Biomarkers of Aggressive Prostate Cancer at Diagnosis

Potential of sonobiopsy as a novel diagnosis tool for brain cancer

Brain tumors have a poor prognosis. Early, accurate diagnosis and treatment are crucial. Although brain surgical biopsy can provide an accurate diagnosis, it is highly invasive and risky and is not suitable for follow-up examination. Blood-based liquid biopsies have a low detection rate of tumor biomarkers and limited evaluation ability due to the existence of the blood-brain barrier (BBB). The BBB is composed of brain capillary endothelial cells through tight junctions, which prevents the release of brain tumor markers to the human peripheral circulation, making it more difficult to diagnose, predict prognosis, and evaluate therapeutic response through brain tumor markers than other tumors. Focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) is an emerging technique using FUS to promote the release of tumor markers into the circulatory system and cerebrospinal fluid, thus facilitating tumor detection. The feasibility and safety data from both animal models and clinical trials support sonobiopsy as a great potential in the diagnosis of brain diseases.

Effects on molecular interactions of hollow gold nanoparticles and antibody for sensitizing P24 antigen determination

In recent years, with the rapid development of point-of-care testing, the application of lateral flow immunochromatography assay (LFIA) has become increasingly widespread. The key to the success of these detection technologies is the effective binding with diagnostic materials and detection antibody proteins. Although many researchers have tried to optimize antibody binding, a universally accepted strategy that can provide maximum performance has not been determined. In this study, the HIV infection P24 antigen was selected as the detection biomarker. Then the binding mechanism between hollow gold nanoparticles as diagnostic materials and detection antibodies was explored through dynamic light scattering, Fourier transform infrared spectroscopy, circular dichroism spectroscopy, and other methods. It was found that the binding efficiency is related to the change in protein secondary conformation during binding, hydrogen bonding, and van der Waals force maintain the binding mechanism between antibodies and nanoparticles. The main forces of particle complexation and the main binding site of the antibody were discussed and analyzed. Finally, an immunochromatographic system was constructed to evaluate the significant advantages of this platform compared to the common colloidal gold immunochromatographic system.

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study

OBJECTIVE

To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.

METHODS

Three hundred sixty-two inpatients diagnosed with PCa at the First Affiliated Hospital were randomly assigned to training and testing sets in a 3:7 ratio, while 136 PCa patients from People's Hospital formed the external validation set. Imaging and clinicopathologic information were collected. Optimal features distinguishing advanced prostate cancer (APC) and metastatic PCa (mPCa) were identified through logistic regression (LR). ML algorithms were employed to build and compare ML models. The best-performing algorithm established models for PCa progression stage. Models performance was evaluated using metrics, ROC curves, calibration, and decision curve analysis (DCA) in training, testing, and external validation sets.

RESULTS

Following LR analyses, PSA (P = .001), maximum tumor diameter (P = .026), Gleason score (P <.001), and RNF41 (P <.001) were optimal features for predicting APC, while ALP (P <.001), PSA (P <.001), and GS score (P = .024) were for mPCa. Among ML models, the LR models exhibited superior performance. Consequently, the LR algorithm was used for the APC-risk-nomogram and mPCa-risk-nomogram construction, with AUC values of 0.848, 0.814, 0.810, and 0.940, 0.913, 0.910, in the training, testing, and external validation sets, respectively. Calibration and DCA curves affirmed nomograms' consistency and net benefits for clinical decision-making.

CONCLUSION

In summary, ML-based APC-risk-nomogram and mPCa-risk-nomogram exhibit outstanding predictive performance for PCa progression stages. These nomograms can assist clinicians in finely categorizing newly diagnosed PCa patients, facilitating personalized treatment plans and prognosis assessment.

METTL14 inhibits the proliferation, migration and invasion of prostate cancer cells by increasing m6A methylation of CDK4

Background

Methyltransferase-like (METTL) plays an important role in various biological processes, but its role in prostate cancer (PCa) is still unclear. This study aimed to explore the mechanism by which methyltransferase-like 14 (METTL14) inhibits the physiological activity of PCa cells by increasing the N6-methyladenosine (m6A) modification of cyclin-dependent kinase 4 (CDK4).

