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Добірка наукової літератури з теми "Biomarker of prostate cancer"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Biomarker of prostate cancer"

1

Kushwaha, Prem Prakash, Shiv Verma, and Sanjay Gupta. "Aquaporins as Prognostic Biomarker in Prostate Cancer." Cancers 15, no. 2 (January 4, 2023): 331. http://dx.doi.org/10.3390/cancers15020331.

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Prostate cancer is a complex heterogeneous disease that affects millions of males worldwide. Despite rapid advances in molecular biology and innovation in technology, few biomarkers have been forthcoming in prostate cancer. The currently available biomarkers for the prognosis of prostate cancer are inadequate and face challenges, thus having limited clinical utility. To date, there are a number of prognostic and predictive biomarkers identified for prostate cancer but lack specificity and sensitivity to guide clinical decision making. There is still tremendous scope for specific biomarkers to understand the natural history and complex biology of this heterogeneous disease, and to identify early treatment responses. Accumulative studies indicate that aquaporins (AQPs) a family of membrane water channels may serve as a prognostic biomarker for prostate cancer in monitoring disease advancement. In the present review, we discuss the existing prostate cancer biomarkers, their limitations, and aquaporins as a prospective biomarker of prognostic significance in prostate cancer.

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2

Emami, Nashmil, and Eleftherios P. Diamandis. "Utility of Kallikrein-Related Peptidases (KLKs) as Cancer Biomarkers." Clinical Chemistry 54, no. 10 (October 1, 2008): 1600–1607. http://dx.doi.org/10.1373/clinchem.2008.105189.

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Abstract Background: The human kallikrein-related peptidase (KLK) family consists of 15 highly conserved serine proteases, which are encoded by the largest uninterrupted cluster of protease genes in the human genome. To date, several members of the family have been reported as potential cancer biomarkers. Although primarily known for their biomarker value in prostate, ovarian, and breast cancers, more recent data suggest analogous roles of KLKs in several other cancers, including gastrointestinal, head and neck, lung, and brain malignancies. Among the proposed KLK cancer biomarkers, prostate-specific antigen (also known as KLK3) is the most widely recognized member in urologic oncology. Content: Despite substantial progress in the understanding of the biomarker utility of individual KLKs, the current challenge lies in devising biomarker panels to increase the accuracy of prognosis, prediction of therapy, and diagnosis. To date, multiparametric KLK panels have been proposed for prostate, ovarian, and lung cancers. In addition to their biomarker utility, emerging evidence has revealed a number of critical functional roles for KLKs in the pathogenesis of cancer and their potential use as therapeutic targets. Summary: KLKs have biomarker utility in many cancer types but individually lack sufficient specificity or sensitivity to be used in clinical practice; however, groups of KLKs and other candidate biomarkers may offer improved performance.

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3

Wu, Jui-Chuang, and Guang-Jer Wu. "METCAM Is a Potential Biomarker for Predicting the Malignant Propensity of and as a Therapeutic Target for Prostate Cancer." Biomedicines 11, no. 1 (January 13, 2023): 205. http://dx.doi.org/10.3390/biomedicines11010205.

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Prostate cancer is the second leading cause of cancer-related death worldwide. This is because it is still unknown why indolent prostate cancer becomes an aggressive one, though many risk factors for this type of cancer have been suggested. Currently, many diagnostic markers have been suggested for predicting malignant prostatic carcinoma cancer; however, only a few, such as PSA (prostate-specific antigen), Prostate Health Index (PHI), and PCA3, have been approved by the FDA. However, each biomarker has its merits as well as shortcomings. The serum PSA test is incapable of differentiating prostate cancer from BPH and also has an about 25% false-positive prediction rate for the malignant status of cancer. The PHI test has the potential to replace the PSA test for the discrimination of BPH from prostate cancer and for the prediction of high-grade cancer avoiding unnecessary biopsies; however, the free form of PSA is unstable and expensive. PCA3 is not associated with locally advanced disease and is limited in terms of its prediction of aggressive cancer. Currently, several urine biomarkers have shown high potential in terms of being used to replace circulating biomarkers, which require a more invasive method of sample collection, such as via serum. Currently, the combined multiple tumor biomarkers may turn out to be a major trend in the diagnosis and assessment of the treatment effectiveness of prostate cancer. Thus, there is still a need to search for more novel biomarkers to develop a perfect cocktail, which consists of multiple biomarkers, in order to predict malignant prostate cancer and follow the efficacy of the treatment. We have discovered that METCAM, a cell adhesion molecule in the Ig-like superfamily, has great potential regarding its use as a biomarker for differentiating prostate cancer from BPH, predicting the malignant propensity of prostate cancer at the early premalignant stage, and differentiating indolent prostate cancers from aggressive cancers. Since METCAM has also been shown to be able to initiate the spread of prostate cancer cell lines to multiple organs, we suggest that it may be used as a therapeutic target for the clinical treatment of patients with malignant prostate cancer.

