Biomarker For Prostate Cancer Research Articles

Spatial transcriptomics reveals strong association between SFRP4 and extracellular matrix remodeling in prostate cancer

Prostate tumor heterogeneity is a major obstacle when studying the biological mechanisms of molecular markers. Increased gene expression levels of secreted frizzled-related protein 4 (SFRP4) is a biomarker in aggressive prostate cancer. To understand how SFRP4 relates to prostate cancer we performed comprehensive spatial and multiomics analysis of the same prostate cancer tissue samples. The experimental workflow included spatial transcriptomics, bulk transcriptomics, proteomics, DNA methylomics and tissue staining. SFRP4 mRNA was predominantly located in cancer stroma, produced by fibroblasts and smooth muscle cells, and co-expressed with extracellular matrix components. We also confirmed that higher SFRP4 gene expression is associated with cancer aggressiveness. Gene expression of SFRP4 was affected by gene promotor methylation. Surprisingly, the high mRNA levels did not reflect SFRP4 protein levels, which was much lower. This study contributes previously unknown insights of SFRP4 mRNA in the prostate tumor environment that potentially can improve diagnosis and treatment.

Discovery of RNA Biomarkers for Prostate Cancer Using Cross-Platform Transcriptomics

Microarray and Single-Molecule Molecular Inversion Probe (smMIP)-based targeted RNA sequencing are two RNA profiling platforms for identifying disease-associated biomarkers. The microarray uses a GeneChip array with oligonucleotide probes to measure expression levels across thousands of genes, while smMIPs capture and quantify RNA transcripts and transcript variants via next-generation sequencing. To evaluate the strengths and weaknesses of both platforms, a comparative gene expression profiling study was conducted using RNA samples from 52 prostate tissues (normal, benign prostatic hyperplasia (BPH) and various prostate cancer (PCa) grades). Of all genes covered by both platforms, only 35% of the expression levels aligned, with 45% showing discrepancies. Both platforms identified the same 17 genes as potential PCa biomarkers. Microarray analysis identified an additional 253 genes that were not covered or not identified by smMIP technology, while smMIP technology identified eight markers not covered or not identified in the microarray core gene analysis, including fusion genes and splice variants. For high-grade prostate cancer (HG-PCa), the smMIP-method identified 8 markers, and the microarray identified 17 markers, with FOLH1, FAP and CLDN3 being common across both platforms. The choice of RNA expression analysis technology depends on research objectives; microarray technology is useful for the evaluation of a wide range of genes but has low throughput. In contrast, smMIP-based RNA sequencing enables sensitive analysis with minimal RNA in a medium- to high-throughput setting.

Identifying causal relationships between 35 blood and urine biomarkers and urologic cancers: MR-meta combined with Bayesian colocalization Mendelian randomization analysis

BackgroundBlood and urine biomarkers have been associated with urologic tumors, but their causal relationship with urologic tumors is unclear.MethodsWe performed a bidirectional Mendelian randomization (MR) analysis of the association between 35 blood and urine biomarkers and urological tumors in our discovery cohort. A Bayesian weighting approach was used to validate the positive results identified in the discovery cohort, and steiger filtering analysis was used to distinguish causality from reverse causality. Bayesian colocalization analysis was used to analyze which single nucleotide polymorphisms (SNPs) specifically co-located between the positive blood and urine biomarkers and the disease phenotypes were driven, and MR-positive results from the discovery cohort and the validation cohort were combined using the MR-meta method.ResultsSeveral blood and urine biomarkers were found to be significantly and causally associated with urologic cancers. Notably, calcium (OR: 1.34, 95%CI 1.10–1.63, P = 0.0040) and sex hormone-binding globulin (SHBG) (OR: 0.81, 95%CI 0.70–0.95, P = 0.0092) were associated with bladder cancer; gamma glutamyl transferase (GGT) (OR: 0.91, 95%CI 0.83–0.99, P = 0.0209), lipoprotein A (Lp(a)) (OR: 1.12, 95%CI 1.01–1.24, P = 0.0399), and insulinlike growth factor 1 (IGF 1) (OR: 1.10, 95%CI 1.01–1.20, P = 0.0220) were linked to prostate cancer (PCa); non albumin protein (OR: 0.78, 95%CI 0.65–0.93, P = 0.0065) and total protein (OR: 0.80, 95%CI 0.64–0.99, P = 0.0380) were linked to renal cancer; and apolipoprotein A (Apo-A) (OR: 0.56, 95%CI 0.32–0.98, P = 0.0426) and urate (OR:1.89, 95%CI 1.03–3.47, P = 0.0399) were associated with renal pelvis cancer. These associations were validated in an independent cohort, with GGT, IGF 1, and Lp(a) being consistently linked to PCa.ConclusionThis study identified significant biomarkers associated with urological cancers in blood and urine. These include GGT, IGF 1, and Lp(a), which are strong biomarkers for PCa. In addition, the findings of this study provide evidence for a handful of risk and protective factors for the development of urologic cancers.

