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Genitourinary Cancer

Relevant Articles About Research and Clinical Trials in Genitourinary Cancer

RELATED :Bladder Cancer:Prostate Cancer:RCC (Renal Cell Carcinoma)

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OncWeekly Featured Trials

Prostate CancerDarolutamide(Nubeqa, BAY1841788)ActiveNot RecruitingArgentinaAustriaBelgiumBrazilCanadaChinaColombiaDenmarkFranceGermanyGreeceItalyJapanRussian FederationSpainTaiwanUnited States 0NCT04122976DAROL
A Study in Which Non-metastatic Castration-resistant Prostate Cancer (nmCRPC) Patients for Whom a Decision to Treat With Darolutamide Has Been Made Before Enrollment Are Observed and Certain Outcomes Are Described

A Study in Which Non-metastatic Castration-resistant Prostate Cancer (nmCRPC) Patients for Whom a Decision to Treat With Darolutamide Has Been Made Before Enrollment Are Observed and Certain Outcomes Are Described

RCC (Renal Cell Carcinoma)Cabozantinib tabletsEverolimus (Afinitor) tabletsCompletedArgentinaAustraliaAustriaBelgiumCanadaChileCzechiaDenmarkFinlandFranceGermanyHungaryIrelandItalyKorea, Republic ofNetherlandsPolandPortugalRussian FederationSlovakiaSpainSwedenTaiwanTurkeyUnited KingdomUnited States10NCT01865747Phase 3METEOR
A Study of Cabozantinib (XL184) vs Everolimus in Subjects With Metastatic Renal Cell Carcinoma

A Study of Cabozantinib (XL184) vs Everolimus in Subjects With Metastatic Renal Cell Carcinoma

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BPH Increases PCa Risk: A Mendelian Study

KEY TAKEAWAYS

  • The study aimed to investigate the causal relationships between prostatitis, BPH, and prostate cancer using MR.
  • Researchers noticed that BPH has a positive causal effect on PCa, while prostatitis does not.

Although it is thought that prostatitis or benign prostatic hyperplasia (BPH) is related to prostate cancer (PCa), the underlying causal effects of these diseases are unclear.

Jiaguo Huang and the team conducted this Mendelian randomization (MR) study to clarify these relationships by analyzing genetic data to determine whether there are direct causal links between BPH, prostatitis, and PCa.

They performed an inclusive analysis to assess the causal relationship between prostatitis or BPH and PCa using a 2-sample MR approach. The data utilized in this study were sourced from genome-wide association studies. The association of genetic variants from cohorts of prostatitis or BPH and PCa patients was determined using inverse-variance weighted and MR Egger regression techniques. The direction of chance was determined using independent genetic variants with genome-wide significance (P < 5 × 10-6). The accuracy of the results was confirmed using sensitivity analyses.

MR analysis showed that BPH had a significant causal effect on PCa (Odds Ratio = 1.209, 95% Cl: 0.098-0.281, P = 5.079 × 10-5) while prostatitis had no significant causal effect on PCa (P > 0.05). Additionally, the pleiotropic test and leave-one-out analysis showed the two-sample MR analyses were valid and reliable.

The study concluded that BPH has a positive causal effect on PCa, while genetically predicted prostatitis has no causal effect on PCa. Nonetheless, further studies should explore the underlying biochemical mechanisms and potential therapeutic targets for the prevention of these diseases.

This study was supported by the Foundation of Xiaoshan Science and Technology Bureau of Hangzhou, China, and Zhejiang Provincial Natural Science Foundation of China.

Source: https://pubmed.ncbi.nlm.nih.gov/38937672/

Huang J, Sun J, Wang K, et al. (2024). “Causal relationship between prostatic diseases and prostate cancer: a mendelian randomization study.” BMC Cancer. 2024 Jun 27;24(1):774. doi: 10.1186/s12885-024-12551-9. PMID: 38937672.

POLD1: Key Biomarker in Prostate Cancer Metastasis

KEY TAKEAWAYS

  • The study aimed to identify metastasis-related genes by WGCNA.
  • The POLD1 reported to be a novel biomarker related to PCa metastasis, enhancing treatment avaenues for PCa.