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4

Fox, Natalie S., Emilie Lalonde, Julie Livingstone, Julia Hopkins, Yu-Jia Shiah, Vincent Huang, Takafumi Yamaguchi, et al. "Integrated somatic subtypes of localized intermediate-risk prostate cancer." Journal of Clinical Oncology 35, no. 6_suppl (February 20, 2017): e560-e560. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.e560.

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e560 Background: Approximately two thirds of intermediate risk prostate cancer patients are over- or under- treated because we cannot correctly prognose this risk group; therefore we require novel biomarkers to better direct patient therapies and avoid subjecting patients to side effects without benefit. One reason genomic biomarkers are not currently used in clinical settings is because they are notoriously difficult to validate in follow-up studies. Furthermore, the lack of clear prostate cancer subtypes prevents the development of subtype specific biomarkers as is standard practice in breast cancer. We aim to improve biomarker validation rates by defining prostate cancer subtypes that can be used to create subtype specific biomarkers. Methods: First, we assess large scale genomic patterns using whole genome sequencing and methylation data and create integrative subtypes for intermediate risk prostate cancer. Second, we assess associations between specific aberrations and subtypes, and determine whether certain types of molecular aberrations are more important background aberrations for subtype specific biomarker development. Finally, we assess biases in prognostic performance of the published Lalonde biomarker between groups associated with patient subtypes to show that subtype aware biomarkers are necessary. Results: We demonstrate that the Lalonde biomarker is biased by the cohorts’ proportion of TMPRSS2-ERG (T2E) aberrations illustrating the need to develop different biomarkers for patients with T2E and patients without T2E. Further, we suggest integrative subtypes can be used to select patients with similar genomic profiles for biomarker analysis to improve biomarker validation rates. Conclusions: This analysis provides direct guidance for future biomarker development and addresses an important barrier to clinical use of genomic biomarkers for prostate cancer.

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5

Jakobsen, Niels Asger, Freddie Charles Hamdy, and Richard John Bryant. "Novel biomarkers for the detection of prostate cancer." Journal of Clinical Urology 9, no. 2_suppl (December 2016): 3–10. http://dx.doi.org/10.1177/2051415816656121.

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Анотація:

Prostate-specific antigen (PSA) is widely used as a biomarker in the detection of prostate cancer and for decision making regarding treatment options, response to therapy, and clinical follow-up. Despite its widespread use, it is well recognised that PSA has suboptimal performance as a screening tool due to poor specificity, resulting in high negative biopsy rates and potential ‘over-diagnosis’ and ‘over-treatment’ of clinically insignificant cancers. In particular, PSA does not reliably distinguish either cancer from benign prostatic conditions, or ‘clinically significant’ from ‘indolent cancers’, and it is inaccurate in predicting disease burden and response to treatment. There is an urgent demand for novel biomarkers to address these clinical needs. This article provides an update on the novel candidate biomarkers in development, which have shown potential for improving the detection of clinically significant cases of this malignancy.

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6

Bettin, Alfonso, Ismael Reyes, and Niradiz Reyes. "Gene Expression Profiling of Prostate Cancer–Associated Genes Identifies Fibromodulin as Potential Novel Biomarker for Prostate Cancer." International Journal of Biological Markers 31, no. 2 (April 2016): 153–62. http://dx.doi.org/10.5301/jbm.5000184.

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Background The aim of this study was to evaluate the gene expression profiles of a set of prostate cancer–associated genes in prostate cancer cell lines, to determine their association with different cancer phenotypes and identify potential novel biomarkers for this disease. Methods Quantitative real-time PCR was used to determine the expression profiles of 21 prostate cancer–associated genes in the human prostate cancer cell lines PC-3 and LNCaP, using the nontumorigenic cell line PWR-1E as control cell line. Genes evaluated were ESM-1, SERPINE2, CLU, BGN, A2M, PENK, FMOD, CD81, DCN, TSPAN8, KBTBD10, F2RL1, TMSB4X, SNCG, CXXC5, FOXQ1, PDPN, SPN, CAV1, CD24 and KLK3. A potential biomarker from this set of genes, the FMOD gene, encoding the small leucine-rich proteoglycan fibromodulin, was selected for further evaluation in clinical samples from patients diagnosed with benign or malignant prostatic disease. Results Several of the evaluated genes showed significantly altered expression in the prostate cancer cell lines, compared with nontumorigenic PWR-1E cells. Further evaluation of FMOD transcript in prostate clinical samples from patients diagnosed with benign or malignant prostatic disease identified a significant difference in the expression levels of this proteoglycan between benign and malignant tissue (p<0.05). Conclusions A number of gene transcripts were differentially expressed by the cell lines assayed. Among them, FMOD was further evaluated in clinical samples and was found to be differentially expressed between benign and prostate cancer tissue. Further validation of FMOD transcript in a larger population is required to ascertain its usefulness as biomarker for prostate cancer.