Comprehensive cataloging of miR-363 as a therapeutic & non-invasive biomarker of prostate cancer

Background & objectives Overcoming the challenge of early diagnosis of prostate cancer (PCa) by exploring molecular biomarkers is urgently needed. With this objective, this study was designed to explore the biomarker and therapeutic potential of miRNA (miR)-363-3p in PCa pathogenesis. Methods Total participants (n=188) were enrolled, and blood and tissue samples were collected from individuals categorized into the control group (n=55), benign prostate hyperplasia (BPH) group (n=60), PCa group (n=48), and castration-resistant PCa (CRPC) group (n=25). MiR expression profiling was carried out using quantitative polymerase chain reaction (qPCR), and biomarker analysis was conducted employing receiver operating characteristics (ROC) curve. The miR-363 target genes were predicted by in silico tools like Target Scan and starBasev 2.0 and its expression was validated by qPCR and association among them was established by using the STRING database. Results The results showed that the tumour-suppressive nature of miR-363-3p in both PCa tissues and serum were significantly higher than the control with a greater area under curve (AUC) was 0.969 (sensitivity: 85%; specificity 100%) and 0.988 (sensitivity: 97.5%; specificity: 87.5%), respectively. The targetome analysis of miR-363-3p revealed five target genes-NRAS, E2F3, PTEN, MDM2, and CCNE2 which were strongly associated with cell division and proliferation. The expression analysis of the target genes showed a significant tumour-suppression of PTEN gene and significant upregulation of oncogenic genes such as NRAS, E2F3, MDM2, and CCNE2. Interpretation & conclusions Collectively, the findings of this study suggest that miR-363-3p may be a potential biomarker in differentiating individuals with PCa and CRPC from healthy controls. The miR-363-3p triggers various oncogenic genes (MDM2, NRAS, E2F3, CCNE2) and tumour suppressor genes (PTEN) that are actively involved in PCa progression and development.

Blood Protein Ratios Reveal New Diagnostic Biomarkers for Prostate Cancer: A Study from the Perspective of Mendelian Randomization

Prostate cancer (PCa) is a leading malignancy affecting men globally, contributing significantly to cancer-related morbidity and mortality. Our study aims to explore causal relationships between blood protein ratios (BPR) and PCa using a Mendelian randomization (MR) approach, potentially identifying new diagnostic and therapeutic targets. Methods: A two-sample MR method was employed, utilizing genetic variants as instrumental variables (IVs) to infer causality between circulating BPR and PCa. Data on BPR were obtained from a proteomics study of the UK Biobank, while PCa data were sourced from FinnGen, the PRACTICLE Consortium, and the GWAS Catalog. Stringent criteria were applied for IV selection, and statistical analyses included the inverse-variance weighted (IVW) method with sensitivity analyses to address pleiotropy and heterogeneity. Results: Significant causal associations were identified between several BPR and PCa. Notably, the ratios of CEBPB/PXN, APBB1IP/NCF2, APP/EGF, and CRKL/ EGF were found to be protective against PCa, while the ratios of ARHGAP1/RAD23B, EGF/ TNFSF14, and GOLM2/STC1 were identified as risk factors. Reverse MR analysis suggested that PCa might act as a protective factor for the GOLM2/STC1 ratio. Sensitivity analyses confirmed the robustness of these findings. Conclusions: This study elucidates significant causal relationships between 7 BPR and PCa, offering new insights for diagnosis, treatment evaluation, and personalized therapeutic strategies. Future research should focus on validating these findings and exploring the underlying biological mechanisms to improve PCa management.

