Exosomes-based biomarker discovery for diagnosis and prognosis of prostate cancer

10.2741/4565 ◽  
2017 ◽  
Vol 22 (10) ◽  
pp. 1682-1696 ◽  
Author(s):  
Gagan Deep
Keyword(s):  
Prostate Cancer ◽  
Biomarker Discovery ◽  
Diagnosis And Prognosis

scholarly journals Metabolomics Contributions to the Discovery of Prostate Cancer Biomarkers

Metabolites ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 48 ◽  
Author(s):  
Nuria Gómez-Cebrián ◽  
Ayelén Rojas-Benedicto ◽  
Arturo Albors-Vaquer ◽  
José López-Guerrero ◽  
Antonio Pineda-Lucena ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Biomarker Discovery ◽  
Tca Cycle ◽  
Tricarboxylic Acid ◽  
Metabolic Signature ◽  
Diagnosis And Prognosis ◽  
Clinical Biomarkers ◽  
Specificity And Sensitivity ◽  
Fatty Acids Metabolism ◽  
Patient Prognosis

Prostate cancer (PCa) is one of the most frequently diagnosed cancers and a leading cause of death among men worldwide. Despite extensive efforts in biomarker discovery during the last years, currently used clinical biomarkers are still lacking enough specificity and sensitivity for PCa early detection, patient prognosis, and monitoring. Therefore, more precise biomarkers are required to improve the clinical management of PCa patients. In this context, metabolomics has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids. Thus, changes in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism have been reported in different studies analyzing PCa patients’ biofluids. The review provides an up-to-date summary of the main metabolic alterations that have been described in biofluid-based studies of PCa patients, as well as a discussion regarding their potential to improve clinical PCa diagnosis and prognosis. Furthermore, a summary of the most significant findings reported in these studies and the connections and interactions between the different metabolic changes described has also been included, aiming to better describe the specific metabolic signature associated to PCa.

scholarly journals Prostate Cancer Liquid Biopsy Biomarkers’ Clinical Utility in Diagnosis and Prognosis

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3373
Author(s):  
Milena Matuszczak ◽  
Jack A. Schalken ◽  
Maciej Salagierski
Keyword(s):  
Prostate Cancer ◽  
Liquid Biopsy ◽  
Current Knowledge ◽  
Prostate Gland ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Digital Rectal Examination ◽  
Cost Effective ◽  
Non Invasive ◽  
Diagnosis And Prognosis

Prostate cancer (PCa) is the most common cancer in men worldwide. The current gold standard for diagnosing PCa relies on a transrectal ultrasound-guided systematic core needle biopsy indicated after detection changes in a digital rectal examination (DRE) and elevated prostate-specific antigen (PSA) level in the blood serum. PSA is a marker produced by prostate cells, not just cancer cells. Therefore, an elevated PSA level may be associated with other symptoms such as benign prostatic hyperplasia or inflammation of the prostate gland. Due to this marker’s low specificity, a common problem is overdiagnosis, which leads to unnecessary biopsies and overtreatment. This is associated with various treatment complications (such as bleeding or infection) and generates unnecessary costs. Therefore, there is no doubt that the improvement of the current procedure by applying effective, sensitive and specific markers is an urgent need. Several non-invasive, cost-effective, high-accuracy liquid biopsy diagnostic biomarkers such as Progensa PCA3, MyProstateScore ExoDx, SelectMDx, PHI, 4K, Stockholm3 and ConfirmMDx have been developed in recent years. This article compares current knowledge about them and their potential application in clinical practice.

scholarly journals Promoter Methylation of PRKCB, ADAMTS12, and NAALAD2 Is Specific to Prostate Cancer and Predicts Biochemical Disease Recurrence