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7

Tosoian, Jeffrey, and Stacy Loeb. "PSA and Beyond: The Past, Present, and Future of Investigative Biomarkers for Prostate Cancer." Scientific World JOURNAL 10 (2010): 1919–31. http://dx.doi.org/10.1100/tsw.2010.182.

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Анотація:

The discovery of prostate-specific antigen (PSA) as a biomarker represented a major discovery in the early diagnosis and monitoring of prostate cancer. However, the use of PSA is limited by the lack of specificity and an inability to differentiate indolent from life-threatening disease reliably at the time of diagnosis. A multitude of studies have aimed to improve the performance of PSA as well as identify additional biomarkers. The purpose of this study is to review available data on prostate cancer biomarkers for prostate cancer screening and prognostication, including prostatic acid phosphatase, PSA, PSA derivatives (PSA density, free PSA, pro PSA, and PSA kinetics), PCA3, GSTP1, AMACR, and other newly emerging molecular and genetic markers.

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8

Barnett, Christine L., Scott A. Tomlins, Daniel J. Underwood, John T. Wei, Todd M. Morgan, James E. Montie, and Brian T. Denton. "Two-Stage Biomarker Protocols for Improving the Precision of Early Detection of Prostate Cancer." Medical Decision Making 37, no. 7 (March 31, 2017): 815–26. http://dx.doi.org/10.1177/0272989x17696996.

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Background. New cancer biomarkers are being discovered at a rapid pace; however, these tests vary in their predictive performance characteristics, and it is unclear how best to use them. Methods. We investigated 2-stage biomarker-based screening strategies in the context of prostate cancer using a partially observable Markov model to simulate patients’ progression through prostate cancer states to mortality from prostate cancer or other causes. Patients were screened every 2 years from ages 55 to 69. If the patient’s serum prostate-specific antigen (PSA) was over a specified threshold in the first stage, a second stage biomarker test was administered. We evaluated design characteristics for these 2-stage strategies using 7 newly discovered biomarkers as examples. Monte Carlo simulation was used to estimate the number of screening biopsies, prostate cancer deaths, and quality-adjusted life-years (QALYs) per 1000 men. Results. The all-cancer biomarkers significantly underperformed the high-grade cancer biomarkers in terms of QALYs. The screening strategy that used a PSA threshold of 2 ng/mL and a second biomarker test with high-grade sensitivity and specificity of 0.86 and 0.62, respectively, maximized QALYs. This strategy resulted in a prostate cancer death rate within 1% of using PSA alone with a threshold of 2 ng/mL, while reducing the number of biopsies by 20%. Sensitivity analysis suggests that the results are robust with respect to variation in model parameters. Conclusions. Two-stage biomarker screening strategies using new biomarkers with risk thresholds optimized for high-grade cancer detection may increase quality-adjusted survival and reduce unnecessary biopsies.

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9

Davalieva, Katarina, та Momir Polenakovic. "Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином". PRILOZI 36, № 1 (1 травня 2015): 5–36. http://dx.doi.org/10.1515/prilozi-2015-0027.

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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.

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Aliyu, Mansur, Ali Akbar Saboor-Yaraghi, Shima Nejati, and Behrouz Robat-Jazi. "Urinary VPAC1: A potential biomarker in prostate cancer." AIMS Allergy and Immunology 6, no. 2 (2022): 42–63. http://dx.doi.org/10.3934/allergy.2022006.

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<abstract> <p>Prostate cancer is ranked as the fourth most prevalent cancer commonly diagnosed among males over 40 years of age, according to the WHO Cancer Fact Sheet 2020, and it is additionally a leading cause of cancer mortality among males. The incidence of prostate cancer and mortality varied significantly across the globe. Diagnosis of prostate cancer hinders easier management of cases, and prostate-specific antigen (PSA) use for screening of prostate cancer has poor specificity and sensitivity, thereby yielding overdiagnosis and unnecessary biopsies. Radiologically guided (ultrasound/MRI) prostate biopsy, considered the gold standard, is invasive and can miss a significant number of metastatic cancers. Even though mild, other prostate biopsy complications occur on a large scale, and few severe ones are often recorded. Scientists intensify their search for biomarker(s) for non-invasive diagnosis of prostate cancer using proteomics, metabolomics, genomics, and bioinformatics—urinary biomarkers were uniquely on the lookout. Vasoactive intestinal peptide (VIP)/pituitary adenylate cyclase-activating peptide (PACAP) receptor 1 (VPAC1), which is overexpressed (a thousandfold) in prostate cancer at the onset of oncogenesis and is excreted in the urine on tumor cells, is a contender in the prostate cancer biomarker quest. VPAC1 is ubiquitous, expressed by normal and malignant cells, and interwoven in their cell membranes. Therefore, using urine samples limits the possibility of making the wrong diagnosis, since VPAC1 is not normally excreted in the urine. Nevertheless, studying transmembrane receptors is intricate. However, producing monoclonal antibodies against the N-terminal end of VPAC1 can provide a promising target for designing a non-invasive diagnostic assay for early detection of prostate cancer using a urine sample.</p> </abstract>

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Більше джерел

Дисертації з теми "Biomarker of prostate cancer"

1

Teahan, Orla. "A metabonomic approach to biomarker discovery in prostate cancer." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506046.