N-linked fucosylated glycans are biomarkers for prostate cancer with a neuroendocrine and metastatic phenotype.

Prostate cancer (PCa) is a heterogeneous disease with a spectrum of pathology and outcomes ranging from indolent to lethal. Although there have been recent advancements in prognostic tissue biomarkers, limitations still exist. We leveraged Matrix Assisted Laser Desorption Ionization (MALDI) imaging of formalin-fixed, paraffin embedded (FFPE) prostate cancer specimens to determine if N-linked glycans expressed in the extracellular matrix of lethal neuroendocrine prostate cancer were also expressed in conventional prostate adenocarcinomas that were associated with poor outcomes. We found that N-glycan fucosylation was abundant in neuroendocrine prostate cancer as well as adenocarcinomas at time of prostatectomy that eventually developed recurrent metastatic disease. Analysis of patient derived xenografts revealed that this fucosylation signature was enriched differently across metastatic disease organ sites, with the highest abundance in liver metastases. These data suggest that N-linked fucosylated glycans could be an early tissue biomarker for poor PCa outcomes. Implications: These studies identify that hyper-fucosylated N-linked glycans are enriched in neuroendocrine prostate cancer and conventional prostate adenocarcinomas that progress to metastatic disease, thus advancing biomarker discovery and providing insights into mechanisms underlying metastatic disease.

Non-enzymatic electrochemical detection of sarcosine in serum of prostate cancer patients by CoNiWBO/rGO nanocomposite

Selective and sensitive sarcosine detection is crucial due to its recent endorsement as a prostate cancer (PCa) biomarker in clinical diagnosis. The reduced graphene oxide-cobalt nickel tungsten boron oxides (CoNiWBO/rGO) nanocomposite is developed as a non-enzymatic electrochemical sensor for sarcosine detection in PCa patients’ serum. CoNiWBO/rGO is synthesized by the chemical reduction method via a one-pot reduction method followed by calcination at 500 °C under a nitrogen environment for 2 h and characterized by UV-Vis, XRD, TGA, and SEM. CoNiWBO/rGO is then deposited on a glassy carbon electrode, and sarcosine sensing parameters are optimized, including concentration and pH. This non-enzymatic sensor is employed to directly determine sarcosine in serum samples. Differential pulse voltammetry (DPV) and linear sweep voltammetry (LSV) are employed to monitor the electrochemical behavior where sarcosine binding leads to oxidation. Chronoamperometric studies show the stability of the developed sensor. The results demonstrate a wide linear range from 0.1 to 50 µM and low limits of detection, i.e., 0.04 µM and 0.07 µM using DPV and LSV respectivel. Moreover, the calculated recovery of sarcosine in human serum of prostate cancer patients is 78–96%. The developed electrochemical sensor for sarcosine detection can have potential applications in clinical diagnosis.

Prognostic Impact of H19/Cell Adhesion Molecules Circuitry on Prostate Cancer Biopsy.

Metastatic prostate cancer (PCa) presents a significant challenge in oncology due to its high mortality rate and the absence of effective biomarkers for predicting patient outcomes. Building on previous research that highlighted the critical role of the long noncoding RNA (lncRNA) H19 and cell adhesion molecules in promoting tumor progression under hypoxia and estrogen stimulation, this study aimed to assess the potential of these components as prognostic biomarkers for PCa at the biopsy stage. This research utilized immunohistochemistry and droplet digital PCR to analyze formalin-fixed paraffin-embedded (FFPE) biopsies, focusing on specific markers within the H19/cell adhesion molecules pathway. A novel multivariate analysis led to a "BioScore", a composite biomarker score to predict disease progression. This score is based on evaluating five key markers: the expression levels of Hypoxia-Inducible Factor 2 Alpha (HIF-2α), endothelial Nitric Oxide Synthase (eNOS), β4 integrin, E-cadherin transcript (CDH1), and lncRNA H19. The criteria for the "BioScore" involve identifying three out of these five markers, combining elevated levels of HIF-2α, eNOS, β4 integrin, and CDH1 with reduced H19 expression. This finding suggests the possibility of identifying, at the time of biopsy, PCa patients at higher risk of metastasis based on dysregulation in the H19/cell adhesion molecules circuitry. This study provides a valuable opportunity for early intervention in managing PCa, potentially contributing to personalized treatment strategies.

