Unleashing novel horizons in advanced prostate cancer treatment: investigating the potential of prostate specific membrane antigen-targeted nanomedicine-based combination therapy.

Prostate cancer (PCa) is a prevalent malignancy with increasing incidence in middle-aged and older men. Despite various treatment options, advanced metastatic PCa remains challenging with poor prognosis and limited effective therapies. Nanomedicine, with its targeted drug delivery capabilities, has emerged as a promising approach to enhance treatment efficacy and reduce adverse effects. Prostate-specific membrane antigen (PSMA) stands as one of the most distinctive and highly selective biomarkers for PCa, exhibiting robust expression in PCa cells. In this review, we explore the applications of PSMA-targeted nanomedicines in advanced PCa management. Our primary objective is to bridge the gap between cutting-edge nanomedicine research and clinical practice, making it accessible to the medical community. We discuss mainstream treatment strategies for advanced PCa, including chemotherapy, radiotherapy, and immunotherapy, in the context of PSMA-targeted nanomedicines. Additionally, we elucidate novel treatment concepts such as photodynamic and photothermal therapies, along with nano-theragnostics. We present the content in a clear and accessible manner, appealing to general physicians, including those with limited backgrounds in biochemistry and bioengineering. The review emphasizes the potential benefits of PSMA-targeted nanomedicines in enhancing treatment efficiency and improving patient outcomes. While the use of PSMA-targeted nano-drug delivery has demonstrated promising results, further investigation is required to comprehend the precise mechanisms of action, pharmacotoxicity, and long-term outcomes. By meticulous optimization of the combination of nanomedicines and PSMA ligands, a novel horizon of PSMA-targeted nanomedicine-based combination therapy could bring renewed hope for patients with advanced PCa.

Chimeric antigen receptor-modified T cells therapy in prostate cancer: A comprehensive review on the current state and prospects.

Recent immunotherapy research has focused on chimeric antigen receptor-modified T cells (CAR-Ts). CAR-T therapies have been clinically applied to manage hematologic malignancies with satisfactory effectiveness. However, the application of CAR-T immunotherapy in solid tumors remains challenging. Even so, current CAR-T immunotherapies for prostate cancer (PCa) have shown some promise, giving hope to patients with advanced metastatic PCa. This review aimed to elucidate different types of prostate tumor-associated antigen targets, such as prostate-specific membrane antigen and prostate stem cell antigen, and their effects. The current status of the corresponding targets in clinical research through their applications was also discussed. To improve the efficacy of CAR-T immunotherapy, we addressed the possible applications of multimodal immunotherapy, chemotherapy, and CAR-T combined therapies. The obstacles of solid tumors were concisely elaborated. Further studies should aim to discover novel potential targets and establish new models by overcoming the inherent barriers of solid tumors, such as tumor heterogeneity and the immunosuppressive nature of the tumor microenvironment.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Assessment of biochemical outcomes in patients with primary aldosteronism after adrenalectomy based on CT scan diagnosis of unilateral adenoma without adrenal vein sampling.

Purpose: The purpose of this study was to assess the surgical outcomes of patients with primary aldosteronism when surgery was based only on CT finding of unilateral adenoma without adrenal vein sampling (AVS). Methods: This is a retrospective review of the records of patients who had undergone retroperitoneal laparoscopic adrenalectomy for primary aldosteronism based on CT scan finding of unilateral adenoma and had a follow-up of at least 6-12 months from January 2012 to December 2020 in a single center; decision for adrenalectomy was based on CT scan, and AVS was not used. The clinical and biochemical outcomes were accessed using the standardized primary aldosteronism surgical outcome (PASO) criteria. Patient's demographics and preoperative factors were analyzed to assess for independent predictor of surgical success. Results: According to the PASO criteria, 172 patients finally enrolled in the training dataset, and 20 patients enrolled in the validation dataset. In the training dataset, complete clinical success was achieved in 71 patients (41.3%), partial success in 87 (50.6%), and absent success in 14 (8.1%). Biochemical outcomes showed that 151 patients (87.8%) were completely cured, 14 patients (8.1%) got a partial biochemical success, and an absent biochemical success was found in seven patients (4.1%). Multivariate logistic regression analysis showed that age, body mass index (BMI), tumor size, mean arterial pressure (MAP), and serum potassium were the most independent factors for incomplete biochemical success. Based on the results of statistical analysis, our study constructed a nomogram prognostic evaluation model for patients after unilateral primary aldosterone surgery. Conclusions: Laparoscopic adrenalectomy for patients with primary aldosteronism base on CT scan finding of a unilateral adenoma without AVS had a high rate of complete biochemical cure at 12 months. Risk factors for incomplete biochemical success include age, BMI, tumor size, MAP, and serum potassium. Our study constructed a nomogram prognostic evaluation model for patients after unilateral primary aldosterone surgery. The nomogram accurately and reliably predicted the incomplete biochemical success.

Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.

Purpose: PSA is currently the most commonly used screening indicator for prostate cancer. However, it has limited specificity for the diagnosis of prostate cancer. We aim to construct machine learning-based models and enhance the prediction of prostate cancer. Methods: The data of 551 patients who underwent prostate biopsy were retrospectively retrieved and divided into training and test datasets in a 3:1 ratio. We constructed five PCa prediction models with four supervised machine learning algorithms, including tPSA univariate logistic regression (LR), multivariate LR, decision tree (DT), random forest (RF), and support vector machine (SVM). The five prediction models were compared based on model performance metrics, such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curve, and clinical decision curve analysis (DCA). Results: All five models had good calibration in the training dataset. In the training dataset, the RF, DT, and multivariate LR models showed better discrimination, with AUCs of 1.0, 0.922 and 0.91, respectively, than the tPSA univariate LR and SVM models. In the test dataset, the multivariate LR model exhibited the best discrimination (AUC=0.918). The multivariate LR model and SVM model had better extrapolation and generalizability, with little change in performance between the training and test datasets. Compared with the DCA curves of the tPSA LR model, the other four models exhibited better net clinical benefits. Conclusion: The results of the current retrospective study suggest that machine learning techniques can predict prostate cancer with significantly better AUC, accuracy, and net clinical benefits.

PSMA conjugated combinatorial liposomal formulation encapsulating genistein and plumbagin to induce apoptosis in prostate cancer cells.

Although the biomedical sciences have achieved tremendous success in developing novel approaches to managing prostate cancer, this disease remains one of the major health concerns among men worldwide. Liposomal formulations of single drugs have shown promising results in cancer treatment; however, the use of multi drugs has shown a better therapeutic index than individual drugs. The identification of cancer-specific receptors has added value to design targeted drug delivering nanocarriers. We have developed genistein and plumbagin co-encapsulating liposomes (∼120 nm) with PSMA specific antibodies to target prostate cancer cells selectively in this work. These liposomes showed >90 % decrease in PSMA expressing prostate cancer cell proliferation without any appreciable toxicity to healthy cells and human red blood cells. Release of plumbagin and genistein was found to decrease the expression of PI3/AKT3 signaling proteins and Glut-1 receptors (inhibited glucose uptake and metabolism), respectively. The decrease in migration potential of cells and induced apoptosis established the observed anti-proliferative effect in prostate cancer cell lines. The discussed strategy of developing novel, non-toxic, and PSMA specific antibody conjugated liposomes carrying genistein and plumbagin drugs may also be used for encapsulating other drugs and inhibit the growth of different types of cancers.

A novel GPCR target in correlation with androgen deprivation therapy for prostate cancer drug discovery.

Most prostate carcinomas require androgen stimulation to grow, and for nearly 70 years, androgen ablation therapy has been one of the central therapeutic strategies against advanced prostate cancer. Although most tumours initially respond to this therapy, some will be acquired resistant and progress to metastatic castration-resistant (mCRPC) disease which clinically tends to progress more rapidly than earlier disease manifestations. The underlying molecular biology of mCRPC is highly complex, and numerous mechanisms have been proposed that promote and retain androgen independence. In various clinical and preclinical data explored, the nature of intracellular signalling pathways mediating mitogenic acquired resistant effects of GPCRs in prostate cancer is poorly defined. G-protein-coupled receptor kinase 2 (GRK2) contributes to the modulation of basic cellular functions-such as cell proliferation, survival or motility-and is involved in metabolic homeostasis, inflammation or angiogenic processes. Moreover, altered GRK2 levels are starting to be reported in different tumoural contexts and shown to promote breast tumourigenesis or to trigger the tumoural angiogenic switch. Thus, we are exploring recent findings that present unexpected opportunities to interfere with major tumourigenic signals by manipulating GPCR-mediated pathways.

Locally Controlled Release of Methotrexate and Alendronate by Thermo-Sensitive Hydrogels for Synergistic Inhibition of Osteosarcoma Progression.

