Molecular mechanisms and clinical applications of exosomes in prostate cancer

Extracellular vesicle-derived biomarkers in prostate cancer care: Opportunities and challenges

Prostate cancer (PCa) is the second most prevalent cancer in men worldwide, presenting a significant global public health challenge that necessitates early detection and personalized treatment. Recently, non-invasive liquid biopsy methods have emerged as promising tools to provide insights into the genetic landscape of PCa and monitor disease progression, aiding decision-making at all stages. Research efforts have concentrated on identifying liquid biopsy biomarkers to improve PCa diagnosis, prognosis, and treatment prediction. This article reviews recent research advances over the last five years utilizing extracellular vesicles (EVs) as a natural biomarker library for PCa, and discusses the clinical translation of EV biomarkers, including ongoing trials and key implementation challenges. The findings underscore the transformative role of liquid biopsy, particularly EV-based biomarkers, in revolutionizing PCa diagnosis, prediction, and treatment.

Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study

INTRODUCTION

This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection.

METHODS

We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation.

RESULTS

The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa.

CONCLUSIONS

The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.

Nanomedicines in diagnosis and treatment of prostate cancers: an updated review

Prostate cancer (PC) is the third most common male cancer in the world, which occurs due to various mutations leading to the loss of chromatin structure. There are multiple treatments for this type of cancer, of which chemotherapy is one of the most important. Sometimes, a combination of different treatments, such as chemotherapy, radiotherapy, and surgery, are used to prevent tumor recurrence. Among other treatments, androgen deprivation therapy (ADT) can be mentioned, which has had promising results. One of the drawbacks of chemotherapy and ADT treatments is that they are not targeted to the tumor tissue. For this reason, their use can cause extensive side effects. Treatments based on nanomaterials, known as nanomedicine, have attracted much attention today. Nanoparticles (NPs) are one of the main branches of nanomedicine, and they can be made of different materials such as polymer, metal, and carbon, each of which has distinct characteristics. In addition to NPs, nanovesicles (NVs) also have therapeutic applications in PC. In treating PC, synthetic NVs (liposomes, micelles, and nanobubbles) or produced from cells (exosomes) can be used. In addition to the role that NPs and NVs have in treating PC, due to being targeted, they can be used to diagnose PC and check the treatment process. Knowing the characteristics of nanomedicine-based treatments can help design new treatments and improve researchers' understanding of tumor biology and its rapid diagnosis. In this study, we will discuss conventional and nanomedicine-based treatments. The results of these studies show that the use of NPs and NVs in combination with conventional treatments has higher efficacy in tumor treatment than the individual use of each of them.

SYT4 binds to SNAP25 to facilitate exosomal secretion and prostate cancer enzalutamide resistance

Prostate carcinoma represents a predominant malignancy affecting the male population, with androgen deprivation therapy (ADT) serving as a critical therapeutic modality for advanced disease states, but it often leads to the development of resistance. Enzalutamide (Enz), a second-generation antiandrogen drug, initially offers substantial therapeutic benefit, but its efficacy wanes as drug resistance ensues. In this study, we found that synaptotagmin 4 (SYT4) is an upregulated gene in enzalutamide-resistant (EnzR) cell lines. The downregulation of SYT4, in combination with enzalutamide therapy, substantially enhances the antiproliferative effect on resistant prostate cancer cells beyond the capacity of enzalutamide monotherapy. SYT4 promotes vesicle efflux by binding to the synaptosome-associated protein 25 (SNAP25), thereby contributing to cell resistance against enzalutamide. The elevated expression of SYT4 is mediated by bromodomain-containing protein 4 (BRD4), and BRD4 inhibition effectively suppressed the expression of SYT4. Treatment with a therapeutic dose of enzalutamide combined with ASO-1, an antisense oligonucleotide drug targeting SYT4, shows promising results in reversing the resistance of prostate cancer to enzalutamide.

Knowledge mapping of exosomes in prostate cancer from 2003 to 2022: a bibliometric analysis

BACKGROUND

Prostate cancer (PCa) is highly prevalent among males worldwide. The investigation of exosomes in PCa has emerged as a dynamic and important research area. To visually depict the prominent research areas and evolutionary patterns of exosomes in PCa, we performed a comprehensive analysis via bibliometric methods.

METHODS

Studies were retrieved from the Web of Science Core Collection. CiteSpace, VOSviewers, and the R package "bibliometrix" were employed to analyze the relationships and collaborations among countries/regions, organizations, authors, journals, references, and keywords.

RESULTS

Over the past 20 years (2003-2022), 995 literatures on exosomes in PCa have been collected. The findings indicate a consistent upward trend in annual publications with the United States being the leading contributor. Cancers is widely recognized as the most prominent journal in this area. In total, 5936 authors have contributed to these publications, with Alicia Llorente being the most prolific. The primary keywords associated with research hotspots include "liquid biopsy", "identification", "growth", "microRNAs", and "tumor-derived exosomes".