Single-cell sequencing analysis revealed that NEAT1 was a potential biomarker and therapeutic target of prostate cancer.

Prostate cancer (PCa) usually manifests atypical symptoms in the early stage, and once symptoms appear, most PCa patients have developed to the advanced stage, failing to undergo radical surgery. In this study, PCa occurrence-related biomarkers were explored based on single-cell RNA sequencing (scRNA-seq) data. scRNA-seq data of prostate normal (Normal), benign prostatic hyperplasia (BPH), and PCa (Tumor) samples were acquired from the Gene Expression Omnibus (GEO). Cellular subsets associated with PCa occurrence were obtained using cell annotation. Additionally, the mRNA expression of nuclear enriched abundant transcript 1 (NEAT1) was detected by quantitative real-time PCR (qRT-PCR). The effects of NEAT1 on cell proliferation and apoptosis were analyzed by 5-ethynyl-2-deoxyuridine (EdU) and flow cytometry. Subsequently, cell-derived xenograft (CDX) models were constructed and divided into the LV-NC and LV-shNEAT1 groups. After the tumor tissues of CDX model mice in each group were extracted, the cell growth and Ki67 expression were observed separately using hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). Ten cellular subsets were obtained via cell annotation, and significantly differential changes were observed between Basal intermediate and Luminal during the course of BPH to PCa. NEAT1-Luminal was highly recruited in the Tumor group with low stemness and high malignancy scores. Matrix metallopeptidase 7 (MMP7)- keratin 17 (KRT17)-Basal intermediate had high ratios in the Tumor group with low stemness and high malignancy scores. The results of pseudotime analysis revealed that NEAT1-Luminal in the Tumor group were consistently distributed with tumor stage cells. In vitro assays showed that NEAT1 expression was elevated in PCa cells, and NEAT1 knockdown could inhibit cell proliferation and induce apoptosis. CDX assays indicated that silencing NEAT1 could reduce the growth rate of PCa tumor volume in CDX model mice. H&E staining results showed that nuclei of tumor cells were reduced and exhibited lighter color in the LV-shNEAT1 group compared with the LV-NC group. IHC results showed that Ki67 positivity was significantly lower in the LV-shNEAT1 group than in the LV-NC group. NEAT1 expression is increased in PCa, and NEAT1 can be a potential biomarker and therapeutic target for PCa.

Circulating Tumor Cell Count and Overall Survival in Patients With Metastatic Hormone-Sensitive Prostate Cancer