Prostate cancer (PCa) ranks as the second leading cause of cancer-related death in men. The main cause of mortality in patients (pts) with advanced PCa is metastasis. Bringing insights into novel and effective biomarkers is needed for understanding metastasis driving mechanisms in patients advanced PCa and for managing to design successful treatment strategies.

YaXuan Wang and the team designed this study to identify metastasis-related genes in PCa using data from GSE8511 and GSE27616 through weighted gene co-expression network analysis (WGCNA).

Researchers employed 2 datasets, GSE8511 and GSE27616, to identify 21 metastasis-related genes using the WGCNA method. Functional analysis of these genes was performed on the Gene Set Cancer Analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy.

Further multivariate COX regression analysis advocated the significant role of POLD1 and established its association with DNA methylation. Molecular docking and immunohistochemistry experiments were performed to obtain the binding affinity of POLD1 to PCa drugs and reveal its impact on PCa prognosis.

The study identified 21 metastasis-related genes using the WGCNA method, which were associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms demonstrated strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results.

POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.

The study concluded with the development of diagnostic and metastasis models that provide potential value for patients with PCa. The discovery of POLD1 as a novel biomarker related to PCa metastasis offers a promising avenue for enhancing the treatment of prostate cancer metastasis.

This study was supported by the Natural Science Foundation of Heilongjiang Province (LH2019H030) and National Natural Science Foundation of China (82002680).

Source: https://pubmed.ncbi.nlm.nih.gov/38918844/

Wang Y., Ji B., Zhang L., et al. (2024). “Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.” Biol Direct. 2024 Jun 25;19(1):50. doi: 10.1186/s13062-024-00494-x. PMID: 38918844

ML Model Predicts LNM in Patients With BUC

KEY TAKEAWAYS

  • The study aimed to investigate ML models for predicting LNM in patients with BUC preoperatively.
  • Researchers noticed that the SVM model with 14 variables was the most effective for predicting LNM.

Lymph node metastasis (LNM) is associated with worse prognosis in patients with bladder urothelial carcinoma (BUC).

Junjie Ji and the team aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in patients with BUC treated with radical cystectomy (RC).

They performed an inclusive analysis by retrospectively collecting demographic, pathological, imaging, and laboratory information of patients with BUC who underwent RC and bilateral lymphadenectomy. Patients were randomly categorized into training and testing sets. Five ML algorithms were utilized to establish prediction models.

The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, the corresponding variable coefficients were calculated based on the optimal model to reveal the contribution of each variable to LNM.

About 524 and 131 patients with BUC were finally enrolled into the training set and testing set, respectively. They identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903-0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95% CI: 0.777-0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node imaging contributed the most to the prediction of LNM in patients with BUC.

The study concluded that the developed and validated ML models effectively preoperatively predict LNM in patients with BUC treated with RC, with the SVM model containing 14 variables demonstrating the best performance and high clinical applicability.

This study was partly funded by the Natural Science Foundation of Shandong Province (ZR2021MH354), Medical and health research program of Qingdao (2021-WJZD170).

Source: https://pubmed.ncbi.nlm.nih.gov/38872141/

Ji J, Zhang T, Zhu L, et al. (2024). “Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma.” BMC Cancer. 2024 Jun 13;24(1):725. doi: 10.1186/s12885-024-12467-4. PMID: 38872141; PMCID: PMC11170799.

POLD1: Key Biomarker in Prostate Cancer Metastasis

POLD1: Key Biomarker in Prostate Cancer Metastasis

KEY TAKEAWAYS

  • The study aimed to identify metastasis-related genes by WGCNA.
  • The POLD1 reported to be a novel biomarker related to PCa metastasis, enhancing treatment avaenues for PCa.

Prostate cancer (PCa) ranks as the second leading cause of cancer-related death in men. The main cause of mortality in patients (pts) with advanced PCa is metastasis. Bringing insights into novel and effective biomarkers is needed for understanding metastasis driving mechanisms in patients advanced PCa and for managing to design successful treatment strategies.

YaXuan Wang and the team designed this study to identify metastasis-related genes in PCa using data from GSE8511 and GSE27616 through weighted gene co-expression network analysis (WGCNA).

Researchers employed 2 datasets, GSE8511 and GSE27616, to identify 21 metastasis-related genes using the WGCNA method. Functional analysis of these genes was performed on the Gene Set Cancer Analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy.