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2

Tamboli, Vibha. "Detection of prostate cancer biomarker using molecularly imprinted polymers." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/103518/.

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Анотація:

Successful treatment of prostate cancer (PCa) depends on early diagnosis and screening, which currently relies on the measurement of serum prostate specific antigen (PSA) levels. The overarching aim of the project was to generate molecularly imprinted polymers for PCa biomarkers, with subsequent integration with a sensing platform to allow for rapid, point of care detection and monitoring. The initial work involved the use of simple PSA epitopes for epitope imprinting using conventional imprinting techniques. A four amino acid sequence from the Cterminus of PSA was imprinted with MAA, Aam and Urea monomers to obtain bulk imprinted polymers. Apparent Kd of 102 μM, 154 μM, 194 μM was obtained for MAA, AAm, Urea based bulk mini-MIPs respectively. Epitope imprinting was further developed using a surface imprinting approach, via electropolymersiation of dopamine to detect an epitopic sequence from pro-PSA. An improvement in Kd from bulk-imprinted polymers, with an apparent Kd of 2.9 μM was obtained with the surface electrochemical MIP sensor. However, both epitope imprinting technique lacked sensitivity to measure clinical relevant concentrations of PSA (nM range). As a consequence, a more sophisticated technique called hybrid imprinting was developed to build an electrochemical MIP sensor. Hybrid MIP imprinting utilised an aptamer with established affinity towards PSA to trap the aptamer-PSA complex into a surface grown electropolymer (polydopamine). The resulting aptamer lined polymer pockets exhibited high selectivity and affinity towards PSA (apparent Kd 0.3 nM). The apta-MIP sensor was also able to discriminate from a homologous protein (human Kallikrein 2) and was resilient to fouling from serum proteins. The apta-MIP sensor was further translated to a MOSFET device whereby successful detection of PSA at clinically relevant concentration was obtained in human plasma. Although good sensitivity and selectivity was obtained with the hybrid-MIP sensors, further research is required to understand the binding mechanism of the template to the MIP.

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3

Sharpe, Benjamin Peter. "Prostate cancer stem cells : potential new biomarkers." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.698969.

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Prostate cancer is a leading cause of cancer-related death in men, and while many men diagnosed with the disease will have an indolent clinical course, 20-25% of men will experience disease recurrence which is invariably lethal. There is an urgent need for prognostic biomarkers that will predict disease recurrence and risk-stratify patients upon diagnosis, allowing for personalised therapies. This thesis attempts to identify new prognostic biomarkers for prostate cancer and investigates their patterns of protein expression in human primary prostate tumour tissue. Cancer stem cells are cancer cells thought to be uniquely capable of self-renewal and tumorigenicity, and may have a role in tumour recurrence. Using a literature searching approach, potential biomarkers related to stem cells, cancer stem cells or recurrence in prostate cancer were identified, and ALDH7A1, BMI1, SDC1, MUC1-C, Nestin and ZSCAN4 were chosen for investigation. An in silico approach was also used for biomarker identification, with RS1 and SLC31A1 selected as their mRNA was found to be upregulated in recurrent tumours. The expression patterns of all 7 potential biomarkers were examined by immunohistochemistry on prostate tumour tissue and benign tissue from prostate biopsies and prostatectomies. BMI1, ALDH7A1, MUC1-C and Nestin showed no relationship to recurrence or other clinical features. RS1 protein levels increased in patients with recurrence within 5 years, negatively correlated with AR expression, and a meta-analysis showed that the RS1 gene was amplified in up to 32% of castration-resistant prostate tumours. ZSCAN4 was heterogeneously expressed in a subset of 26% of prostate tumours with unclear characteristics and was not expressed in benign tissue, but was not associated with recurrence. Finally, SDC1 expression was lost in tumour epithelium, but a population of unidentified SDC1-expressing cells were found in the stroma of a third of tumours, and an increased burden of these cells was associated with primary Gleason pattern 5 tumours. These cells do not overlap with common epithelial, mesenchymal or stromal lineages, but may be migratory. In summary, the data presented in this thesis identifies 3 potential new biomarkers for prostate cancer, and provides the basis for future characterisation of their wider roles in the disease.

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4

Thermaenius, Elisabeth. "Prostasome ELISA - a potential marker for prostate cancer diagnosis." Thesis, Uppsala universitet, Institutionen för medicinsk biokemi och mikrobiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179493.