In metastatic hormone-sensitive prostate cancer (mHSPC), new first-line combination therapies have enhanced overall survival (OS), but clinical outcomes for individual patients vary greatly and are difficult to predict. Peripheral blood circulating tumor cell (CTC) count is the most extensively validated prognostic liquid biomarker in metastatic castration-resistant prostate cancer (mCRPC), and recent studies have suggested that it may also be informative in mHSPC. To examine the prognostic value of CTC count in men with mHSPC. In this prognostic study, peripheral blood was drawn at registration (baseline) and at progression to mCRPC in the S1216 study (March 1, 2013, to July 15, 2017), a phase 3, prospective, randomized clinical trial in men with mHSPC. The CTCs were enumerated using a US Food and Drug Administration-cleared isolation platform. Counts were categorized as 0, 1 to 4, or 5 or more CTCs per 7.5 mL based on the prognostic value of these cut points in prior studies. The data analysis was performed between October 28, 2022, and June 15, 2023. Metastatic hormone-sensitive prostate cancer. Circulating tumor cell count was evaluated for an association with 3 prespecified trial end points: OS, progression-free survival, and 7-month prostate-specific antigen, after adjusting for other baseline covariates using proportional hazards and logistic regression models. Of 1313 S1216 participants (median [IQR] age, 68 [44-92] years), evaluable samples from 503 (median [IQR] age, 69 [46-90] years) with newly diagnosed mHSPC were collected at baseline, and 93 samples were collected at progression. Baseline counts were 5 or more CTCs per 7.5 mL in 60 samples (11.9%), 1 to 4 CTCs per 7.5 mL in 107 samples (21.3%), and 0 CTCs per 7.5 mL in 336 samples (66.8%). Median OS for men with 5 or more CTCs per 7.5 mL was 27.9 months (95% CI, 24.1-31.2 months) compared with 56.2 months (95% CI, 45.7-69.8 months) for men with 1 to 4 CTCs per 7.5 mL and not reached at 78.0 months follow-up for men with 0 CTCs per 7.5 mL. After adjusting for baseline clinical covariates, men with 5 or more CTCs per 7.5 mL at baseline had a significantly higher hazard of death (hazard ratio, 3.22; 95% CI, 2.22-4.68) and disease progression (hazard ratio, 2.46; 95% CI, 1.76-3.43) and a lower likelihood of prostate-specific antigen complete response (odds ratio, 0.26; 95% CI, 0.12-0.54) compared with men with 0 CTCs per 7.5 mL at baseline. Adding baseline CTC count to other known prognostic factors (covariates only: area under the curve, 0.73; 95% CI, 0.67-0.79) resulted in an increased prognostic value for 3-year survival (area under the curve, 0.79; 95% CI, 0.73-0.84). In this prognostic study, the findings validate CTC count as a prognostic biomarker that improved upon existing prognostic factors and estimated vastly divergent survival outcomes regardless of subsequent lines of therapy. As such, baseline CTC count in mHSPC may serve as a valuable noninvasive biomarker to identify men likely to have poor survival who may benefit from clinical trials of intensified or novel regimens.

Development of transition metal oxide platforms for aptasensing of PSA in cell cultures.

In this study, a novel aptasensor based on a transition metal oxide-modified pencil graphite electrode (PGE) was developed for the diagnosis of early-stage prostate cancer (PCa) via monitoring the prostate-specific antigen (PSA), which is the main biomarker for PCa. Single-use PGEs modified with pulsed deposited manganese oxide (MnOx) film were used to attach the amino-terminated aptamer specific to the PSA via carbodiimide chemistry. The designed aptasensor was placed in an electrochemical cell containing ferri/ferrocyanide ions as a redox probe to measure the charge transfer resistances (Rct) of the electrode surface by electrochemical impedance spectroscopy (EIS) to follow the response of each modification step. The effect of the medium pH on the ionic structure of the aptamer molecule according to its pI value and, thus, the reversing of the direction of the response (ΔRct) by the pH change was also discussed. The level of PSA secreted from PCa cells was investigated using impedimetric transduction. The specificity of the aptasensor was validated through selectivity studies against non-specific tumor markers like VEGF and different cancer cell lines including breast cancer and androgen-insensitive prostate cancer. The developed system showcases a label-free, fast, specific, and cost-effective approach for PSA detection, highlighting the importance of medium pH and the electrostatic environment on the aptamer's response. Our work emphasizes the potential for such aptasensors in clinical diagnostics and paves the way for further exploration into using transition metal oxides in biosensing applications.

Serum levels of prostate specific antigen, free PSA, [-2]proPSA, fPSA/tPSA ratio, Prostate Health Index, and glycosylation patterns of free PSA in patients with benign prostatic hyperplasia pharmacotherapy.

The medication used to treat benign prostate hyperplasia (BPH), a common condition in men over 50 years of age, can alter the levels of biomarkers used in prostate cancer detection. Commonly used medications for BPH include alpha-blockers, 5-alpha reductase inhibitors (5-ARIs), and muscarinic antagonists. We studied the impact of these drugs on total prostate-specific antigen (tPSA), free PSA (fPSA), [-2]proPSA, fPSA/tPSA ratio, and the Prostate Health Index (PHI), as well as novel potential biomarkers in the form of glycan composition of fPSA. Serum samples were collected from 564 males with BPH, with a mean age of 68.5 years. The samples were used to measure levels of tPSA, fPSA, and [-2]proPSA. The fPSA/tPSA and PHI were then calculated. The glycan composition of fPSA was analyzed using lectin-based glycoprofiling. Pharmacotherapy data was collected from the patients' medical records. Alpha-blocker monotherapy was associated with higher fPSA and fPSA/tPSA ratio, and decreased PHI. Levels of tPSA were not impacted. Alpha-blocker and 5-ARI dual therapy was associated with reduced levels of fPSA, [-2]proPSA, and PHI. Therapy combining alpha-blockers and antimuscarinic agents did not significantly influence biomarker levels apart from an increase in a Maackia amurensis lectin-recognized glycan originating in fPSA. BPH pharmacotherapy notably affects prostate cancer biomarkers. Recognizing the impact of pharmacotherapy is crucial for achieving an accurate diagnosis of prostate cancer and for planning treatment.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification.

