Yinjie Zhu

As one of the predominant male malignancies globally, prostate cancer (PCa) transitions to a treatment‐refractory phase upon metastasis, for which no curative modalities currently exist. Tumor‐associated macrophages (TAMs), a crucial component of the tumor microenvironment (TME), primarily adopt a metastasis‐promoting M2 phenotype. However, the mechanisms underlying the TAM‐cancer cell crosstalk and resultant PCa metastasis remain elusive. In this study, primary lesions of metastatic PCa (mPCa) exhibit both greater infiltration of M2 macrophages and a higher proportion of M2 macrophage‐derived extracellular vesicles (M2 EVs) compared to those of non‐metastatic PCa (nmPCa). Furthermore, M2 EVs can be internalized by PCa cells, promoting a mesenchymal‐like state (MLS) in PCa and affecting tumor metastasis. Mechanistically, thioredoxin domain‐containing 5 ( TXNDC5 ) mRNA encapsulated in M2 EVs contributes to MLS of DU145 and PC3 cells, enhancing migration and invasion. Single‐vesicle particle analysis confirms that TXNDC5 mRNA encapsulated within M2 EVs can be horizontally transferred to target cells, where it is translated to produce functional proteins. In conclusion, our study demonstrates that M2 macrophages can promote MLS and metastasis of PCa through EV‐mediated horizontal mRNA transfer. A novel role of EVs in the communication between the TME and tumor cells is discovered, offering new insights into tumor metastasis.

Background: Although artificial intelligence (AI) algorithms provide reliable prostate volume (PV) measurements across various magnetic resonance imaging devices, their impact on prostate cancer risk stratification for patients with a Gleason score of 6 remains unclear. This study aimed to evaluate the benefits of integrating AI-derived PV and transitional or peripheral zone volume (TZV/PZV) measurements with clinical factors to improve prostate-cancer risk stratification. Methods: Our retrospective cohort included 560 patients with biopsy-confirmed Gleason score 6, stratified based on the outcome of radical prostatectomy as clinically significant prostate cancer (csPC) and clinically insignificant prostate cancer (insPC). We used AI methods for accurate PV and TZV/PZV estimation based on the origin virtual net (Vnet) and with cascade (coarse and fine) Vnet, the best-performing volume segmentation network in the subsequent analysis. We then developed predictive models incorporating clinical factors including age, prostate serum antigen levels, Prostate Imaging Reporting and Data System, positivity in transitional-zone or peripheral-zone biopsy, number of positive cores (foundational model), and novel models integrating PV (model 1) and TZV/PZV (model 2). The efficacy of these models was assessed by the receiver operating characteristic area under the curve (AUC). Results: For prostate segmentation, the fine cascade Vnet performed best with a Dice similarity coefficient of 0.93 for the whole prostate, 0.82 for the transitional zone, and 0.85 for the peripheral zone. The comparative discriminative power of the three models between csPC and insPC was assessed using the test dataset, indicating an AUC of 0.698 for the foundational model, 0.712 for model 1, and 0.730 for model 2. Model 2 significantly outperformed model 1 (P=0.045) and the foundational model (P=0.005) in distinguishing between csPC and insPC. Model 1 also showed statistically significant improvement over the foundational model (P=0.023). Conclusions: Incorporating AI-driven PV and TZV/PZV measurements with clinical parameters improves prostate-cancer risk stratification.

Objectives: Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making. Methods: Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models. Results: Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk. Conclusions: Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Background T cell stress response state (TSTR), as a novel immune concept previous studies have proposed, has not yet been explored in prostate cancer (PC). As a type of cellular efflux, exosomes play important roles in the occurrence and development of PC. Method Here, we conducted a combined analysis on extracellular vesicle related genes (EVRGs) in PC using data from single-cell RNA (scRNA), spatial transcriptome (ST), and bulk RNA sequencing. Result Preliminary findings have revealed that heat shock protein family H (Hsp110) member 1 (HSPH1) possesses two identities, one being EVRGs and the other being a member of the heat shock protein family involved in TSTR, which may promote the differentiation of conventional T cells towards Th1 or Th2 cells through the pathway of IL2-MYC-IL2RA, thereby promoting the increase of CD8 + T cells in the tumor area, especially in the invasive zone, and inhibiting the invasion of PCs. We also notice the negative response of HSPH1 + CD8 + T cell related genes in immune checkpoint blockade (ICB). Western blot (WB) and droplet digital Polymerase Chain Reaction (ddPCR) demonstrated that the mRNA and protein levels of HSPH1 in EVs of PCs were significantly higher than those in adjacent tissues. Conclusion Results above indicate the potential of HSPH1 as a critical therapeutic target in PC.

To explore the diagnostic value of the Prostate Imaging–Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CSPCa) in patients with a history of transurethral resection of the prostate (TURP), we conducted a retrospective study of 102 patients who underwent systematic prostate biopsies with TURP history. ROC analyses and logistic regression analyses were performed to demonstrate the diagnostic value of PI-RADS v2.1 and other clinical characteristics, including PSA and free/total PSA (F/T PSA). Of 102 patients, 43 were diagnosed with CSPCa. In ROC analysis, PSA, F/T PSA, and PI-RADS v2.1 demonstrated significant diagnostic value in detecting CSPCa in our cohort (AUC 0.710 (95%CI 0.608–0.812), AUC 0.768 (95%CI 0.676–0.860), AUC 0.777 (95%CI 0.688–0.867), respectively). Further, PI-RADS v2.1 scores of the peripheral and transitional zones were analyzed separately. In ROC analysis, PI-RADS v2.1 remained valuable in identifying peripheral-zone CSPCa (AUC 0.780 (95%CI 0.665–0.854; p < 0.001)) while having limited capability in distinguishing transitional zone lesions (AUC 0.533 (95%CI 0.410–0.557; p = 0.594)). PSA and F/T PSA retain significant diagnostic value for CSPCa in patients with TURP history. PI-RADS v2.1 is reliable for detecting peripheral-zone CSPCa but has limited diagnostic value when assessing transitional zone lesions.