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Abstract The prostate gland, a male organ, situated right under the urine bladder, is involved in male reproduction. It can also be the place for more or less serious diseases such as inflammation, abnormal growth and cancer. Especially prostate cancer is very common in the Western world. Today PSA is the most widely used marker for detection of prostate cancer. Unfortunately, this method is not specific enough. Therefore, there is a need for a better marker for screening of malignant prostate cancer. The marker should be specific both for the organ prostate and for the cancer disease. One promising marker is the prostasome, a small vesicle emanating from epithelial cells in the ejaculatory ducts in the prostate. The aim of this project was to set up an ELISA and test a number of antibodies for their ability to work as suitable capture or detection antibodies. As blocking agent different concentrations of BSA were tested. Biotin-Streptavidin conjugate was used in the detection step. Two surface proteins, PSCA and PSMA were used as capture antigens; they are specific for prostasomes. Clusterin, a prostasomal surface-bound protein, was used as antigen for the secondary antibody in the assay. With this experimental setup the detection limit was 2500ng/mL, which is probably not enough to detect prostasomes in cancer. The development of the ELISA did not reach its final stage, a ready-to-use assay, during this project. We have not yet the knowledge of optimal antibody concentrations and the other test parameters are also at experimental state.

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5

Mak, Blossom Po Sum. "The role of lipid metabolism in advanced prostate cancer." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29713.

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In metastatic castration-resistant prostate cancer (mCRPC), elevated plasma sphingolipids and a 3-lipid signature (3LS) are associated with worse clinical outcomes. The thesis aims to: assess the association between 3LS, somatic genetic aberrations and clinical outcomes in mCRPC; determine whether simvastatin therapy modulates the circulating lipidomic profile in men with mCRPC; and characterise the effect of extracellular sphingolipids on prostate cancer cells in vitro. Elevated circulating sphingolipids were found to be associated with AR, TP53, RB1 and PI3K aberrations in mCRPC, which are somatic aberrations linked to poor outcomes. Notably, the combination of lipid and genetic abnormalities conferred a worse prognosis. This suggests that certain genotypes in mCRPC may benefit from metabolic therapies. A proof-of-concept study of simvastatin in addition to standard therapy for mCRPC showed that simvastatin can modulate the circulating lipid profile by reducing plasma sphingolipids. Simvastatin eliminated the 3LS in almost half the men with this poor prognostic biomarker, thus demonstrating its potential as a metabolic targeting strategy. Treatment of prostate cancer cells with plasma from men with mCRPC and the 3LS increased cell viability in vitro. This phenotype was associated with gene expression and lipidomic changes that likely involve the NF-κB, sphingosine-1-phsosphate and sphingomyelin pathways. Therefore, plasma lipids may have a direct effect on prostate cancer growth and be a potential therapeutic target. In conclusion, elevated plasma sphingolipids are associated with worse clinical outcomes in mCRPC, providing prognostic information in addition to established genomic biomarkers. Extracellular sphingolipids increase prostate cancer cell viability, potentially through activation of ceramide metabolism towards pro-survival sphingolipids. This can be therapeutically targeted with simvastatin, which can eliminate the poor prognostic 3LS in men with mCRPC.

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6

Hazan, Allon. "Delineating a functional role for the urinary biomarker Lipocalin 2 in prostate cancer." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8246.

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Анотація:

Prostate cancer (PCa) is the most commonly diagnosed cancer amongst Western males. PCa progression is strongly linked to steroid receptor signalling, however the modulation of steroid receptor expression in PCa is incompletely understood. Lipocalin 2 (LCN2) is a secreted protein which binds to Fe3+-containing siderophores and was originally identified as part of the innate immune response. LCN2 has been proposed as a potential biomarker for a range of cancers. However, LCN2 effects appear to be tissue specific. LCN2 expression is associated with poor prognosis in breast cancer, but with good prognosis in pancreatic cancer where it has been used therapeutically. The role of LCN2 in prostate cancer is poorly understood, in particular its effects on steroid receptor regulation. To elucidate the role of LCN2 in prostate cancer, the LCN2 gene was ectopically expressed in LNCaP cells to generate the LNCaP-LCN2 cell line. LNCaP-LCN2 cells had elevated androgen receptor expression which was linked to increased levels of KLK3 (PSA). LNCaP-LCN2 cells also had reduced levels of Estrogen receptor α (ERα), but increased expression of ERβ. This was combined with higher levels of E-cadherin, but not to changes in other EMT markers. Reciprocally, LCN2 was suppressed using RNAi in the PC3 cell line to generate PC3-shLCN2 cells. PC3-shLCN2 displayed a distinct change in morphology, with increased cell size and a sub-population of multi-nucleated and highly enlarged cells. PC3-shLCN2 cells had reduced proliferation, and lost the ability to form colonies in a 3D substrate. With regards to steroid receptors, PC3-shLCN2 cells had increased ERα expression, but reduced ERβ expression. This was also combined with a loss of E-cadherin and EGFR. Microarray analysis of PC3-shLCN2 cells identified changes to expression of a wide range of genes including VEGF-R, SPARC and KLK6. Functional grouping of differentially expressed genes suggests that LCN2 in involved in a range of cellular processes including hormone receptor response, Wnt signalling and cell cytoskeletal integrity. Many, but not all genes identified by microarray were responsive to recombinant LCN2 protein indicating a paracrine function for the protein. Treatment of PC3 cells with the iron chelator Deferoxamine resulted in phenotypic changes similar to those found in PC3-shLCN2 cells which suggest that LCN2 functions in part due to intracellular iron regulation. In summary, the data presented in this thesis suggests that LCN2 has both pro- and anti- tumourigenic properties in prostate cancer and that the protein is involved in a much wider range of functions than previously described.