Methods

Clinical samples were collected for bioinformatics analysis. A PCa mouse model was constructed. Cell counting kit-8 (CCK-8), flow cytometry, colony formation assays, scratch assays, Transwell assays, real-time quantitative polymerase chain reaction (RT-qPCR), immunofluorescence and western blotting were used to detect the corresponding indicators.

Results

METTL14 was found to be beneficial to inhibit the proliferation, invasion, and migration of PCa cells. When the m6A RNA increased, the half-life of CDK4 mRNA decreased after oe-METTL14 (overexpression of METTL14). Overexpression of CDK4 reversed the effect of oe-METTL14. Coimmunoprecipitation experiments revealed there were interactions between CDK4 and forkhead box M1 (FOXM1). Transfection of si-CDK4 was similar to transfection of oe-METTL14. After transfection with oe-FOXM1, the invasion and migration ability of cells increased, and cell apoptosis decreased. After transfection with si-FOXM1 alone, autophagy related 7 (ATG7) expression was significantly downregulated, and autophagy levels were reduced. The overexpression of ATG7 reversed the effect of si-FOXM1. The tumor volume and weight of the oe-METTL14 group mice were significantly reduced, and tumor proliferation was decreased in comparison to untreated tumor-bearing mice.

Conclusions

METTL14 inhibits the invasion and migration of PCa cells and induces cell apoptosis by inhibiting CDK4 stability and FOXM1/ATG7-mediated autophagy.

Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer

Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

A Synergistic Strategy Combining Chemotherapy and Photodynamic Therapy to Eradicate Prostate Cancer

Prostate cancer is the most prevalent cancer among men in the United States and is a leading cause of cancer-related death. Prostate specific membrane antigen (PSMA) has been established as a biomarker for prostate cancer diagnosis and treatment. This study aimed to develop a novel theranostic agent, PSMA-1-MMAE-Pc413, which integrates a PSMA-targeting ligand, the photosensitizer Pc413, and the microtubular inhibitor monomethyl auristatin E (MMAE) for synergistic therapeutic efficacy. In vitro uptake studies revealed that PSMA-1-MMAE-Pc413 demonstrated selective and specific uptake in PSMA-positive PC3pip cells but not in PSMA-negative PC3flu cells, with the uptake in PC3pip cells being approximately three times higher. In vitro cytotoxicity assays showed that, when exposed to light, PSMA-1-MMAE-Pc413 had a synergistic effect, leading to significantly greater cytotoxicity in PSMA-positive cells (IC50 = 2.2 nM) compared to PSMA-1-Pc413 with light irradiation (IC50 = 164.9 nM) or PSMA-1-MMAE-Pc413 without light irradiation (IC50 = 12.6 nM). In vivo imaging studies further demonstrated the selective uptake of PSMA-1-MMAE-Pc413 in PC3pip tumors. In in vivo studies, PSMA-1-MMAE-Pc413 dramatically improves the therapeutic outcome for prostate cancer by providing a synergistic effect that surpasses the efficacy of each treatment modality alone in PC3pip tumors. These findings suggest that PSMA-1-MMAE-Pc413 has strong potential for clinical application in improving prostate cancer treatment.

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66-6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05-14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

Urinary PSA-ZINC biomarker outperforms standard of care in early detection of prostate cancer

BACKGROUND

Urine is a promising biological fluid for prostate cancer (PCa) diagnostics due to its non-invasive collection and wide range of biomarkers. The aim of this study was to assess the role of urinary PSA (uPSA) and urinary Zinc (uZinc) as biomarkers for the diagnosis of PCa in combination with routine parameters of standard of care (SOC - blood PSA, abnormal DRE, age) and MRI in patients candidates for prostate biopsy.

METHODS

Urine samples after prostatic massages were collected from men with suspected PCa scheduled for prostate biopsy. Quantification of uPSA was performed by ECLIA platform and confirmed by ELISA assay, while uZinc measurement was evaluated by ICP-MS and confirmed by colorimetric in vitro assay. Six multivariate logistic regression analysis were performed to assess diagnostic performance of uPSA and uZinc (urine), SOC and MRI alone, and combination of MRI+SOC, MRI+urine and SOC+MRI+urine. The discriminative power of the logistic models was assessed by calculating the area under the receiver operating characteristic (ROC) curves (AUC).

