision curves and ROC curves. RESULTS: The results of paired sample t-test for 210 PCa patients showed statistically significant differences between the Gleason scores from preoperative biopsy and those from postoperative pathology (P < 0.001). There were significant differences in the body weight, BMI and PSA level as well as in all other factors but prostate calcification between the patients with consistent and those with inconsistent Gleason scores (all P < 0.05). An 8-factor prediction model was successfully constructed, which could predict the consistency of Gleason scores, with a better predicting performance than the single indicator within the model. The nomogram exhibited a C-index value of 0.85, with the calibration curve similar to the standard one, the threshold of the decision curve 0.10－0.92, and the area under the ROC curve higher than other predictive indicators. CONCLUSION: Based on the demographic and clinical data on PCa patients, a risk prediction model and a column chart were successfully constructed, which could effectively predict the difference between the Gleason scores from preoperative prostate biopsy and those from postoperative pathology.

[Basement membrane-related gene signature to predict biochemical recurrence in patients with prostate cancer after radical prostatectomy].

OBJECTIVE: To construct and verify a key gene signature of the basement membrane of prostate cancer (PCa) to predict the progression and biochemical recurrence of the malignancy after radical prostatectomy. METHODS: Based on the PCa-related transcriptome, gene mutation and clinical data from the Cancer Genome Atlas Project (TCGA) database, we analyzed the differentially expressed genes (DEG) related to the basement membrane in the PCa and adjacent normal prostate tissues, and subjected them to GO function enrichment and KEGG pathway enrichment analyses. We identified prognosis-related genes from the DEGs and analyzed their mutations. According to the follow-up data and biochemical recurrence after prostatectomy, we established a prognostic risk scoring model, verified its accuracy using the Gene Expression Omnibus (GEO) database, and performed survival analysis, principal component analysis (PCA), independent prognostic analysis and ROC curve analysis of the model. We constructed a protein-protein interaction network after verifying the correctness of the model by immunohistochemistry. We also established a nomogram and tested its accuracy using ROC and calibration curves. RESULTS: Totally, 85 DEGs were identified, among which 18 were up-regulated and 67 down-regulated. The prognostic risk scoring model was established with 11 of the genes. The risk of biochemical recurrence PCa was significantly higher in the high-risk than in the low-risk group (HR: 3.51, 95% CI: 2.32－5.32, P < 0.01), which was verified with the GEO database data (P < 0.01). In addition, the patients in the high-risk group were older with higher clinical T-stage, higher Gleason score, higher positive rate, larger numbers of positive lymph nodes, and a larger proportion of residual tumors than those in the low-risk group (P < 0.05). The nomogram constructed with the patients' age, pN, pT and cT stages, Gleason score and prognostic risk score manifested that the area under the ROC curve was higher than the other predictors. The calibration chart showed consistency of the predicted outcomes to the actual results. CONCLUSION: A prognostic risk scoring model of basement membrane-related genes and an effective nomogram were successfully constructed, which can predict the risk of biochemical recurrence in PCa patients after radical prostatectomy.

NRP-1 expression in bladder cancer and its implications for tumor progression.

Neuropilin-1 (NRP-1) overexpression has been reported in a variety of human cancers. However, the role of NRP-1 in bladder cancer (BC) remains unclear. The aim of present study was to analyze NRP-1 protein expression in BC tissues and to assess its prognostic significance for BC. NRP-1 messenger ribonucleic acid (mRNA) and protein expression were determined by real-time quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and immunohistochemistry in specimens of primary cancer and their adjacent noncancerous tissues in BC patients. Additionally, NRP-1 protein expression in 139 archived paraffin-embedded BC samples was analyzed by immunohistochemistry and correlated with clinicopathological characteristics and survival. Student's t test, Spearman's rank correlation, Kaplan-Meier plots, and Cox's proportional hazards regression model were used to analyze the data. By qRT-PCR and immunohistochemistry, the levels of NRP-1 mRNA and protein were significantly higher in BC, compared to that in adjacent noncancerous tissues (P<0.001). High expression of NRP-1 was significantly associated with histologic grade (P=0.016) and tumor stage (P=0.001). Multivariate analysis showed that high expression of NRP-1 was an independent prognostic factor for overall survival. Our study suggests that overexpression of NRP-1 may play an important role in the progression of BC, and NRP-1 expression may serve as a biomarker for poor prognosis for BC.