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7

Lu, Xiaoling [Verfasser]. "Reduced Graphene Oxide Biosensors for Prostate Cancer Biomarker Detection / Xiaoling Lu." Gießen : Universitätsbibliothek, 2019. http://d-nb.info/1189582759/34.

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8

Nkuna, Lerato Precious. "Overoxidized polypyrrole-osmium telluride quantum dots immunosensor for prostate specific antigen – A cancer biomarker." University of the Western Cape, 2014. http://hdl.handle.net/11394/4433.

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Анотація:

>Magister Scientiae - MSc
Prostate cancer is a deadly disease that occurs in the male’s prostate gland. A prostate gland is a walnut structure that forms part of the male’s reproductive system. Prostate cancer is caused by high level than normal of PSA (Gleason score > 4 ng ml-1) in human blood. Some symptoms associated with high levels of PSA include blood in urine, pain when urinating, difficulty in getting and keeping an erection, blood in semen and pain in upper thigh. An immunosensor is a type of biosensor that has an antigen or antibody fragment as its biological recognition component. The specificity of the molecular recognition of antigen by antibodies to form a stable complex is the basis of immunosensor technology. In this work, overoxidized polypyrrole (OvoxPpy) was electrosynthesized as a novel sensor platform on glassy carbon electrode (GCE). The OvoxPpy was then doped with osmium telluride quantum dots(OsTe2QDs) by drop-coating method to form OsTe2QDs|OvoxPpy|GCE system. The morphology and the size of OsTe2QDs|OvoxPpy|GCE nanocomposite were determined using scanning electron microscopy. The size of thioglycolic acid capped osmium telluride quantum dots (TGA-OsTe2QDs) used as support material for the biosensor was about 2.289 nm. These quantum dots showed an excellent photo-absorption properties with an ultraviolet- visible (UV-Vis) photo absortion band occurring at 406nm associated with high band energy of 3.05 eV. The electrochemical immunosensor for PSA was prepared by immobilizing anti- PSA-antibody onto the OsTe2QDs|OvoxPpy|GCE by drop-coating and allowing it to dry for 2h. The nanocomposite sensor platform and the immunosensor were electrochemically characterised by voltammetric and impedimetric techniques. The phase shift in Bode diagram at maximum frequency was indicative of kinetic changes. Charge transfer resistance, Rct, was used as the analytical parameter for measuring the interfacial kinetics which occurred as a result of the bio-recognition event between anti-PSA-antibody and PSA. The impedance of the quantum dot electrode (TGA-OsTe2QDs-Nafion|GCE) was lower (1.490 x 104 kΩ) than the impedance of the immunosensor platform (BSA-Anti-PSA-antibody|TGA-OsTe2 QDs|OvoxPpy|GCE), 2.754 x 104. The Rct of the immunosensor was found to increase with increasing concentration of PSA. The linearity of the immunosensor at the very low concentration range (1.266 - 4.207 fg ml-1) tested, confirms its high sensitivity for PSA.

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9

Kench, James Geoffrey. "Prognostic Factors, Molecular Biomarkers and Mechanisms of Progression in Prostate Cancer." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25103.

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Prostate cancer (PC) is the most commonly diagnosed non-cutaneous cancer in men in developed countries. Although most prostate cancers behave in an indolent fashion, approximately 20% are more aggressive, a situation which is reflected in the large gap between the incidence and mortality figures. The 42 publications included in this thesis revolve around a key issue in the management of prostate cancer: how to identify those cancers which are more likely to manifest adverse behaviour so that men with such tumours can be offered appropriate therapeutic interventions, while those patients with more indolent cancers can be managed with surveillance and avoid the risk of toxicities associated with more intensive treatments. The first section includes studies of morphological parameters, including tumour grade, local spread and histological type, as well as proposing the novel prognostic factor of Gleason pattern at a positive surgical margin. The latter has now been incorporated into routine clinical practice and is included in the 2016 WHO Classification and College of American Pathologists reporting protocol for prostate cancer. Other articles in this section analyse structured reporting protocols for prostate cancer, analyse deficiencies and discrepancies, and propose evidence-based international standards. In section 2 the ability of molecular biomarkers to supplement or replace traditional morphological parameters in predicting biological behaviour is investigated. Several of these publications focus on AZGP1, which was found to be a robust independent predictor of prostate cancer recurrence in both retrospective and multicentre prospective studies. The final section includes investigations of the molecular mechanisms underpinning the progression of prostate cancer, since an understanding of these mechanisms facilitates the identification of potential biomarkers that stratify patients into different risk categories, enabling better targeting of therapy.

