Detection and Prognosis of Prostate Cancer Using Blood-Based Biomarkers

Overexpression of sialyl Lewisa carrying mucin-type glycoprotein in prostate cancer cell line contributes to aggressiveness and metastasis

Metastasis-promoting Lewis and sialyl Lewis antigens expressed on glycoproteins such as mucins are frequently displayed on the surface of prostate cancer cells and could thus be ideal candidates as measures of prostate cancer aggressiveness. The current study describes the altered expression of sialyl Lewisa (sLea) antigen attached to glycoproteins and key glycosyltransferases between normal prostate (RWPE-1) and cancerous cell lines (LNCaP and DU145). Our results suggest that the expression of sLea on different glycoproteins correlates with the aggressiveness of prostate cancer cells, as determined by flow cytometry and fluorescence microscopy. Blotting studies revealed that sLea-bearing glycoproteins, similar to mucins, are predominantly expressed in the more aggressive DU145 cells, followed by LNCaP cells. Immunohistochemistry technique showed a gradient of sLea expression, with low levels in low-grade prostate cancer (stage II/III) and increasing levels in high-grade cancer (stage IV), indicating its potential as a prognostic marker. Additionally, in qRT-PCR analysis significant upregulation of the glycosyltransferases GALNT5 and ST3GAL6 was observed, correlating with the increased sLea expression in LNCaP (3.2- and 14.5-fold) and DU145 (3.3- and 23.75-fold) cells. Our data indicates a correlation between sLea selectin ligand expression and prostate cancer aggressiveness. Furthermore, GALNT5 and ST3GAL6 could serve as benchmarks in PCa malignancy.

Extrachromosomal circular DNA promotes prostate cancer progression through the FAM84B/CDKN1B/MYC/WWP1 axis

BACKGROUND

Extrachromosomal circular DNA (eccDNA), a kind of circular DNA that originates from chromosomes, carries complete gene information, particularly the oncogenic genes. This study aimed to examine the contributions of FAM84B induced by eccDNA to prostate cancer (PCa) development and the biomolecules involved.

METHODS

The presence of eccDNA in PCa cells and the FAM84B transcripts that eccDNA carries were verified by outward and inward PCR. The effect of inhibition of eccDNA synthesis on FAM84B expression in PCa cells was analyzed by knocking down Lig3. The impact of FAM84B on the growth and metastases of PCa cells was verified by Cell Counting Kit-8 (CCK8), EdU, transwell assays, and a xenograft mouse model. Chromatin immunoprecipitation quantitative PCR (ChIP-qPCR) and dual-luciferase reporter assays were carried out to examine the effect of FAM84B/MYC on WWP1 transcription, and a co-immunoprecipitation (Co-IP) assay was conducted to verify the modification of CDKN1B by WWP1. The function of this molecular axis in PCa was explored by rescue assays.

RESULTS

The inhibited eccDNA synthesis significantly downregulated FAM84B in PCa cells, thereby attenuating the growth and metastasis of PCa. FAM84B promoted the transcription of WWP1 by MYC by activating the expression of MYC coterminous with the 8q24.21 gene desert in a beta catenin-dependent approach. WWP1 transcription promoted by MYC facilitated the ubiquitination and degradation of CDKN1B protein and inversely attenuated the repressive effect of CDKN1B on MYC expression. Exogenous overexpression of CDKN1B blocked FAM84B-activated MYC/WWP1 expression, thereby inhibiting PCa progression.

CONCLUSIONS

FAM84B promoted by eccDNA mediates degradation of CDKN1B via MYC/WWP1, thereby accelerating PCa progression.

Deletion in a regulatory region is associated with underexpression of miR-148b‑3p in patients with prostate cancer

Prostate cancer (PCa) is the leading cause of cancer-related death in men. This pathology is complex and heterogeneous; therefore, elucidating the molecular mechanisms that lead to its origin and progression is imperative. MicroRNAs (miRNAs or miRs) are part of the epigenetic machinery that regulates the expression of human genes, therefore, mutations in the genes that encode them can lead to a dysregulation in their expression, which directly impacts their target genes, which could be oncogenes or tumor suppressor genes. In PCa several dysregulated expression levels of miRNAs are associated with perturbed cellular processes. A differential expression of miRNAs such as miR-145-5p and miR-148-3p has been observed in PCa, possibly due to mutations in regions near the miRNAs. However, the molecular mechanisms that lead to the dysregulation of these miRNAs still need to be clarified. Therefore, the present study aimed to analyze the expression of miRNAs and their relationship with mutations in patients with and without PCa. In total, 71 patients were analyzed: 41 of whom had PCa (CAP group) and 30 with benign pathology (BPD group). Underexpression was observed in miR-145-5p and miR-148b-3p in PCa patients (P=0.03 and P=0.001, respectively). In miR-145-5p, no mutations related to its expression were identified. For miR-148b-3p, a set of mutations were identified in the chr12:54337042/54337043 region, which were grouped into the mutation named DelsAAG. Although this mutation's abnormal allele is related to PCa (P=0.017), a statistically significant difference was observed in the expression of miR-148b-3p between carriers and non-carriers of the mutated allele, identifying a mechanism likely to be involved in the miR-148b-3p dysregulation.