Further multivariate COX regression analysis advocated the significant role of POLD1 and established its association with DNA methylation. Molecular docking and immunohistochemistry experiments were performed to obtain the binding affinity of POLD1 to PCa drugs and reveal its impact on PCa prognosis.

The study identified 21 metastasis-related genes using the WGCNA method, which were associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms demonstrated strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results.

POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.

The study concluded with the development of diagnostic and metastasis models that provide potential value for patients with PCa. The discovery of POLD1 as a novel biomarker related to PCa metastasis offers a promising avenue for enhancing the treatment of prostate cancer metastasis.

This study was supported by the Natural Science Foundation of Heilongjiang Province (LH2019H030) and National Natural Science Foundation of China (82002680).

Source: https://pubmed.ncbi.nlm.nih.gov/38918844/

Wang Y., Ji B., Zhang L., et al. (2024). “Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.” Biol Direct. 2024 Jun 25;19(1):50. doi: 10.1186/s13062-024-00494-x. PMID: 38918844

E-PASS Score Effectively Predicts Complications in RARP

KEY TAKEAWAYS

  • The study aimed to investigate the effectiveness of the E-PASS score in predicting postoperative complications in patients undergoing RARP.
  • Researchers noticed that the E-PASS scoring model effectively predicts postoperative complications.

Robot-Assisted Radical Prostatectomy (RARP) is increasingly becoming the standard surgical treatment for prostate cancer. While some risk factors for postoperative complications of RARP have been identified, no scoring model that incorporates both preoperative physical status of the patient and intraoperative risk factors has been developed. The Estimation of Physiologic Ability and Surgical Stress (E-PASS) score was initially described to predict postoperative complications after gastrointestinal surgical procedures.

Süleyman Bulut and the team aimed to assess the effectiveness of the E-PASS score in predicting postoperative complications of RARP.

Researchers performed an inclusive analysis on 204 patients who underwent RARP between 2019 and 2022. Demographic data, parameters indicating patients’ preoperative physical condition, and intraoperative risk factors were analyzed. The E-PASS score and subscores were calculated for each patient to evaluate their effectiveness in predicting postoperative complications.

About 164 patients (80.4%) were discharged without any postoperative complications (Group 1), and 40 patients (19.6%) experienced various degrees of complications (Group 2). Patients in Group 2 had higher rates of previous abdominal surgery, elevated Eastern Cooperative Oncology Group (ECOG) performance scores, longer surgical durations, and higher E-PASS scores.

To assess the effectiveness of the Comprehensive Risk Score (CRS) as a predictive factor for postoperative complications, a receiver operating characteristic (ROC) curve was constructed with a 95% confidence interval (CI), and a cut-off value was established. The cut-off value for CRS was determined to be -0.0345 (AUC=0.783, CI: 0.713-0.853; P<0.001). Patients with a CRS higher than the cut-off value had a 16.4 times higher rate of postoperative complications after RARP (95% CI: 5.58-48.5).

The study concluded that the E-PASS scoring model effectively predicts postoperative complications in patients undergoing RARP, leveraging preoperative patient physical status and surgical risk factors. The E-PASS score and its subscores offer objective criteria for assessing the risk of complications both before and immediately after surgery.

No funding information was provided.

Source: https://pubmed.ncbi.nlm.nih.gov/38863296/

Bulut S, Kizilkan Y, Gültekin H, et al. (2024). “Efficiency of the estimation of physiologic ability and surgical stress (E-PASS) score in predicting postoperative complications after robot-assisted radical prostatectomy.” Ulus Travma Acil Cerrahi Derg. 2024 Jun;30(6):423-429. English. doi: 10.14744/tjtes.2024.36332. PMID: 38863296.

Enhanced RFS With ERBT Over cTURBT in NMIBC

KEY TAKEAWAYS

  • The study aimed to investigate the comparative efficacy and perioperative outcomes of ERBT vs cTURBT in the treatment of patients with NMIBC.
  • Researchers noticed ERBT yielded better or comparable outcomes and improved RFS.

En-Bloc transurethral resection of bladder tumor (ERBT) was clinically used to resect non-muscle-invasive bladder cancer (NMIBC). However, discrepancies persist regarding the comparisons between ERBT and conventional transurethral resection of bladder tumor (cTURBT).