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10

Mohamed, M. "Epigenetic biomarkers in prostate cancer." Thesis, Queen's University Belfast, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426926.

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Книги з теми "Biomarker of prostate cancer"

1

Chung, Leland W. K., William B. Isaacs, and Jonathan W. Simons. Prostate Cancer. New Jersey: Humana Press, 2000. http://dx.doi.org/10.1385/1592590098.

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2

Sotelo, René, Juan Arriaga, Raed A. Azhar, and Inderbir S. Gill, eds. Prostate Cancer. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05600-5.

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3

Pestell, Richard G., Marja T. Nevalainen, and Michael Milken, eds. Prostate Cancer. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-079-3.

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4

Tindall, Donald J., ed. Prostate Cancer. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6828-8.

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5

Hricak, Hedvig, and Peter Scardino, eds. Prostate Cancer. Cambridge: Cambridge University Press, 2008. http://dx.doi.org/10.1017/cbo9780511551994.

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6

Tewari, Ashutosh K., Peter Whelan, and John D. Graham, eds. Prostate Cancer. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118347379.

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7

Chung, Leland W. K., William B. Isaacs, and Jonathan W. Simons, eds. Prostate Cancer. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-224-3.

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8

Hofmann, Reiner, Axel Heidenreich, and Judd W. Moul, eds. Prostate Cancer. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-56321-8.

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9

Chang, Sam S., and Michael S. Cookson, eds. Prostate Cancer. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78646-9.

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10

Ramon, Jacob, and Louis J. Denis, eds. Prostate Cancer. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-40901-4.

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Частини книг з теми "Biomarker of prostate cancer"

1

Ankerst, Donna P. "Pitfalls in Prostate Cancer Biomarker Evaluation Studies." In Prostate Cancer Screening, 319–29. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-281-0_23.

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2

Clegg, Nigel, and Peter S. Nelson. "Transcriptional Profiling of Prostate Cancer: Biomarker Identification and Clinical Applications." In Prostate Cancer Screening, 243–59. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-281-0_17.

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3

Feng, Ziding, Jacob Kagan, and Sudhir Srivastava. "Toward a Robust System for Biomarker Triage and Validation – EDRN Experience." In Prostate Cancer Screening, 297–306. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-281-0_21.

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4

Kaplan, Rick, and Sarah Brown. "Selection by Biomarker in Prostate Cancer." In A Practical Guide to Designing Phase II Trials in Oncology, 163–73. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118763612.ch10.

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5

Kagan, Jacob, Ian M. Thompson, and Daniel W. Chan. "Prostate Cancer." In Biomarkers in Cancer Screening and Early Detection, 197–206. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118468869.ch16.

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6

Kearns, James T., and Daniel W. Lin. "Utilizing Biomarkers in Patients with Prior Negative Prostate Biopsy." In Prostate Cancer, 43–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78646-9_3.

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7

Gunelli, Roberta, Eugenia Fragalà, and Massimo Fiori. "PCA3 in Prostate Cancer." In Urinary Biomarkers, 105–13. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1354-2_9.

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8

Dijkstra, S., R. J. Hendriks, G. H. J. M. Leyten, P. F. A. Mulders, and J. A. Schalken. "Biomarkers for Prostate Cancer." In Management of Prostate Cancer, 77–96. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42769-0_5.

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9

Leyten, Gisele H. J. M., Peter F. A. Mulders, and Jack A. Schalken. "Biomarkers for Prostate Cancer." In Management of Prostate Cancer, 55–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27597-5_5.

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10

Akakura, Koichiro. "Biomarkers of Prostate Cancer." In Biomarkers in Cancer Therapy, 125–32. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7295-7_12.

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Тези доповідей конференцій з теми "Biomarker of prostate cancer"

1

Zhou, Yinglu, Chaoran Ma, Giuseppe Nicoló Fanelli, Lavinia Stefanizzi, Elizaveta Gazeeva, Lorelei A. Mucci, Massimo Loda, Svitlana Tyekucheva, and Kathryn L. Penney. "Abstract 2702: Prostate stromal transcriptome as biomarker of aggressive prostate cancer." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-2702.

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2

Bhagirath, Divya, Thao Yang, Kirandeep Sekhon, Nathan Bucay, Shahana Majid, Yutaka Hashimoto, Priyanka Kulkarni, et al. "Abstract 5448: An exosomal biomarker for prostate cancer." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-5448.