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

Identification and validation of diagnostic and prognostic biomarkers in prostate cancer based on WGCNA

BackgroundProstate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management.MethodsThe datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value.ResultsThe blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity.ConclusionsIn summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.

Semen sEV tRF-Based Models Increase Non-Invasive Prediction Accuracy of Clinically Significant Prostate Cancer among Patients with Moderately Altered PSA Levels.

PSA screening has led to an over-diagnosis of prostate cancer (PCa) and unnecessary biopsies of benign conditions due to its low cancer specificity. Consequently, more accurate, preferentially non-invasive, tests are needed. We aim to evaluate the potential of semen sEV (small extracellular vesicles) tsRNAs (tRNA-derived small RNAs) as PCa indicators. Initially, following a literature review in the OncotRF database and high-throughput small RNA-sequencing studies in PCa tissue together with the sncRNA profile in semen sEVs, we selected four candidate 5'tRF tsRNAs for validation as PCa biomarkers. RT-qPCR analysis in semen sEVs from men with moderately elevated serum PSA levels successfully shows that the differential expression of the four tRFs between PCa and healthy control groups can be detected in a non-invasive manner. The combined model incorporating PSA and specific tRFs (5'-tRNA-Glu-TTC-9-1_L30 and 5'-tRNA-Val-CAC-3-1_L30) achieved high predictive accuracy in identifying samples with a Gleason score ≥ 7 and staging disease beyond IIA, supporting that the 5'tRF fingerprint in semen sEV can improve the PSA predictive value to discriminate between malignant and indolent prostate conditions. The in silico study allowed us to map target genes for the four 5'tRFs possibly involved in PCa. Our findings highlight the synergistic use of multiple biomarkers as an efficient approach to improve PCa screening and prognosis.

Extracellular RNAs from Whole Urine to Distinguish Prostate Cancer from Benign Prostatic Hyperplasia.

RNAs, especially non-coding RNAs (ncRNAs), are crucial players in regulating cellular mechanisms due to their ability to interact with and regulate other molecules. Altered expression patterns of ncRNAs have been observed in prostate cancer (PCa), contributing to the disease's initiation, progression, and treatment response. This study aimed to evaluate the ability of a specific set of RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), and mRNAs, to discriminate between PCa and the non-neoplastic condition benign prostatic hyperplasia (BPH). After selecting by literature mining the most relevant RNAs differentially expressed in biofluids from PCa patients, we evaluated their discriminatory power in samples of unfiltered urine from 50 PCa and 50 BPH patients using both real-time PCR and droplet digital PCR (ddPCR). Additionally, we also optimized a protocol for urine sample manipulation and RNA extraction. This two-way validation study allowed us to establish that miRNAs (i.e., miR-27b-3p, miR-574-3p, miR-30a-5p, and miR-125b-5p) are more efficient biomarkers for PCa compared to long RNAs (mRNAs and lncRNAs) (e.g., PCA3, PCAT18, and KLK3), as their dysregulation was consistently reported in the whole urine of patients with PCa compared to those with BPH in a statistically significant manner regardless of the quantification methodology performed. Moreover, a significant increase in diagnostic performance was observed when molecular signatures composed of different miRNAs were considered. Hence, the abovementioned circulating ncRNAs represent excellent potential non-invasive biomarkers in urine capable of effectively distinguishing individuals with PCa from those with BPH, potentially reducing cancer overdiagnosis.