CONCLUSION

Our analysis reveals that investigating the intrinsic mechanisms of exosomes in PCa pathogenesis and exploring the potential of exosomes as biomarkers of PCa constitute the principal focal points in this domain of research.

The protein composition of exosomes released by prostate cancer cells is distinctly regulated by androgen receptor-antagonists and -agonist to stimulate growth of target cells

BACKGROUND

Prostate cancer (PCa) is a prevalent malignancy in men worldwide, ranking as the second leading cause of cancer-related death in Western countries. Various PCa hormone therapies, such as androgen receptor (AR)-antagonists or supraphysiological androgen level (SAL) reduce cancer cell proliferation. However, treated cells may influence the growth of neighboring cells through secreted exosomes in the tumor microenvironment (TME). Here, the change of protein content of exosomes secreted from PCa cells through treatment with different AR-antagonists or SAL has been analyzed.

METHODS

Isolation of exosomes via ultracentrifugation of treated human PCa LNCaP cells with AR-agonist and various AR-antagonists; analysis of cellular senescence by detection of senescence associated beta galactosidase activity (SA β-Gal); Western blotting and immunofluorescence staining; Mass spectrometry (MS-spec) of exosomes and bioinformatic analyses to identify ligand-specific exosomal proteins. Growth assays to analyze influence of exosomes on non-treated cells.

RESULTS

MS-spec analysis identified ligand-specific proteins in exosomes. One thousand seventy proteins were up- and 52 proteins downregulated by SAL whereas enzalutamide upregulated 151 proteins and downregulated 42 exosomal proteins. The bioinformatic prediction indicates an up-regulation of pro-proliferative pathways. AR ligands augment hub factors in exosomes that include AKT1, CALM1, PAK2 and CTNND1. Accordingly, functional assays confirmed that the isolated exosomes from AR-ligand treated cells promote growth of untreated PCa cells.

CONCLUSION

The data suggest that the cargo of exosomes is controlled by AR-agonist and -antagonists and distinct among the AR-antagonists. Further, exosomes promote growth that might influence the TME. This finding sheds light into the complex interplay between AR signaling and exosome-mediated communication between PCa cells.

Identification of cell differentiation trajectory-related gene signature to reveal the prognostic significance and immune landscape in prostate cancer based on multiomics analysis

Background

In the context of prostate cancer (PCa), the occurrence of biochemical recurrence (BCR) stands out as a pivotal factor significantly impacting prognosis, potentially leading to metastasis and mortality. However, the early detection of BCR poses a substantial challenge for PCa patients. There is an urgent need to pinpoint hub genes that can serve as predictive indicators for BCR in PCa patients.

Methods

Our primary goal was to identify cell differentiation trajectory-related gene signature in PCa patients by pseudo-time trajectory analysis. We further explored the functional enrichment of overlapped marker genes and probed clinically relevant modules and BCR-related genes using Weighted Gene Co-expression Network Analysis (WGCNA) in PCa patients. Key genes predicting recurrence-free survival were meticulously identified through univariate and multivariate Cox regression analyses. Subsequently, these genes were utilized to construct a prognostic gene signature, the expression, predictive efficacy, putative functions, and immunological landscape of which were thoroughly validated. Additionally, we employed immunohistochemistry (IHC) and a western blotting assay to quantify the expression of PYCR1 in clinical samples.

Results

Our single-cell RNA (scRNA) sequencing analysis unveiled three subgroups characterized by distinct differentiation trajectories, and the marker genes associated with these groups were extracted from PCa patients. These marker genes successfully classified the PCa sample into two molecular subtypes, demonstrating a robust correlation with clinical characteristics and recurrence-free survival. Through WGCNA and Lasso analysis, we identified four hub genes (KLK3, CD38, FASN, and PYCR1) to construct a risk profile of prognostic genes linked to BCR. Notably, the high-risk patient group exhibited elevated levels of B cell naive, Macrophage M0, and Macrophage M2 infiltration, while the low-risk group displayed higher levels of T cells CD4 memory activated and monocyte infiltration. Furthermore, IHC and western blotting assays confirmed the heightened expression of PYCR1 in PCa tissues.

Conclusion

This study leveraged the differentiation trajectory and genetic variability of the microenvironment to uncover crucial prognostic genes associated with BCR in PCa patients. These findings present novel perspectives for tailoring treatment strategies for PCa patients on an individualized basis.

Exploring the metastasis-related biomarker and carcinogenic mechanism in liver cancer based on single cell technology

Background

Hepatocellular carcinoma (HCC) is a fatal primary malignancy characterized by high invasion and migration. We aimed to explore the underlying metastasis-related mechanism supporting the development of HCC.