Advancements in Biomarkers of Prostate Cancer: A Review

Prostate cancer (PCa) is one of the most prevalent and deadly cancers among men, particularly affecting men of African descent and contributing significantly to cancer-related morbidity and mortality worldwide. The disease varies widely, from slow-developing forms to highly aggressive or potentially fatal variants. Accurate risk stratification is crucial for making therapeutic decisions and designing adequate clinical trials. This review assesses a broad spectrum of diagnostic and prognostic biomarkers, many of which are incorporated into clinical guidelines, including the Prostate Health Index (PHI), 4Kscore, STHLM3, PCA3, SelectMDx, ExoDx Prostate Intelliscore (EPI), and MiPS. It also highlights emerging biomarkers with preclinical support, such as urinary non-coding RNAs and DNA methylation patterns. Additionally, the review explores the role of tumor-associated microbiota in PCa, offering new insights into its potential contributions to disease understanding. By examining the latest advancements in PCa biomarkers, this review enhances understanding their roles in disease management.

Clinical characteristics analysis and prognostic nomogram for predicting survival in patients with second primary prostate cancer: a population study based on SEER database

BACKGROUND AND AIMS

Second primary prostate cancer (SPPCa) is a common type of secondary malignancy that negatively impacts patient prognosis. This study aimed to identify prognostic factors for SPPCa patients and develop nomograms to assess their prognosis.

METHODS

Patients diagnosed with SPPCa between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The study cohort was randomly divided into a training set and a validation set. Cox regression analysis, Kaplan‒Meier survival analysis, and least absolute shrinkage and selection operator regression analysis were used to identify independent prognostic factors and develop the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curve, area under the curve (AUC), and Kaplan-Meier analysis.

RESULTS

A total of 5342 SPPCa patients were included in the study. Independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) were identified as age, interval between diagnoses, first primary tumor site, and AJCC stage, N stage, M stage, PSA, Gleason score, and SPPCa surgery. Nomograms were constructed based on these prognostic factors, and the performance was evaluated using the C-index (OS: 0.733, CSS: 0.838), AUC, calibration curve, and Kaplan-Meier analysis, which demonstrated excellent predictive accuracy.

CONCLUSION

We successfully established and validated nomograms to predict OS and CSS in SPPCa patients using the SEER database. These nomograms provide an effective tool for risk stratification and prognosis assessment in SPPCa patients, which will aid clinicians in optimizing treatment strategies for this patient population.

The Impact of Prostate Imaging Reporting and Data System Version 2.1 and Prostate-Specific Antigen Density in the Prediction of Clinically Significant Prostate Cancer

OBJECTIVE

The aim of this study was to evaluate the diagnostic performance of multiparametric magnetic resonance imaging for clinically significant prostate cancer and to determine whether applying Prostate Imaging Reporting and Data Systems version 2.1 score could improve the diagnostic pathway besides the biochemical characteristics.

MATERIALS AND METHODS

In this study, 199 patients with clinically suspected prostate cancer who underwent multiparametric magnetic resonance imaging were included. Logistic regression analyses and receiver operating characteristic curve were performed to determine independent predictors and to compare diagnostic performance of indicators for clinically significant prostate cancer. Two models were established. In model 1, the diagnostic performance of prostate-specific antigen- and prostatespecific antigen density-derived parameters were evaluated. In model 2, the prediction potential of model 1 plus Prostate Imaging Reporting and Data Systems version 2.1 score was analyzed.

RESULTS

Sixty-four patients were positive for clinically significant prostate cancer by histopathological analysis (32.1%). In model 1, a prostate-specific antigen density >0.15 was labeled as the strongest predictor of malignancy. In model 2, a prostatespecific antigen density >0.15, a Prostate Imaging Reporting and Data Systems score ≥3, and a Prostate Imaging Reporting and Data Systems score ≥4 demonstrated the strongest association with malignancy. Among these parameters, a Prostate Imaging Reporting and Data Systems score ≥4 (P=.003) was found to be the most robust predictor for malignancy, followed by a Prostate Imaging Reporting and Data Systems score ≥3 (P=.012). The multivariate analysis revealed higher accuracy in model 2 (76.9%) than in model 1 (67.8%). The area under curve values with respect to prostatespecific antigen, prostate-specific antigen density, model 1, and model 2 were 0.632, 0.741, 0.656, and 0.798, respectively.

