Prostate cancer (PCa) is a widespread malignancy with global significance, which substantially affects cancer-related mortality. Its spectrum varies widely, from slow-progressing cases to aggressive or even lethal forms. Effective patient stratification into risk groups is crucial to therapeutic decisions and clinical trials. This review examines a wide range of diagnostic and prognostic biomarkers, several of which are integrated into clinical guidelines, such as the PHI, the 4K score, PCA3, Decipher, and Prolaris. It also explores the emergence of novel biomarkers supported by robust preclinical evidence, including urinary miRNAs and isoprostanes. Genetic alterations frequently identified in PCa, including BRCA1/BRCA2, ETS gene fusions, and AR changes, are also discussed, offering insights into risk assessment and precision treatment strategies. By evaluating the latest developments and applications of PCa biomarkers, this review contributes to an enhanced understanding of their role in disease management.

Prostate cancer (PCa) is a critical global public health issue with its incidence on the rise. Radiation therapy holds a primary role in PCa treatment; however, radiation resistance has become increasingly challenging as we uncover more about PCa’s pathogenesis. Our review aims to investigate the multifaceted mechanisms underlying radiation therapy resistance in PCa. Specifically, we will examine how various factors, such as cell cycle regulation, DNA damage repair, hypoxic conditions, oxidative stress, testosterone levels, epithelial–mesenchymal transition, and tumor stem cells, contribute to radiation therapy resistance. By exploring these mechanisms, we hope to offer new insights and directions towards overcoming the challenges of radiation therapy resistance in PCa. This can also provide a theoretical basis for the clinical application of novel ultra-high-dose-rate (FLASH) radiotherapy in the era of PCa.

Background: Stereotactic ablative body radiotherapy (SABR) is one of the treatment options for oligometastatic renal cell carcinoma (RCC) but is limited by a lack of data to evaluate high-dose SABR to all/multiple sites. Objective: This study retrospectively investigated the efficacy and prognostic factors of high-dose SABR for oligometastatic RCC patients. Design, setting, and participants: Patients with oligometastatic RCC on systemic therapy were retrospectively collected. Intervention(s): All patients were treated with SABR (40–50 Gy/5 fractions) for small tumors or partial-SABR (tumor center boosted with 6–8 Gy/3–5 fractions with 50–60 Gy/20–25 fractions to the whole tumor volume) for bulky tumors or tumors adjacent to critical organs. Outcome measurements and statistical analysis: Progression-free survival (PFS) and overall survival (OS) were calculated. Results and limitations: In total, 35 patients were enrolled, of which 88.5% had intermediate- or high-risk disease, with 60% on second- to fourth-line systemic therapy. The median follow-up time was 17 months. The median PFS and OS times were 11.3 and 29.7 months, respectively. Univariate analysis showed that an OS benefit was found in patients who received radiation before tyrosine kinase inhibitor (TKI) failure (p = 0.006) and where there was a short time interval (<six months) from being diagnosed with metastatic disease to undergoing radiotherapy (p = 0.046). Similar results were also found in PFS in patients who received radiation before TKI failure (p = 0.049) or within eight months (p = 0.047). There were certain differences in PFS (p = 0.033) between patients receiving radiotherapy with all lesions and those with selected tumors. In multivariate analysis, OS benefits were found in patients who received radiotherapy before TKI failure (p = 0.028). The limitations of this study include its retrospective design and the small patient cohort. Conclusions: The early use of high-dose SABR to multi-lesions may improve survival. Partial-SABR for bulky lesions close to critical organs could be safely and effectively applied under certain circumstances.