RESULTS

Two hundred thirty-eight patients were included in the analysis; 145 of them were diagnosed with PCa. Urine test showed a better discrimination of HS from CP, in respect of uPSA and uZinc alone, both for PCa of any grade and Gleason Score ≥7 (4+3) (AUC 0.804 and 0.823 respectively). ROC curve combining SOC+MRI+urine showed an AUC=0.882, that is statistically different from SOC or MRI alone, or MRI+SOC (P=0.0001, P=0.0001, and P=0.008 respectively). PCa risk algorithm designed considering SOC+MRI+urine results in potential reduction of 57% of unnecessary biopsies compared to the current standard parameters.

CONCLUSIONS

The loss of uPSA and Zinc production and secretion during neoplastic transformation of the prostate could potentially represent a hallmark of PCa. Its combination with age, PSA and DRE, as well as with mpMRI could represent an interesting approach to improve the diagnostic accuracy of PCa.

Molecular diagnostics of prostate cancer: impact of molecular tests

ABSTRACT

Prostate cancer (PCa) is the second leading cause of cancer-related death among men. Prostate-specific antigen (PSA) testing is used in screening programs for early detection with a consequent reduction of PCa-specific mortality at the cost of overdiagnosis and overtreatment of the nonaggressive PCa. Recently, several assays have been commercially developed to implement PCa diagnosis, but they have not been included in both screening and diagnosis of PCa. This review aims to describe the actual and novel commercially available molecular biomarkers that can be used in PCa management to implement and tailor the screening and diagnosis of PCa.

Primary cancer prevention for cancers with no known infectious etiology: Time for a new paradigm

Vaccines developed for hepatitis B and human papilloma virus infections have been very successful in reducing the burden of cancer due to these infections. In the past decade, our understanding of the immunology of cancer has greatly improved and important progress has been made in the use of immunotherapy for several cancers. However, for the majority of cancers, an infectious etiology is either unknown or does not exist. Prostate cancer, for which no infectious etiology is known, is the most common cancer in men in the United States. Here we discuss the rationale for developing a preventive vaccine for prostate cancer, discuss a possible approach for further work in this area and a means of testing the effectiveness of a prostate cancer prevention vaccine in a clinical trial.

VOC-based detection of prostate cancer using an electronic nose and ion mobility spectrometry: A novel urine-based approach

BACKGROUND

Many diseases leave behind specific metabolites which can be detected from breath and urine as volatile organic compounds (VOC). Our group previously described VOC-based methods for the detection of bladder cancer and urinary tract infections. This study investigated whether prostate cancer can be diagnosed from VOCs in urine headspace.

METHODS

For this pilot study, mid-stream urine samples were collected from 56 patients with histologically confirmed prostate cancer. A control group was formed with 53 healthy male volunteers matched for age who had recently undergone a negative screening by prostate-specific antigen (PSA) and digital rectal exam. Headspace measurements were performed with the electronic nose Cyranose 320TM . Statistical comparison was performed using principal component analysis, calculating Mahalanobis distance, and linear discriminant analysis. Further measurements were carried out with ion mobility spectrometry (IMS) to compare detection accuracy and to identify potential individual analytes. Bonferroni correction was applied for multiple testing.

RESULTS

The electronic nose yielded a sensitivity of 77% and specificity of 62%. Mahalanobis distance was 0.964, which is indicative of limited group separation. IMS identified a total of 38 individual analytical peaks, two of which showed significant differences between groups (p < 0.05). To discriminate between tumor and controls, a decision tree with nine steps was generated. This model led to a sensitivity of 98% and specificity of 100%.

CONCLUSIONS

VOC-based detection of prostate cancer seems feasible in principle. While the first results with an electronic nose show some limitations, the approach can compete with other urine-based marker systems. However, it seems less reliable than PSA testing. IMS is more accurate than the electronic nose with promising sensitivity and specificity, which warrants further research. The individual relevant metabolites identified by IMS should further be characterized using gas chromatography/mass spectrometry to facilitate potential targeted rapid testing.