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3

Maya, K., Lalita Rane, Tousief Irshad Ahmed, Mohammad Javed Ansari, Chandra Kumar Dixit, and Rahul Kanaoujiya. "L-Cysteine Passivated Carbon Quantum Dots as Biosensor for early Stage Detection of Prostate Cancer." In International Conference on Recent Advancements in Biomedical Engineering. Switzerland: Trans Tech Publications Ltd, 2022. http://dx.doi.org/10.4028/p-x65kwp.

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Анотація:

Green synthesized surface passivated carbon dots for detection of Citrate as biomarker for prostate cancer. The carbon sources of CQDs are passivated with L-cysteine via a one-pot hydrothermal route. The quenching in emission intensity of the synthesized carbon dots (CQDs) is observed for Citrate samples. The hydroxyl and carboxylic functional groups of Citrate showed a binding affinity with amino and free carboxyl cysteine passivated over the surface of carbon dots. The CQDs showed a high sensitivity for detection of Citrate in a continuous range of 1.0 μM–500 μM. The CQDs showed good level of selectivity, repeatability, and stability for the detection of Citrate. We successfully detected the Citrate content for prostate cancer cells using an L-cysteine passivated carbon quantum dots various incubation durations. As a result, quenching in fluorescence intensity CQDs are noted to analyze extent of cancer cells in biological samples.

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4

Kagohara, Luciane T., Prakash Kulkarni, Takumi Shiraishi, Robert Vessella, Robert W. Veltri, and Elana J. Fertig. "Abstract 4525: Cancer/testis antigens: A biomarker panel for prostate cancer screening." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-4525.

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5

Mantena, Vamsi, Wenjuan Jiang, Jiang Li, and Rick McKenzie. "Prostate cancer biomarker identification using MALDI-MS DATA: Initial results." In 2009 IEEE/NIH Life Science Systems and Applications Workshop (LiSSA) Formerly known as LSSA and. IEEE, 2009. http://dx.doi.org/10.1109/lissa.2009.4906723.

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6

Takita, Sarra, Alexei Nabok, Anna Lishchuk, Magdi H. Mussa, and David Smith. "Detection of Prostate Cancer Biomarker PCA3 with Electrochemical Apta-Sensor." In IECB 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/iecb2022-12257.

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7

Jedinak, Andrej, Camille Vuichoud, Andrew El-Hayek, Katherine Kaplan, Jason Savage, Sarah Prophet, Adam S. Feldman, Kevin A. Camphausen, Kevin R. Loughlin, and Marsha A. Moses. "Abstract 711: Mechanistic implications of COL1A1 as a prostate cancer biomarker." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-711.

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8

Chuang, Shao-Hui, Xiaoyan Sun, Lisa Cazares, Julius Nyalwidhe, Dean Troyer, O. John Semmes, Jiang Li, and Frederic D. McKenzie. "Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis." In SPIE Medical Imaging, edited by Nico Karssemeijer and Ronald M. Summers. SPIE, 2010. http://dx.doi.org/10.1117/12.844403.

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9

Reyes, Niradiz, Alfonso Bettin, Juan Rebollo, Raj Tiwari, and Jan Geliebter. "Abstract 5185: Evaluation of biomarker candidate genes for prostate cancer metastasis." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-5185.

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10

Putriyuni, Anandia, Meta Z. Oktora, Nurwiyeni Nurwiyeni, and Tofrizal Tofrizal. "Potential Forkhead Box O 3a as Prognostic Biomarker in Prostate Cancer." In 1st International Conference on Health Sciences and Biotechnology (ICHB 2021). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/ahsr.k.220303.004.

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Звіти організацій з теми "Biomarker of prostate cancer"

1

Hemstreet, George P., and III. Biomarker Based Individual Risk Assessment for Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada430343.

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2

Hemstreet, George P., and III. Biomarker Based Individual Risk Assessment for Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada420787.

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3

Hemstreet, III, and George P. Biomarker Based Individual Risk Assessment for Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada472804.

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4

Mercurio, Arthur M. Neuropilin-2: Novel Biomarker and Therapeutic Target for Aggressive Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada611823.

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5

Bishopric, Nanette H. Novel Biomarker Discovery for Diagnostic and Therapeutic Strategies in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada612044.

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6

Mercurio, Arthur M. Neuropilin-2: Novel Biomarker and Therapeutic Target for Aggressive Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada599091.

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7

Bishopric, Nanette H., and Betty Diamond. Novel Biomarker Discovery for Diagnostic and Therapeutic Strategies in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada604489.

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8

Singal, Rakesh. Aberrant Promoter Methylation in Serium DNA as a Biomarker for Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada452528.

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9

Dasgupta, Subhamoy, and Jamboor Vishwanatha. Novel gene C17orf37 in Prostate Cancer Progression and Metastasis: A Prospective Biomarker. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada523179.

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10

Kyprianou, Natasha, and Haining Zhu. Biomarker Discovery and Mechanistic Studies of Prostate Cancer Using Targeted Proteomic Approaches. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada561372.

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