Study provides evidence for a causal genetic link between prostate cancer and urothelial carcinoma

Prostate cancer is among the most common cancers in men and a leading cause of cancer-related deaths. With improved detection and therapies, survival rates have risen, but this progress comes with a new concern: the risk of secondary primary malignancies. These cancers, unrelated to metastasis or recurrence, occur in approximately 11-22% of prostate cancer survivors and often shorten life expectancy. Identifying high-risk patients and understanding the genetic and clinical factors behind these outcomes are critical to improving care. Based on these challenges, there is a need to develop robust predictive tools and uncover genetic insights to guide personalized management for prostate cancer patients with SPMs.

Researchers from Guangzhou Medical University and collaborators published (DOI: 10.1002/uro2.70017) their findings on March 27, 2025, in UroPrecision. Using data from the U.S. Surveillance, Epidemiology, and End Results (SEER) program, the team constructed a prognostic nomogram to predict survival outcomes for prostate cancer patients diagnosed with SPMs. They further applied two-sample Mendelian randomization (TSMR) analysis to examine genetic associations between prostate cancer and ten common secondary primary malignancies (SPMs). The study not only enhances survival prediction but also provides novel evidence for a causal genetic link between prostate cancer and urothelial carcinoma.

The team analyzed records of 6,363 prostate cancer patients with SPMs identified in the SEER database. These patients were divided into training and validation cohorts to ensure accuracy. Key prognostic factors—including age, marital status, SPM site, M stage, AJCC stage, PSA levels, and prostate cancer surgery history—were selected using rigorous statistical methods such as LASSO regression and Cox proportional hazards modeling. Based on these variables, the researchers developed a visual nomogram that translates complex risk factors into an intuitive survival prediction tool. The model demonstrated strong predictive power, with area under the curve (AUC) values of 0.84-0.87 across both cohorts, outperforming traditional AJCC staging. A dynamic, web-based calculator was also created for easy clinical use. Importantly, risk stratification showed the nomogram's ability to distinguish high- and low-risk patients more effectively than AJCC staging. Beyond clinical modeling, the study's genetic analysis revealed a significant causal relationship between prostate cancer and urothelial carcinoma, particularly bladder and upper tract cancers, providing mechanistic insights into their co-occurrence.

Our research addresses two critical gaps—clinical prediction and genetic causality—in prostate cancer patients who develop second malignancies. The nomogram offers physicians a practical tool to personalize risk assessment, while the Mendelian randomization analysis uncovers genetic evidence linking prostate cancer to urothelial carcinoma. These findings emphasize the importance of integrating clinical and genetic data for better patient management. Ultimately, the study provides a foundation for precision medicine strategies that could significantly improve outcomes for this vulnerable patient group."

Dr. Di Gu, lead author

The newly developed nomogram holds promise for real-world oncology practice. By supplementing traditional staging with individualized survival predictions, it allows clinicians to tailor follow-up schedules, treatment intensity, and supportive care strategies. The online calculator enhances accessibility, enabling broader application in diverse healthcare settings. Moreover, the genetic findings provide a rationale for closer monitoring of urinary system cancers in prostate cancer survivors, especially bladder cancer and upper tract urothelial carcinoma. Together, these advances support a shift toward precision oncology—where treatment and surveillance strategies are guided not only by tumor stage but also by patient-specific clinical and genetic profiles.