Evaluation of HNF1B, KLK3, ELAC2, TMPRSS2-ERG, and CTNNB1 polymorphisms associated with prostate cancer in samples of patients from HUPE-UERJ

PURPOSE

Prostate cancer (PCa) is the leading cause of death among men in 48 countries. Genetic alterations play a significant role in PCa carcinogenesis. For the hypothesis of this research, five unique polymorphisms (SNP) were investigated in different genes that showed to be associated in different ways with PCa: rs4430796, rs2735839, rs4792311, rs12329760, and rs28931588, respectively for the genes HNF1B, KLK3, ELAC2, TMPRSS2-ERG, and CTNNB1.

PATIENTS AND METHODS

Blood samples from 426 subjects were evaluated: 290 controls (161 females and 129 males) and 136 PCa patients. SNP were determined by real-time polymerase chain reaction. TaqMan SNP genotyping assay. In the control samples, the SNPs were defined in association with the self-reported ethnicity, and in 218 control samples with markers with ancestry indicators. The genes were in Hardy-Weinberg equilibrium. One hundred and seventy control samples were matched by ethnicity for comparison with the PCa samples.

RESULTS

The G allele at rs28931588 was monomorphic in both patients and controls studied. Significant differences were observed in allelic and genotypic frequencies between the control and Pca samples in rs2735839 (KLK3; p = 0.002 and χ2  = 8.73 and p = 0.01, respectively), by the global frequency and in the dominant model rs2735839_GG (odds ratio [OR] = 0.51, p = 0.02). AA and GA genotypes at rs4792311 (ELAC2) were more frequent in patients with Gleason 7(4 + 3), 8, and 9 (n = 37%-59.7%) compared to patients with Gleason 6 and 7(3 + 4) (n = 26%-40.0%) conferring a protective effect on the GG genotype (OR = 0.45, p = 0.02). The same genotype showed an OR = 2.71 (p = 0.01) for patients with low severity. The HNF1B-KLK3-ELAC2-TMPRSS2-ERG haplotypes: GAAT, AAAT, GAGT, and AAGT were more frequent in patients with Pca with OR ranging from 4.65 to 2.48.

CONCLUSIONS

Higher frequencies of risk alleles were confirmed in the SNPs, KLK3 rs2735839_A, ELAC2 rs4792311_A, and TMPRSS2 rs12329760_T in patients with Pca. Rs2735839_A was associated with risk of Pca and rs4792311_A with severity and Gleason score of 7(4 + 3) or greater. There is a need for careful observation of rs2735839 and rs4792311 in association with the prostatic biopsy due to the increased risk of Pca.

Lymphovascular Invasion at the Time of Radical Prostatectomy Adversely Impacts Oncological Outcomes

Lymphovascular invasion, whereby tumour cells or cell clusters are identified in the lumen of lymphatic or blood vessels, is thought to be an essential step in disease dissemination. It has been established as an independent negative prognostic indicator in a range of cancers. We therefore aimed to assess the impact of lymphovascular invasion at the time of prostatectomy on oncological outcomes. We performed a multicentre, retrospective cohort study of 3495 men who underwent radical prostatectomy for localised prostate cancer. Only men with negative preoperative staging were included. We assessed the relationship between lymphovascular invasion and adverse pathological features using multivariable logistic regression models. Kaplan-Meier curves and Cox proportional hazard models were created to evaluate the impact of lymphovascular invasion on oncological outcomes. Lymphovascular invasion was identified in 19% (n = 653) of men undergoing prostatectomy. There was an increased incidence of lymphovascular invasion-positive disease in men with high International Society of Urological Pathology (ISUP) grade and non-organ-confined disease (p < 0.01). The presence of lymphovascular invasion significantly increased the likelihood of pathological node-positive disease on multivariable logistic regression analysis (OR 15, 95%CI 9.7-23.6). The presence of lymphovascular invasion at radical prostatectomy significantly increased the risk of biochemical recurrence (HR 2.0, 95%CI 1.6-2.4). Furthermore, lymphovascular invasion significantly increased the risk of metastasis in the whole cohort (HR 2.2, 95%CI 1.6-3.0). The same relationship was seen across D'Amico risk groups. The presence of lymphovascular invasion at the time of radical prostatectomy is associated with aggressive prostate cancer disease features and is an indicator of poor oncological prognosis.