2021 ◽  
Vol 22 (11) ◽  
pp. 6091
Author(s):  
Kristina Daniunaite ◽  
Arnas Bakavicius ◽  
Kristina Zukauskaite ◽  
Ieva Rauluseviciute ◽  
Juozas Rimantas Lazutka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Practice ◽  
Promoter Methylation ◽  
Disease Recurrence ◽  
The Cancer Genome Atlas ◽  
Cancer Dataset ◽  
Protein Coding ◽  
Diagnosis And Prognosis ◽  
Genome Wide ◽  
Cancer Genome Atlas

The molecular diversity of prostate cancer (PCa) has been demonstrated by recent genome-wide studies, proposing a significant number of different molecular markers. However, only a few of them have been transferred into clinical practice so far. The present study aimed to identify and validate novel DNA methylation biomarkers for PCa diagnosis and prognosis. Microarray-based methylome data of well-characterized cancerous and noncancerous prostate tissue (NPT) pairs was used for the initial screening. Ten protein-coding genes were selected for validation in a set of 151 PCa, 51 NPT, as well as 17 benign prostatic hyperplasia samples. The Prostate Cancer Dataset (PRAD) of The Cancer Genome Atlas (TCGA) was utilized for independent validation of our findings. Methylation frequencies of ADAMTS12, CCDC181, FILIP1L, NAALAD2, PRKCB, and ZMIZ1 were up to 91% in our study. PCa specific methylation of ADAMTS12, CCDC181, NAALAD2, and PRKCB was demonstrated by qualitative and quantitative means (all p < 0.05). In agreement with PRAD, promoter methylation of these four genes was associated with the transcript down-regulation in the Lithuanian cohort (all p < 0.05). Methylation of ADAMTS12, NAALAD2, and PRKCB was independently predictive for biochemical disease recurrence, while NAALAD2 and PRKCB increased the prognostic power of multivariate models (all p < 0.01). The present study identified methylation of ADAMTS12, NAALAD2, and PRKCB as novel diagnostic and prognostic PCa biomarkers that might guide treatment decisions in clinical practice.

scholarly journals Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином

PRILOZI ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 5-36 ◽  
Author(s):  
Katarina Davalieva ◽  
Momir Polenakovic
Keyword(s):  
Prostate Cancer ◽  
Tumor Tissue ◽  
Biomarker Discovery ◽  
Molecular Level ◽  
Specific Antigen ◽  
Shotgun Proteomics ◽  
Comparative Proteomics ◽  
Label Free ◽  
The Past ◽  
Proteomics Research

Abstract Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly increased the number of men diagnosed with PCa but at the same time, as a result of the low specificity, led to overdiagnosis, resulting to unnecessary biopsies and high medical cost treatments. The primary goal in PCa research today is to find a biomarker or biomarker set for clear and effecttive diagnosis of PCa as well as for distinction between aggressive and indolent cancers. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, MALDI MS profiling, shotgun proteomics with label-based (ICAT, iTRAQ) and label-free (SWATH) quantification, MudPIT, CE-MS have been applied to the study of PCa in the past 15 years. Various biological samples, including tumor tissue, serum, plasma, urine, seminal plasma, prostatic secretions and prostatic-derived exosomes were analyzed with the aim of identifying diagnostic and prognostic biomarkers and developing a deeper understanding of the disease at the molecular level. This review is focused on the overall analysis of expression proteomics studies in the PCa field investigating all types of human samples in the search for diagnostics biomarkers. Emphasis is given on proteomics platforms used in biomarker discovery and characterization, explored sources for PCa biomarkers, proposed candidate biomarkers by comparative proteomics studies and the possible future clinical application of those candidate biomarkers in PCa screening and diagnosis. In addition, we review the specificity of the putative markers and existing challenges in the proteomics research of PCa.

scholarly journals Circular RNA Expression Profiling Identifies Prostate Cancer- Specific circRNAs in Prostate Cancer