Platinum-perovskite nanocomposite-based Exosensor for specific detection of prostate cancer in clinical settings.

Exosomes, extracellular vesicles (EVs) with an average size of 50-150nm, transfer various biomolecules and exchange signaling molecules between cells in a paracrine manner. Molecular investigations have revealed that EVs can reflect real-time metabolic changes in normal- and cancer-origin cells and thus harbor valid diagnostic biomarkers. Despite these advantages, the detection of low concentrations of cancer cell EVs in biological fluids is still a great challenge. Here, a new electrochemical Exosensor based on platinum-perovskite is developed for the direct detection of EVs using a biotinylated monoclonal CD63 antibody as a capture element. The label-free method exhibited higher sensitivity with a lower limit of quantification of 2000 EVs/µL with a dynamic linear range (LDR) of 2000 to 14,000 EVs/μL compared with other available methods. To enhance the selectivity of detection, EVs were simultaneously sandwiched between secondary antibodies of PSA (prostate-specific antigen), as an FDA-approved prostate cancer biomarker. Data indicated that this Exosensor can distinguish normal and cancer EVs in samples from healthy individuals and prostate cancer patients. Taken together, this technology offers a unique approach to label-free quantification of EVs and cancer detection in the early stages.

Multi-parametric magnetic resonance imaging of prostate cancer: correlation between Ktrans, Gleason score, and PI-RADS score

PurposeTo measure Ktrans and correlate it with Gleason score (GS) and PI-RADS score in patients with prostate cancer.MethodsThis retrospective study included patients with pathologically proven prostate cancer who had undergone clinically indicated 1.5 Tesla multi-parametric magnetic resonance imaging (MRI) examination between February and December 2020. T2-weighted (T2w) images, diffusion-weighted images (DWI), and dynamic contrast-enhanced (DCE) sequences were obtained. PI-RADS score was calculated for all tumor lesions. From DCE-MRI dataset, Ktrans was computed and compared between patients with clinically insignificant (GS ≤ 6) and clinically significant (GS ≥ 7) prostate cancer. Spearman rank-order correlation coefficient (ρ) was used to assess the correlation strength between Ktrans and GS and between Ktrans and PI-RADS score.ResultsTwenty-one patients (age: 67 ± 12 years; BMI: 26.63 ± 4.04 kg/m2) with a PSA of 7.91 ± 3.01 were included in the study. Seven patients (33.3%) had clinically insignificant prostate cancer, while 14 patients (66.7%) were diagnosed with clinically significant prostate cancer. Mean Ktrans value was 0.42 ± 0.20 min−1 (range: 0.15–0.75). Ktrans was significantly higher (0.50 ± 0.17 min−1) in clinically significant prostate cancer compared to clinical insignificant prostate cancer (0.23 ± 0.15 min−1; P = 0.001). Ktrans showed moderate significant correlation with GS (ρ = 0.575, P = 0.006), but showed no significant correlation with PI-RADS (ρ = 0.386, P = 0.069).ConclusionKtrans may discriminate between clinically insignificant and significant prostate cancer and shows moderate correlation with GS. Thus, MP-MRI may serve as an imaging biomarker in prostate cancer.

Measuring the impact of new risk factors within survival models

Abstract Survival is poor for patients with metastatic cancer, and it is vital to examine new biomarkers that can improve patient prognostication and identify those who would benefit from more aggressive therapy. In metastatic prostate cancer, 2 new assays have become available: one that quantifies the number of cancer cells circulating in the peripheral blood, and the other a marker of the aggressiveness of the disease. It is critical to determine the magnitude of the effect of these biomarkers on the discrimination of a model-based risk score. To do so, most analysts frequently consider the discrimination of 2 separate survival models: one that includes both the new and standard factors and a second that includes the standard factors alone. However, this analysis is ultimately incorrect for many of the scale-transformation models ubiquitous in survival, as the reduced model is misspecified if the full model is specified correctly. To circumvent this issue, we developed a projection-based approach to estimate the impact of the 2 prostate cancer biomarkers. The results indicate that the new biomarkers can influence model discrimination and justify their inclusion in the risk model; however, the hunt remains for an applicable model to risk-stratify patients with metastatic prostate cancer.