CONCLUSION

These results indicated that Prostate Imaging Reporting and Data Systems version 2.1 score and prostate-specific antigen density are independent predictors for the presence of clinically significant prostate cancer. Both prostate-specific antigen density and Prostate Imaging Reporting and Data Systems version 2.1 score should be risen to prominence in the decision of biopsy instead of PSA.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Comparing Urinary Glycoproteins among Three Urogenital Cancers and Identifying Prostate Cancer-Specific Glycoproteins

Prostate cancer, bladder cancer, and renal cancers are major urogenital cancers. Of which, prostate cancer is the most commonly diagnosed and second leading cause of cancer death for men in the United States. For urogenital cancers, urine is considered as proximate body fluid to the tumor site for developing non-invasiveness tests. However, the specific molecular signatures from different urogenital cancers are needed to relate changes in urine to various cancer detections. Herein, we utilized a previously published C4-Tip and C18/MAX-Tip workflow for enrichment of glycopeptides from urine samples and evaluated urinary glycopeptides for its cancer specificity. We analyzed 66 urine samples from bladder cancer (n = 27), prostate cancer (n = 4), clear cell renal cell carcinoma (ccRCC, n = 3), and benign plastic hyperplasia (BPH, n = 32) and then compared them with a previous publication that reported glycopeptides associated with aggressive prostate cancer (Gleason score ≥ 8). We further demonstrated the cancer specificity of the glycopeptides associated with aggressive prostate cancer. In this study, a total of 33 glycopeptides were identified to be specifically differentially expressed in prostate cancer compared to other urogenital cancer types as well as BPH urines. By cross-comparison with our previous urinary glycoproteomic dataset for aggressive prostate cancer, we reported a total of four glycopeptides from glycoproteins DSC2, MGAM, PIK3IP1, and CD55, commonly identified to be prostate cancer-specific. Together, these results deepen our understanding of the urinary glycoproteins associated with urogenital cancer types and expand our knowledge of the cancer specificity of urinary glycoproteins among urogenital cancer progression.

Preterm birth buccal cell epigenetic biomarkers to facilitate preventative medicine

Preterm birth is the major cause of newborn and infant mortality affecting nearly one in every ten live births. The current study was designed to develop an epigenetic biomarker for susceptibility of preterm birth using buccal cells from the mother, father, and child (triads). An epigenome-wide association study (EWAS) was used to identify differential DNA methylation regions (DMRs) using a comparison of control term birth versus preterm birth triads. Epigenetic DMR associations with preterm birth were identified for both the mother and father that were distinct and suggest potential epigenetic contributions from both parents. The mother (165 DMRs) and female child (136 DMRs) at p < 1e-04 had the highest number of DMRs and were highly similar suggesting potential epigenetic inheritance of the epimutations. The male child had negligible DMR associations. The DMR associated genes for each group involve previously identified preterm birth associated genes. Observations identify a potential paternal germline contribution for preterm birth and identify the potential epigenetic inheritance of preterm birth susceptibility for the female child later in life. Although expanded clinical trials and preconception trials are required to optimize the potential epigenetic biomarkers, such epigenetic biomarkers may allow preventative medicine strategies to reduce the incidence of preterm birth.

NLRP3 inflammasome promoted the malignant progression of prostate cancer via the activation of caspase-1