Objective: To establish the prognostic nomogram for locally advanced prostate cancer (LAPC) patients treated by radical prostatectomy (RP) based on clinical and multiparametric‐MRI (mp‐MRI) metrics.Methods: One hundred and twenty‐one patients diagnosed with LAPC were included in this study. They were all examined by mp‐MRI within one week before surgery and treated by RP (36 with RP alone, 48 with neoadjuvant hormonal therapy (NHT) and 37 with neoadjuvant chemohormonal therapy (NCHT)). The biochemical progression‐free survival (bPFS) was analyzed by Kaplan–Meier method. Univariate and multivariate analysis were used to determine prognostic factors that were related with bPFS. The prognostic nomogram was established by factors that were significant in multivariate analyses.Results: The median bPFS had significant difference in the subgroup of treatment (RP alone: 2 [0.00–5.04] vs. NHT: 9.3 [6.746–11.854] vs. NCHT: 11.17 [0.000–25.075] months [Log rank p < .001]), the subgroup of hyperintensity within prostate in DWI (negative: 15.97 [11.202–20.731] vs. positive: 5.2 [2.952–7.448] months [Log rank p < .001]) and the subgroup of pelvic lymph node metastasis (negative: 10.2 [8.404–11.996] vs. unilateral: 4.43 [0.000–11.086] vs. Bilateral: 1.83 [0.636~3.031] [Log rank p < .001]). The method of treatment (hazards ratio [HR], 0.566; 95% confidence interval [CI], 0.356–0.899; p = .016), hyperintensity within prostate in DWI (HR, 2.539; 95% CI, 1.349–4.779; p = .004) and the metastasis burden of pelvic lymph node (HR, 2.492; 95% CI, 1.645–3.777; p < .001) were identified as independent predictors with significance in multivariable Cox regression analysis. The nomogram was established based on these three factors.Conclusion: We established a nomogram based on three significant prognosis factors including the neoadjuvant therapeutic schedule, hyperintensity within prostate in DWI and the metastasis burden of pelvic lymph nodes, which were associated with the clinical outcomes in LAPC patients after surgery.

Neuroendocrine prostate cancer is one of the most aggressive subtypes of prostate tumor. Although much progress has been made in understanding the development of neuroendocrine prostate cancer, the cellular architecture associated with neuroendocrine differentiation in human prostate cancer remain incompletely understood. Here, we use single-cell RNA sequencing to profile the transcriptomes of 21,292 cells from needle biopsies of 6 castration-resistant prostate cancers. Our analyses reveal that all neuroendocrine tumor cells display a luminal-like epithelial phenotype. In particular, lineage trajectory analysis suggests that focal neuroendocrine differentiation exclusively originate from luminal-like malignant cells rather than basal compartment. Further tissue microarray analysis validates the generality of the luminal phenotype of neuroendocrine cells. Moreover, we uncover neuroendocrine differentiation-associated gene signatures that may help us to further explore novel intrinsic molecular mechanisms deriving neuroendocrine prostate cancer. In summary, our single-cell study provides direct evidence into the cellular states underlying neuroendocrine transdifferentiation in human prostate cancer.

Chronic lymphocytic leukemia is a malignancy with good prognosis. However, the incidence of secondary tumors increases every year after the diagnosis of chronic lymphotcytic leukemia. One of the induced secondary tumors is prostate cancer. For high-risk prostate cancer in particular, the standard therapy is radical prostatectomy and extended lymphadenectomy, which carries high risks of lymphatic leakage and reduced quality of life. Currently, there has been no study reporting the necessity of extended lymphadenectomy for high-risk prostate cancer in patients with chronic lymphocytic leukemia. We reported two cases with concomitant high-risk prostate cancer and chronic lymphocytic leukemia. The first patient was a 60-year-old male diagnosed with synchronous prostate cancer and chronic lymphocytic leukemia. The second patient was a 70-year-old male initially presented with chronic lymphocytic leukemia alone but was then diagnosed with high-risk prostate cancer nine years later. Both patients received neoadjuvant androgen deprivation therapy and robot-assisted radical prostatectomy. The first patient underwent extended lymphadenectomy and developed prolonged postoperative lymphatic cyst. Histology showed chronic lymphocytic leukemia infiltration in resected lymph nodes. Serum prostate-specific antigen levels at one and 13 months post-operation were both 0.01 ng/ml. The second patient received positron emission tomography/computed tomography before androgen deprivation therapy, which showed mild fluorodeoxyglucose-avidity in lymph nodes across the entire body. Lymph node biopsy showed only chronic lymphocytic leukemia. The patient experienced no postoperative complication. Serum prostate-specific antigen levels at one and nine months post-operation were both 0.02 ng/ml. Extended lymphadenectomy may not be necessary for patients with concomitant high-risk prostate cancer and chronic lymphocytic leukemia, but such patients must undergo thorough preoperative assessment and mindful postoperative follow-up. Positron emission tomography/computed tomography may be valuable in detecting nodal metastases. A lymph node biopsy is necessary for patients with an ambiguous positron emission tomography/computed tomography in the metastatic involvement of lymph node.