2018 ◽  
Vol 50 (5) ◽  
pp. 1903-1915 ◽  
Author(s):  
Qianlin Xia ◽  
Tao Ding ◽  
Guihong Zhang ◽  
Zehuan Li ◽  
Ling Zeng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Polymerase Chain Reaction Analysis ◽  
Circular Rna ◽  
Differentially Expressed ◽  
Pcr Analysis ◽  
High Morbidity ◽  
Qrt Pcr ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers ◽  
Rna Expression Profiling

Background/Aims: Prostate cancer (PCa) is one of the main cancers that damage males’ health severely with high morbidity and mortality, but there is still no ideal molecular marker for the diagnosis and prognosis of prostate cancer. Methods: To determine whether the differentially expressed circRNAs in prostate cancer can serve as novel biomarkers for prostate cancer diagnosis, we screened differentially expressed circRNAs using SBC-ceRNA array in 4 pairs of prostate tumor and paracancerous tissues. A circRNA-miRNA-mRNA regulatory network for the differential circRNAs and their host genes was constructed by Cytoscape3.5.1 software. Quantitative real-time polymerase chain reaction analysis (qRT-PCR) was performed to confirm the microarray data. Results: We found 1021 differentially expressed circRNAs in PCa tumor using SBC-ceRNA array and confirmed the expression of circ_0057558, circ_0062019 and SLC19A1 in PCa cell lines and tumor tissues through qRT-PCR analysis. We demonstrated that combination of PSA level and two differentially expressed circRNAs showed significantly increased AUC, sensitivity and specificity (0.938, 84.5% and 90.9%, respectively) than PSA alone (AUC of serum PSA was 0.854). Moreover, circ_0057558 was correlated positively with total cholesterol. The functional network of circRNA-miRNA-mRNA analysis showed that circ_0057558 and circ_0034467 regulated miR-6884, and circ_0062019 and circ_0060325 regulated miR-5008. Conclusion: Our results demonstrated that differentially expressed circRNAs (circ_0062019 and circ_0057558) and host gene SLC19A1 of circ_0062019 could be used as potential novel biomarkers for prostate cancer.

scholarly journals Accelerated Protein Biomarker Discovery from FFPE tissue samples using Single-shot, Short Gradient Microflow SWATH MS

2019 ◽  
Author(s):  
Rui Sun ◽  
Christie Hunter ◽  
Chen Chen ◽  
Weigang Ge ◽  
Nick Morrice ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Biomarker Discovery ◽  
Single Shot ◽  
Protein Biomarkers ◽  
Differential Abundance ◽  
Tissue Samples ◽  
Peptide Precursors ◽  
High Consistency ◽  
Method R ◽  
Acquisition Method

ABSTRACTWe report and evaluated a microflow, single-shot, short gradient SWATH MS method intended to accelerate the discovery and verification of protein biomarkers in clinical specimens. The method uses 15-min gradient microflow-LC peptide separation, an optimized SWATH MS window configuration and OpenSWATH software for data analysis.We applied the method to a cohort 204 of FFPE prostate tissue samples from 58 prostate cancer patients and 10 prostatic hyperplasia patients. Altogether we identified 27,976 proteotypic peptides and 4,043 SwissProt proteins from these 204 samples. Compared to a reference SWATH method with 2-hour gradient the accelerated method consumed only 27% instrument time, quantified 80% proteins and showed reduced batch effects. 3,800 proteins were quantified by both methods in two different instruments with relatively high consistency (r = 0.77). 75 proteins detected by the accelerated method with differential abundance between clinical groups were selected for further validation. A shortlist of 134 selected peptide precursors from the 75 proteins were analyzed using MRM-HR, exhibiting high quantitative consistency with the 15-min SWATH method (r = 0.89) in the same sample set. We further verified the capacity of these 75 proteins in separating benign and malignant tissues (AUC = 0.99) in an independent prostate cancer cohort (n=154).Overall our data show that the single-shot short gradient microflow-LC SWATH MS method achieved about 4-fold acceleration of data acquisition with reduced batch effect and a moderate level of protein attrition compared to a standard SWATH acquisition method. Finally, the results showed comparable ability to separate clinical groups.

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