Background: Prostate cancer is a significant clinical issue, particularly for high Gleason score (GS) malignancy patients. Our study aimed to engineer and validate a risk model based on the profiles of high-GS PCa patients for early identification and the prediction of prognosis. Methods: We conducted differential gene expression analysis on patient samples from The Cancer Genome Atlas (TCGA) and enriched our understanding of gene functions. Using the least absolute selection and shrinkage operator (LASSO) regression, we established a risk model and validated it using an independent dataset from the International Cancer Genome Consortium (ICGC). Clinical variables were incorporated into a nomogram to predict overall survival (OS), and machine learning was used to explore the risk factor characteristics’ impact on PCa prognosis. Our prognostic model was confirmed using various databases, including single-cell RNA-sequencing datasets (scRNA-seq), the Cancer Cell Line Encyclopedia (CCLE), PCa cell lines, and tumor tissues. Results: We identified 83 differentially expressed genes (DEGs). Furthermore, WASIR1, KRTAP5-1, TLX1, KIF4A, and IQGAP3 were determined to be significant risk factors for OS and progression-free survival (PFS). Based on these five risk factors, we developed a risk model and nomogram for predicting OS and PFS, with a C-index of 0.823 (95% CI, 0.766–0.881) and a 10-year area under the curve (AUC) value of 0.788 (95% CI, 0.633–0.943). Additionally, the 3-year AUC was 0.759 when validating using ICGC. KRTAP5-1 and WASIR1 were found to be the most influential prognosis factors when using the optimized machine learning model. Finally, the established model was interrelated with immune cell infiltration, and the signals were found to be differentially expressed in PCa cells when using scRNA-seq datasets and tissues. Conclusions: We engineered an original and novel prognostic model based on five gene signatures through TCGA and machine learning, providing new insights into the risk of scarification and survival prediction for PCa patients in clinical practice.

Positive surgical margins (PSMs) after radical prostatectomy (RP) can increase the risk of biochemical recurrence in prostate cancer (PCa) patients. However, the prediction of the likelihood of PSMs in patients undergoing similar surgical procedures remains a challenge. We aim to develop a predictive model for PSMs in patients undergoing non-nerve-sparing RP. In this retrospective study, we analyzed data from PCa patients who underwent minimally invasive non-nerve-sparing RP at our hospital between June 2017 and June 2021. We identified independent risk factors associated with PSMs using clinical and MRI-based parameters in univariate and multivariate logistic regression analyzes. These factors were then used to develop a nomogram for predicting the probability of PSMs. The predictive performance was validated using calibration and receiver operating characteristic curve, area under the curve ,and decision curve analysis. Multivariate analyzes revealed prostate-specific antigen density, tumor size, tumor location at the apex, tumor contact length, extracapsular extension (ECE) level, and apparent diffusion coefficient value as independent risk factors. A nomogram was developed and validated with high accuracy (C-index = 0.78). Furthermore, we found that 44.2% of patients diagnosed with organ-confined disease had ECE after surgery, and 29.1% of patients with Gleason scores ≤7 had higher pathological scores. Interestingly, the tumor burden calculated from PCa biopsy cores was overestimated when compared to postoperative PCa specimens. We developed a reliable nomogram for predicting the risk of PSMs in PCa patients undergoing non-nerve-sparing RP. The study highlights the importance of incorporating these parameters in personalized surgical management.

Patients with advanced renal cancer (RCC) often have limited success with systemic therapy due to tumor heterogeneity. However, stereotactic ablative radiotherapy (SABR) has been shown to have a beneficial therapeutic effect for oligometastatic disease when used early. Despite this, current guidelines recommend the use of tyrosine kinase inhibitors (TKIs) as the first-line therapeutic agent for patients with recurrent or metastatic kidney cancer. Additionally, there is limited data on the combination of systemic treatment and SABR for extensive metastatic RCC due to concerns about high toxicity. Proton therapy offers a promising treatment option as it emits energy at a specific depth, generating high target doses while minimizing damage to normal tissue. This allows for precise treatment of various tumor lesions. In this case report, we describe a high-risk 65-year-old male with extensive pleural and thoracic lymph node metastases and 2 bone metastases of clear cell renal cancer. While the targeted therapy and immunotherapy effectively treated the bone metastases, it was not effective in treating the chest metastases, including the pleural and lymph node metastases. Thus, the patient received full-coverage radiotherapy with photon for primary renal tumor and intensity-modulated proton therapy (IMPT) for thoracic metastases. The patient showed no evidence of disease for 1 year after the initial radiotherapy, and no severe SABR-related adverse effects were observed until now. The combination of targeted therapy and immunotherapy with full-coverage radiotherapy may be a promising treatment option for selected patients with extensive metastatic renal cancer, especially as proton therapy allows for more precise control of the beam and minimal damage to normal tissue. This case has motivated us to investigate the potential advantages of administering proton therapy concurrently with systemic therapy in the management of metastatic renal cell carcinoma patients.