Deng-Xiong Li and the team aimed to assess the overall effectiveness and benefits of ERBT compared to cTURBT in treating NMIBC.

They performed an inclusive analysis by conducting a comprehensive search in PubMed, Embase, Web of Science, Cochrane Database of Systematic Reviews, and manual searches of reference lists to collect and extract data. Data evaluation was carried out using Review Manager 5.4.0, Rx64 4.1.3, and relevant packages.

About 9 eligible meta-analyses and 9 eligible RCTs were included in the study. Patients with NMIBC undergoing ERBT were significantly associated with a lower rate of bladder perforation and obturator nerve reflex compared to those receiving cTURBT. The pooled results indicated that ERBT and cTURBT required similar operation time. Regarding postoperative outcomes, ERBT demonstrated superior performance compared to cTURBT in terms of detrusor muscle presence, catheterization time, and residual tumor.

ERBT exhibited a higher rate of 3-month recurrence-free survival (RFS) compared to those receiving cTURBT (P < 0.05; I2 = 0%). In the bipolar subgroup, ERBT had a significantly better 12-month RFS than cTURBT (P < 0.05; I2 = 0%). Simultaneously, the exclusion of Hybrid Knife data revealed a significant improvement in 12-month RFS associated with ERBT (P < 0.05; I2 = 50%).

The study concluded that, using a combination of umbrella review and meta-analysis, ERBT had better or comparable perioperative outcomes and improved 3 and 12-month RFS compared to cTURBT. ERBT may be a better surgical method for patients with NMIBC.

This study was funded by the Chinese Scholarship Council.

Source: https://pubmed.ncbi.nlm.nih.gov/38819629/

Li DX, Yu QX, Wu RC, et al. (2024). “Efficiency of transurethral en-bloc resection vs. conventional transurethral resection for non-muscle-invasive bladder cancer: An umbrella review.” Cancer Med. 2024 Jun;13(11):e7323. doi: 10.1002/cam4.7323. PMID: 38819629; PMCID: PMC11141332.

Personalized ccRCC Treatment: Genetic Models & RNA Insights

KEY TAKEAWAYS

  • The study aimed to examine ccRCC’s vascularization, immune infiltration, TME roles in disease progression, and treatment outcomes.
  • The results revealed RNA sequencing data is crucial for advancing validation studies and personalized ccRCC therapy.

Clear cell renal cell carcinoma (ccRCC), the prevalent renal cancer in adults, emerges as a highly vascularized and immune-infiltrated solid tumor. The excessive production of vascular endothelial growth factor A fosters unregulated vessel growth and immunosuppression. The tumor microenvironment (TME) significantly influences disease progression, drug targeting, response, and patient outcomes.

Elin Sjöberg and the team aimed to investigate how ccRCC progresses, responds to drugs, and impacts patient outcomes through tumor microenvironment analysis.

The study reviewed experimental models, large-scale omics approaches, and patient prognosis and therapy prediction techniques to understand the endothelium in ccRCC and its interaction with the surrounding TME.

The results revealed that preclinical and clinical studies have unveiled molecular mechanisms of endothelial cross-talk pertinent to disease progression, patient prognosis, and therapy prediction. Despite this, there is a scarcity of representative ccRCC experimental models. Omics approaches have delineated clinically relevant subsets of angiogenic and immune-infiltrated tumors, highlighting distinct molecular signatures and discrete populations of endothelial and immune cells in patients.

The study concluded that recent genetically engineered ccRCC mouse models, along with emerging evidence from single-cell RNA sequencing data, offer opportunities for future validation studies.

These studies, including multiplex imaging of ccRCC patient cohorts, hold significance for enhancing therapy benefits and facilitating personalized treatment approaches for patients with ccRCC.

No funding information was available.

Source: https://pubmed.ncbi.nlm.nih.gov/38863726/

Sjöberg E. (2024). “Molecular mechanisms and clinical relevance of endothelial cell cross-talk in clear cell renal cell carcinoma.” Ups J Med Sci. 2024 May 8;129. doi: 10.48101/ujms.v129.10632. PMID: 38863726; PMCID: PMC11165252.

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