Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive.

Prostate cancer (PCa) patients with lymph node involvement (LNI) constitute a single-risk group with varied prognoses. Existing studies on this group have focused solely on those who underwent prostatectomy (RP), using statistical models to predict prognosis. This study aimed to develop an easily accessible individual survival prediction tool based on multiple machine learning (ML) algorithms to predict survival probability for PCa patients with LNI. A total of 3280 PCa patients with LNI were identified from the Surveillance, Epidemiology, and End Results (SEER) database, covering the years 2000-2019. The primary endpoint was overall survival (OS). Gradient Boosting Survival Analysis (GBSA), Random Survival Forest (RSF), and Extra Survival Trees (EST) were used to develop prognosis models, which were compared to Cox regression. Discrimination was evaluated using the time-dependent areas under the receiver operating characteristic curve (time-dependent AUC) and the concordance index (c-index). Calibration was assessed using the time-dependent Brier score (time-dependent BS) and the integrated Brier score (IBS). Moreover, the beeswarm summary plot in SHAP (SHapley Additive exPlanations) was used to display the contribution of variables to the results. The 3280 patients were randomly split into a training cohort (n = 2624) and a validation cohort (n = 656). Nine variables including age at diagnosis, race, marital status, clinical T stage, prostate-specific antigen (PSA) level at diagnosis, Gleason Score (GS), number of positive lymph nodes, radical prostatectomy (RP), and radiotherapy (RT) were used to develop models. The mean time-dependent AUC for GBSA, RSF, and EST was 0.782 (95% confidence interval [CI] 0.779-0.783), 0.779 (95% CI 0.776-0.780), and 0.781 (95% CI 0.778-0.782), respectively, which were higher than the Cox regression model of 0.770 (95% CI 0.769-0.773). Additionally, all models demonstrated almost similar calibration, with low IBS. A web-based prediction tool was developed using the best-performing GBSA, which is accessible at https://pengzihexjtu-pca-n1.streamlit.app/ . ML algorithms showed better performance compared with Cox regression and we developed a web-based tool, which may help to guide patient treatment and follow-up.

FOXM1-Dependent Transcriptional Regulation of EZH2 Induces Proliferation and Progression in Prostate Cancer.

BACKGROUND: Prostate cancer is one of the most commonly diagnosed cancers and one of the most common causes of cancer-related deaths among men worldwide. Patients who are diagnosed with localized prostate cancer and treated with radical prostatectomy often respond well to therapy. The current standard therapy for prostate cancer involves maximal surgical resection, followed by radiotherapy and chemotherapy. Clarifying the molecular mechanism of tumor proliferation and recurrence becomes more and more important for clinical therapies of prostate cancer. METHODS: Quantitative Real-Time PCR and Western-blot were used in the detection of mRNA and protein expression. Lentivirus infection was used to overexpress or knockdown the target gene. Flow cytometry analysis was performed to test protein expression and apoptosis level. Immunohistochemistry was used to identify protein expression in tissue. Statistical differences between the two groups are evaluated by two-tailed t-tests. The comparison among multiple groups is performed by one-way Analysis of Variance (ANOVA) followed by Dunnett's posttest. The statistical significance of the Kaplan-Meier survival plot is determined by log-rank analysis. RESULTS: In this study, we identified that FOXM1 expression was significantly enriched in prostate cancer compared with normal tissue. Additionally, FOXM1 was functionally required for tumor proliferation and its expression was associated with poor prognosis in prostate cancer patients. Mechanically, FOXM1-dependent regulation of EZH2 is essential for proliferation and progression in prostate cancer. CONCLUSION: Taken together, our data suggest that oncogenic transcription factor FoxM1 is up-regulated in prostate cancer, suggesting that the growth of cancer cells may depend on FOXM1 activity. FOXM1 may serve as a clinical prognostic factor and a therapeutic target for prostate cancer.

BUB1B Promotes Proliferation of Prostate Cancer via Transcriptional Regulation of MELK.

BACKGROUND: Prostate cancer remains one of the most common and deadliest forms of cancer, generally respond well to radical prostatectomy and associated interventions, up to 30% of individuals will suffer disease relapse. Although BUB1B was found to be essential for cell growth and proliferation, even in several kinds of tumor cells, the specific importance and mechanistic role of BUB1B in prostate cancer remain unclear. METHODS: Quantitative Real-Time PCR and Western-blot were used in the detection of mRNA and protein expression. Lentivirus infection was used to overexpression or knock down the target gene. Flow cytometry analysis was performed to test protein expression and apoptosis level. Immunohistochemistry was used to identify protein expression in tissue. Statistical differences between the two groups are evaluated by two-tailed t-tests. The comparison among multiple groups is performed by one-way Analysis of Variance (ANOVA) followed by Dunnett's posttest. The statistical significance of the Kaplan-Meier survival plot is determined by log-rank analysis. RESULTS: In the present report, we found BUB1B expression to be highly increased in prostate cancer tissues relative to normal controls. We further found BUB1B to be essential for efficient tumor cell proliferation, and to correlate with poorer prostate cancer patient outcomes. From a mechanistic perspective, the ability of BUB1B to regulate MELK was found to be essential for its ability to promote prostate cancer cell proliferation. CONCLUSION: Altogether, our data suggest that BUB1B is up-regulated in prostate cancer, suggesting that the growth of cancer cells may depend on BUB1B-dependent regulation of MELK transcription. BUB1B may serve as a clinical prognostic factor and a druggable target for prostate cancer.