It is widely accepted that inflammation is an important risk for the development of prostate cancer (PCa). The objective of this study was designed to investigate the potential molecular mechanism of NLR family, pyrin domain-containing protein 3 (NLRP3) inflammasome in the malignant progression of PCa. The expression level of NLRP3 was evaluated by quantitative real-time polymerase chain reaction (qRT-PCR) and fluorescence in situ hybridization. The effects of NLRP3 in the development of PCa by applying gain- and loss-of-function assays in LNCaP and PC3 cell lines were detected by CCK-8, TUNEL, and Transwell migration assays. The underlying mechanism of NLRP3 and caspase-1 in PCa was examined by the rescue experiments, western blotting, and qRT-PCR assays. In addition, the promoting effect of NLRP3 inflammasome was performed with an animal subcutaneous tumorigenesis experiment in vivo. The upregulation of NLRP3 was confirmed in PCa tissues and cell lines. Functionally, using CCK-8, TUNEL, and Transwell migration assays, these results showed that activation of NLRP3/caspase-1 inflammasome by LPS + ATP could enhance the ability of proliferation and migration; and decrease the apoptosis of LNCaP and PC3 cell lines. Western blotting assay showed that the activation of caspase-1 would increase after the stimulation of NLRP3 inflammasome by LPS + ATP. Moreover, the overexpression of NLRP3 promoted, while the knockdown of NLRP3 inhibited the malignant progression in PCa cell lines by positively regulating caspase-1. In addition, the rescue experiments revealed the association among NLRP3 and caspase-1, which showed that the overexpression vectors/inhibitors of caspase-1 could reverse the effect of knockdown/overexpression of NLRP3 in PCa cell lines in vitro. Finally, In in vivo experiment, the suppression of NLRP3 knockdown impaired tumor growth of PCa. Collectively, these results indicated that NLRP3 inflammasome played a vital role in promoting the malignant progression of PCa via the activation of caspase-1. Together, our findings provided insight into the mechanisms of NLRP3/caspase-1 inflammasome and revealed an alternative and potential target for the clinical diagnosis and treatment of PCa.

MiR-622 acts as a tumor suppressor to induce cell apoptosis and inhibit metastasis in human prostate cancer

Growing evidence indicating the critical modulator roles of microRNAs (miRNAs) involved in prostate cancer (PCa) metastasis that holds great promise as therapeutic targets. Herein, we transfected the miR-622 mimic into PC3 cells and evaluated the effects of this interference on these tumour cells' growth and the expression of specific metastatic genes. Transfecting of miR-622 mimic and inhibitor, negative control (NC) inhibitor and NC was established using Lipofectamine 2000. The mRNA levels of miR-622 and metastatic genes were evaluated using the qRT-PCR and Western blot. Cytotoxic effects of miR-622 were assessed by MTT. Apoptosis was detected using an ELISA cell death assay kit. miR-622 is down-regulated in PC3 cells. As expected, cell viability effects after transfection were described as miR-622 inhibitor >NC and NC inhibitor >miR-622 mimic (p < .01). Importantly, we showed that transfected miR-622 mimic could enhance the apoptosis of PC3 cells, while transfected miR-622 inhibitor could decrease cell apoptosis (p < .01). Furthermore, miR-622 overexpression could increase significantly down-regulated the MMP2, MMP9, CXCR-4, c-Myc and K-Ras expression levels. Findings demonstrate a novel mechanism by which miR-622 modulates PCa cells' metastasis by targeting metastatic genes. These data confirm the tumour-suppressive function of miR-622 in PCa cells by enhancing apoptosis and reducing metastasis.

BAIAP2L2 facilitates the malignancy of prostate cancer (PCa) via VEGF and apoptosis signaling pathways

BACKGROUND

Prostate cancer (PCa) is the second most common type of male cancer in western. Despite key roles of brain-specific angiogenesis inhibitor 1-associated protein like 2 (BAIAP2L2) in several cancers, the function of BAIAP2L2 in PCa is never reported.

OBJECTIVE

We aimed to investigate the role of BAIAP2L2 in the progression of PCa and decipher the underlying mechanisms.

METHODS

RNA sequencing data from TCGA database were used to evaluate the expression of BAIAP2L2 in PCa. Survival analysis and Cox regression model analysis were conducted to evaluate the prognostic value of BAIAP2L2. BAIAP2L2-associated pathways were preliminary analyzed by Gene Set Enrichment Analysis (GSEA) method and confirmed by western blot assays. Cell proliferation and transwell assays were performed to determine biological behaviors in BAIAP2L2 knocked-down or overexpressed PCa cell lines including LNCaP and PC-3 cells.

RESULTS

In our study, BAIAP2L2 was significantly up-regulated in PCa tissues and cell lines and independently associated with the poor prognosis of PCa patients. Knockdown of BAIAP2L2 notably repressed proliferation, migration and invasion of PCa cells. And overexpression of BAIAP2L2 obtained the contrary results. Mechanically, GSEA method and western blot results of key molecules in signaling pathways implicated that the depletion of BAIAP2L2 inactivated the vascular endothelial growth factors (VEGFs) and induced apoptosis signaling pathways in PCa cells.

CONCLUSIONS

Overall, these findings revealed that BAIAP2L2 may support tumorigenesis and malignant development of prostate cancer cells via VEGF and apoptosis signaling pathways, and it could be considered as a promising biomarker and independent prognostic predictor of prostate cancer.