Osteosarcoma (OS) is a serious primary bone malignant tumor that can easily affect children and adolescents. Chemotherapy is one of the important and feasible clinical treatment strategies for the treatment of OS at present, which is severely limited due to insufficient retention time, poor penetration ability, and serious side effects of current anti-tumor drug preparations. In this work, a novel injectable thermo-sensitive hydrogel (mPEG45-PLV19) loaded with methotrexate and alendronate, and the sustained release at the tumor site synergistically inhibited the progression of OS. The mPEG45-PLV19 shows excellent physical and chemical properties. Compared with other treatment groups, the in vivo treatment of gel+ methotrexate + alendronate effectively inhibited the growth of tumor. More importantly, it significantly reduced bone destruction and lung metastasis caused by OS. Therefore, this injectable thermo-sensitive hydrogel drug delivery system has broad prospects for OS chemotherapy.

Prognostic value of microRNA-20b expression level in patients with prostate cancer.

BACKGROUND: miR-20b is a member of the miR-106a-363 gene cluster located in the mammalian X chromosome, the larger miR-17 family, and the miR-17-92 and miR-106b-25 gene clusters. Previous studies have indicated that miR-20b may function as oncogene or tumor suppressor in different types of cancers. The present study analyzed the association between miR-20b and clinicopathological characteristics of patients with prostate cancer. METHODS: A total of 127 pairs of prostate cancer tissue samples and adjacent prostate tissue samples were collected from April 2013 to March 2018. The associations between miR-20b expression levels and clinicopathological factors were assessed using the χ2­test. Survival was estimated using the Kaplan-Meier method, and the differences in survival according to miR-20b expression were compared using the log-rank test. Prognostic values of miR-20b expression and clinical outcomes were evaluated by Cox regression analysis. RESULTS: The relative expression of miR-20b in prostate cancer tissues was significantly higher than that in adjacent noncancerous prostate tissues (P<0.001). miR-20b expression was observed to be significantly associated with Gleason score (P<0.001), lymph node metastasis (P<0.001), and TNM stage (P=0.002). The log-rank test indicated that patients with increased miR-20b expression experienced poor overall survival (P=0.037). Multivariate Cox regression analysis showed that miR-20b expression level (HR=2.181, 95% CI: 1.772-9.021, P=0.016) was an independent factor in predicting the overall survival of prostate cancer patients. CONCLUSION: The present study demonstrated that tissue miR-20b expression level could be a promising biomarker of prognosis in prostate cancer.

Advances in Drug Delivery via Biodegradable Ureteral Stent for the Treatment of Upper Tract Urothelial Carcinoma.

Drug eluting ureteral stent is an effective means for local drug delivery to the urinary tract. It can potentially solve a variety of upper urinary tract problems, such as stent-related urinary tract infections and discomfort, ureteral stricture, and neoplastic diseases. However, the release of drug elutes on the surface of biostable stents is unsustainable and uncontrollable. With the development of biomaterial science, the emergence of biodegradable ureteral stents (BUSs) provides a new approach for local drug delivery in the urinary tract. The drugs can be continuously released in a controlled manner from a drug-eluting BUS, when the stent degrades. Especially for the delivery of anti-tumor drugs, the stents can obviously improve the therapeutic effectiveness of the drugs by prolonging the contact duration of the drug and tumor cells. In addition, a secondary stent removal procedure can be avoided. The purpose of this review article is to provide an overview of anti-tumor drug-eluting BUSs and discuss the biomaterials and drug delivery systems of BUS that are currently being developed to deliver anti-tumor drugs for upper tract urothelial carcinoma.

lncRNA BCAR4 sponges miR­370­3p to promote bladder cancer progression via Wnt signaling.

Accumulating evidence suggests that the aberrant expression of long non­coding RNAs (lncRNAs) is involved in the initiation, development and metastasis of bladder cancer (BC). Although several differentially expressed lncRNAs have been identified via lncRNA expression profiling of BC tissues, their functions and the molecular mechanisms underlying these functions remain to be fully elucidated. In the present study, elevated levels of lncRNA breast cancer anti­estrogen receptor 4 (BCAR4) were identified in BC tissues compared with matched healthy tissues. Silencing of BCAR4 inhibited cell proliferation and induced apoptosis in BC cell lines 5637 and T24. Downregulation of BCAR4 led to the inactivation of Wnt signaling. Mechanistically, BCAR4 directly sponged microRNA (miR)­370­3p and elevated Wnt7a expression. Endogenous expression of Wnt7a reversed BCAR4 silencing­mediated cell growth arrest and induction of apoptosis in BC cells accompanied with a re­activation of Wnt signaling. Reverse transcription­quantitative PCR indicated that there was a strong association between BCAR4, miR­370­3p and Wnt7a expression in tumors from patients with BC compared with healthy control tissues. In conclusion, results of the present study suggest that lncRNA BCAR4 promoted proliferation and survival of BC cells via downregulation of miR­370­3p. Therefore, lncRNA BCAR4 may be a lncRNA of oncogenic potential in BC.