Unlocking Early Cancer Detection: Exploring Biomarkers, Circulating DNA, and Innovative Technological Approaches

Research and development improvements in early cancer diagnosis have had a significant positive impact on health. In the treatment and prevention of cancer, early detection is essential. In this context, biomarkers are essential because they offer important information on the state of cells at any particular time. Cells go through unique changes when they shift from a healthy condition to a malignant state, changes that appropriate biomarkers may pick up. Recent advancements have been made to identify and characterize circulating cancer-specific mutations in cell-free circulating DNA derived from tumors and tumor cells. A patient's delay between the time they first detect symptoms and the time they contact a doctor has been noted for many cancer forms. The tumor's location and features significantly impact the presentation of symptoms judged appropriate for early diagnosis. Lack of knowledge of the severity of the symptoms may be one cause for this delay. Our review is largely focused on the ongoing developments of early diagnosis in the study of biomarkers, circulating DNA for diagnosis, the biology of early challenges, early symptoms, liquid biopsies, detectable by imaging, established tumor markers, plasma DNA technologies, gender differences, and artificial intelligence (AI) in diagnosis. This review aims to determine and evaluate Indicators for detecting early cancer, assessing medical conditions, and evaluating potential risks. For Individuals with a heightened likelihood of developing cancer or who have already been diagnosed, early identification is crucial for enhancing prognosis and raising the likelihood of effective treatment.

Biomarkers for Prostate Cancer: From Diagnosis to Treatment

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.

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

PURPOSE

Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk.

METHODS

Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC).

RESULTS

In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS.

CONCLUSION

This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

Prostate Cancer: Genetics, Epigenetics and the Need for Immunological Biomarkers

Epidemiological data highlight prostate cancer as a significant global health issue, with high incidence and substantial impact on patients' quality of life. The prevalence of this disease is associated with various factors, including age, heredity, and race. Recent research in prostate cancer genetics has identified several genetic variants that may be associated with an increased risk of developing the disease. However, despite the significance of these findings, genetic markers for prostate cancer are not currently utilized in clinical practice as reliable indicators of the disease. In addition to genetics, epigenetic alterations also play a crucial role in prostate cancer development. Aberrant DNA methylation, changes in chromatin structure, and microRNA (miRNA) expression are major epigenetic events that influence oncogenesis. Existing markers for prostate cancer, such as prostate-specific antigen (PSA), have limitations in terms of sensitivity and specificity. The cost of testing, follow-up procedures, and treatment for false-positive results and overdiagnosis contributes to the overall healthcare expenditure. Improving the effectiveness of prostate cancer diagnosis and prognosis requires either narrowing the risk group by identifying new genetic factors or enhancing the sensitivity and specificity of existing markers. Immunological biomarkers (both circulating and intra-tumoral), including markers of immune response and immune dysfunction, represent a potentially useful area of research for enhancing the diagnosis and prognosis of prostate cancer. Our review emphasizes the need for developing novel immunological biomarkers to improve the diagnosis, prognosis, and management of prostate cancer. We highlight the most recent achievements in the identification of biomarkers provided by circulating monocytes and tumor-associated macrophages (TAMs). We highlight that monocyte-derived and TAM-derived biomarkers can enable to establish the missing links between genetic predisposition, hormonal metabolism and immune responses in prostate cancer.

Early Detection of Prostate Cancer: The Role of Scent

Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.

Analysis of Clinical Trials and Review of Recent Advances in Therapy Decisions for Locally Advanced Prostate Cancer

Despite the implementation of screening and early detection in many countries, the prostate cancer mortality rate remains high, particularly when the cancer is locally advanced. Targeted therapies with high efficacy and minimal harms should be particularly beneficial in this group, and several new approaches show promise. This article briefly analyses relevant clinical studies listed on ClinicalTrials.gov, combined with a short literature review that considers new therapeutic approaches that can be investigated in future clinical trials. Therapies using gold nanoparticles are of special interest in low-resource settings as they can localize and enhance the cancer-cell killing potential of X-rays using equipment that is already widely available.