Long Non-Coding RNA (lncRNA) RAMS11 Promotes Metastatis and Cell Growth of Prostate Cancer by CBX4 Complex Binding to Top2α

Introduction

Studies have confirmed that parts of the non-coding genes in the human genome play an important role in the pathogenesis and metastasis of prostate cancer. Among them, long non-coding RNAs (lncRNAs) are vitally involved in the biological regulation of prostate cancer. In addition, lncRNAs are closely associated with the recurrence, metastasis and prognosis of prostate cancer. However, the molecular pathogenesis of lncRNAs in regulating cell growth and metastasis of prostate cancer remains unclear. Therefore, this study was designed to explore the function and mechanism of lncRNA RAMS11 in cell growth and metastasis of prostate cancer.

Methods

Prostate cancer and para-carcinoma tissue samples were obtained from 42 patients who were diagnosed from March 2013 to September 2014 at Quanzhou First Hospital Affiliated to Fujian Medical University. Microarray experiments and real-time polymerase chain reaction (PCR) measured the expression of lncRNA. RWPE-2, LNCap, PC3 and DU145 cells were used for an in vitro model.

Results

The expression of lncRNA RAMS11 was up-regulated in prostate cancer tissue samples. LncRNA RAMS11 promoted cell growth and metastasis of prostate cancer cells. Down-regulation of lncRNA RAMS11 attenuated cell growth and metastasis of prostate cancer cells. We also demonstrated that lncRNA RAMS11 bound to CBX4 to activate expression of Top2α. LncRNA RAMS11 promoted tumor growth of prostate cancer in the mouse model. The inhibition of CBX4 attenuated the pro-cancer effects of lncRNA AMS11 in prostate cancer cells, while the activation of Top2α attenuated the anti-cancer effects of si-lncRNA RAMS11 in prostate cancer cells.

Discussion

Our results indicated that lncRNA RAMS11 promoted cell growth and metastasis of prostate cancer by CBX4 complex via binding to Top2α, and might be developed for the treatment of prostate cancer.

A Systematic Review of Artificial Intelligence in Prostate Cancer

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Highly Selective Optical Sensor Eu (TTA)3 Phen Embedded in Poly Methylmethacrylate for Assessment of Total Prostate Specific Antigen Tumor Marker in Male Serum Suffering Prostate Diseases

A low-cost, simple, and highly selective method was used for the assessment of total prostate specific antigen (tPSA) in the serum of prostate cancer patients. This method is based on quenching the intensity of luminescence displayed by the optical sensor Eu (TTA)₃ phen/poly methylmethacrylate (PMMA) thin membrane or film upon adding different concentrations of tPSA. The luminescent optical sensor was synthesized and characterized through absorption, emission, scanning electron microscopy (SEM), and x-ray diffraction (XRD), and is tailored to present red luminescence at 614 nm upon excitation at 395 nm in water. The fabricated sensor fluorescence intensity is quenched in the presence of tPSA in aqueous media. The fluorescence resonance energy transfer (FRET) is the main mechanism by which the sensor performs. The sensor was successfully utilized to estimate tPSA in the serum of patients suffering prostate cancer in a time and cost effective way. The statistical results of the method were satisfactory with 0.0469 ng mL^-1 as a detection limit and 0.99 as a correlation coefficient.

PSA Based Biomarkers, Imagistic Techniques and Combined Tests for a Better Diagnostic of Localized Prostate Cancer

Prostate cancer represents the most encountered urinary malignancy in males over 50 years old, and the second most diagnosed after lung cancer globally. Digital rectal examination and prostatic specific antigen were the long-time standard tools for diagnosis but with a significant risk of overdiagnosis and overtreatment. Magnetic resonance imaging recently entered the diagnosis process, but to this date, there is no specific biomarker that accurately indicates whether to proceed with the prostate biopsy. Research in this area has gone towards this direction, and recently, serum, urine, imagistic, tissue biomarkers, and Risk Calculators promise to help better diagnose and stratify prostate cancer. In order to eliminate the comorbidities that appear along with the diagnosis and treatment of this disease, there is a constant need to implement new diagnostic strategies. Important uro-oncology associations recommend the use of novel biomarkers in the grey area of prostate cancer, to better distinguish the next step in the diagnostic process. Although it is not that simple, they should be integrated according to the clinical policies, and it should be considered that statistical significance does not always equal clinical significance. In this review, we analyzed the contribution of prostate-specific antigen (PSA)-based biomarkers (PHI, PHID, 4Kscore, STHLM3), imagistic techniques (mp-MRI and mp-US), and combined tests in the early diagnosis process of localized prostate cancer.