Methods

The dataset of single cell RNA-seq (GSE149614) were collected for cell clustering by using the Seurat R package, the FindAllMarkers function was used to find the highly expression and defined the cell cluster. The WebGestaltR package was used for the GO and KEGG function analysis of shared genes, the Gene Set Enrichment Analysis (GSVA) was performed by clusterProfiler R package, the hTFtarget database was used to identify the crucial transcription factors (TFs), the Genomics of Drug Sensitivity in Cancer (GDSC) database was used for the drug sensitivity analysis. Finally, the overexpression and trans-well assay was used for gene function analysis.

Results

We obtained 9 cell clusters from the scRNA-seq data, including the nature killer (NK)/T cells, Myeloid cells, Hepatocytes, Epithelial cells, Endothelial cells, Plasma B cells, Smooth muscle cells, B cells, Liver bud hepatic cells. Further cell ecological analysis indicated that the Hepatocytes and Endothelial cell cluster were closely related to the cancer metastasis. Subsequently, the NDUFA4L2-Hepatocyte, GTSE1-Hepatocyte, ENTPD1-Endothelial and NDUFA4L2-Endothelial were defined as metastasis-supporting cell clusters, in which the NDUFA4L2-Hepatocyte cells was closely related to angiogenesis, while the NDUFA4L2-Endothelial was related with the inflammatory response and complement response. The overexpression and trans-well assay displayed that NDUFA4L2 exhibited clearly metastasis-promoting role in HCC progression.

Conclusion

We identified and defined 4 metastasis-supporting cell clusters by using the single cell technology, the specify shared gene was observed and played crucial role in promoting cancer progression, our findings were expected to provide new insight in control cancer metastasis.

The regulatory process and practical significance of non-coding RNA in the dissemination of prostate cancer to the skeletal system

Prostate cancer is a major contributor to male cancer-related mortality globally. It has a particular affinity for the skeletal system with metastasis to bones seriously impacting prognosis. The identification of prostate cancer biomarkers can significantly enhance diagnosis and patient monitoring. Research has found that cancer and metastases exhibit abnormal expression of numerous non-coding RNA. Some of these RNA facilitate prostate cancer bone metastasis by activating downstream signaling pathways, while others inhibit this process. Elucidating the functional processes of non-coding RNA in prostate cancer bone metastasis will likely lead to innovative treatment strategies for this malignant condition. In this review, the mechanistic role of the various RNA in prostate cancer is examined. Our goal is to provide a new avenue of approach to the diagnosis and treatment of bone metastasis in this cancer.

Prostate cancer-derived small extracellular vesicle proteins: the hope in diagnosis, prognosis, and therapeutics

Current diagnostic tools for prostate cancer (PCa) diagnosis and risk stratification are insufficient. The hidden onset and poor efficacy of traditional therapies against metastatic PCa make this disease a heavy burden in global men's health. Prostate cancer-derived extracellular vesicles (PCDEVs) have garnered attention in recent years due to their important role in communications in tumor microenvironment. Recent advancements have demonstrated PCDEVs proteins play an important role in PCa invasion, progression, metastasis, therapeutic resistance, and immune escape. In this review, we briefly discuss the applications of sEV proteins in PCa diagnosis and prognosis in liquid biopsy, focus on the roles of the PCa-derived small EVs (sEVs) proteins in tumor microenvironment associated with cancer progression, and explore the therapeutic potential of sEV proteins applied for future metastatic PCa therapy.

Genomic and Phenotypic Biomarkers for Precision Medicine Guidance in Advanced Prostate Cancer

OPINION STATEMENT

Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.

Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

Small Extracellular Vesicles Derived from Induced Pluripotent Stem Cells in the Treatment of Myocardial Injury

Induced pluripotent stem cell (iPSC) therapy brings great hope to the treatment of myocardial injuries, while extracellular vesicles may be one of the main mechanisms of its action. iPSC-derived small extracellular vesicles (iPSCs-sEVs) can carry genetic and proteinaceous substances and mediate the interaction between iPSCs and target cells. In recent years, more and more studies have focused on the therapeutic effect of iPSCs-sEVs in myocardial injury. IPSCs-sEVs may be a new cell-free-based treatment for myocardial injury, including myocardial infarction, myocardial ischemia-reperfusion injury, coronary heart disease, and heart failure. In the current research on myocardial injury, the extraction of sEVs from mesenchymal stem cells induced by iPSCs was widely used. Isolation methods of iPSCs-sEVs for the treatment of myocardial injury include ultracentrifugation, isodensity gradient centrifugation, and size exclusion chromatography. Tail vein injection and intraductal administration are the most widely used routes of iPSCs-sEV administration. The characteristics of sEVs derived from iPSCs which were induced from different species and organs, including fibroblasts and bone marrow, were further compared. In addition, the beneficial genes of iPSC can be regulated through CRISPR/Cas9 to change the composition of sEVs and improve the abundance and expression diversity of them. This review focused on the strategies and mechanisms of iPSCs-sEVs in the treatment of myocardial injury, which provides a reference for future research and the application of iPSCs-sEVs.